Conference Agenda

Artificial intelligence in education (AIED) promises personalized flexible and engaging learning. With real-time automated feedback students can learn and develop skills in a manner and at a speed that suits them, not confined by the synchronous nature of traditional vocational and higher education practices.

This research explores the benefits in learning the financial analysis inherent in property valuation, asynchronously, with the help of AIED. The review extends to consider the potential for unintended consequences by employing AI in property education. The later part demonstrates the research informed design of an online asynchronous unit focused on the apply side of the Apply Evaluate Plan (AEP) sequence. The financial analysis unit incorporates the mathematics applied in the valuation of residential and commercial property in Australia. It is designed to benefit from tailoring and engagement through an AI partnered learning system. The unit content, structure and design process are presented for subsequent analysis.

Much discussion in the lay, and academic, press suggests that a strong relationship exists between the covid pandemic and home prices - that the pandemic directly contributed to rising values in such communities as Miami, Florida. As families fled the risks of covid, they bought homes in popular suburbs and caused home value increases. Using publicly available data, we find the actual relationship is not so simple. We find no significant relationships between covid infections, hospitalizations, covid nor covid deaths and home values. None of these three proxies exhibits a statistically meaningful relationship with house prices. We find, however, that the "usual suspects" - personal income, unemployment and inflation - all help to describe increasing home prices in Southeast Florida. This is contrary to the popular belief that the pandemic caused those changes in home values. Our discoveries invite extended examinations of this topic.

Mechanisms driving movement in real estate home prices

Ksenija Bogosavljevic Anita K. Pennathur

Florida Atlantic University

Determining the list price when selling a house is typically a thoughtfully considered decision made by both the seller and the real estate broker. From the seller's standpoint, she needs to carefully determine the optimal pricing strategy for the property. An exact initial price ensures the ideal equilibrium. Finding a balance between setting a price that is sufficiently attractive to attract multiple buyers, while also pricing it at a level that ensures the seller receives what she perceives to be the actual worth of the property. However, the original listing price is subject to change. A seller may inadvertently undervalue their property if they initially set the price too low and subsequently learn from multiple offers that there is significant demand. On the other hand she might have overestimated the worth of her house, resulting in no offers. In both situations, the seller has the choice to change the original listing price and to adapt to prevailing market conditions. We employ unique cross-sectional data set obtained from an Multiple Listing Service (MLS) of properties in all Florida counties listed between January 1, 2009 and May 31, 2022. Thus, this data provides a rich period around both the up and down markets of the last decade and pandemic Covid19 Approximately 35% of owners changed their list price during observed marketing period. 3% of marketed properties have an increase in list price, while 30% homeowners decrease list price. We employ multinomial logit model to estimate the factors that will impact the likelihood of a price decrease/increase. With the use of the probability of a price change model, we hope to be able to capture seller behavior. We investigate factors that capture seller motivation, housing attributes, market cycle, selling circumstances, and characteristics of the buyer seller listing agent and buyer agents. In addition to this, we are interested in observing how Seller altered their methods to the current state of the economy. We hope to gain insights into how sellers adapt their strategies and pricing decisions in response to economic conditions.

Property taxes, a crucial revenue source for local governments, particularly in funding education, rely on fair and accurate assessments. This study examines six measures of horizontal and vertical inequity in property tax assessments, including Spline, Clapp, Birch/Sunderman, Quintos Approach, PRD, and PRB. Using Shelby County, Tennessee data, we provide a detailed comparison of these methods, offering stakeholders valuable insights for addressing persistent issues in property tax assessments.

Abstract: This paper considers the relationships between disruptions, dual agency in housing markets, and house sales prices in the state of Virginia. Disruptions – either policy or natural – can impact housing markets. Examples of disruptions include legislative disruptions, such as new regulations; and more global disruptions due to economy-wide shocks, such as health or natural disaster disruptions. We first develop an identification strategy to test the hypotheses that these types of disruptions can impact how dual agency affects house prices. Then we examine how two specific disruptions impact the relationships between dual agency listings and house sales prices. The first set of disruptions includes the 2011 announcement and the subsequent implementation of new Virginia legislation requiring disclosure of dual agency to homebuyers, and how these events affected the relationship between dual agency and Virginia house sales prices. Second, we consider how the Covid pandemic that began in March of 2020 led to differential house price impacts for listings with dual agents. We also control for the simultaneity between sales prices and time on the market. In our full-blown specification, we find no significant effect of the 2011 disclosure legislation on the relationship between dual agency and house prices. But there is a significant negative impact on sales prices, of approximately $9,400 to $9,900 on average, for dual agency residential listings after the start of the Covid pandemic. This finding implies for listings with dual agents, home buyers were able to purchase homes for less due to this global disruption. In other words, dual agency benefitted home buyers in the context of the Covid disruption.

This study examines the impact of an information shock on the listing outcomes of digital trading platforms. It reports a unique case of the suspension of a data sharing agreement between two Multiple Listing Services (MLS) in South Florida, and investigates the impact of such a (negative) information shock on the sales outcomes of properties on the MLS platforms. The paper first develops a theoretical model involving search and matching frictions, which predicts discounted trading prices being correlated to the degree of cross-MLS brokers' collaboration. Using data involving property listings from the two MLSs, the paper then presents a series of regression analyses and depicts that there is approximately a 3% price reduction during the data suspension period. This result confirms the theoretical model's predictions, and remains stable and consistent under various robustness checks. Our results also reveal heterogeneous pricing and liquidity effects due to such an information shock.

The purpose of this paper is to investigate some important aspects of the profession of real estate agents on the residential housing market. Based on a survey among the in all 7,260 registered real estate agents in Sweden, two specific types of encounters experienced in the exercise of their profession are explored. The paper focuses on the experiences of real estate agents with regard to being threatened by a buyer or being offered a bribe by a buyer to close a deal before an sales auction.

As markets for real estate agency function quite differently between countries around the world, we also give a short overview of the specific attributes of the Swedish model of dual agency on the residential real estate market as compared to literature on the same models for the profession in other countries. As there are no (or at least so few that they can be neglected when studying the functioning of the market) buyer agents on the Swedish market, the assigned seller agent advertises the home and gets a number of interested buyers with whom he or she holds an auction to find the best price. The seller has the last word in choosing with whom to make the deal, but in fact the highest bidder is the winner and the agent closes the deal filling out all necessary documents for both parties, making contacts with both parties’ banks and setting up meetings for closing the deal.

However, the results from the survey show that the investigated real estate agents report being offered special benefits as well as having been threatened while practicing their profession.

Two logistic regressions were run to identify the effects of a model consisting of 15 independent variables (10 context-specific variables and 5 respondent-specific variables) on the two studied dependent variables (bribes and threaths) describing possible problematic situations accounted by the Swedish real estate agents. The results of these regressions are independently presented in the paper.

Direct logistic regression was performed to assess the impact of the chosen factors on the likelihood of respondents reporting having been offered “a bribe” (extra money or the possibility to get hte contract to sell another home) to close a deal before an auction while working as a real estate agent. Six of the independent variables made a unique statistically significant contribution to the model: having colleagues that was giving misleading information to sellers; respondents in major cities; male respondents; reporting working to much; more experienced agents; younger agents.

Direct logistic regression was also performed to assess the impact of a number of factors on the likelihood of respondents reporting having been threatened by a client while working as a real estate agent. The results show a statistically significant effect of seven different factors: Agents reported having colleagues giving misleading information to buyers; agents working in a major city; reporting having colleagues giving misleading information to sellers; agents that did not see the rules and regulations for agents as clear; agents reporting having a too heavy work load; agents reporting that they worried about their daily work; and agents with more experience.

Consumer protection relating to activities of real estate agents have been discussed widely since the financial crisis of 2007-2008 as they are important mediators of knowledge in any local residential housing market and as their customers – lay consumers – often lack knowledge on market functions as well as on the specific regulations and rules regarding the sale of residential property. Knowledge on the professional circumstances under which real estate agents are working is of interest for the business as well as for policy-makers and the public.

Keywords: real estate agent, threats, bribe, Sweden, residential housing market

Real estate agents who are interested in maximizing their sales commissions may want to consider the impact that their brokerage firm’s organizational culture may have on their incomes. They may also be interested to know whether the size and age of their brokerage firms correspond to four popular organizational culture types (clan, hierarchical, market, and adhocracy). We analyzed these relationships via a survey we distributed to United States real estate agents. Results provided support for hypotheses indicating that features of clan cultures were prevalent in small firms with 20 or fewer employees and in younger firms under six years, while features of market cultures were prevalent in larger and older firms. Market and adhocracy cultures correlated positively with brokerage firm size. We also found that adhocracy culture features in younger firms. Real estate commissions were highest in market cultures, followed by clan, adhocracy, and hierarchical cultures. Linear regressions and bootstrapping further supported a negative relationship between hierarchical cultures and real estate commissions and a negative relationship between adhocracy cultures and the age of firms. Theoretical and practical implications are offered.

This paper explores urban development on challenging city center blocks above and around light rail or transit lines. Federal Transit Administration (FTA) is a funding partner in many major transit projects in the US. Policy changes around joint developments and incidental use on FTA funded property allows for more creativity on putting land around transit projects to productive use. This paper digs into local policy decisions, creative financing, and market conditions that put transit oriented land to productive use and shares what local governments should consider for their excess land around transit.

Understanding whether and how markets price environmental risk is critical to policy design, particularly as the projections of climate risks change rapidly.
Commercial real estate (CRE) has constantly been exposed to extreme climate events. Still, an increase in those events will increase the financial risk of the asset for owners, occupiers, and insurers.
Employing a (total return) repeated sales framework with the newest climate and real estate financial performance data, we estimate how CRE markets adjust the price of properties at climate risk. We use the latest property-level climate data to consider the exposure to floods, hurricanes/cyclones, sea-level rise, wildfires, and extreme temperatures.
The findings of the project provide unique insights into the channels through which climate risks damage the value of CRE properties.
Addressing climate risk gaps in pricing contributes to overall market functioning where assets are exposed to physical climate risk and facilitates adaptation strategies to increase the preparedness of buildings for future climate shocks.
Our results also inform policymakers, regulators, and practitioners in their efforts to increase resilience, advance socially equitable markets, and meet net-zero commitments.

The impact of firm location on land markets has received little attention in the literature. Our research focuses on the effects of a local large wholesale retailer’s relocation on land sales. Costco Wholesale Corporation, the multi-national membership warehouse, was first located in the city of Clovis, California, in 1990. In 2018, after almost 30 years, Costco announced plans to relocate within the city limits to a new location only two miles away from its original location. This situation provides an opportunity to explore the effects of two simultaneous events: first, the localized effects of closing an old warehouse location, and second, the localized effects of the construction and reopening of a new location. Since the relocation takes place within a few miles, it is likely that various effects will take place. Conversations with local city government officials indicate that the announcement and relocation resulted in significant movements in the sales of land, especially undeveloped land, near the new Costco location.

Our empirical methodology follows a spatial difference-in-difference (spatial DID) design strategy. Our basic model will compare commercial land prices near and far from the original and new Costco locations before and after the relocation. We plan to set different distance thresholds for the treatment and control areas and consider a triple difference quasi-experimental design as explained in Guinet et al. (2023). These different specifications provide robustness checks to alternative threshold distance specifications (Guinet et al., 2023).

Crime in CBDs of major US cities has accelerated over the last years, particularly due to COVID-19, and affected investors, tenants, and developers. We investigate the impact of different types of crimes (e.g., property, personal, society) on rental rates of office and multifamily property over the period of 2000 to 2023 by combining neighborhood-level crime data and Costar data for the City of Portland, OR and City of Seattle, WA. Our findings will have implications for industry professionals and policymakers.

Leases are wasting assets that die out with time and will always, at one point, require the lessee to hand over the land to the lessor with all improvements and structures permanently attached thereto or renegotiate a new lease to secure continual enjoyment of the property. Since imposing restrictions and fetters on creating freehold interest in Ghana, many landowners and institutions have resorted to the leasehold arrangement. It has become evident that many of such leases granted decades back are folding up, and it appears the land sector is still fraught with several challenges in terms of lease renewal. This calls for land sector agencies to develop a coherent, standardized policy framework and guidelines to facilitate lease renewal. The study seeks to comparatively study the lease renewal processes in the land sector agencies in Ghana and make recommendations on the best practices. The study adopted the mixed research methodology and embedded design. The purposive sampling technique was used to select three institutions, including the Lands Commission, State Housing Company, and Asantehene Part One Lands Secretariat, where in-person interviews were conducted. At the same time, questionnaires were distributed to lessees via Google Forms. The study's findings revealed that many lessees know about the surrender and renewal of leases and are willing to renew them when they expire. The study also revealed that the selected land sector agencies all do capital valuations during the renewal of leases. It was found that delays in approving renewal applications, the inability of lessees to pay the premiums, and other associated costs, among others, are some of the challenges encountered during the renewal process. In light of these findings, the study recommended that there should be constitutional and statutory provisions that outline the ideal processes for lease renewal and the need for collaboration and coordination between the land sector agencies to create a unified system for the renewal of leases.

Even as increased demand and misallocation of housing are established consequences of rent controls, there is limited research on how household demand responds to rent subsidies. In a setting with heterogenous households and rent controls that not solely target low-income households, a likely consequence is overconsumption of housing that results in a less-than-optimal household- apartment matching.

Data from Stockholm, Sweden, with both household and apartment characteristics is analyzed. There is considerable variation in how much apartments are subsidized, household size, and income. On average, regulated rents are 36.1% lower than the estimated market rents. Apartment size is regressed on both household characteristics at the time of moving-in, and a measure of the rent subsidy. Controlling for household size, age(s), income and location, it is found that the average difference between an estimated counterfactual market rent and the regulated rent results in an average household renting an apartment that is 15.9% larger compared to if paying market rent. When regressing the number of rooms, rather than apartment size, the results are consistent. The average annual subsidy per square meter will, on average, result in households renting an apartment with .28 additional rooms.

The observed behavior is a consequence of ineffective targeting of rent subsidies. The shift in demand will amplify housing shortages and decrease access to housing and therefore contradict the policy goals. If tenants in the more subsidized segment of rental apartments where to consume the equivalent amount of space as new tenants in the least subsidized apartments, the renter population would increase with 8.1%.

Keywords: rent control, housing supply, housing demand, housing misallocation

JEL classification numbers: D4, R21, R28, R31

This paper examines the distributional consequences of rent regulation. We estimate counterfactual free market rents for households benefiting from rent control in their current tenancy. The gap between the paid rent and free market rent represents the benefit that a household draws from the rent control policy. We document how this measure is allocated along various household characteristics, providing novel evidence on the distributional consequences of rent control policies. Our results show that rent control mainly benefits older renters at the expense of younger ones.

The issue of heirs’ property has been widely discussed in literature spanning disciplines like economics, legal studies, and agricultural sciences. In 2010, the Uniform Law Commission introduced the Uniform Partition of Heirs’ Property Act (UPHPA), which is designed to reduce partition sales by bolstering the preference for partition in kind, creating provision for the buyout of cotenants that request the sale, and requiring the open market sale of property that is partitioned by sale. This paper examines the economic impact of the UPHPA and focuses on the economic impacts of the UPHPA on disadvantaged populations. We find the UPHPA to have a negative and statistically significant relationship with home values suggesting that the UPHPA does not improve the wealth of disadvantaged populations.

This study contributes to existing literature by examining how carbon regulation initiatives influence the corporates’ ESG actions in real estate sector, with a special focus on Environmental (E) performance. Specifically, it investigates if stringent carbon regulations like emissions trading systems (ETS) enhance corporates' ESG performance by analysing data from 927 Real Estate Investment Trusts (REITs) across 30 countries rated by MSCI. Our findings indicate that implementing ETS leads to heightened environmental responsibility among REITs. This supports North (1990)'s institutional theory, highlighting the impact of regulations on organizational behavior and business strategies. Notably, our channel analysis suggests that REITs leverage ETS-driven regulations to participate in green building initiatives. However, the study does not find comparable effects for carbon taxes. This research highlights the pivotal role of regulations in shaping sustainable practices in the real estate sector.

In an urbanizing world, food is consumed in cities to an increasing extent. However, the food system is based almost entirely on rural agriculture that is vulnerable to externalities and linked to sustainability issues ranging from emissions to natural degradation. Local solutions are needed to complement the system and making it more resilient. Simultaneously, real estate markets are amid changes calling for strategies to renew. Thus, the aim of this paper is to explore opportunities of urban food systems and the current and future roles of real estate in the systemic transition. The intention is not to add juxtaposition between urban and rural areas but rather to take a holistic view on the whole system for sustainable futures and prosperous real estate strategies.

To explore opportunities, boundaries, and impacts concerning real estate, a future-oriented and multidisciplinary workshop was held in Finland with experts from different fields: 20 professionals from the built environment (private and public sectors) and 12 professionals from the food industry (startups and experts). The results of the workshops were developed into a futures map to support different stakeholders in the transition. The workshop revealed several opportunities for value creation, especially in terms of utilizing vacant spaces, and several ways for real estate actors to contribute and profit. It was also found that the boundary conditions of the built environment are not known by many key actors representing different parts of the food system and need to be communicated to enable the transition.

There continues to be a strong interest in the value of “Green” in real estate. Most papers assess green as the value that energy or water efficiency brings to the built environment. Another thread of literature studies a more literal measure of green, typically that being the positive value of trees bring to residential real estate. This paper adds to that literature by measuring the value of property and neighborhood tree canopy when a house is sold. We employ the 1-meter resolution tree canopy data from the 2017 LiDAR, from which we derive the percentage of tree canopy on each Property, and the percentage of tree canopy at the block group level (which we use as our Neighborhood measure). Tree canopy is somewhat different than other variables that contribute to house value. Homebuyers have precise measures of the square feet, garage spaces, bathrooms, etc. that they are purchasing. From their personal observation they gain a visual estimate of the tree canopy for the property and for the neighborhood which the property is located in. Given this imprecise purchaser estimate of tree canopy we partition the tree canopy by property and neighborhood into the lowest 25%, the middle 50% and the highest 25%. Interacting these we create nine indicator variables to measure the combination of property and neighborhood tree canopy. We then employ a semi log hedonic pricing model to analyze about 96,000 existing home sales to estimate the value of tree canopy. In addition, we posit the value of tree canopy may depend on the overall Area value conjecturing that lower valued Areas may be more concerned with the housing space while higher valued areas may place increasing value on what may be seen as the amenity of tree canopy. After segmenting our neighborhoods into approximate value quintiles, we run a separate regression for each Area. Our results show that in the lowest value Area, home sales show no statistically reliable price increase with increased Property tree canopy. There is a modest gain from having a moderate or high Neighborhood tree canopy. In the remaining Areas, we find that increasing both Property and Neighborhood tree canopy is valuable and the most valuable is to have both high Property and high Neighborhood tree canopy.

This study presents empirical evidence of the impacts of climate change on residential real estate and insurance in the continental U.S. By utilizing regression analysis and incremental F-tests, this study establishes a non-linear correlation between housing price growth and temperature anomalies. The paper assesses the impact of diverse climate beliefs, represented by political ideology, on this association and recognizes that housing markets respond more strongly to abnormally high temperatures in counties leaning conservative. Furthermore, the research underscores a significant underestimation of climate risks in insurance pricing models, underscoring the pressing call for recalibration. These empirical findings enhance the general comprehension of climate-related vulnerabilities in the residential real estate sector and indicate the necessity for preventative actions to modify insurance policies and avoid instability within the housing market.

This paper explores potential causes of wealth inequality using the family background and real property records of a sample of American individuals. I use real property records to measure how sensitive one’s ability to possess real property in adulthood is to childhood environment, specifically one’s parents’ ability to access credit. To do so, I traced individuals from the 1940 U.S. census to present day public records, mapped the individuals’ childhood homes onto HOLC credit maps from 1940, and analyzed using a Poisson pseudo maximum likelihood model and an instrumental variables approach. I find that one’s parents’ access to credit (as proxied by their position on the HOLC credit maps) is positively and significantly correlated with one’s ability to own real property in adulthood and with the median value of homes in one’s adult neighborhood.

This paper studies how local pricing dispersion affects the housing transactions for both buyers and sellers. Due to the heterogeneity in the housing market, the extreme market prices of comparable properties affect the market participants’ reservation prices. Specifically, we conjecture that sellers are biased towards the higher listing price among the comparable houses, while buyers towards the lower selling price. Using the housing transaction data from the greater Toronto area, we find that, after controlling for the housing characteristics and market conditions, sellers and buyers are both subject to confirmation biases from comparable properties. Given the same median and average prices of comparable homes, the listing price is positively affected by the price level of the top 5% of comparable houses, while the selling price is positively affected by the price level of the bottom 5% of comparable houses. The time on market is longer when the local price dispersion is large.

This study explores information asymmetry in the residential housing market, primarily arising from the disparity in property information accessible to buyers and sellers as well as real estate agents. We investigate whether the advance of online information contributes to reducing this information asymmetry. The information asymmetry often results in gaps in property valuations, measured through price spreads. By analyzing residential property sales in Philadelphia, our study confirms prior findings that agents have superior access to property information and sell their own properties at a premium. However, the agent premium fades out during more recent periods, indicating the reduced information asymmetry. We also observe a significant reduction in price spreads due to the availability of online information, highlighting the crucial role of online information in mitigating information asymmetry in the real estate market.

Company Towns In the US:

A Unique Case Study of Corning, New York

Abstract

In this paper I examine the rise and fall of company towns that developed across resource rich rural areas in the US around the start of the Industrial Revolution. Most of these company towns failed. The process started with fast success and good labor-capital relationships, at times, until economic stress hit. The towns’ dominant firms would then typically fire employees and cut the pay of the remaining workers, leading to strained relationships and finally firm failure. One company town, Corning, NY and its flagship company, Corning Inc., had a distinct experience and has successfully operated for over 150 years. I find strong historical accounts that Corning Inc. consistently enjoyed quality relationships with labor, engaged in conservative business practices, sought innovation, and for a large part of its history had low leverage which put less stress on the firm in tough economies. Corning Inc., like dominant firms in other parts of the US, participated in the housing market, but they did not control the market which is in stark contrast to other company towns where the dominant firm did control the housing market and most all other aspects of life in the town, leading to a closed and monitored community. Given that housing is the largest asset for most Americans, and housing’s impact on life, it is important to understand housing in a company town to understand life in a company town. In Corning, NY, I find that the housing market has been much more stable compared to the nation at large and I theorize that this could be due to Corning Inc.’s investments into housing and the community. Utilizing regression analysis, I find that when Corning’s share price rises, the housing market also increases in value, while controlling for national housing market conditions, overall stock market conditions, and the general economy. I also find that Corning Inc.’s share price underperformed the S&P500, but the firm enjoys longevity. In all Corning, NY was quite different than other company towns of the day. The case study of Corning, NY is useful in today’s economy, as employers struggle with keeping and retaining talent, finding quality housing in markets where prices have skyrocketed, and still being economically viable.

About 60% of the Dutch live in areas exposed to flood risk. We examine different levels of flood risk and connect them with mortgage data to assess if and how lenders account for flood risk in Dutch mortgage markets. Our study uses a granular set of more than 200,000 securitized mortgages issued between 2013 and 2021. Our results suggest that flood risk has so far played only a minor role in setting interest margins or LTVs. Lenders cut LTVs, on average, by 26 bps for mortgages in the floodplains facing the highest inundation levels. However, lenders appear not to charge higher premiums on interest margins for properties more exposed to flood risk. Going beyond average responses, we uncover that the distribution of mortgage credit risk is not equally distributed among borrowers. The LTV cuts are most pronounced for lower-income borrowers (up to 74 bps) in flood-prone areas and for relatively low property values (up to 53 bps).

In this paper we investigate the importance of borrower’s liquid reserves accounts at the time of loan closure in explaining mortgage delinquency within two years of loan acquisition. Using a logistic model, we find that reserves are important determinant of mortgage delinquency hence credit risk. Coupled with lower levels of downpayment, lower levels of reserves significantly increase credit risk relative to high reserve and high downpayment regardless of time-period investigated. Credit risk generally gets flat after 6 months of reserves, equivalent to 6 months of mortgage payments, for lower levels of downpayment in recent years superseding home price decline period. First-time homebuyers and low-income first-time homebuyers at 95% LTV margin but with zero liquid reserves can benefit from a potential tradeoff between equity and reserves as our findings show that would improve their loan performance regardless of home price environment. In general, trading downpayment for higher level of reserves in some cases can also help borrowers with zero levels of reserves; however, we find this to depend on the home price environment and the level of tradeoff between reserves and downpayment.

There has been a significant growth of fintech lenders in the mortgage
lending space recently. I study if fintech lenders have the potential to broaden
the credit access to the borrowers who would have been denied by the traditional
lenders. I find that fintech lenders tend to lend to the borrowers with higher
debt-to-income ratios, lower loan to value ratios, lower income, higher age
and more minorities. Fintech lenders are less likely to deny the loan due to
DTI ratios and credit history. I find that credit access has expanded with the
emergence of fintech lenders. I also show that fintech lenders are not charging
higher interest rates to these borrower.

We analyze the costs and benefits of intermediaries for government-sponsored enterprise (GSE) mortgages using regulatory data. We find evidence of lenders pricing for observable and unobservable default risk independently from the GSEs. These findings are explained using a model of competitive lending in which lenders have skin-in-the-game and acquire information beyond the GSEs' underwriting criteria, but also charge markups. We find that most borrowers are better off in a counterfactual in which the GSEs' underwriting criteria are implemented passively. Finally, the observed differences between banks and nonbanks are more consistent with differences in their skin-in-the-game rather than screening quality.

This cross-country empirical study examines the phenomenon of growth convergence and
its relationship with early urbanization. Conflicting findings from various studies have
raised questions about whether poorer countries tend to grow faster than richer ones,
leading to income level convergence over time. We analyze a panel dataset covering 199
countries from 1990 to 2022, exploring the impact of early urbanization on economic
growth and its role in shaping growth convergence outcomes. Our findings offer valuable
insights for policymakers and researchers seeking to promote inclusive and sustainable
development, underscoring the significance of early urbanization as a potential factor
influencing economic growth. Policy implications and recommendations are discussed to
address the challenges and harness the opportunities presented by early urbanization.

Building on the classical monocentric city model of Muth/Mills which generates a spatial price gradient, we ask whether there is similarly a time-on-the-market (TOM) gradient. To test this, we construct granular TOM indices using regional multiple listings services data and measure their variation both within and across cities.

Preliminary results indicate the existence of a TOM gradient, though only for certain property types (e.g., single-family homes) and not others (e.g., condominiums). Additionally, the TOM gradient is nonmonotonic, flat, or even negative in some cities, especially in the South, suggesting important heterogeneity across cities. Finally, we see evidence of a COVID-19 shock, as the tendency of city centers to experience shorter TOM was weak during the Great Recession and the work-from-home boom.

In terms of mechanisms, previous findings suggest that homes are sold more quickly in neighborhoods with more homogeneous housing units (i.e., less “atypical’’ homes) or in neighborhoods with more demand relative to supply (i.e. “tighter” markets). Our preliminary results suggest that these may not be satisfactory explanations unless relative to their urban counterparts, suburban homes are more atypical and suburban housing markets are less “tight” markets.

Our results have immediate implications for understanding residential price movements, particularly spatial variations thereof, as price and TOM are known to be jointly determined. Knowledge of the TOM gradient can paint a more complete picture of how worsening liquidity has exacerbated affordability concerns in recent years.

Real estate plays a vital role in sustainable development, particularly in developing countries. This study aims to explore the role of real estate in the sustainable development of Nigeria, a developing country in West Africa; which is experiencing a rapid urbanization, with a population projected to reach 440 million by 2050, with a developing economy that as at 2021 sits as 26th in the world and number one in Africa. Findings from this research can be interpretive and used to make long call shots for other developing countries especially in Africa. The research employs a qualitative approach, using a case study method to examine the current state of real estate in Nigeria and its impact on sustainable development. Data was collected through interviews with key stakeholders in the real estate sector, such as developers, investors, and government officials, as well as a review of relevant literature.

The findings of the study indicate that real estate in Nigeria has the potential to contribute significantly to sustainable development, particularly in terms of economic growth and job creation. However, the sector is currently facing several challenges, such as a lack of proper regulations and infrastructure, which hinder its ability to fully realize this potential. Additionally, the study found that there is a lack of awareness and understanding among stakeholders about the importance of sustainable practices in the real estate sector.

The study recommends that the Nigerian government should take steps to address these challenges and promote sustainable development in the real estate sector. This can be done by implementing regulations and policies that encourage sustainable practices, such as green building standards and energy efficiency measures. Additionally, the government should invest in infrastructure development, such as transportation and power supply, to support the growth of the real estate sector. Furthermore, the study suggests that stakeholders in the real estate sector should be educated and sensitized about the importance of sustainable development to help them understand the benefits of sustainable practices.

Additionally, the study put forward that sustainable real estate development can also contribute to the achievement of several of the United Nations' Sustainable Development Goals (SDGs). For example, it can help to achieve SDG 11, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable. It can also contribute to SDG 7, which focuses on affordable and clean energy, and SDG 9, which promotes resilient infrastructure and industrialization.

Overall, the study highlights the importance of the real estate sector in the sustainable development of Nigeria, and the need for government intervention to address the challenges facing the sector. It also emphasizes the importance of raising awareness and understanding among stakeholders about the benefits of sustainable development in the real estate sector. The findings of this study could be useful for policymakers, developers, investors and other stakeholders in the real estate sector, as well as other developing countries seeking to promote sustainable development in their real estate sectors.

Early-stage evaluation of mega projects is essential for policymaking and follow-up policy adjustment. Using an announcement of the Northern Metropolis Plan in Hong Kong on 6 October 2021 as a quasi-natural experiment, we discern its immediate impact on housing price, consumption, school enrolment, and longer-term implications on inequality. Drawing from a rich dataset spanning 38,214 housing transaction records, 4.7 million consumption records, and school enrolment data across 18 districts, our difference-in-differences estimates unveil three significant patterns. First, we find a marked 3.9% rise in housing prices in the Northern Metropolis region one year after the announcement, with a profound 6.9% surge in the initial 4 months. Additionally, we discover a swift 4.1% uptick in consumption for residents in the treated region in the same initial 4 months, predominantly within the private housing market possibly with expected housing wealth realization. Last, we observe a rise in school enrolments, mostly likely from bordering mainland China, spanning primary and secondary school levels in the subsequent year. While our findings hint at narrowing cross-region inequality in the global superstar city, they also suggest burgeoning within-region inequality, as benefits seem skewed towards private home occupants. We thus call for policy attention on the affordability and welfare of individuals with lower socioeconomic status in the Northern Metropolis region.

There is a contentious debate on whether ride-hailing complements or substitutes public transportation. We address this question using novel data and an innovative identification strategy. Our identification strategy relies on exogenous variation in local transit availability caused by rail expansions. Using proprietary, anonymized trip data from Uber for 35 countries, we use a dynamic difference-in-differences strategy to estimate how transit expansions affect local Uber ridership in 100 m distance bands centered on the new train station.
Our estimates compare Uber ridership within a distance band before and after a train station opens relative to the next further out distance band. Total effects are obtained by aggregating relative effects at all further distance bands. We find that a new rail station opening increases Uber ridership within 100 m of the station by 60%, and that this effect decays to zero for distances beyond 300 m.

This paper decomposes raw fire sale discounts in housing markets into a house quality component and a quality-adjusted fire sale discount. We develop a novel computer-assisted text analysis algorithm to show that houses liquidated via fire sales have deferred maintenance stemming from reduced homeowner investment. We find this quality impairment explains approximately one-third to half of the raw fire sale discount.

This paper applies meta-analysis of school quality capitalization estimates reported in the literature to examine the relationship between state approaches to protecting property rights and price discovery in housing markets. After controlling for possibly confounding influences from the housing supply elasticity and unrelated regional effects, the analysis shows that restricting local government powers of eminent domain for redevelopment is associated with house prices doing a better job conveying information about school quality differences across neighborhoods. While the property rights variables are narrowly defined, we argue they likely indicate the state’s broader approach to protecting private property rights in general.

This paper examines the impact of e-grocery’s entry on the housing market, focusing on Amazon Fresh, an e-grocer that provides both grocery items and delivery services without establishing a physical store. We find that areas that receive Amazon Fresh services experience a 5.4% increase on housing prices when compared to areas that did not receive Amazon Fresh service but within the same metropolitan area following the entry of Amazon Fresh. We also find supporting evidence that local households increase online consumption following the advent of Amazon Fresh, indicating the amenity effect provided by Amazon Fresh. Additionally, we provide evidence that the entry of e-grocers can potentially reduce socio-spatial inequalities within the housing market. We find more pronounced effects on home values and online consumption in socio-economically disadvantaged areas such as lower income, lower education levels, higher rates of rental occupancy, minority populations, lower vehicle ownership, and lower retail accessibility. While Amazon Fresh boosts housing prices, Instacart, another type of e-grocery that only offers delivery services for existing stores, does not significantly raise housing prices, suggesting that delivery services within existing stores do not yield an additional amenity effect in the local housing market. These findings imply the importance of considering technological advancements' unequal distribution of benefits and their effects on housing affordability.

The loan-to-value (LTV) ratio is arguably the most commonly used gauge to measure financial leverage and credit risk around the world. LTV ratio is calculated as percentage of loan amount to assess value of the property. The purpose of this paper is to analyze the effects of loan-to-value ratio on home prices. Previous literature is focused on LTV ratio’s effect on mortgage (Xun, Lin, & Liu, 2018) and credit supply (De Araujo, Barroso, & Gonzalez, 2020). The paper contributes the market points of view on LTV ratio. High LTV ratio suggest that in case of sale larger part of the sale price belongs to the bank leaving seller with less control over the price. On the other hand, low LTV ratio gives seller financial flexibility and provides a bargaining opportunity for buyers. Our initial results indicate that there is a negative relationship between LTV ratio and sale price is negative. Indicating that lower LTV ratio homes have lower sale price as owners have more equity in the property providing them with more financial flexibility.

We study the effect on economic outcomes resulting from locational advantages of intermediaries through the lens of brokered transactions in the commercial real estate market. Specifically, we explore how locational advantage influences pricing outcomes and highlight the underlying channel. The empirical analysis is conducted on a dataset of over 210,000 commercial real estate transactions across the US. The results from our analysis suggest that brokers that have a locational advantage generate sales price premiums. Our findings are robust to a range of alternative explanations. Furthermore, we present evidence that transactions that involve brokers that have a locational advantage sell faster, the negative effect of disrupted markets is less pronounced when a broker that has a locational advantage is involved, and brokers that have a locational advantage are more likely to bring distant buyers to the table. Thus, we highlight the channel relating to locational advantage as a reduction in search and matching frictions due to an expansion in the auction house through an expanded network of the broker.

This paper examines housing market outcomes of transaction brokerage relationships. In contrast to single agency brokerage where a real estate broker represents either a buyer or a seller in a housing transaction with complete loyalty, transaction brokerage is an arrangement recognized in some U.S. states whereby a broker provides limited representation to both the seller and buyer in the same transaction with only limited loyalty to each party. In such an arrangement, the broker is presumed to merely be facilitating a transaction between the parties without working to the detriment of either party at the expense of the other party. The broker’s loyalty to both parties is limited to confidentiality regarding the price the parties are willing to pay/accept in a transaction, the motivations of the parties to enter a transaction, and the terms the parties are willing to accept in a transaction. Transaction brokerage also stands in contrast to dual agency arrangements, which supposedly allow a broker (supposedly, because it is illogical) to represent both parties simultaneously with complete loyalty and confidentiality to both parties. While dual agency requires written acceptance from both parties, transaction brokerage arrangements do not require disclosure to either party unless the broker is transitioning from a preexisting single agency relationship with either or both parties. (In all three forms of representation discussed here, brokers owe all parties fair and honest dealing and accurate accounting for all funds entrusted to them.) This study investigates whether transaction brokerage impacts price and/or time-on-market using a large data sample of house transactions from Florida, a state that prohibits dual agency but permits a very similar acting transaction brokerage. The results indicate that prices are inversely related to transaction brokerage arrangements, to the benefit of the buyer and detriment to the seller.

This study presents findings regarding the influence of information asymmetry on the dynamics of housing search and matching. We use a unique dataset to gather detailed observations of property-related information, real estate agents, and the involved buyers and sellers.
Discrepancies in the prices buyers pay for nearly identical houses prompt an exploration of information asymmetries in housing markets. It is plausible that buyers from outside the area face higher search costs and possess less knowledge about the local market compared to current residents. Additionally, non-local buyers might base their price expectations on market conditions in their hometowns, thereby affecting the final transaction outcomes. Furthermore, variations in demographic characteristics, such as income, age, education, and ethnicity among homebuyers, may result in differing abilities and willingness to engage in effective search and negotiations.
We focus on real estate agents' role in mitigating information asymmetry in housing markets. In particular, we examine the role of real estate agents in the matching (or not) of counterparties of different nationalities or racial or ethnic groups and the effects on transaction outcomes.

Purchasing a house represents an economic and emotional investment for homebuyers. Besides financial considerations, the ability to see themselves and their families live in a house and make it a home impacts homebuying decisions. The purpose of this study is to assess the extent to which “home”-related features as opposed to “house” characteristics mentioned in MLS listing texts impact sales prices. In particular, we assess the pricing effects of words relating to home (kids & pets, recreation) and house features (kitchen, baths, condition, architecture) in listings.

To derive our measures of home and house characteristics, we employ a textual analysis (dictionary) approach. Our study brings together the residential brokerage literature, which focuses mainly on the economic aspects of homebuying, and other literatures that focus on the emotional and social aspects of a home. It hereby not only contributes to the literature on textual analysis in real estate and residential brokerage, but also has implication for brokers and their marketing strategies.

We look at the effect of taxation and broad-based spending on the redevelopment of commercial property. Using regression discontinuity and close votes on local government current expense tax levies, we find that cities that vote to cut taxes and spending have less redevelopment of commercial property, relative to the cities that renew them. There is less demolition two years after the vote, and less new construction two, three, and four years later. The drop in demolition is at least 85%, and the drop in new construction is about 40%. This pattern of results holds for cities with below-average population growth, suggesting that on the margin firms value local government spending more than they value cutting local government taxes.

Exposure to natural disaster risk is an important consideration for participants in the commercial real estate market. In the past 30 years, in the state of Florida alone, the total estimated economic loss from hurricanes is over $250 billion, which doesn't directly consider insured losses that commercial properties may face. While Florida has adopted many new policies, such as revisions to building codes, changes to insurance regulations, and incentives to develop greater resiliency throughout the physical building stock, our knowledge is limited in regards to how commercial properties, in aggregate, are impacted by disaster risk. In this study, we examine three major dimensions of possible reallocation in the FL CRE market from 1995 to 2022. First, we find that there is a 3.9% decline in the likelihood of a property's designated use in the three years after exposure to a hurricane. Specifically, we find that use code changes to commercial property are significantly less likely compared to changes to industrial property. Next, we examine the impact of these exposures on market dynamics, such as sales price and volume. Preliminary results suggest that both sales price and volume experience a significant decline. Finally, we plan to utilize these results to examine how disasters via commercial real estate can impact industry agglomeration in fourteen major Florida MSAs.

To what extent is the pricing of natural disaster risk in commercial real estate influenced by investors’ views on climate change? It is well-known that perceptions on climate change vary widely in the population and are influenced by a myriad of factors (Howe, Mildenberger, and Marlon, 2015), such as political orientation. Figure 1 illustrates this point. Right-leaning ideologies often correlate with skepticism towards climate change, leading to a potential underestimation of disaster risk in property valuation. Conversely, left-leaning political inclinations generally align with a greater acknowledgment of climate risks, potentially translating into a more cautious approach to real estate investment in areas prone to climate-related disasters.

In this paper, we examine the extent to which investors incorporate natural disaster risk in the pricing of commercial real estate properties. We find that, among all properties in our sample, natural disaster risk positively impacts capitalization rates (cap rates), indicative of greater risk and lower valuation (see Table 1 and Figure 4). This suggests that properties at greater risk inherently carry a higher likelihood of damage or destruction, which can lead to significant financial losses, increased insurance costs, and potential revenue interruption.

We further investigate whether the pricing of natural disaster risk varies across locations based on local political orientation and perceptions of climate change. Importantly, we find that disaster risk is only priced in areas that are politically left-leaning and/or have a strong belief in climate change (see Tables 2 and 3 and Figure 5). Even conditional on a high belief in climate change, cap rates in right-leaning areas exhibit little relation to natural disaster risk (see Table 4), suggesting that the influence of political ideology is substantial. These findings indicate that the pricing of natural disaster risk in the real estate market is not uniform but is instead influenced by the local political climate and perceptions on climate change. This disparity suggests that market efficiency may be compromised by ideological biases, potentially leading to undervalued (overvalued) risk in politically conservative (liberal) areas.

To better understand the geographic variation, we explore other demographic and economic drivers of climate change beliefs. We show that belief is higher in areas with a higher GDP, higher population density, higher household income, and a more educated population (see Figures 2 and 3). Education, which promotes independent thought, could mitigate the effects of political ideology. Furthermore, a higher level of education, particularly in environmental science or related fields, may correlate with a greater awareness of climate change (see Figure 2), leading to more pronounced pricing of disaster risks in real estate. We intersect political ideology with education level and find that pricing of disaster risk is primarily based on political ideology, not level of education (see Table 5). In fact, disaster risk is most prominent in the cap rates of low education, left-leaning areas.

Lastly, we find that the pricing of natural disaster risk is only in areas that both believe in climate change and expect climate change to have harmful effects (see Table 6). In other words, investors appear to factor natural disaster risk into property prices when there is both acknowledgment of and expectation for the harmful effects of climate change. Importantly, expectations about harmful effects of climate change are more correlated with left-leaning political ideology than more education (compare Panel B of Figure 1 to Panel B of Figure 2).

This paper studies how the development of internet infrastructure and firms' demand for higher internet speeds impact real asset pricing. We document significantly higher transaction prices for office properties that are near the establishment of speed-related internet infrastructure. Using an instrumental variable approach, multivariate and dynamic staggered difference-in-difference analyses, and saturated fixed effects, we sharply identify our findings. As an important demand-driven channel, we show that the pricing effects reflect increases in capitalized effective rents, and the mechanism behind the increased demand is knowledge and technology intensive firms that most value their need for internet speed.

Rapid advancements in automation, artificial intelligence, and computer-based technologies are poised to transform the modern workplace. The adoption of these advanced technologies (AT) would inevitably impact corporate real estate (CRE) decisions in firms as they allocate production factors to capital and labor. On the one hand, advanced technologies increase human productivity, leading to a higher demand for CRE spaces (complementary effect). On the other hand, improvements in technology might directly replace workers, reducing CRE demand (substitution effect). Which effect dominates is an empirical question. Our preliminary findings suggest that firms in industries with higher exposure to artificial intelligence-related positions are associated with lower usage of corporate real estate, suggesting the substitution effect dominates. In terms of contribution, we are the first to investigate how the adoption of advanced technologies in the workplace affects corporate real estate decisions. Given the size of the corporate real estate market, understanding the space demand from the corporation side would help predict the trends of commercial real estate in the intermediate and long term. The study will also contribute to the existing literature on corporate real estate holdings and shed light on remote work and its impact on real estate values.

This paper investigates the association between REIT board characteristics and REIT board meeting frequencies (total board meetings, monitoring sub-committee meetings, and advisory sub-committee meetings). Using REIT data during the period of 2010 to 2019, we provide evidence that REITs’ board composition matters as it relates to how board committees and sub-committees’ work. We find consistent evidence that the longer the board members’ tenure, the less engaged they tend to be. For large REITs, we find that the frequencies of board meetings are positively associated with board size. We also find that outside board directors and CEO-Chair are positively associated with board meetings. However, for small REITs, board meeting frequencies are negatively associated with outside board directors and CEO chair. Our results provide empirical evidence that the corporate governance for small REITs is relatively weak.

This article investigates the impacts of the POC (Percentage of Completion) methodology on Brazilian public companies in the Real Estate segment. The POC methodology is widely used for revenue recognition in real estate projects, but its application can generate significant impacts on companies' financial results. Through an exploratory analysis, the influences and consequences of the POC methodology on Brazilian public companies in the Real Estate segment were investigated. The results show that the application of the POC methodology can lead to distortions in revenue recognition, especially in long-term projects. Additionally, the improper use of the methodology can result in significant impacts on the evaluation of companies' performance, financial indicators analysis, and investors' decision-making. In summary, this article contributes to the debate about the use of the POC methodology in the Brazilian real estate sector and highlights the importance of a careful and critical analysis in its application by public companies. The analysis presented in the article remains relevant, even with the recent CVM Curriculum Office that maintained accounting standards with the requirements of company performance and internal controls. The CVM's guidelines and requirements provide a regulatory framework for companies to follow, but it is still crucial for them to analyze and understand the implications of the POC methodology on their financial results and decision-making processes. Therefore, a careful and critical analysis of the POC methodology's application is still necessary for companies to ensure transparency and accuracy in their financial reporting.

This research compares various balance sheet and income statements pre-COVID 19 (1st Quarter 2020) to post-COVID (1st Quater 2023) testing for significant differences of corporate real estate holdings, leases, and other factors depending on data availability. We identify numerous sectors of publicly-held comparines such as manufacturing, information,, wholesalers, retailers, financial, and professional and business services.

Amidst the ever-evolving landscape of regional Australia, the challenges surrounding key worker housing have emerged as a pivotal factor influencing economic resilience and social well-being. This paper analyzes the issues plaguing key worker housing in regional areas, drawing upon an integrated approach incorporating quantitative survey data, in-depth interviews, and policy implications.

Key workers, encompassing a diverse range of professionals vital to social reproduction, face mounting hurdles in securing affordable, suitable housing. As this study reveals, this issue stems from a supply-side predicament resulting from decades of insufficient investment and a prevailing policy vacuum. Notably, the shortage of affordable housing for key workers is inextricably linked to neo-liberal economic policies that have, over the years, financialized the housing market. This phenomenon, as observed in Australia and worldwide, has distorted housing prices in central business districts and sought-after residential areas.

The impacts of this housing crisis reverberate throughout regional economies. Key workers, who make up a significant portion of the workforce, are burdened by the lack of affordable housing options that do not exceed 30% of their family income. This paper underscores that these challenges are especially pronounced in regional Australia, where housing affordability remains a pressing concern.

Through a synthesis of survey responses, interview insights, and policy recommendations, this research proposes a Key Worker Housing and Employment Synergy Framework (KW-HEFS) designed to address the housing affordability and employment accessibility conundrum key workers face. This framework advocates for collaboration among stakeholders, recognizing their pivotal roles in the regional housing ecosystem. By pooling their expertise and resources, stakeholders can inform policy directives, investments, and interventions tailored to the unique challenges encountered by key workers.

In conclusion, this paper illuminates the urgency of addressing key worker housing challenges and their far-reaching implications for regional economies. It provides a roadmap for policymakers, academics, and society to navigate this issue, promoting economic resilience and social well-being in regional Australia. Bridging the divide between key worker housing challenges and economic resilience is essential for shaping a more equitable and sustainable future for regional communities.

Prior research used data from the 100 largest MSAs to analyze whether there is a link between regional industry concentration and regional housing price indices. In this paper, we allow the housing price index to identify for us those “normal” markets that experienced a peak in the years 2005, 2006, or 2007. Using the year identified as the peak for each MSA (Metropolitan Statistical Area), we subset sample into pre-peak and post-peak periods. We define the beginning point of the pre-peak subsample as 2003(the year the bubble really got started) and the ending point as the end of the sample. We find that during a bubble, the level of industrial concentration contributes to the decrease of house price index and negative impacts are attenuated after a burst.

The Ghanaian Constitution, after 1992, prohibited the outright sale of “Stool Land” (i.e., land held in trust by tribal chiefs) to any individual, entity, or corporation. This means that the fee simple (or freehold) is to be held by the chiefs in perpetuity, and that land for real estate development can only be obtained through a leasehold arrangement. In a typical residential lease in this setting, the lessor lets a site to a tenant for 99 years at a peppercorn (nominal) rent, and the tenant puts up a building. The ground lease term for commercial real estate development is often shorter. While the Constitution of Ghana specifies the required lease terms for residential and commercial real estate, it is “quiet” on how and to whom the reversionary title to the “improvement(s)” with remaining economic life will revert upon the expiration of the lease. This creates ambiguity regarding the rights and obligations of lessors and lessees and insecurity in the future transfer of property rights. Therefore, our research aims to address some questions to extend the literature in this area. First, given this lack of clarity, how have the relevant stakeholders handled issues arising from the current set-up? How will lease renewals be tackled? What are the best practices of the leasehold system from other jurisdictions? Do real estate appraisers consider the ownership structure distinction in their valuations? Does it pose title insecurity problems for mortgage lending institutions?

The Porter Hypothesis suggests that environmental regulations can encourage firms to invest in Research & Development (R&D), resulting in innovations and improving firms’ competitiveness. Is this true? Are there any alternative strategies for firms to mitigate carbon risks? This article addresses the question by evaluating firms’ likelihood to choose either environmental Mergers and Acquisitions (M&As) or environmental R&D to cope with increasing greenhouse gas (GHG) emissions. We investigate the environmental M&As and environmental R&D investments of 7,903 companies at the global scale during the period from 2000 to 2021. It is found that firms’ carbon strategies are attributed to the extent of carbon risks. Specifically, firms with larger GHG footprints favor environmental M&As over environmental R&D investments, irrespective of economic and institutional contexts. This strategy, which presents an external alternative to building in-house capabilities through environmental R&D investments, can better align with a firm's resources, business model, or regulatory landscape it operates. Our findings also underscore the importance of a careful evaluation of the most cost-effective approach between environmental M&As versus environmental R&D investments for firms grappling with rising GHG emissions. By providing a more nuanced understanding of these choices, our study can assist firms in selecting the option that best serves their corporate interests and strategies.

I create a climate risk index for banks based on the climate exposure of their asset portfolio. Banks highly exposed to climate risk perform worse than those not exposed from an accounting, stock, and bond perspective. Around significant climate events, banks with more climate exposure are associated with lower cumulative abnormal re- turns in affected and unaffected areas, suggesting salience plays a role. Furthermore, banks with higher climate risk are more likely to fail, merge, or be acquired by those with lower climate risk. Overall, this paper suggests that climate risk is an important factor in pricing banks’ securities.

The recent increase in sargassum seaweed on the beaches of Southwest Florida poses significant challenges for the coastal real estate market, highlighting the need for effective beach clean-up initiatives. This study investigates the community's willingness to contribute financially to these efforts, which is crucial for preserving the value and appeal of coastal properties. A non-market valuation approach, specifically the dichotomous-choice elicitation format, is employed to quantify the economic value that residents place on maintaining clean beaches. The research, involving a survey of local residents, examines factors such as environmental attitudes, personal connection to the beaches, income levels, and perceived benefits of clean-up programs. The findings provide insights into the level of community-driven financial support for environmental preservation and its direct impact on coastal property values and development strategies. This study is particularly relevant to real estate professionals and policymakers, offering valuable perspectives on the interplay between environmental challenges and real estate market dynamics, essential for guiding future coastal development and conservation efforts.

This study employs a quasi-experimental design to explore the relationship between the timing of vacant lot investment and land options. We investigate the timing effects of two options: housing investment, which involves building residential housing on vacant lots, and greening investment, which entails converting vacant lots into community gardens. By analyzing time-to-event data at the lot level from a long-term vacant lot greening program in Philadelphia, we address the questions of whether and why greening can serve as a more effective investment option than housing in accelerating investment and reducing vacancy. We examine the presence of the real options channel and provide policy insights regarding the effects of price uncertainty, price growth and landownership on the timing of vacant lot investment. Our findings show that greening investment occurs sooner than housing investment and is associated with lesser effects of price uncertainty and price growth on the investment timing. Vacant lot greening counteracts the deceleration of investment in high-uncertainty and low-growth neighborhoods, effectively reducing vacancy through the real options channel, particularly for privately held land.

Several studies have examined the impact of market fundamentals on housing prices. However, the effect of economic sectors on housing price is limited despite the existence of two-speed economies in some countries such as South Africa. We investigated the effect of mining activities on housing prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an Auto-Regressive Distributed Lag model (ARDL). In the short run, we found that changes in mining activities, as measured by the contribution of this sector to GDP, impacts housing price of mining towns directly after the first quarter, and after the second quarter in non-mining towns. Second, we found that inflationary pressure is instantaneous and impact housing prices in mining towns only in the short run but not in the long run, while increasing housing supply will help to cushion housing prices in both submarkets. We extended the analysis by examining possible spillover in housing prices between mining and non-mining towns. We found evidence of spillover in housing price from mining towns to non-mining towns without any reciprocity. In the long run, mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant. These results reveal that enhanced mining activities will increase housing price in mining towns after the first quarter and this is expected to spillover to non-mining towns in the next quarter. These findings will inform housing policy makers in stabilising the housing market of mining and non-mining towns.

Natural disasters disrupt housing markets, damaging homes and reducing property values. Lacking granular, property-level data immediately after a natural disaster, it is common to resort to publicly available data when estimating price effects. Unfortunately, this requires treatment aggregation and introduces measurement error. We exploit an experimental design to measure price effects after Hurricane Ian unexpectedly strikes southwest Florida in September 2022 while the state is recovering from the the COVID-19 pandemic. Using leading difference-in-differences and synthetic control approaches under various treatment definitions, we argue the combination of measurement error and scarcity of housing data pose substantial challenges for understanding whether natural disasters cause permanent changes to real estate markets. While property owners and insurance companies have immediate needs to document economic damages, governmental entities and investors have longer-horizon concerns about asset valuations. Our preliminary results suggest that Hurricane Ian led to positive price effects that vary over time, although further refinements are needed to pin down precise magnitudes and timing. These findings underscore the importance of a battery of robustness checks, data quality standards, the importance of functional form, and the sensitivity of quickly accessible and high-quality public information when estimating the impacts of natural disasters.

Many urban universities provide on-campus housing, but the amount of student housing available is limited and is usually unaffordable for the majority of the students living in the city, leading most students to live off-campus with long commutes. This study, based on 700,000 student records covering eleven years in a metropolitan city, examines on-campus housing benefits. This analysis is complemented by single-year, self-reported data on student satisfaction, off-campus living situations, and commuting time. The results from a fixed-effects panel data model show positive effects on GPA, particularly for first-year students. This effect is more pronounced for students with higher grades. Off-campus living and commuting significantly affect students’ satisfaction. We argue that the unaffordability of on-campus housing creates housing stress, disengaging students from university activities and impacting their overall satisfaction. The study emphasizes the need for universities to address housing challenges to enhance student experiences and bridge the disparities between on- and off-campus living conditions.

Previous research has consistently shown that homeowners have higher levels of life satisfaction than renters. This study investigates the underlying factors responsible for this disparity. Using data from the Panel Study of Income Dynamics (PSID), our findings reaffirm a positive association between homeownership and life satisfaction. However, after accounting for various homeownership benefits, such as household wealth, consumption, housing quality, health, and residential stability, the previously observed positive homeownership effect becomes statistically insignificant and approaches zero in magnitude. This finding indicates that these homeownership benefits can completely explain why homeowners have higher levels of life satisfaction. When controlling for these homeownership benefits, the insignificant effect of homeownership on life satisfaction persists across various housing cycles and within the context of the COVID-19 pandemic. Among these homeownership benefits, residential stability is critical in explaining the impact of homeownership on life satisfaction. Furthermore, even after accounting for homeownership benefits, homeowners still express greater satisfaction with their houses but lower satisfaction with their jobs compared to renters.

The aim of this article is to shed new light on the analysis of real estate investment trust (REIT) returns. To improve the contribution of standard asset pricing models, we develop and suggest the use of potential common factors related to REITs specific risk exposure: leverage, cash flow volatility and investment growth. We group REITs into portfolios based on their market capitalization, book-to-market ratios, and loan-to-value ratios. Using unconditional and conditional asset pricing models, our results show that the leverage factor is a relevant risk factor in REIT returns. This conclusion remains when other REITs risk factors are considered, including the Fama and French factors.

In this paper we investigate whether expected idiosyncratic volatility (IV) and expected idiosyncratic skewness (IS) risk are both present and significant in the cross-section of expected REIT returns. Our firm-level empirical tests indicate a significant and negative relationship between REIT returns and both IV and IS risk. The observed risk-return trade-off remains significant even after controlling for firm-level characteristics and common risk factors. The empirical results document that firm-level IS risk is consonant with firm-level IV and that IS risk not subsumed by IV and vice versa. Using Hou and Loh’s (2016) cross-sectional decomposition analysis, we find that firm-level IV and IS capture a very small percentage of each other’s average return premium, while a residual component accounts for the rest.

The treatment of real estate investment trust (REIT) dividends as pass-through distributions to shareholders implies that an optimal dividend policy is to pay distributions of 100% of income both from operations and from capital gains. However, cash flows related to the depreciation allow REITs to pay more than 100% of earnings, and a majority of REITs regularly distribute more than 100% of income. A relevant question therefore is: How much of depreciation-related cash-flow should a REIT distribute to shareholders? This paper uses an after-tax discounted cash-flow framework to develop a REIT valuation that depends on a REIT’s property turnover rate (holding period). Analysis of the model shows that there exists an optimal target holding period for a REIT’s property portfolio, but regardless of the optimal holding period, REITs should avoid paying dividends from depreciation cash flows (capital).

This paper delves into the relationship between media content sentiments and returns of Real Estate Investment Trusts (REITs). Leveraging proprietary investor sentiment measures from Thomson Reuters, including dimensions such as "stress," "emotion vs. fact," "dividends," and "price direction," we employ a multi-step approach to examine their impact on REIT returns. Through time series regression and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, we establish the statistical significance of media content sentiments in explaining REIT returns and market volatility. Employing Lasso analysis, we identify the sentiment related to "price direction" as the most influential factor impacting excess REIT returns consistently across various REIT types and weighting schemes. Our analysis enhances traditional asset pricing models, improving the adjusted R-squared, and provides insights into the role of media sentiment in shaping REIT returns.

This case study examines the complex refinancing process of a Class A office build-
ing in Washington, DC, occupied by the Federal Emergency Management Agency
(FEMA). Facing imminent foreclosure, the property owner sought a maturity exten-
sion of the loan held by a commercial mortgage-backed securities (CMBS) and serviced
by PNC. The case delves into the challenges and negotiations led by Kevin Gibbons,
Asset Resolution Consultant at PNC, and the collaboration between CMBS investors,
borrower representatives, and legal advisors. The study showcases the critical roles
played by PNC and Iron Hound Management Company and illustrates how innova-
tive refinancing strategies can save a property from financial distress. With a loan
amount of $130 million, the dramatic story provides valuable insights into real estate
deal dynamics, especially when future lease uncertainties loom.

In the ever-evolving landscape of real estate, staying updated, connected, and ahead of market trends is crucial. Plus, with so much competition for a finite number of listings, strategic and creative marketing tactics are necessary to stand out. Podcasts can offer a solution.

This session will explore two ways podcasts can be used within the industry. By leveraging internal podcasts, even nationwide agencies can keep their teams equipped with the latest industry insights, company intel, and best practices–without alerting the competition. And, through hyperlocal podcasting, agents can bring attention, traffic, and engagement to their area to become the go-to for new business.

Key learning objectives:

• Understand the benefits of internal podcasts in providing real estate professionals with essential industry insights, sales tips, and best practices, enhancing their skills and knowledge.
• Explore the role of hyper-local podcasting in marketing, focusing on creating content that resonates with specific communities, showcases local market expertise, and attracts potential clients.
• Learn practical steps to implement both internal and hyper-local podcasting, from concept development to technical execution, tailored to enrich the professional growth of agents and amplify their marketing efforts.

This paper presents a three-stage property investment evaluation project that integrates the CoStar database into real estate finance courses. Focusing on market analysis, demand-supply dynamics, real estate pro-forma, IRR estimation, and DCF valuation, the project enhances student understanding of foundational real estate investment knowledge and prepares them for evaluation of real estate investments. Students conduct an in-depth market and submarket analysis, formulate assumptions for their financial model, estimate returns, value the property, conduct sensitivity analyses, and present their findings in a professional investment prospectus. The project’s scaffolding design ensures active student engagement with the instructor feedback. Integration of CoStar and a hands-on evaluation of actual property investments deepens understanding of commercial real estate dynamics and KPIs, fosters active learning, and provides students with essential job-ready skills.

<p>There is an extensive but contradictory literature on the capitalization of property taxes. These wide-ranging estimates reflect the difficulty of isolating the effect of taxes on home prices and calculating the present value of tax payments over long and uncertain time horizons. Philadelphia's abatement policy exempts new development from 10 years of property taxes. The policy provides an ideal natural experiment to test property tax capitalization by creating contemporaneous intra-jurisdiction tax variation where the duration of the tax benefit is finite and known. Consistent with theory, the abatement's tax benefits are initially fully capitalized into home prices. However, as abatements near expiration, the benefits become <em>over-capitalized</em> in home prices. The paper also finds that homeowners are likelier to become delinquent on their mortgages when abatements expire.</p>

The recent experience with pandemic related commuting restrictions and wide fluctuations in gasoline and diesel fuel prices highlight the fact that commuting costs are uncertain when location and housing decisions are made. Even without these high-profile recent events, commuting cost uncertainty is ubiquitous. Periodic variation in traffic congestion from road repairs or accidents, changes in household commuting patterns from changing jobs, and modifications in public transit infrastructure or schedules create commuting cost uncertainty. The question is: do households take this type of commuting cost uncertainty into account when making housing and location decisions and, if so, how are their decisions reflected in the urban land market? This paper provides a theoretical and empirical analysis of how commuting cost uncertainty affects the spatial configuration of American urbanized areas.

<p>The literature offers little rigorous empirical evidence about how large scale civil disturbances affect housing markets. This study begins to fill this gap, examining the housing market consequences of the violent disturbances in Ferguson, MO. House sales in the immediate area decrease significantly in the year after the riots, making any short-term analysis difficult. Nonetheless, full sample and repeat sales analysis of pre- and post-event pricing show surprisingly localized price effects, significant declines within one mile of the rioting epicenter that fall off beyond one mile. The price discounts fade after 3-5 years as destroyed commercial properties are rebuilt over time.</p>

<p>This study seeks to understand gentrification trends during the Covid-19 years of 2020-2022. We employ a novel measure of &ldquo;gentrification pressure&rdquo; derived from HMDA mortgage application data. This method allows us to capture annual trends in income level changes at the geographic level of census tracts. We focus on low-income, central-city areas in a handful of cities in the U.S. The pandemic contributed to extremely high demand and in the residential real estate markets. Compared to the pre-Covid years of 2018-2019, we expect to see an acceleration of gentrification from home purchasers in some census tracts. Across cities we find different levels of relative gentrification pressure based on different migration patterns during the pandemic times.</p>

This paper assesses the role of local greenness in attracting ESG capital into infrastructure deals in the US. Local greenness refers to state-level policies and incentives to increase energy efficiency, local capabilities to develop green assets, and local citizen’s green ideology. We identify ESG investors as infrastructure private equity (PE) fund management firms who have signed the United Nations Principles for Responsible Investment (UN PRI). We find that in states with higher greenness, ESG PE investors are more likely to be involved in an infrastructure deal. We show that green policies and incentives contribute to a boost in green capabilities and green ideology thus reducing political and regulatory risk for ESG investors interested in low-risk-low-return way of “greenifying” their portfolio.

This paper examines how the COVID-19 pandemic impacted the demand for green buildings in the wake of migration and work-from-home shifts. Particularly, we attempt to identify whether the change is attributed to sustainability or the search for economy. Although COVID-19 negatively impacted the office buildings' occupation rates, we show that LEED certified buildings were less affected. We provide comprehensive evidence showing that after the pandemic, the market is willing to pay more to go greener, even in the states with falling rents. While “green” premiums are falling on a bearish office space market, the occupancy rates hold higher than for non-certified comparable buildings and make such investments less at risk of getting bankrupt. We conclude that LEED certification helps to shape more sustainable building design even for older spaces, and helps to reduce the negative environmental impact.

The green living phenomenon has long been taking human attention. Extensive research has evaluated its impact on human life. In real estate, due to a lack of data, the effect of environmental life and policies on the real estate industry is narrowly investigated. By using city greenness data for the 100 most popular cities in the USA, we explore the city greenness effect on the development of new multifamily houses. Our data ranges from 2019 to 2023, excluding the year 2020. By using panel data for multi-years and cities, we can obtain a robust measure of the relationship between city greenness and real estate development. To further strengthen our findings, we also include city environmental laws in our study.

Investors in real estate have significant exposure to climate risk due to the innate characteristics of real property. Real property is a tangible and immovable asset class; hence they are financially costly to physically protect against extreme weather events. Furthermore, as the frequency of these extreme events become more intense, the financial cost of hedging climate risk through standard insurance contract may become prohibitive or even unavailable as insurers exit markets that are prone to climate-related disasters. Commercial and luxury residential estate investments are often concentrated in areas with high exposure to climate-related risks such as coastal areas which are exposed to flooding, flat lands which are exposed to high winds, and scenic areas that have high exposure to drought. Given this, investors may also be exposed to future legal risk for damages by extreme weather. Lastly, real estate accounts for about 40% of all greenhouse gas emissions, therefore, they may also be exposed to the transitional risk from future regulations that could mandate changes to building form and interior facilities to comply with lower emission standards.

In reality, real estate investors would find that very few counterparties will be willing to trade in climate risk-related risk claims because they are difficult to quantify, non-diversifiable, and may require multiple payments from one climate event. This is a particularly important issue for real estate investors because hedging the financial risk of real estate investment is already challenging under the current structure of financial markets.

In this research, we explore whether real estate investors can self-insure against climate risk, by holding long (short positions) in portfolios of real-estate-related stocks whose pay-offs are closely related to climate-related events. The research delineates climate risk by employing the climate policy uncertainty (CPU) index that was developed by Konstantinos Gavriilidis (2021). The CPU is created from the textual analysis of climate-related news articles[1] published in 8 major US newspapers (namely: Boston Globe, Chicago Tribune, Los Angeles Times, Miami Herald, New York Times, Tampa Bay Times, USA Today, and the Wall Street Journal) from 1987 to 2022. Furthermore, the study also examines the heterogeneity in climate risk exposure of real estate firms by sector, size, and geographic dispersion of assets.

We provide estimates of the impact of different water supply and sewer systems on residential house prices in non-urban areas in Fresno County, California. We also explore the impact of groundwater availability on rural house prices. These topics have been extensively explored in developing countries (den Berg and Nauges, 2012; Anselin et al., 2010; North and Griffin, 1993; Yousuf and Koundouri, 2005), yet surprisingly, little attention has been given in developed countries. Most studies in the United States focus on the impact of water quality on house prices (McLaughlin, 2011), particularly properties with private wells. Though we acknowledge the importance of water quality, our paper sets out to explore how different water sources and water availability impact residential house prices in rural areas. Local organizations advocating for the importance of safe drinking water are voicing concerns over home buyers purchasing in areas where water provision is limited. Not taking proper measures prior to acquiring a property dependent on private wells, for example, may eventually result in considerable loss of property value.

This study uses contextual analysis to explain the effects of media coverage on immigrant homeownership in the United States. The creation, sharing, and exchange of information over networks and virtual communities among immigrants and local natives is facilitated by media coverage of the housing market, but there are significant differences across ethnic groups and generations in their preferred media outlet's country of origin. We document that: a) first-generation Asian immigrants are influenced by positive housing news from the media in their country of origin, whereas second-generation Asian immigrants rely more on local housing news from U.S.-based media sources; and b) first-generation African and Hispanic immigrants rely on positive housing news from both the US and their country of origin to make house purchase decisions. The result is robust after we adopted an exogenous shock to media coverage measured by nationwide media strike. Education, employment, and income are all channels that influence the geographic origin of one's preferred source of media. Positive local housing-related news from U.S.-based media outlets has a greater impact on well-educated and/or high-income immigrants than on less educated and/or lower-income earning immigrant peers. Similarly, employed immigrants are more likely than unemployed immigrants to be affected by housing news disseminated by U.S. media outlets. This paper offers novel insights into information assimilation and immigrant homeownership in the United States.

This paper seeks to understand the dynamics of affordable housing problems in Lahore, Pakistan, with a focus on heterogeneity by household income and neighbourhood characteristics (i.e. housing markets, amenities, etc.). Quantile analysis results suggest that while lower-income households may enjoy greater affordability by sorting into poorer-quality housing and neighbourhoods, their affordability becomes a lot less significant if they maintain better housing quality. Housing unaffordability is highest for the middle-upper class (i.e. 60th-80th quantile for household income) as they face the choice of higher-quality housing than their income level. We believe that supply constraints in the mid- to upper-level housing markets would be the main cause of this heterogeneity. In the real-world situations, households with different incomes do compete for lower-quality housing to enhance their affordability and we investigate who win in this competition. Some higher-income households won in the competition while some lower-income households ended up facing higher unaffordability. Affordability for lower-income households is worse if they sort into higher-quality neighbourhoods with more homogeneous housing markets. We also analyse which components (housing vs. neighbourhood quality) different income groups make trade-offs for their housing choices.

It is often argued that the “relationship between law and transgression is more complex than usually interpreted” (Chiodelli and Moroni, 2014, p. 168). In post-colonial Small Island Developing States (SIDS) like Jamaica, housing policies are operationalized within a society’s unique institutional framework, creating behavioral outcomes largely determined by the interaction between the rules-of-the-game and the policy provisions.

Termed ‘nomotropism’ or “acting in light of rules” (Conte, 2011), this behavior has been instrumental in our evolving understanding of urban informality and the spatial form of informal developments, especially in the Global South. I extend this argument and posit that in many formal housing markets, nomotropism can explain risky, sub-optimal or ‘irrational’ decision-making and risk preferences and by extension, poor housing outcomes in institutionally constrained environments. This paper, an update of last year’s submission and the last in a series of three, interrogates the decision behavior of actors in response to the underlying principles of two unique Jamaican housing policies- mandatory contributions to the National Housing Trust and arbitrary loan limits on the funds accessed from the Trust- using controlled lab-in-the-field experiments. I rely on the findings of the first 2 papers and of a pilot study to customize four experimental exercises, programmed and administered using oTree (Chen et al, 2016) targeting 120 subjects in the parish of St. Elizabeth, Jamaica. These exercises included risk elicitation, the ten-item personality index, a private auction and survey on housing norms and beliefs.

It is expected that when subjects are given scenarios with the option for informal transactions, they will choose informal channels over formal channels and riskier investments than their base risk preferences. It is also expected that the findings will show that the mandatory contributions create a statistically significant endowment effect that increases participants’ willingness-to-pay; and appetite for risk. It is expected that when the loan limits are situated as reference prices, reference dependent behavior will be evident in the auction and in the risk preferences of subjects. The results will provide evidence of a behavioral effect caused by these two policies that have existed in the Jamaican market over the past 50 years and might lend key contextual insights for testing the effects in field experts and also provide key insights to real estate analysts who struggle to interpret current market trends for the Jamaican housing market. It also facilitates causal inference re the behavioral effect of housing policies which, in the absence of detailed housing market data in the Jamaican context, would otherwise be impossible.

The emergence of the first Multifamily buildings reveals a recent dynamic in the Brazilian real estate market. Within this context, this research aims to explore and comprehend the demand for Multifamily developments in Brazil, focusing on the fundamentals that drive the growth of this specific sector.

Multifamily pertains to residential buildings designed exclusively for income purposes, aiming to accommodate multiple families in a professionally managed building, with institutional investors involved. The research seeks to provide a comprehensive analysis of the demand for multifamily housing in the Brazilian context, identifying fundamental factors influencing this phenomenon. At the conclusion of this study, the goal is to offer a deeper understanding of the driving elements in this market, contributing to a solid theoretical foundation.

Situating itself within the realm of Real Estate, this study endeavors to outline the specific importance of multifamily housing in the Brazilian landscape, emphasizing its integration into broader discussions on urbanization, sustainable development, and housing needs. Related themes such as demographic trends, lifestyle changes, and economic dynamics will be addressed to contextualize the demand for multifamily housing.

The uniqueness of this study lies in the detailed and critical analysis of the underlying fundamentals behind the growth of the multifamily sector in Brazil. The central hypothesis is that socioeconomic factors, cultural shifts, and evolutions in the real estate market play crucial roles in the dynamics of multifamily housing demand.

As the research progresses, it will seek to validate and refine this hypothesis through rigorous methods of data collection and analysis, thereby enabling significant contributions to both the academic and practical understanding of this constantly evolving phenomenon.

We use the U.S. Department of Housing and Urban Development’s Housing Choice Voucher program to evaluate whether homeownership preserves wealth among low-income and minority senior households. We apply a within-treatment difference-in-differences framework to establish that homeownership leads to wealth formation for households with children, which reduces the wealth disparities between low-income households. These wealth gains do not occur for senior households without children. We observe similar wealth dynamics for low-income and minority senior households. Thus, we provide evidence that homeownership can be a driver of financial inclusion among low-income senior households that does not inherently increase disparities in wealth.

We consider the disparate outcomes of minority groups in the reverse mortgage market. Using least squares regressions, we analyze the lender’s decision to approve or deny a reverse mortgage loan application in the 2018, 2019, and 2020 reporting years and estimate the interest rate members of different minority groups typically pay. Because slope coefficients for important regressors may differ across racial/ethnic groups along with individual endowments of these variables, we repeat the analysis using a propensity score method called inverse probability weighting. In general, we find that minority applicants are less likely to be approved than non-Hispanic white applicants and, when approved, pay a higher interest rate.

In this paper, we investigate the optimized decisions made by the originators of residential mortgage-backed security (RMBS). Conditional on default and prepayment risks, originators choose (i) the asset pool to be securitized from their entire mortgage obligations, (ii) coupon interest rates of the senior tranche, and (iii) disclosure of the condition and risks of the asset pool to achieve highest benefits from reinvestment opportunity and reputation. We find that myopic originators conceal true risks or even exaggeratedly disclose high risks to attract investors and to ensure the current issuance. We also discover that repeated originators are concerned about the dispersion of risks and disclose true risks because reputation enlarges their total profits from facilitating their future originations.

Recent work has demonstrated that the U.S. mortgage market is characterized by significant heterogeneity in the interest rates that are offered to borrowers as well as mortgage refinancing behavior. In this study we contribute to the mortgage heterogeneity literature by providing the first systematic analysis of regional differences in transaction costs in the mortgage market. Using the Uniform Closing Database---a unique repository of loan-level closing cost information---we demonstrate that there is a tremendous amount of regional variation in transaction costs in the mortgage market, most of which is driven by differences in local mortgage stamp taxes and recording fees.

In the second part of our paper, we take up the question of how failing to account for such heterogeneity might affect studies of borrower behavior in the mortgage market. We do so through the lens of the failure-to-refinance literature on optimal refinancing activity. Accounting for rate and closing cost heterogeneity significantly reduces estimates of suboptimal refinancing behavior, particularly among borrowers with high-risk credit profiles and those living in states with high closing costs. Because regional variation in closing costs is driven by the state and municipal policies, our results suggest that local governments play a role in the pass through of monetary policy via the mortgage market that has not been previously documented. Our findings also provide a potential mechanism above and beyond the home equity channel that could explain regional variation in refinancing activity and consumer spending during recoveries.

During 2022, U.S. commercial banks reported more than $500 billion in unrealized losses on their investment securities portfolios as the Federal Reserve Board raised its target interest rate by 400 basis points to combat inflation. In many ways, this was strikingly similar to the unrealized losses on residential mortgages experienced by savings & loans in the early 1980s as the Federal Reserve Board raised interest rates to combat inflation. In this study, we analyze the role of investments by banks in different types of securities (Treasuries, Munis, RMBS and CMBS) and different types of mortgages (commercial and residential) in explaining these losses. We find that investments in RMBS were most pernicious, as banks “reached for yield” during 2020-2021 as they coped with massive deposit inflows associated with pandemic relief programs. In contrast, we find that investments in actual mortgages were negatively related to these losses.

We investigate the presence of herding in the listed real estate stock markets of fast-growing African exchanges such as South Africa, Nigeria, Egypt, Morocco, and Mauritius. Daily data on securitized real estate company assets for the respective samples were used, spanning from Jan. 01, 2020, to Oct. 13, 2023. We found a weak correlation relationship between market return and return dispersion for all the samples, with a negative coefficient for Nigeria and Mauritius. From the results of the benchmark test, we found no evidence of herding in the listed property stock market for Egypt, South Africa, and Morocco. There is partial evidence of herding in Nigeria, while significant herding behavior was detected in the Mauritius listed property stock market. The relatively advancing property stock markets herd less than developing ones in the region.

The question investigated in this paper is how vulnerable are US households today to the risk of rent increases. The short answer is that US households in heavily regulated markets like Boston, Los Angeles, Seattle, and San Francisco are, on average, more vulnerable to the risk of rent increases today than they were in the 1940s and 1950s. To measure vulnerability requires a normative framework. We follow the approach developed by Sinai and Souleles (2005). We find that the rent premium that would leave households indifferent between the discounted cost of renting and its expected value is greater today, on average, than it was over the past eighty years. There is therefore a clear public policy implication from this research.

Capital Flows have been shown to affect real estate prices. Using World Bank macroeconomic data releases, we analyze whether standard macroeconomic variables used in Foreign Direct Investment (FDI) models also explain global real estate capital flows. Then we add real estate related variables to the standard FDI macroeconomic variables to improve the global RE capital flow models. We find that prior year macroeconomic data can act as an “informed signal’” on global capital flows to real estate.

The London office market is a primary destination for international real estate capital and a key global city and financial centre for international real estate investment. However, the increase in global uncertainties in recent years due to political events and economic and inflationary challenges highlights the need for more insights into the behaviour of international real estate capital flows. The purpose of this study is to evaluate the influence of the global and domestic environment on international real estate investment activities within the London office market over the period 20016–2023, concentrating on the separation of the UK from Europe due to the Brexit referendum, which seemingly have influenced investment flow patterns in and out of London. We employ an auto-regressive distributed lag approach using quarterly MSCI cross-border investment transactions within the central London office market for the period 2016-2023. We measure both long-run and short-run co-integrating effects and causality relative to Long-term interest rates, real effective exchange rates, total returns and yields from the London office market, GDP, Stock Market Capitalisation, the VIX index and Global Liquidity.

We provide novel evidence that the irrational dividend preference of some public market investors, commonly referred to as the free money fallacy, leads to elevated price-to-NAV (PNAV) ratios for US Real Estate Investment Trusts (REITs). The PNAV ratio proxies for the divergence of valuations in the public markets (price) and the private real estate market (NAV). While the traditional view purports the public market efficiently prices the real estate portfolio and the private market is an imperfect lagging indicator, a large body of behavioral finance literature has identified various ways in which public market investors make inefficient decisions. One such way is how investors appear to value dividend returns more than capital gains returns, when in reality they are equally valuable, all else equal. Given REITs are specialized investment vehicles requiring high dividend payout ratios, they are ideal candidates for a subset investors with significant dividend preferences to flock to. If investors have an irrational preference for dividends, they may over-pay for REITs in the public markets, leading to a divergence in pricing with the private markets, where dividends are not considered in the pricing equation.

To test this hypothesis, we test whether variables that capture when REIT dividends are potentially more attractive leads to higher PNAV ratios. First, we find that PNAV ratios are significantly higher in low interest rate environments. This captures the view that dividend income is a substitute for bond income. Second, we measure the relative dividend yield between a REIT and the S&P500. When a REITs dividend yield is particularly high relative to alternatives, PNAV ratios are significantly higher. These unique findings provide nuance to the ongoing debate regarding what drives public and private pricing divergence in the US real estate market.

This paper investigates the relationship between motivated institutional investors and REIT operational efficiency. We find that total institutional ownership appears to have minimal impact on REIT operational efficiency. However, motivated institutional shareholders are significantly associated with operational efficiency gains, a finding that is robust across multiple methodologies. We find the relationship between motivated institutional investors and REIT efficiency more pronounced in larger REITs and those with more financial leverage. This research advances the REIT literature by highlighting the impact of motivated institutional investors on REIT efficiency and offering actionable insights for industry stakeholders.

Traditional capital structure theories face severe limitations when applied to securitized real estate and REIT markets. Most notably, the regulatory environment faced by these firms dramatically alters their economic incentives and limits their ability to self-finance growth and expansion activities. As such, firms in this industry with continuing needs for external capital are uniquely positioned to benefit from reduced valuation uncertainty engendered by enhanced information flow. Against this backdrop, the current investigation examines whether, and to what extent, options market trading intensity serves as a value-relevant, noise reducing information signal which may be used to inform REIT borrowing and capital structure decisions. Specifically, we document increased REIT options market trading activity is associated with reductions in both overall firm leverage levels and changes in firm leverage. Additionally, increased options market trading intensity is associated with a relative increase in the use of unsecured debt and corresponding reduction in the use of collateralized bank debt and term loans. Together, these findings suggesting the enhanced information flow and resulting price discovery attributable to option market activity allows REITs to retain financial flexibility and ensure continuing access to credit. Finally, these results appear to be most pronounced in geographic regions free from market disruptions associated with political corruption, as well as within firms that are financially constrained and/or characterized by enhanced growth opportunities.

We analyze the relationship between retail sales and fees in the non-traded REIT market. We find evidence that brokers direct retail clients to high-fee products. Retail investors purchase more shares in non-traded REIT offerings that pay brokers higher compensation, including commissions and dealer manager fees, although these upfront fees reduce the amount available for investment. Our results are based on regressions that compare sales and fees both across REITs and for different share classes within the same REIT offering, mitigating concerns of omitted variables. The non-traded REIT offerings awarding brokers higher fees also tend to pay the REIT’s advisor a higher annual management fee which reduces cash distributions, indicating conflicts of interest between brokers and retail investors.

Previous studies show that the correlation of stocks with a number of other asset classes is asymmetric and higher on the downside than the upside. If the REIT/stock correlation displays a similar asymmetric profile, especially in periods of financial crisis, the value of REITs as a diversifier is questionable. This paper therefore examines the asymmetric correlation between REITs and stocks, using the concept of exceedance correlations.

Using daily data, the empirical results indicate that the correlation between REITs and stocks is time varying and asymmetric. In addition, downside exceedance correlations were higher than the upside exceedance correlations. Lastly, the exceedance correlations became substantially higher in periods of financial crises. This indicates that if fund managers ignore the time varying nature of the REIT/stock asymmetric correlation profile, during the portfolio construction process, it will reduce their ability to mitigate risk through diversification when needed most.

Although land rent is not a focal point of general economic discussions during the 20th century (Gaffney, 1994), it maintains its importance in academic circles and is gaining popularity as the digital age progresses (Vejchodska et al., 2022). Rent classically was a measure of land’s physical productivity, however, today the source of creativity and wealth shifts directly into its equivalent of knowledge which this research is ultimately interested in, as opposed to the physical land itself. We specifically aim to explore how physical assets derived from land rent have mutated into digital assets from digital rent, providing also some preliminary insights on various issues this transition leads to.

Land rent conception is complex and changes over time as digitization, processing and communication with modernized equipment and technology drives socioeconomic transformation and productivity (OECD, 2017). Billions of people around the world use digital, algorithmic, and computerized information for their everyday activities, including shopping at virtual marketplaces (Peitz and Waldfogel, 2012), digital transactions (Thanh et al., 2022), virtual consultations (Rzepka, 2023) or online education (Okeye et al., 2022). As technology progressed, it enabled a sufficient level of provision for basic needs, which then led to an increased focus on consuming information. This shift has resulted in a heightened importance of digital information as the cornerstone of a knowledge-based economy (Delices, 2010). In today’s digital, knowledge-based society, the economy is increasingly driven by the expectation of scarcity in novel information, rather than just the physical quantity or quality of commodities. As basic needs are met and comfort increases, the value of information becomes more important as a tool for competition and even survival itself. Rent, which measures the net potential for knowledge that can be extracted for the continuation of progress, is likely to transition more directly into digital information. Instead of physically extracting resources from the land, we now directly seek to access knowledge in digital rent. As the physical space becomes more a-spatial and as environments, production, education and general human life move to the digital space, we see the emergence of a “digital type of rent”, where the desired asset become knowledge. Whereas classical landowners may have exploited rent from land, we progressively realize that digital rent owners own information and effectively becoming “mindowners”, where value systems and expectations may be owned by powerful digital conglomerates.

References

Delices, P., 2010. The digital economy. Journal of International Affairs, 64(1), 225–226.

Gaffney, M., 1994. Neo-classical economics as a stratagem against Henry George. Corruption of Economics, 29–163.

OECD, 2017. OECD Digital Economy Outlook 2017, OECD Publishing, Paris.

Okoye, K., Hussein, H., Arrona-Palacios, A. et al., 2022. Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks. Education and Information Technologies. doi.org/10.1007/s10639-022-11214-1.

Peitz, M., Waldfogel, J., 2012. The Oxford handbook of the digital economy. Oxford University Press.

Rzepka, A., 2023. Innovation in the Digital Economy, Routledge.

Thanh, T.T., Ha, L.T., Dung, H.P. et al., 2022. Impacts of digitalization on energy security: evidence from European countries. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-022-02545-7

Vejchodska, E. et al., 2022. Bridging land value capture with land rent narratives, Land Use Policy, 114, 1-11.

The murder of George Floyd occurred in the intersection of two streets surrounded by retail and commercial uses in a largely urban area of Minneapolis, Minnesota. To generalize the George Floyd murder to all police uses of force would assume that urban areas with commercial uses may be magnets for higher levels of policing resulting in a greater likelihood of police use of force. In reality, this generalization is far from accurate regarding the police use of force. While the exact intersection was surrounded by street-level commercial uses with residential above, characteristics within one mile of the site show health care and social assistance, retail, scientific and technology services, construction, and hospitality and food services as the major business types. Within one mile of this intersection, the area’s population is 38,598, consisting of 63.4 percent white, 22.7 percent black, a median household income of $77,755, a median home value of $261,559, and an unemployment rate of 3.0 percent.

This paper examines 24,840 cases of police use of force to determine whether specific locations result in a higher likelihood of police use of force. Bars and related establishments that serve alcohol only account for 1.8 percent of these use of force cases, compared to 3.2 percent of incidents occurring in hospitals and another 8.9 percent occurring in businesses. The two most prevalent police use of force locations – streets (34.8 percent) and residence (30.7 percent) – beg the question as to whether there is a geography of police use of force based on where these streets and homes are located.

Additional data in the model specifically related to the police-subject incident include the time of day, weather, whether the incident occurred inside or outside, whether any recording existed (camera/body worn camera/motor vehicle, lighting, type of incident, perceived condition of the subject, the type of use of force, whether the police officer or subject required medical treatment, race, age, and gender. The data from the police use of force incident is coupled with county and city data, including demographic data such as population and population density, percent of the city’s population on Temporary Assistance to Needy Families (TANF) and/or Supplemental Nutrition Assistance Program (SNAP) benefits, violent crimes per 100,000 people, unemployment rate, residential property values, vacancy rates, per capita taxable property values, municipal budget per capita, municipal tax rate, and the percentage of that city’s population that voted Democrat or Republican in the 2020 U.S. Presidential Election. The goal of this exploratory analysis is to determine whether a geography of police use of force exists or if the police use of force is completely random.

Future research resulting from this exploratory analysis will focus on police use of force and impacts on surrounding real estate values and development. An exploratory analysis of the data shows that police use of force does not solely occur in urban areas, but also occurs in suburban and rural areas. Moreover, while the conflict may occur between the police officer and the subject, where that conflict occurs may shed light on alternative policies to reduce the likelihood of future negative interactions, especially if these types of interactions impact property values.

The residential segregation of immigrants is not a new phenomenon. However, is it driven by native flight or by immigrants' preferences? If the former, who amongst the native population avoids living with immigrants? Utilizing a matched panel dataset with the universe of individuals and residential properties in Denmark from 1987 through 2017, we conclusively show that the presence of immigrants induces native flight, even in a country with relatively tolerant attitudes. Both immigrant
concentrations at the broader neighborhood level and at very short distances affect native residential behavior. Native flight is stronger among older populations and is mostly a reaction to the arrival of low-income Muslim immigrants.

This project studies the housing market effects of transit development; specifically, we consider the openings and expansions of 30 light rail and streetcar lines across the United States since 1999. These systems are diverse in geographic coverage and capacity, ranging in size from the 4.8 miles of El Paso’s Streetcar System serving 65,400 passengers annually to the 65 miles of San Diego’s Trolley serving 34,053,400 passengers annually. Our empirical approach will leverage a difference-in-differences approach to document the key research questions of (1) the capitalization of fixed guideway public transit investments into real estate prices and (2) the historical dependence of this capitalization on existing or defunct transit lines. Based on past findings, we hypothesize the following, with the caveat that a broad-based geographic approach as outlined in this proposal is necessary to substantiate these empirical patterns. First, light rail investments will see positive capitalization effects for more populous, denser cities with clearly defined central business districts. Capitalization of transit access will also be dependent on the economic diversity and industrial concentration of a metro. Second, there is path dependence to the capitalization of transit. A history of public transportation will encourage acceptance and hence drive valuation, and the more substantive past or current transport networks, the more rapid access to transit will enter into housing market outcomes.

Appraisal accuracy became a major concern following the global financial crisis in the late 2000s. In the United States, legislation and industry guidance was created to reduce intentional and unintentional mis-valuation of residential homes. Stricter professional practices were established including appraiser independence requirements and new conflict of interest standards. Nonetheless, economists have continued to document patterns of systematic mis-valuation.

An important source of mis-valuation arises from inadequate adjustments for rapid house price movements, and economists have extensively studied how this mis-valuation varies over real estate cycles. Here, we examine how mis-valuation varies across neighborhoods, and the role that house price trends play in these patterns. House price appreciation presents a technical challenge to appraisers, who have only limited data available. In addition, when larger adjustments to comparable sales prices are required, appraisers have more scope to unintentionally or intentionally mis-value properties.

To account for recent price changes, appraisers make time adjustments to the prices of comparable properties from past sales, walking forward prices to reflect current market conditions. First, we document that these time adjustments are used too infrequently, are not large enough to correctly reflect current market conditions, and that these shortfalls result in underappraisal. Next, we examine whether time adjustments are made consistently across neighborhoods, and patterns of underappraisal that vary across neighborhoods. Finally, we examine how underappraisal patterns are affected by price growth and time adjustments. We find that disparities in time adjustments are an important source of disparities in underappraisal.

The purpose of this paper is to assess the premium associated with water front properties. We compare the water front properties to non-waterfront properties to observe any premium for water front properties. Previous studies indicate that there is some premium associated with lake views (Bond, Seiler, & Seiler, 2002) or proximity to coast (Conroy & Milosch, 2011). Previous research is focused on single family homes while our objective is to examine whether this premium also exists for condos, townhouses, villas and other types of residential properties. In addition, we are going to include variable for type of water access the property holder has to water. Our results show that there is some premium associated with lake front location and we also find that this premium depends on type of property.

Using the machine learning algorithm VADER (Valence Aware Dictionary for Sentiment Reasoning), we analyze the public remarks section of the Multiple Listing Service (MLS) using a data set of over 1,000,000 housing sales in the Atlanta Metro Area (2000-2018). Focusing on waterfront/water view properties, the raw text comments by real estate agents are both quantitively more expansive and qualitatively more positive in sentiment compared to powerline proximate properties where the textual information is sparser and more neutral in sentiment. Further, textual information improves predictive pricing in our hedonic models for waterfront/water view properties but is of limited significance in predictive pricing models for powerline proximate properties. Our results suggest a heterogeneity of treatment effect for textual information with its predictive impact varying according to the quality of spatial amenity.

Tel Aviv boasts a diverse and generally liberal urban population, with an accessible waterfront. Its UNESCO designated Bauhaus architecture has won the city the title: “The White City.” But every busting city has its downside: noise is one of them.

This research concentrates on the relationship between the negative amenity of noise and residential sales prices.in this metropolis of 2 million.
Noise as a negative attribute includes but is not not limited to traffic noise, school and kindergarten noise, dust and
pollution from active construction, and proximity to hospitals and supermarkets, some of which also present positive attractive features.
We will perform hedonic regression analysis on a database of over 5,000 sales of multifamily units in the metro Tel Aviv market over the 2018-2023 time frame. In addition to the usual unit-specific control variables such as size, rooms and floor, we will use GIS to add neighborhood proximity variables. The main variable of interest, a noise database collected by the Ministry of Health, is based on local decibel counts at street level. This will be supplemented by GIS modeling of available data on construction and early-morning noise generating land uses. Advanced modeling techniques will permit teasing out the nuisance effects of noise in very close proximity from the generally beneficial effects of being near desirable features (but not too close!).

The research employs a dual-method approach, integrating spatial hedonic pricing models and Advanced Interrupted Time Series Difference-in-Differences (AITS DID) analysis, to assess the impact of BRT on residential property values. The study's findings reveal complex and varied impacts of BRT on the housing market. In the Mesa corridor, a positive impact on property values is observed, potentially due to higher residential expectations. Conversely, in the less affluent areas of Alameda and Dyer, an initial negative impact is followed by a gradual positive trend post-BRT implementation, indicating a growing appreciation for the BRT system and associated urban improvements.

These results suggest that the introduction of BRT systems can have differential impacts on housing markets, influenced by the socio-economic characteristics of the areas they serve. The study highlights the importance of considering local context in real estate development and public transportation development. It provides valuable insights for real estate developers and urban planners, emphasizing the need for tailored approaches to infrastructure development to foster equitable real estate development and enhance the overall quality of life in diverse urban settings.

In June 2023, the US Congress introduced a bill that would prohibit public housing agencies from breed restrictions on pets and discourage them from imposing size and weight restrictions. Governor Ron DeSantis signed into law a bill preventing dog weight and breed restrictions in public housing on June 17, 2023. In Ohio, lawmakers are considering a bill that would provide a $750 tax credit to landlords who allow pets (without any breed or weight restrictions) in buildings with less than 10 units.

While the changes to Florida law do not impact privately owned housing units, they do show a shift in attitude and policy that may extend to the broader housing market. Thus, we re-examine the question of pet restrictions in the Florida condominium market using data from 2017-2018. Rather than focusing on price changes, however, this study focuses on the marketability of condominiums with pet restrictions. Previous studies have only focused on price premiums or discounts but not on the changes to time on market or the probability of sale.

This research examines the transitory nature of real estate salespersons based on economic conditions. In particular there is a large percentage of real estate agents that have received their license only to allow it to expire at the end of the two-year renewal period. In one example, a licensee allowed their license to expire and renew it on five different occasions over the period, 1998-2022. We examine the number of licensees over two decades including both the “Great Recession”, the COVID pandemic and the recent run up in interest rates impacting the housing market.

The Taxpayer Relief Act of 1997 amended U.S. Internal Revenue Code Section 121, allowing homeowners to exclude up to $500,000 in realized capital gains from taxation if the homeowner held the property for at least two years. While this is one of many Federal tax law provisions enabling real estate capital gains to be sheltered from taxation, we investigate repeat sales transactions across a number of tax jurisdictions to describe the contexts in which this 1997 policy has created an incentive to hold property longer than desired to qualify for the tax relief. Notably, we find holding period trend discontinuities beginning at exactly the 730th day (two year) threshold for properties realising capital gains after the policy took effect in 1997. These discontinuities disappear for sales realising capital losses and for repeat sales before 1997, suggesting a causal link with the 1997 tax change. Across 25 years of data where the policy was in effect, the number of sales delayed to meet the 730 day requirement is small as a percentage of total sales, suggesting only minor disruption of incentives. Furthermore, the discontinuity is strongest in jurisdictions without local property tax price controls, suggesting that competing tax incentives to hold property for longer than two years may be more valuable to homeowners. These findings have interesting implications in the context of currently historical low housing inventory across the U.S., providing additional evidence that tax policies, along with fixed interest rate financing, motivate longer holding periods and thus, reduce inventory supplied from the existing stock of homes.

Green construction practises can minimise buildings’ significant environmental effects, conserve resources, and provide economic benefits such as lower operating costs and enhanced property values. In Africa, the need for sustainable practices is critical due to the rapid urbanisation and population growth rates. However, research on stakeholders’ influence and receptiveness to green buildings is limited. Our study draws on the experiences of 38 green building experts from five African countries affiliated with either EDGE, LEED, Green Star or DGNB. Our findings indicate that construction professionals and private sector developers are the most receptive, environmental agency regulators are indifferent, and interest groups are the least receptive. In addition to construction professionals and private sector developers, local regulatory authorities are among the most influential stakeholders in construction projects. Further analysis of the nexus between receptiveness and influence suggests local regulatory authorities and landowners are less receptive than influential, highlighting a disconnect inhibiting green building adoption. This disparity suggests that policies and sensitisation campaigns must actively consider the different stakeholders' motivations and the balance of power in the African context towards promoting sustainable building practices to secure a more sustainable future.

This study presents a framework on the effects of economic diversity and hurricane shock on house prices. Data from Florida's metropolitan statistical areas over a course of 32 years is analyzed. Preliminary findings of the main and iinteraction effects are presented.

In the context of the UN Climate Change Conferences, participating countries have agreed to nationally determined contributions (NDCs) for legally binding obligations to reduce greenhouse gas emissions. The reductions are split up for sectors such as buildings, energy, industry, transport, agriculture and waste management. There are considerable challenges of reducing greenhouse gas emissions in the building sector. Necessary modernization is often seen as too expensive relative to property value. Targets of net zero emissions by the year 2050 are rather ambitious in a sector with a life usage of 70+ years. 75% of buildings in Europe are energy inefficient and the annual renovation rate of buildings is only 1.2%. Most of today’s buildings will still be in use in 2050. This transition will cause pain for enterprises, society and government due to necessary adjustments and the political backlash caused by the financial burden. The transition will have both direct and indirect effects on owners and tenants as high subsidies will have to be covered by tax payments. Sectors with shorter life span of products (e.g. vehicles) might be more efficient in reducing further emissions and a stronger focus on emissions trading will be helpful to reduce the greenhouse emissions in an efficient way. Overall, ambitious targets in the real estate sector might be necessary to move in the right direction, even if the targets are not achievable.

This interactive simulation case study immerses college-level students in the dynamic world of sustainable real estate, providing a comprehensive exploration of decision-making processes in property acquisition, development, operations, and strategic initiatives. The simulation case is rooted in the intersection of environmental stewardship, social responsibility, and economic viability; the simulation presents five distinct scenarios challenging students to balance diverse considerations. Participants define and explain key sustainability concepts, unraveling the interconnectedness between environmental, social, and economic aspects. They apply sustainability principles to navigate decisions on property acquisition, development, operations, and proactive initiatives, evaluating the long-term impact of choices on environmental and financial performance metrics. The simulation cultivates financial management and budgeting skills, encouraging students to develop and implement effective accounting strategies within a simulated business environment.

Furthermore, students learn to integrate sustainability considerations into overall business strategy and decision-making, identifying opportunities aligning sustainable practices with financial objectives for long-term success. Proficiency in problem-solving and decision-making is enhanced as students address challenges amid dynamic and uncertain market conditions, making informed decisions considering trade-offs between short-term financial gains and long-term sustainability goals. The simulation also emphasizes communication and collaboration skills, necessitating persuasive communication of sustainability initiatives to diverse stakeholders and fostering consensus on strategies within virtual teams. The experience concludes with students reflecting on personal learning, identifying areas for improvement and further exploration, and promoting continuous growth and adaptability in navigating the complex landscape of sustainable real estate.

Housing has long been recognized as having positive investment attributes for homeowners. Only in the mid 20th century was housing acknowledged as having investment appeal to non-occupant, third-party investors. Initially, housing as an investment was viewed primarily through the prism of real estate opportunity promoters: essentially an entrepreneurial endeavor: buy advantageously, fix and improve, then hold or flip and put the proceeds to work on another housing venture.

Gradually, housing entrepreneurial ventures/ownership evolved to be a favored individual career/business path. Major institutional investors, however, participated more on the debt than the equity side. This orientation shifted in the later years of the first decade of the 21st-century, as creative entrepreneurs attracted institutional backing to acquire substantial numbers of REO(real estate owned) houses from banks and troubled RMBS, following the major market disruptions triggered by the 2007–9 Great Recession.

Now, investment managers offer institutional investors the opportunity to invest in professionally-managed portfolios of single-family residences. Increasingly, homebuilders are offering houses not solely for sale to individuals but as a build-to-rent product. Significant technology-induced threats to the viability of retail and commercial office properties have reduced demand for, scale, and appeal of those two long-favored real estate asset classes. Not readily recognized is that nearly 1/3 of the single-family housing stock is owned by investors rather than by the occupants of those houses.

This paper explores the implications of these significant developments that make housing a fifth primary asset class

We investigate the relationship between vacancy rates and property prices in the residential and commercial real estate markets. We find that in Singapore's residential market, the lagged differenced vacancy rate negatively affects the future differenced house price, a relation that results from what we term the ``fundamental-driven effect.'' As housing vacancies can be regarded as excess supply in real estate market, vacancies will result in decreased prices to reduce excess supply and achieve long-term equilibrium when supply exceeds the fundamental demand for residential housing. By considering the two types of property prices in Hong Kong, we identify a long-term relationship between residential prices and office building prices.

In a recent paper Lee (2023) found that found that using the approach of Nadler and Schmidt (2016), the in-sample mean-variance REIT portfolios based Partial correlation coefficients (PACC) are more diversified and more stable than those based on Pearson correlation coefficients (PCC). However, while the results of the in-sample performance are encouraging, they are unattainable, and investors are more concerned about out-of-sample performance. This paper therefore evaluates the out-of-sample mean-variance PACC and PCC portfolio performance using monthly return data on 11 REIT sectors, over the period from 1994:1 to 2022:12. Using a fixed length rolling window of 36 months, producing 312 out-of-sample portfolios, the results show that PCC-portfolios are concentrated and unstable. In contrast PACC-portfolios are more diversified, show greater stability. Therefore, PACC portfolios are probably more acceptable to investors than the PCC portfolios. Nonetheless, none of the portfolio strategies used in this paper consistently outperforms the equally-weighted naïve portfolio, in the period under investigation.

This paper analyzes the time-varying volatility of European listed real estate returns across sectors and countries. We also examine whether volatility and the price to net asset value ratio dynamics can be exploited by tactical allocation schemes to improve the performance of a listed real estate portfolio and whether this affects the allocation to listed real estate in mixed-asset portfolios. Using GARCH models, we find that volatility shocks are synchronous across listed real estate sectors and countries, albeit with differences in magnitude. The high-volatility regime of the global financial crisis lasts longer than that of the COVID-19 pandemic. All countries but Germany experience higher volatility during the COVID-19 pandemic than during the global financial crisis, while the results across sectors are more nuanced. When considering sectors, our results indicate that implementing tactical asset allocation is beneficial for strategies that have stable and well-balanced allocations. However, when allocating to countries, tactical rebalancing has overall a detrimental effect on performance. Tactical rebalancing leads to higher allocations to listed real estate in a mixed-asset portfolio when sectors are considered, while the opposite is true for countries. Overall, the allocation to listed real estate ranges from 4% to 26% when sectors are considered, while it is slightly higher for countries.

ABSTRACT

This research seeks to determine if including leveraged equity real estate ETFs in leveraged mixed asset ETF portfolios improves the return/risk performance over the long term, and during up and down financial market periods. Curcio and Guirguis [2023] recently demonstrated that bull (long) equity real estate leveraged exchange traded funds, (LETFs): including the 3x DRN, benchmarked to MSCI U.S. REIT Index and the 2x URE benchmarked to Dow Jones U.S. Real Estate Index, (1) produced significant rates of return, well in excess of their leverage factors during long-term upward trending equity real estate stock prices; (2) declined less than expected during Covid-19 pandemic crash; (3) recovered faster than expected following Covid-19 pandemic crash. Their results further showed that the return/volatility rose to (5.8x return)/(3x volatility) for DRN and (3.3x return)/(2x volatility) for URE over the test period. Curcio and Dickerson [2017] found similar long term test results on LETFs benchmarked to the S&P 500. In their study, under conditions of upward trending stock prices in the broad market, bull (long) LETFs benchmarked to the S&P 500 produced significant rates of return, in excess of their leverage factors over an approximate 8-year period. An early study demonstrating the long-term return attributes of real estate LETFs was reflected in Curcio et.al. [Dec. 2012]. This research will empirically examine the return/risk performance from combining leveraged real estate ETFs, representing the MSCI U.S. REIT Index, through its tracking ETF, VNQ, and the Dow Jones U.S. Real Estate Index, via its tracking ETF, IYR, with 2 leveraged stock index ETFs. The two leveraged stock indexes will include: (1) the S&P 500, through its tracking ETF, SPY, and (2) the high beta, NASDAQ 100, through its tracking ETF, QQQ. These real estate, common stock mixed asset leveraged ETF portfolios will be evaluated over the test period, 2009-2022. Results will be analyzed and conclusions presented.

This research study involves single-family residences' annual data from 2019 through 2023 on MLS between the selling price of $500,000 to $1,100,000 in the diverse region of Orange County, California. The purpose of the price range was to determine new home buyers activity within pre-COVID (2019), COVID (2021), and post-COVID (2023). The goal was to determine the effects of low-interest rates, high-interest rates, low home supply, and hyper-appreciation on entry-level home buyer prices. This analysis also involved city location differences of the properties within the county over the time periods. We noticed various trends that will be presented. The hypothesis is that the types of financing are a significant predictor of entry-level sales. The main financings reviewed are conventional financing (Fannie Mae), VA, FHA, and cash. As the number of cash buyers increases, the number of available entry-level homes becomes less, and varies over types of locations and time. Home financing types affect entry-level housing in different ways.

The cities in Orange that were analyzed were representative of entry-level home buyers in Orange County from 2019 to 2023. These cities were: Anaheim, Buena Park, Fullerton, Garden Grove, Orange, and Santa Ana. These cities in Orange County were chosen because of their large total population and high minority concentrations. Also, these are all part of northern Orange County in more affordable single-family housing areas compared to other parts of the county.

<p>The concept of economic freedom has been long studied in a wide spectrum of research. Many existing studies focused on whether economic freedom brings prosperity and comfort to the citizens or hurts them by creating income inequality, and exhausting resources. However, in real estate, the research on economic freedom and its impact on the real estate market is surprisingly limited. In this study, to fill this gap, we investigate the effect of economic freedom on real estate market instability. Our findings show that economic freedom leads to an increase in instability in the real estate market. The effect is more pronounced during economic downturns such 2008 financial crisis. We also find that economic freedom leads to higher price premiums in the real estate market which is consistent with the risk and return concept.</p>

<p>Households and industries are two vital components of the local economy. Industrial turnover serves as an important indicator that reflects the local economy. Housing price dynamics, on the other hand, are greatly related to household housing wealth. However, few studies discuss whether housing price dynamics can influence business turnover. Thus, this paper aims to investigate the relationship between industrial turnover and housing price dynamics, and further reveal how housing wealth affects urban growth. The empirical study is based on the panel dataset from Statistics Sweden (SCB) of statistics from 2013-2019 for each spatial grid, including daytime employment, residential population, and industrial turnover in grid space. Besides, this paper introduces the Location Quotient (LQ) value into the econometric model to estimate how housing wealth effects differ across local and non-local industrial sectors. Considering household consumption behaviors, this paper additionally utilized a spatial lag model to address the spillover effects of housing wealth on industrial turnover. Finally, we find that housing price changes can significantly influence the local economy's growth. Besides, housing wealth effects on local business turnover do not show significant differences among city spaces of different local industrial business concentrations.</p>

<p>The aim of this study is to analyze the effect of conventional and unconventional<br />monetary policy shocks on housing price dynamics in Europe (2000-2020). We propose<br />a pan-European comparative analysis at the country and market levels. Therefore,<br />we build a quarterly national dataset for six countries (France, Germany, Italy,<br />the Netherlands, Spain and the United Kingdom) and a quarterly submarket dataset for<br />thirteen European cities (Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam,<br />Madrid, Barcelona, Seville, London, Birmingham and Manchester). We proceed in two<br />steps. First, we develop a Structural VAR model. Second, we conduct a forecast error<br />variance decomposition analysis. At the country-level, we show that a contractionary<br />policy rate has a negative influence on house prices. Moreover, an expansionary balance<br />sheet shock exhibits a positive impact on housing prices. At the market-level, the role<br />of contractionary policy rate shock is confirmed with r elevant differences. A balance<br />sheet shock displays a heterogeneous effect on housing prices. Globally, we observe<br />that a conventional monetary policy shock explains a larger share of total housing price<br />variance than an unconventional monetary policy shock. Finally, our results report that<br />conventional and unconventional monetary policy shocks have a greater impact in more<br />liberalized credit markets. These results may have significant economic policy implications.</p>

Recent media attention and business headlines have highlighted the increased role institutional investors have played within U.S. housing markets; particularly as it pertains to single-family rental accommodations. Common themes have addressed supply concerns, local housing affordability, and the lack of adequate property management. This research seeks to challenge the latter criticism by highlighting key performance indicators for both real estate investment trusts and real estate corporations, as compared to all other ownership types. These indicators include, but are not limited to, such items as operating expenses, capital expenses, and reserves set aside. Furthermore, a demand-based measure such as vacancy rates can serve as a proxy for consumer demand. Such results may suggest that corporate forms of ownership perform better than average when controlling for the number of units in operation. Contrary to media criticism, the research shows how this particular form of corporate ownership actually serves as an extension of housing choice, especially for those areas suffering from what is often labeled in today’s industry as “the missing middle” of U.S. housing.

BTR developers are challenged to build appealing rental communities for consumers in an expanding market. Attracting tenants has become more complex given the ease of access to data available for tenants to evaluate competitors in the marketplace, savvy new market entrants, and an increased focus on branding, etc. The competitive landscape requires a detailed analysis of choice drivers and trade-offs evaluated in the tenant decision-making process, especially as pricing pressures emerge and inventory creeps up. This session will present a model for creating the best tenant experiences and for evaluating value creation drivers in BTR markets. Selected insights from several recently completed and ongoing studies in the US and UK will be discussed.

Summary:

In light of potential challenges to our real estate sales income, it is crucial to actively explore additional ways to earn. Despite being often overlooked, property management now presents a savvy opportunity for substantial earnings. Let's discuss how integrating property management into your services can shield you from market changes, safeguarding your income from potential threats and ensuring your resilience in the face of uncertainties.

Learning Objectives:

What are the dangers of a single source income model and how can property management shield agents from market fluctuations?

Learning objectives include:

Building an antifragile income model

Strategic fusion of sales & property management

Client centric relationship building

Adaptive strategies for market changes

Holistic understanding of wealth building

Tools: Income Fragility Scorecard

Bio:

Rhianna Campbell has been supporting the growth of small businesses for over twenty years. As a business coach, speaker, and best-selling author of “The Best Kept Secret of How Property Management Can Boost Real Estate Team Profits,” Rhianna Campbell travels the country sharing her knowledge and experience with real estate entrepreneurs everywhere.

After building, growing, and then selling a multimillion dollar real estate business, she started Proper Planning LLC, a consultancy assisting entrepreneurs across the world build profitable businesses.

Rhianna is a teacher at heart. She has developed and led workshops for a variety of organizations and presented at national conferences. She has been interviewed numerous times on internationally based podcasts and quoted in many online articles.

For more information visit: https://properplanning.realestate/

Mixed-use development has become more prevalent in the United States over the last several decades due to shifting consumer preferences that favor the integration of residential and commercial space in live, work, play environments. According to estimates, the number of mixed-use properties nationwide is approximately 450,000, which has grown 39% from 2012 to 2021. Along the way, local and regional governments have encouraged such compact forms of development in order to reduce automobile dependence and urban sprawl. Despite such lofty goals, mixed-use development projects still present challenges. Among these challenges is a perception of higher operating expenses that emerge as a result of unique design, construction, and management issues that come along with this real estate product type.

Despite the popularity of mixed-use communities, there are few empirical studies on the effectiveness of such projects from a property management stand point. Our contribution to the literature is an empirical examination of whether development projects that integrate residential and commercial space do in fact have higher operating expenses than developments that are solely residential, and whether operating expenses tend to increase as developments become "more mixed" in terms of commercial rent receipts. We conduct this analysis with the help of the 2021 Rental Housing Finance Survey, a survey of over 5,000 multifamily residential owners and operators nationwide.

This study revisits the 2004 guidelines and 2010 draft revision of the "Real Estate Management Definitions Catalog" and conducts a thorough review and update to establish a shared understanding of key activities and outcomes across all management levels in real estate.

Led by an interdisciplinary team of academic and industry experts, the research employs a Delphi-study-like approach, involving a broad network of professionals, and concludes with a public workshop.

Findings contribute to an updated catalog for real estate management definitions, key activities and outcomes, incorporating contemporary elements like digitalization, ESG considerations, and circular economy tasks. The collaboration between academia and practitioners ensures methodologically rigorous yet practically relevant outcomes, fostering widespread acceptance.

The proposed framework serves as a foundational resource for communication, practical documentation, R&D projects, and an updated reporting and key performance indicators catalog. This research bridges the gap between theory and practice, providing a valuable tool for stakeholders in the real estate industry.

This study investigates changes in the risk profile of Real Estate Investment Trusts (REITS) over a twenty-year span. During this time span, REITs have seen structural changes and faced different economic regimes. REITs regularly go through both boom-and-bust cycles and these changes can vary the performance and riskiness of these companies in a significant manner. This was clearly highlighted during the global financial crisis which was largely a real estate related meltdown of the financial markets.

REIT capital raising and dividend policies during the COVID-19 pandemic crisis

The COVID-19 pandemic was an unprecedented market disruption that had significant global economic consequences. In the face of uncertainty, the U.S. and virtually all other governments around the world halted economic activity and found innovative ways to support individuals, families, and companies during the time of crisis. The crisis crippled all corners of the market and forced a generalized shutdown. The U.S. REIT sector was not an exemption, the industry experienced a significant temporary shock to revenue and to the supply of capital which is crucial for operations and growth. In this paper, we examine the impact of the COVID-19 pandemic crisis on REIT dividend policy and capital raising. For our analyses, we employ accounting and dividend data for S&P Global Market Intelligence (Formerly SNL Financial), and debt and equity offerings from the National Association of Real Estate Investment Trusts (NAREIT). We seek to determine the severity of the COVID-19 pandemic crisis on the halt or partial disruption in REIT dividend payments and uncover the ways REIT managers coped with the short-term market shocks. In addition, we will determine whether the capital markets provided REITs with capital during these uncertain times.

This study examines connectedness and volatility spillover effects among real estate investment trusts (REIT) index and real estate stocks. Even though a recent study examined the connectedness and volatility spillovers of global REITs, no research has concentrated only on the U.S. market and its country-specific implications about connectedness and volatility spillovers among REIT index and real estate stocks. We also analyze hedging effectiveness and optimal weights for the REIT volatility transmission across major real estate companies to understand portfolio diversification and risk management. Our results suggest the existence of significant volatility spillover effects among REIT and real estate companies’ stock volatility. We find that DJREIT is the net transmitter of volatility to real estate markets, with EQR, REALO, PLD, and WELL being major volatility transmitters, and AMT, ARE, CBRE, CCI, PSA, and SPG being major receivers. We also examine a transmission mechanism of volatility spillover in pre-, during-, and post-COVID-19 periods, and find that DJREIT exhibits a net transmitter role across the different periods. Our findings support the benefits of diversification and imply that the hedging strategy could be the preferred optimal weight strategy against volatility for real estate investors.

With the global implementation of disclosure requirements in Environmental, Social, and Governance (ESG) reporting for companies, there are growing concerns about whether all initiatives have substance or are ‘greenwashing’ or ‘value washing’ attempts. These concerns are caused mainly by the lack of consistent, reliable measures, especially for Real Estate Investment Trusts (REITs). Our main objective is to (1) create a measure to identify REITs’ ESG initiatives from their company reports and (2) examine how industry experts perceive/evaluate such information by measuring their sentiment toward REITs’ ESG initiatives embedded in real estate industry news articles in the UK using machine learning models. Specifically, we will examine the effects of sentiment on REITs’ stock price performance (i.e., investors’ reactions to ESG sentiment) and those on REITs’ fund-raising actions (i.e., REIT managers’ reactions to ESG sentiment). Our project will provide new evidence on the impacts of REITs’ ESG disclosure activities.

Urban public facilities provide the very basic infrastructure which support the living needs and development of urban sustainable growth. These facilities include roads (streets), parking areas, green spaces, public elementary schools, and open spaces. However, how to expropriate land from private properties with the proper location and size of public facilities still remains uncertain in capital cities, such as in Taiwan. Under a big gap of insufficient government budget, many lots of public facilities on the officially announced urban plan are not yet been expropriated for many years, even over 30 years, and is so-called the “reserved land of public facilities (RLPF)”. The issues concerning the RLPF policies has been continuously debated by the Taiwanese government. Since 2013, the government has perform projects concerning to re-evaluate and release some of the “less function” RLPF from the viewpoints of the needs of local community residents, especially in response to the living environment facilities for the older adults and also in responding to the decreasing birth rate. This real challenging task includes addressing the requests of official bureau administrators, occupants with property or rental rights of the current land use status, and also NGOs, along with the means of development related stakeholders. To simplify the decision of acquire or release the public facilities, in this study, we selected public facilities that are only needed for living in the local residential community, including parks, green spaces, plaza squares (open spaces), and land for kindergartens, schools, and local stadiums. This study categorized these spaces as community’s “leisure public facilities” (LPF). By constructing an accessibility index of the services of LPF, we computed and produced a GIS map of spatial buffer zones for each LPF. Through these procedures, the spatial area of service needs provided by each LPF could be clearly identified. We then overlap these spatial buffer zones to create the envelope mapping area, so that whole community service areas can be identified. The results obtained from the envelope mapping can be used to help to decide which RLPF should be acquired or released, so that community services can be maximized under a limited budget.

Realtor associations across the country widely shared maps that coded geographies within multiple listing services (MLS) into regions called MLS Areas. Following the Black Lives Matter protests in the Summer of 2020, Realtor associations began to remove the MLS Area field citing that they are arbitrary, functionally obsolete, and vestiges of the redlining maps. We observe that housing demand in Black and Hispanic neighborhoods increased upon the removal of the MLS Area field. Furthermore, we find evidence that appraisers' choice of comparable sales for houses in Black and Hispanic neighborhoods was influenced by the availability of this field.

This study explores how the proximity of houses to universities influences neighborhood prices and the volatility of returns. Previous research indicates that residential real estate in college towns exhibits resilience during economic downturns compared to non-college-town residences, demonstrating that economic stability correlates with higher home prices and lower return volatility in college towns.

Using transaction data from Boulder County, Colorado, we employ GIS data to examine whether homes near the University of Colorado command higher prices than those located farther away in the county. Additionally, we assess whether returns on properties near the university exhibit lower volatility compared to those situated at a distance. The sample covers the period from 2000 to 2022 and includes more than 90,000 single-family home transactions.

Preliminary price information for Boulder County, Co. shows the median sale price in 2000 is $242,900 and grows to $776,000 in 2022. This equals a geometric average return of 5.4% per year. However, annual median price housing returns are as high as 20.9% in 2021 and as low as -4.5% in 2009.

To test our hypothesis, we utilize a hedonic pricing model to estimate the value of single-family properties in Boulder County. This model incorporates neighborhood fixed effects, as well as property-specific information like the number of bedrooms, bathrooms, square footage, and lot size. In addition to exploring spatial differences, our analysis examines price trends during both economic booms and downturns within the sample period.

This research contributes to a deeper understanding of housing markets in U.S. college towns, offering insights that may benefit homebuyers, investors, and policymakers in their decision-making processes. The findings may also have broader implications for property rents in close proximity to universities.

Looking at the DBSCAN clustering on industrial property, by comparing to the submarket definition from market granularity, responsiveness to the market changes and spatial precision.

Today, successful building owners and landlords understand that tenant loyalty requires leveraging a building’s products and services to create a memorable customer experience. This has become known as operational real estate. Operational real estate is driving meaningful change in the commercial real estate sector and more institutional capital is moving towards this as the experience economy burgeons.

We study the relationship between hotel managerial ability and its asset value. The value of a hotel asset depends on many factors including location and market, property characteristics, and brand affiliation. In this paper, we investigate whether managerial ability is valued and whether managerial efficiency is reflected in market transactions. We collected our data by merging the following two sources. The hotel transaction data is from CoStar, a large real estate transaction dataset that covers all historical hotel sales and real estate characteristics over the period of 2000-2023. The hotel managerial performance information is from CBRE hotels.

In this paper, we first measure different hotels’ managerial abilities. We use Data Envelop Analysis proposed by Demerjian, Lev, and McVay 2012, a non-parametric statistical model used to evaluate the relative efficiency of separable hotels, known as “decision-making units” (DMUs), where each DMU converts certain inputs (labor cost, marketing, and sales, etc.) into outputs (total hotel revenue). Our results show that a one-standard-deviation increase in the hotel's managerial ability is associated with a 20% higher sale price.

Reference:

Peter Demerjian, Baruch Lev, Sarah McVay, (2012) Quantifying Managerial Ability: A New Measure and Validity Tests. Management Science 58(7):1229-1248.

Artificial intelligence (AI) has the potential to redefine the way we value property, presenting the contemporary valuer with a new suite of skills to pursue. This research seeks to define the residual skills necessary to complete a valuation in today’s technology augmented practice.

This paper commences with a revisit of research into skills development within Australian universities and the emerging role of technology, specifically AI, in preparing valuations. The latter part of the research presents valuation as a framework, or environment, where tasks and roles are allocated. The mapping and allocation of tasks requires careful consideration of ecological and symbiotic relationships, between the human valuer and AI. The residual role of the property valuer defines the new skills necessary to complete a practical valuation.

This abstract presents an overview of our research paper which focuses on the analysis of models and factors that influence housing prices. Previous studies conducted by Sirmans, Macpherson, and Zietz (2005) have reviewed papers published in real estate journals up until 2005. Building upon their work, Khoshnoud, Sirmans, and Zietz (2023) have extended the analysis to include the most recent papers published in real estate journals. However, our paper specifically examines articles published in non-real estate journals that utilize hedonic regression analysis to estimate house prices.

In our review, we compare the determinants of house pricing and assess their frequency of appearance in the studies. Furthermore, we discuss the different models and techniques employed by researchers in these non-real estate journals. Our analysis is based on a comprehensive review of house pricing articles published in non-real estate journals from 2005 to 2023.

To broaden our perspective, we explore various journal categories, including Geography, Environment, Economics, Insurance, and Transportation. By comparing studies published in real estate and non-real estate journals, our objective is to provide insights into the similarities and differences in how housing prices are examined and understood across different academic disciplines.

Eminent domain litigation requires experts to understand the nuances of "Special benefits" that may be positive or negative to a property owner. It is imparative to know the federal and state laws pertaining to a particular compensation. When a condemning authority initiates a process for a taking, a time window is implicitly created for valuation estimates. We investigate the criteria and potential effects of the pre announcement, announcement, and execution of a taking. We then use cases and sales of property around the dates of announcements to illustrate when there is sufficient knowldge of a future project to justify excluding price effects from compensation measurements.

This study evaluates the impact of the Covid-19 pandemic on housing sale prices in Oahu Island, Hawaii, focusing on the period from January 1, 2016, to April 30, 2023. Based on data from HiCentral MLS, Ltd., we analyzed 50,394 observations representing 43,057 unique units. Findings indicate a varied Covid premium between different types of housing units. Specifically, single-family units experienced a 4% premium or roughly a $50,000 increase in property value post-pandemic, whereas townhouse/condo units showed no evident premium. This distinction suggests that consumer preferences shifted towards single-family units during and post-pandemic. This study enhances understanding by incorporating extended post-pandemic data and applying spatial methods to a diverse sample. However, the underlying reasons for these preferences, whether linked to the virus spread or another factor, remain undetermined. The results also hint at potential avenues for future research, especially in spatial sensitivity concerning unit types.

Background:

In post-pandemic Australia, it is unclear how building occupants have changed their behaviour in their interaction with buildings and other occupants towards three domains of indoor respiratory transmission routes: (1) fomite: handshaking and hand hygiene behaviours; (2) airborne: individual interventions to indoor air quality including face masking and window openings; (3) droplets: social distancing and reducing working hours in the workplace. This paper explores Occupant Behaviour Change (OBC) and its determinants in this context.

Methods:

From March to April 2023, 2298 survey responses were collected from occupants working in 90 different buildings in Melbourne. This paper analyses a sub-set of 530 occupant responses from buildings with operable windows to effectively capture OBC on window openings. Uni and multi-variable regressions were conducted on potential determinants or OBCs.

Findings:

Regressions show that OBC towards different transmission routes share the same primary determinants, including risk perception and co-worker’s behaviour change, while the influence from personal factors and Indoor Environmental Quality (IEQ) factors differs significantly towards the three routes: (1) Older age significantly influences air-related OBC, while fomite-related OBC was more associated with the gender factor. By contrast, the personal factor was negligible for droplet-related OBC. (2) Droplet-related OBCs were more affected by perceived occupant density in the workplace, while the influence of perceived IAQ and occupant density on air-related OBCs was limited. Fomite-related OBC was negatively correlated with perceived environmental cleanliness.

Implications:

This study informs modellers about the significant change in occupants’ behaviour towards all the respiratory transmission routes, such as increased window opening operations, the improvement of occupant respiratory hygiene and hand hygiene, and the reduced frequency of occupant social contacts, to build robust building energy and respiratory infection risk assessment models. The findings also inform building designers of occupant preference for touchless public interface and more individual space in the post-pandemic workplace. The paper contributes to the knowledge of Human-Building Interaction (HBI) and Occupant Behaviour

This paper examines how the shift in social dynamics that characterized the early COVID-19 pandemic impacted the informational role of option trading activity in predicting equity REIT returns. We first document a significant increase in REIT option volume during this period and provide initial evidence that a REIT’s relative option volume continues to be an important predictor of return performance in the underlying stock. We also find that the return predictability of option volume is amplified amongst firms facing a higher degree of financial constraint and with an investment focus in property types most adversely impacted by the pandemic. Interestingly, the negative return predictability of REIT option volume dissipates when we condition our analysis on an individual firm’s geographic exposure to the spread of the pandemic. We find this result to be driven by a significant increase in the proportion of retail traders participating in option trading of these firms. Taken together, these results highlight the broader importance of investor composition in determining the return predictability of option market activity during periods of market volatility.

We developed a supervised machine learning Random Forest model to assess the significance of housing features in predicting property sales during the COVID-19 pandemic lockdown. Our comprehensive dataset includes 100% of all documented residential property sales recorded in the Probate Court of Mobile County, Alabama. The substantial dataset includes 104,727 transactions spanning 52 years from February 24, 1971, to February 27, 2023.

We trained our artificial intelligence model using a subset of 83,781 property sales. This training resulted in a robust model that accurately predicted 94.6% of property transaction results between March 15, 2020 (commencement of the State of Alabama mask and lockdown mandates) and April 9, 2021 (termination of the last State of Alabama mask mandate).

Interestingly, our modeling results confirm our research hypothesis that because of the many limited personal freedoms, such as being able to more freely move about or host private events in and around one’s home, buyers wanted larger spaces and, more importantly, larger “open spaces.” Results show that the paramount housing feature distinguishing property sales during the lockdown period was lot size; dwelling age and property price were second and third in importance, respectively.

For added robustness, we extended our hypothesis by conducting an event study to determine the marginal economic effects of homebuyers’ choices during the lockdown. We employed a parametric hedonic model incorporating lot size as a primary housing attribute. With a statistical confidence level of 99.99%, our results indicate that homebuyers who purchased properties during pandemic-related restrictions were willing to pay, on average, $13,656 more per additional lot-acre (in 2024 US dollars) compared to similar properties sold outside of the restriction period.

The policy, economic, and business implications of our study are clear. Severe, government-imposed restrictions alter homebuyers’ preferences and purchasing behaviors in favor of housing alternatives that will enhance their personal freedoms. Understanding that there is an evident willingness to pay more for homes with larger lot sizes during highly restrictive periods will enable real estate agents to provide greater value and service to their clients. Similarly, sellers and buyers who are better informed about the monetary effects of highly restrictive periods will be better equipped to make economic decisions related to property sale listing prices and purchase offers.

When public policies affect markets in a non-neutral manner they result in distortions. One such policy, the Opportunity Zone (OZ) program, is structured with the direct intent of distorting real estate markets by spatially channeling real property investments through tax breaks. This research focuses on how the OZ program, a place-based federal level economic development program with local inputs, has influenced real estate markets. Although not the first look at place-based economic development policy impacts, this is one of the few detailed analyses exploring impacts from OZ designation; while utilizing a nuanced approach that recognizes the operative word “place,” or proximity, along with time. This is essential to represent the diversity across OZ designated census tracts. Relying on a series of hierarchical linear equations, the results indicate there are significant commercial office price impacts in OZ under certain conditions.

The first amendment of the constitution guarantees the right of the people to “…peaceably assemble and to petition the government for a redress of grievances”. Unfortunately, sometimes such peaceful assemblies turn violent. This was the case in September 2016 in Charlotte, NC where for the first time in the city’s history a protest (of the Keith Lamont Scott shooting) that started peacefully turned violent. One protestor was killed, numerous police officers were injured, and substantial property damage was incurred in a specific section of Uptown Charlotte over a three day period. On the second day, the NC Governor declared a state of emergency in order to bring in the National Guard and State Highway Patrol. By the third day, the conflicts subsided, although the city-wide curfews remined in place for three more days. It is within this setting that this study examines the effects of violent social unrest on the nearby housing market. The study employs a difference-in-difference, propensity score, and a variety of other estimations to quantify the impact. Results from the study reveal a substantial negative impact on nearby housing values. However, this impact is temporal, lasting for about a year. These results are depicted within the framework of a change in risk perceptions, where the shock of violent social unrest is perceived to increase the possibility of future such events, which causes a decrease in demand for nearby housing and thus a decrease in house prices. To my knowledge, this is the first attempt in the literature to document the effects of violent social unrest on housing markets.

Land bank programs aid in rejuvenating and revitalizing communities. Historical redlining has been shown to have negative long term economic, health, and environmental justice outcomes for poorly graded neighborhoods. This article shows that the State Land Bank Authority of Michigan (SLBA) holds parcels disproportionately in historically redlined neighborhoods. Further, the data reveals that the SLBA holds parcels in neighborhoods across all historical redlined categories with higher social vulnerability and lower mortality rates, in many cases also with higher minority representation. The potential policy impact is that the SLBA, and possibly other land banks following suitable research, can serve as effective channels to reach the most at-risk populations.

This research examines the role of the built environment on the spread of COVID-19 in a large urban area, the Orlando metropolitan area. Our daily lives are surrounded by our built environment. We have to take a lesson from this pandemic and create a disaster-resilient infrastructure to reshape our built environment. The attributes, that are important for house price premiums and regarded as environmentally friendly, such as walkability, located in a Principal city, and highway access, also serve as potential transmission vectors for the spread of COVID-19. Walkability significantly predicts infection rates even after allowing for spatial spillovers. The major finding of the paper is that a 1-standard deviation increase in the Walk Score directly increases the incidence roughly by 72 and that addition spills over to a further increase in incidence rate by 32, with a total average increase of 104 confirmed cases per 10,000 population. Due to the pandemic, the quality of walkable places will become even more important if we can live and work from anywhere. Similarly, we find that a 1% increase in SF detached housing units directly decreases the incidence on average by approximately 90, but the spillover effect associated with SF detached units lowers roughly an additional 40 cases, summing to a total average impact of roughly 130 confirmed cases per 10,000 population. One of the important findings of this research is that local accessibility is a robust factor in spreading infection predominantly through air than geographic mobility. Contemporary Principal cities need to be reshaped and urbanization can expand horizontally into city suburbs and countryside with more available open spaces, which could reduce the density and community infections in cities. It is important to synergize the efforts of all stakeholders of the society such as the government, industry, civil society, and residents to promote and practice behaviors that reduce the spread of COVID-19.

We study the effect of immigration on home values in the U.S. Applying a county-level instrument for immigration, we find that immigration increases local house price appreciation and decreases its within-county spatial dispersion. Our estimates suggest that, on average, a one percentage point increase in the immigrant share of the local population raises house price appreciation by approximately 7 percent and reduces the dispersion of housing return within a county by about 1.5 percentage points. We also show that such effects are strikingly heterogeneous across counties and appear to be determined by local culture. Using several proxies for attitudes toward immigration at the county level, we find that immigration boosts housing returns and limits its spatial dispersion only in counties with residents who are younger, more educated, and less racially biased. Our findings highlight that the effect of immigration on home values (or the lack thereof) is highly contingent on natives' attitudes toward immigrants.

This study explores the relation between individual wealth ranking and the financial return on housing using detailed administrative data from Denmark combined with housing transactions. Individual wealth is positively related to unlevered housing returns: individuals in the 90th percentile of the wealth distribution earn a 8.4% higher return than individuals in the 10th percentile over a 10-year holding period. The entire gap in returns is eliminated when we control for location choice. Our results are important for understanding the determinants of wealth inequality, and suggest that we should focus on understanding differences in location choice between rich and poor households.

Despite extensive research on the impact of first home buyers (FHBs) on housing prices, there is a notable gap in the literature regarding the influence of FHBs on the rental housing market. This study aims to investigate the potential of FHBs' actions in bolstering the supply of rental properties through a filtering effect. This effect describes the process where FHBs vacate their homes, subsequently providing housing for the next income group (i.e., renters), thus expanding the pool of rental properties. More specifically, it examines whether the trading behaviour of FHBs offers a rent stabilisation effect as posited by the filtering process. The activities of FHBs in Sydney and Melbourne were gauged and the effect, if any, of these activities on rental markets was assessed using a panel vector autoregressions (VAR) model in a generalised method of moments (GMM) framework. Our empirical results showed that the activities of FHBs do not provide any discernible rent stabilisation. Further, no considerable rent reduction is observed in areas with high levels of FHB involvement. In fact, almost all FHB-concentrated areas remained unaffordable to renters. In other words, the filtering process does not appear to be an effective way to address the rental housing affordability issue in Sydney or Melbourne. The implications of the findings have been discussed.

House price indexes are a widely used tool for understanding the aggregate movements in housing markets. They play an important role in decision-making by government bodies, federal and local, as well as individual market actors. While a robust collection of prior research on methods for index creation exist, the set of work that addresses fundamental questions of measuring index quality is more limited. In this work, we extend the prior criteria and metrics to include relative accuracy, volatility and local specificity. We illustrate our new criteria through an empirical example comparing two common approaches – Repeat Sales and Hedonic – against a novel Interpretable Random Forest (IRF) method. We find that choosing the best index should consider the use case of the index combined with measurable objective qualities of the index itself.

Using semantic analysis of newspaper content, we introduce a novel index that reflects the public perception of the real estate uncertainty NewsREU . The {\it NewsREU is model-free and offers straightforward interpretability. We benchmark the NewsREU against the model-based real estate uncertainty REU recently developed by Nguyen Thanh et al. (2020) and show that the NewsREU contains incremental information about the real estate sector beyond what the REU captures. Moreover, we utilize a state-of-the-art topic model, leveraging neural inference network and contextual embeddings, to effectively unveil the underlying themes in narratives surrounding real estate uncertainty. This approach allows for decomposition of the NewsREU into distinct Topic NewsREU components that may reflect different facets of uncertainty within the realm of real estate. We find that different types of real estate uncertainty may either positively or negatively predict future housing outcomes. These results underscore the distinct contribution of our real estate uncertainty indices in advancing our understanding of the role of real estate uncertainty.

The aim of the research was to identify the preferences of home buyers around the largest airports in Poland. In order to achieve the goal, two stages of research were conducted in 2023, first (i) questionnaire surveys (CAPI - Computer-Assisted Personal Interviews) and then (ii) in-depth interviews (IDI - Individual In-Depth Interviews). The first stage research was based on 125 questionnaire interviews in five locations (25 at each of the airports in Krakow, Warsaw, Poznan, Katowice and Gdansk). From the group of respondents selected in this way, it was possible to select individuals for the second stage of the research; 3 in-depth interviews in each location (15 interviews in total).

The research is part of a broader project related to preventing incorrect intervention in housing markets around restricted use zones around the airports, which is being carried out at the Department of Real Estate and Investment Economics at the Krakow University of Economics (Poland).

Can the general structure of a mortgage-backed security (MBS) contract be programmatically represented through the use of decentralized autonomous organizations (DAOs)? Such an approach could allow for the portfolio of loans to be managed by investors in a trustless and transparent way. The focus and scope of this paper is to explore the potential for applying the tools of modern fintech, such as asset tokenization, smart contracts, and DAOs, to reconstruct traditional structured products that have a greater degree of transparency and traceability. MBS investors face considerable value uncertainty as time increases between the actual occurrence (or non-occurrence) of cash flows and subsequent reporting. Given that an MBS is a financial contract, it should be expressible logically using the Algorithmic Contract Types Unified Standards (ACTUS). Since each underlying mortgage in an MBS derives its cash flows in a prescribed way over the life of the contract, implementation on a public blockchain could enable real-time ratings systems, improving market efficiency. We explore the potential for creating formal algorithmic designs of MBS-DAOs that incorporate individual mortgages, the underlying real estate assets (collateral), and any loan guarantees.

We investigate the implications of non-disclosure laws on single-family housing markets. States regulate the amount of information disclosed from real estate transactions. In some states disclosure is significantly restricted. We find evidence that appraisals are more biased and mortgages have a higher probability of default in states that more heavily restrict public disclosure of house prices. Default probabilities in non-disclosure states are 10.7% higher on average than those in disclosure states. For financially constrained borrowers, this difference increased by 17.39%.

Abstract of Article:

LOOMING DEBT DEFAULTS: LOAN DUE DATES--

Business, Societal, and Tax Implications

Toward the end of 2023 and moving in to 2024, there has been a flurry of defaults on office loans.

These defaults have and are taking place for many reasons. The need to refinance loans that have become due or are coming due in this time frame of the end of 2023 and in to 2024 has been an important subject of discussion in financial circles. The need to refinance loans is not a new event. What is of recent current development is the lack of lenders who are willing and able to refinance the loans in question at rates and on terms that existed when the loans were originated, such as 5 years ago.

If, for example, a loan was placed on office property 5 years prior to this refinancing position in 2024, such loan commonly provided for a loan to value ratio of about 65% or 70%. That is, if a building had a valuation of 100 million, the loan would have normally been around 65 to 70 million. Assume the outstanding balance on the loan, today, is still close to 65 million. Today, the lender who is willing to refinance the loan may only allow a 60% loan to value, i.e., a loan of 60 million, assuming the value of 100 million. Thus, the borrower would need to add additional equity to obtain the refinanced loan to pay off a loan of 65 million.

But the above ratio is not the greatest concern for the borrower, if the market value of the building in question is now only 70 million. That is, in 2024 many office buildings that might have been valued much higher, such as the example with the 100 million valuation that was determined 5 years ago, may now, in 2024, be valued at only 70 million. This means a lender might only be willing to provide a loan on the current 2024 building valuation, and possibly with a ratio of 60% of the valuation of the building. This means in the example presented, the loan would be 60% of 70 million, or a loan of only 42 million.

The combination of only the two variables noted above, the loan to value ratio and the drop in valuation of the building, resulted in a borrower who must provide much more equity to be able to obtain the refinancing.

This example did not consider other potential problems for the borrower, such as an increase in interest rates, a drop in net operating income because of lower rental rates, etc.

The combination of the above financial factors adversely impacts the borrower. In turn, such problems encourage borrowers, a fortiori, to pressure appraisers for higher valuation conclusions that would help the borrower obtain financing.

The defaults that occur if the refinancing does not take place damages investors and tenants in the building, reduces the income stream for many businesses that depend on traffic from the building in question that needs the refinancing, impacts property taxes and other government revenue, and often reduces the quality of property in a given area, such as the core, downtown area. As discussed in this Note, defaults on loans have additional, adverse implications in many cases, such as generating federal taxable income to a defaulting borrower, creating more crime in areas where there is a greater amount of vacant property, and otherwise reducing the appeal of a given area of a city, such as the downtown area.

This study explores the impact of At-the-Market (ATM) programs on debt issuance processes and subsequent reduction in opportunity cost of issuing debts for REITs. Analyzing the U.S. REITs data from 2002 to 2022, I find empirical evidence indicating that ATM programs play a critical role in lowering opportunity costs associated with debt issuance, particularly in cases where REITs face financial constraints. Additionally, this study examines the market dynamics surrounding ATM programs, investigating how ATM programs, by providing a more flexible and cost-efficient way for capital raising, change liquidity management within REITs.

Listed and non-listed REITs over the 1993-2021 period are compared. Listed REITs are larger in terms of assets and revenues, grow more quickly, and have more cash and debt. Listed REIT performance is significantly and economically better in terms of net operating income and FFO. The performance differences between listed and non-listed REITs are about 0.5% in NOI and FFO when deflated by total assets and between 1.8% and 3.7% when deflated by equity. Most important, we find that listed REITs are more efficient/productive. When we relate performance to productivity and listed status, our findings suggest that performance is positively related to productivity in all our specifications and to listed status when we use performance measures deflated by assets.

In this study, we assess the source and dynamic nature of sentiment news on the Nigerian REIT market. The study adopts direct survey method and the Nigerian stockbrokers were surveyed through close-end questionnaire instrument. The data collected were analysed by weighted mean score and stepwise regression model. The result showed that the prominent source of biased news in the property stock market is pronouncements/announcements on economic indices and capital markets. The REIT market is more active, with often buying decisions during the optimistic market mood driven by good news. The pessimism market condition is characterized by the decision to sell, often dominated by pessimistic investors; the optimistic investors take caution, leading to a dull market and low return on investment. Findings also revealed that sentiment news from pronouncements/announcements significantly explains the dynamic behaviour of investors during the optimistic market condition; the media/press report significantly explains investor behaviour towards property stock in the pessimism market condition. The study concluded that the need to examine irrational behaviour attributed to sentiment news is imperative to optimal investment decisions when thinking of investment in the REIT market.

Over time, Real Estate Investment Trusts (REITs) have exhibited a relatively weak correlation with the broader stock market. This study delves into the non-linear association between REITs and the stock market, by going beyond the conventional CAPM and Fama-French model, and introduces a volatility factor proxied by the square of the market risk premium. The regression analysis shows that REITs display a negative association with this volatility term, or in general, a concave relationship versus the market return. The introduction of such a volatility factor helps extract a significant regression intercept, which measures the excess return like the Jensen’s alpha. Further comparative analysis shows that REITs differentiate from other market sectors in their relative concave association with the market overall market return and significant excess returns.

<p>Illiquid investments such as real estate and infrastructure now represent a significant and growing percentage of institutional portfolios. Typically, the underlying values for commercial real estate are appraisal-based leading to reduced volatility and inflated return estimates. Infrastructure valuations are often more opaque since they are often carried at or near their book value. The resulting valuation and return metrics are even more distanced from contemporary market conditions than other asset classes.&nbsp;&nbsp; Given that &ldquo;trading&rdquo; idiosyncratic illiquid assets such as airports, toll roads, bridges, ports, and pipelines is by definition infrequent, the availability of accurate return metrics is critical to investors wishing to understand infrastructure&rsquo;s investment profile.</p>

<p>This paper presents a methodology for estimating risk and return for infrastructure investments using London&rsquo;s Heathrow Airport as a case study. We propose deconstructing the airport&rsquo;s underlying cash flows such that it can be integrated into a factor model sharing a common set of risk factors with liquid assets creating robust exposures to various economic and financial factors. Not only does this approach eliminate the problems associated with traditional valuation and risk estimates, but it also allows investors to seamlessly optimize their infrastructure holdings as an asset class and/or within the context of a multi asset class portfolio. This paper builds on the authors&rsquo; earlier work on real estate and private equity.</p>

<p>Despite some contributions made on the risk management debate, it is clearly evident that risks and opportunities in property development projects most especially in developing nations of Africa are under researched. Therefore this paper, one of the objectives in a doctoral thesis, investigates the risk factors impacting real estate development project. The objective was to determine the important risk factors impacting real estate development projects in the six geopolitical zones of Nigeria. The null hypothesis set is that that there is no significant pattern of risk factors impacting real estate development projects in Nigeria. This study adopts a mixed study research approach which involved collecting both quantitative and qualitative data. For the purpose of this study, a total of eighty four (84) public and private development companies registered with the Real Estate Developers Association of Nigeria (REDAN) were investigated. Both scientific and purposive methods of sampling were adopted to select firms from the six geo-political zones in Nigeria. The primary data retrieved from the investigation were analysed using both inferential and descriptive statistical tools. The descriptive statistical analyses techniques used include frequency, percentage, mean and standard deviation; while the inferential statistic adopted is the exploratory factor analysis which was used to test the hypothesis of the study at 0.05 level of significance. Preliminary results show that there is a significant pattern in the risk factors impacting real estate development in Nigeria. Furthermore results show that while some risk factors are high, other are low within the five broad categories of the risk factors which include social risks, political risks, economic/financial risks, technological/technical risks, and environmental risks.</p>

<p>South Africa still suffers a huge housing deficit which can be worse high inflation episodes. Nonetheless, real estate investment is one attractive vehicle stabilise house prices because they can hedge against inflation. This study examines hedging abilities of house prices in South Africa between 1961Q1 and 2022Q3. We use nominal residential property prices from the Bank for International Settlements, Consumer Price Index, and relevant macroeconomic variables. Baseline cointegration tests and quantile error correction models are used to capture the non-linear, asymmetric, and time-varying short- and long-term relationships between house prices and total, expected, and unexpected inflation. We find that house prices cannot hedge against total inflation and unexpected inflation in both short- and long-terms. However, housing hedges against expected inflation in the short-term at low inflationary episodes. In the extreme inflationary periods, house price is a haven for the US dollar-Rand exchange rate in the short-term. Our findings stimulate policy, investment, and sustainable development implications for South Africa's housing needs.</p>

This paper studies how sentiment affects pricing strategy in single-family home sales for (1) Skagit County and (2) King County which are two different housing markets with different population density and average housing prices. We find that number zero which is proven psychologically more attractive (Lynn, Flynn, and Helion, 2013) is commonly used in ones, tens, hundreds, and thousands even ten-thousands digits in housing transaction selling prices. We find that there is a contemporaneous correlation between the use of zeros in these digits combined as a pricing strategy and market sentiment which ultimately leads to higher transaction selling prices.

Regarding environmental sustainability and market pricing, the energy class is an increasingly more decisive characteristic in the real estate sector. For this reason, a great deal of attention is now devoted to exploring new technologies, energy consumption forecasting tools, intelligent platforms, site management devices, optimised procedures, software, and guidelines. New investments and bright possibilities are currently the object of different research in energy efficiency in building stocks to reach widespread ZEB standards as soon as possible. In this light, this work focuses on analysing 13 cities in Northern Italy to understand the impact of energy class on market values. An extensive data-mining process collects information about 13,093 properties in Lombardia, Piemonte, Emilia Romagna, Friuli Venezia-Giulia, Veneto, and Trentino alto Adige. Then, a feature importance analysis and a machine learning forecasting tool help understand the influence of energy class on market prices today.

Implementation of public projects often results in impoverishment risks including the loss of property and disruption of livelihoods of people due to compulsory land acquisition and involuntary resettlement. Land acquisition and involuntary resettlement, if not well planned, and properly managed has potential to cause a lot of hardships, pain and suffering among project affected persons. As part of planning for land acquisition, Resettlement Action Plans (RAPs) are prepared prior to land acquisition to manage the risks of resettlement. The management of resettlement risks entails ensuring that the risks are mitigated, avoided, and affected properties and livelihoods are adequately compensated through the use of appropriate valuation methodologies and procedures that conform to both the resettlement safeguard polices of projects financiers, and local and international valuation standards. The paper examines the policy framework within which valuation for Resettlement Action Plans are carried out, the basis and methods of valuation, as well as the challenges in carrying out valuations for resettlement planning of public projects funded by international organizations in Kenya and other developing countries in Africa.

A large proportion of the Indian labor force is involved in agricultural activity. As agriculture is an income-producing activity and inherently dependent on land, land parcels derive their value from their potential to generate cash flows. The discounted cash flow (DCF) valuation approach is based on capitalization rate estimates. However, agricultural activity is marred by low-income yields. Therefore, even small errors in the capitalization rate may lead to substantial error in value estimates. In informationally deprived markets, DCF valuation may lead to a low confidence in the resulting estimate. In this paper, I present a hedonic pricing model for valuing agricultural land. Using a unique data set of over 7,000 agricultural land parcels listed for sale across several states of India, I show that such pricing models are feasible.

This research examines the impact of the performance of their football team for Universities in the Southeastern Conference (SEC). In particular, the study looks at how SEC football program performance impacted, if at all, the housing markets in the area. We examine both the housing values and rents in these communities. In particular, we examine the house and rent values relative to the unemployment, population, median household income, university enrollment, number of football wins, total points scored, whether or not the football team played in a bowl game and ultimately whether or not they played in the national championship.

This paper examines the role of agent connectedness in a trading network, using a dataset of over 40,000 housing transactions from Atlanta, GA to construct dynamic agent connectedness measures. Our findings suggest that agents with diversified network connections and a high level of connectedness tend to achieve better transaction outcomes. Specifically, agents with a diversified network of connections tend to sell properties at higher prices, without taking more days-on-market for their listings. The network connectedness of the listing agent is critically important for the transaction outcomes when the house is highly unique relative to other houses in the same market area. Furthermore, we observe that agents with more experience and diversified network connections, relative to other agents, tend to have a higher listing price, a higher sales price, and a smaller discrepancy between the listing price and final sales price. Our study highlights the significance of a listing agent’s network connectedness in achieving successful transaction outcomes in residential real estate.

We examine the effect of brokerage connections in a study of investment property transactions across the United States over the last decade. Specifically, we study the effect of the sale broker being connected either to the seller or the sale broker completing the sale with a connected buyer. Transactions in which the sale broker is connected to the seller trade at a premium and this may be attributed to incentives relating to future business opportunities. Furthermore, transactions in which the sale broker brought a connected buyer to the table sell at a higher price. In terms of the underlying channel relating to sales involving a connected buyer, our findings suggest that brokers enhance the auction house through their Rolodex resulting in a reduction in the time on the market, facilitating sales in adverse markets, and bringing distant buyers to bid. Thus, the analyses suggest that connections facilitate search within and across markets.

This project studies seller’s value of employing real estate agent services and estimates the principal agent problem in the industry taking into account agent heterogeneity. Both the seller (principal) and the real estate agent (agent) would like a transaction to happen at a highest possible price. However, in a case of a failed sale, a seller continues to own the property and can attempt to sell it again, while an agent walks away with nothing. Consequently, agents care more about closing the transaction and are likely to incentivize sellers to lower their asking price to attract more buyers. Using proprietary data on over 60 million listings, I find that the principal-agent problem is more severe in experienced agents as evidenced by slightly lower list prices all else equal for listings they represent. However, experienced agents are much better at selling homes for their clients at any price point. Informed by these findings I build a housing search model to formalize the trade-off between prices and sale probability. I allow experienced agents to have access to “better technology” which endogenously results in a higher chance of finding a buyer at any given price point. Working with experienced agents, a seller would prefer to take advantage of this technology premium and list their home at a particularly high price. However, experienced agents particularly dislike the no-sale outcome because of reputational concerns and their opportunity cost of time. As a result, they choose to list the homes at much lower prices relative to sellers’ optimum. I estimate model parameters using results from the empirical analysis and find that benefits of higher sale probability from working with top agents significantly outweigh the costs of lower list prices. Quantifying these trade-offs allows me to back out the “fair” (relative) commission rates for agents of different experience levels. I find that working with a top agent is worth as much as 2.5% of the house sale price relative to the value of working with an inexperienced agent.

In the present study, we introduce a new approach to forecasting multifamily rents at the SM level motivated by the work of Stock and Watson (2004). Our approach encompasses an optimized forecast methodology derived from minimizing the mean squared error (MSE) of the forecasts generated by ARMA, generalized autoregression (GAR), GARCH, KAR, and Markov chain Monte Carlo (MCMC) models at successive forecasting horizons. It accounts for subsample stabilities and structure changes in the stochastic process of rents by adopting time-varying coefficient estimation techniques and includes conducting forward estimations where the sample-period ending dates rolled from the start of the data series for each SMA. This dynamic forecast methodology exhibits superior out-of-sample performance based on the nonstationary presentation one to four quarters ahead while reducing the average MSE. Additionally, our inclusion of economic leading indicators improves the out-of-sample forecast at longer quarter horizons.

A growing literature investigates the “granular” origins of aggregate fluctuations in a variety of contexts. This paper builds on the theoretical framework developed by Gabaix (1999) and provides the first empirical evidence of the causal link between granular risks, stemming from the size and geographic dispersion of cities, and stock market returns. We first construct a measure of idiosyncratic shocks to individual commercial real estate (CRE) markets, which we label granular property shocks (GPS). We show that this unexpected return risk in local property markets is subsequently capitalized into the prices of listed CRE companies. To establish a causal relationship between GPS and stock returns, we adopt the Granular Instrumental Variable (GIV) approach recently developed by Gabaix and Koijen (2020, 2021). Our results suggest that idiosyncratic shocks from private CRE markets have a large and economically significant effect on listed returns: a one-standard-deviation shock to our instrumented GPS increases quarterly stock returns by 1.34%, which is 40% of its mean value.

Effective Real Estate Investment decision making process requires systematic application of requisite data. The major concern however is that data required for reliable decision-making is subject to fluctuations as a result of the effect of inflation especially in developing economies. This makes investment decision analysis difficult and somewhat unpredictable. This study examines impact of inflation on key variables of real estate investment and investment decisions.

Primary and secondary data were used for the study. Primary data relating to cost, rental and capital values were obtained from practicing estate surveyors and valuers and real estate investment analysts in Lagos, Nigeria. Secondary data covering macroeconomic variables were obtained from records of National Bureau of Statistics, Central Bank of Nigeria and World Bank Reports. Bivariate analyses were used to determine relationships between rental values of two building classes with inflation and exchange rate within the year 2005 – 2021. Results show positive and non-significant relationship between percentage changes in detached and terrace house rental values with inflation rate (r = 0.15, p>0.5) and (r = 0.27, p> 0.05) as well as exchange rate (r = 0.38, p>0.05) and (r = 0.34, p>0.5) respectively. Also, a positive and significant relationship is observed between the exchange rate and the average rental value of detached houses and terraced houses within the period under consideration (r = 0.927, p< 0 .05) and (r = 0.886, p< 0.05) respectively. Again, a negative and insignificant relationship is observed between the inflation rate and the average rental values of detached and terrace houses within the same period (r = -0.151, p>0.05) and (r = - 0.125, p>0.05) respectively. This implies that a rate increase is likely to result in lower average rental values of detached houses but this is not significant at 5% (p>0.05). Also, it shows that income from terraced houses is not a good hedge against inflation. The study recommends appropriate provision for growth in property income due to inflation in every valuation assignment.

Our research objective is to further understand the differential effects of physical & digital space characteristics on job category among team members (Artistic, Technical, and Administrative job categories). This study was conducted within a firm and its new product development (NPD) teams in different combi office. It consists of 55 semi-structured interviews. The results show various perceptions and relationships: Members of the three categories feel and react differently to their workspace which could indicate that there is still needs to improve the support for each profession beyond classic task support.

The originality of this research is to have examined how the diversity of multidisciplinary new product development teams affects the range of reactions to a popular open space office design (combi office) and its associated layouts.

We investigate how fluctuations in values of corporate real estate holdings affect bond
yield spreads. Our findings reveal a negative relationship between real estate values and
yield spreads. Specifically, a one-standard-deviation increase in real estate values as a proportion
of total assets leads to an 11-basis-point reduction in yield spreads for all U.S. public
corporations, increasing to 29 basis points for speculative-grade bonds. Building upon the
existing collateral channel literature, our study provides enhanced measures of real estate
value. Our research sheds light on the significant impact of corporate real estate holdings on
credit spreads, contributing to a deeper understanding of the dynamics between collateral
assets and corporate debt.

The prevailing wisdom is that the global Covid pandemic directly caused the current low office occupancy rates in North America and other developed economies. Since then, a great deal of focus, especially in the commercial real estate industry, has been on how to get office workers back into the actual office. However, the rapidity and ease with which many information economy industries shifted to mobile work practices demonstrates that the technologies and work processes that support this workstyle were already largely in place. Evidence is based upon direct communication with corporate real estate decision-makers during the time of crisis. Beginning in March 2020, a group of approximately 40 corporate real estate executives across a wide range of industries, together with leaders of real estate service companies, held biweekly 90-minute meetings on a Zoom platform to share information, insights, current best practices, and resources to deal with the impact of the Covid pandemic. Their actions during the first 10 months of the pandemic in the US demonstrate that mobile work (also called work from home) allowed information economy companies to maintain operations during the first year of the pandemic with little disruption in services, and with frequent reports of increased productivity. By better understanding how we got to the present situation, we can face the unpleasant reality that there will continue to be a growing overcapacity in the office sector and that demand will not return.

With the implementation or rent control in Berlin, the market for rented housing brocke down. This paper tells about what went wrong with rent control, how the market reacted, and which policy option are left.

While the end of the American Civil War had brought about state-initiated funding and operation of some local schools for black children in the South, the policies emphasizing racial segregation during the Jim Crow era left southern blacks with few opportunities for a truly complete primary education and even fewer secondary school options. and from 1917 to 1932, Rosenwald’s program led to the construction of more than 5300 public schools, teachers’ homes, and instructional shops in fifteen southern states, nearly 500 of which were located in South Carolina. The Rosenwald school program operated out of Tuskeegee University up until 1921. While the Rosenwald program was thought to have been responsible for the construction of these schools, a review of archival records indicates that the fund financed only a small fraction of the total cost of these educational buildings. This study adds to the literature on historic preservation by analyzing newly available data from the South Carolina SC State Historical Preservation Office on the Rosenwald School Construction Program. Perhaps the most important contribution of the program was to initiate a series of statewide public private partnerships that ultimately funded school construction projects with a cumulative cost of more than $2.8 million between 1918 and 1930. This is the equivalent of $51 million in 2023.

The process of condominium termination is subject to regulations that vary from state to state. The termination of a condominium has significant negative implications for unit owners that utilize the units as a principal place of abode. On the other hand, condominium terminations provide a substantial investment opportunity for real estate professionals interested in potential redevelopment of condominium properties. This article provides an overview of the condominium termination process noting some of the challenges likely to be encountered along the way.

Abstract of Article

Real Estate Brokerage Commissions: Big NAR Decision

Many buyers and sellers have become aware of large real estate commissions that are paid to brokers when marketing real estate. Recently a major decision (and we mean MAJOR) was issued out of the District Court in Missouri, holding that the commissions that were charged were in violation of antitrust law and damaged sellers, resulting in a huge dollar judgment against the National Association of Realtors (“NAR”) and a few major real estate brokerage firms. How much were the damages?

“Only” $5.4 Billion! (Yes, with a “B,” Billion!)

The Sellers claimed in a class action (that is, the action by the Plaintiffs/Sellers was undertaken on behalf of the Sellers, as Plaintiffs, AND on behalf of other potential plaintiffs under similar circumstances (as sellers in other transactions)). Thus, not only was the decision important for the Plaintiffs named in the suit, but it was also important for other parties that might have similar claims against the Defendants in question.

The essence of the case was a claim by the Plaintiffs that because the NAR had an Agreement (referred to as the Adversary Commission Rule “NAR Agreement”) between NAR and other Realtor companies that effectively forced such companies to agree that in order for such Realtor companies (and their brokers) to gain the benefit of the Multiple Listing Service (“MLS”) and other benefits of being in NAR, said companies and brokers were required to agree that listing brokers will cooperate and share commissions with the buyer’s broker. The Court held that such arrangement (with other contractual provisions), amounted to colluding by the Defendants to keep the commission rates higher than would have been the case if free competition was present to allow, without the NAR Agreement, a lower, negotiated commission arrangement that would be paid by the Seller.

The Sellers in the Case, the Plaintiffs, argued that because of this arrangement, they, the Sellers, were forced, in order to place their listing into the MLS, to pay their listing broker a greater amount of commission. This amount in turn could be used to pay part of the commission to the cooperating buyer’s broker. (This approach, it was argued, induces the buyer’s broker to be more likely to try and sell the listed home, since the buyer’s broker would be gaining part of the commission).

The Sellers argued that this collusion among NAR and the big firms (Defendants) resulted in a commission that cost the Sellers, unfairly, a commission that was much higher than would have been the result, if NAR and the others did not execute the NAR Agreement. The Sellers asserted that sellers’ brokers are effectively forced into the NAR Agreement and the cooperative position, to gain access to the MLS and make their listings more marketable to buyers’ brokers.

Some cases have raised this issue in past disputes. However, it was the recent Sitzer/Burnett^[1] v National Association of Realtors,^[2] et al^[3] Case (“S/B”) that brought the collusion Case forward to focus on antitrust with NAR and the major Defendants of that Case.

Because the Court concluded for the Plaintiffs, as noted, the entire residential brokerage business is now in a state of uncertainty as to how to handle cooperating commissions within the law and to also meet their agreement with the NAR contract signed by most residential real estate brokers in the United States.

More lawsuits along the lines of S/B have recently been filed; more are coming.^[4]

NAR and others are appealing the S/B case.

Look for more activity in this area, especially when the amount in controversy is in the billions. Some sellers in the class action may be entitled to damages, based on the S/B decision.

This research paper examines the dynamics and performance outcomes for vacation rental properties in Orlando, focusing on listings across two major digital platforms: Airbnb and Vrbo. Airbnb, established in 2008 and a publicly-traded entity as of December 2020, showcases over 7 million listings in 220 countries. Vrbo, originating in 1995 and now part of the Expedia Group, features 2 million listings across 190 countries. Despite Vrbo's earlier establishment, Airbnb has surpassed it in popularity, as reflected in their respective website traffic statistics from January 2023: Airbnb with 37.45 million visits and Vrbo with 27.6 million. This disparity raises critical questions about the impact of platform choice on the performance of vacation rentals.

Our study is rooted in an analysis of monthly performance data from properties in Orlando, employing panel data analysis to discern differences in property performance based on their listing presence on either Airbnb, Vrbo, or both. The methodological approach includes a panel data regression, which leverages comprehensive monthly variables such as the number of reviews and photos, thus providing robust and nuanced insights.

A notable finding of this study is the apparent disparity in pricing and total revenue between properties listed on Vrbo and those on Airbnb. This observation holds even when comparing entire home listings within the same geographic area on both platforms. The paper delves into this phenomenon, considering its implications for vacation rental hosts and the broader rental market dynamics.

This research contributes to the understanding of digital platform strategies in the vacation rental market, offering valuable insights for property owners, platform operators, and policymakers. It underscores the significance of platform selection in influencing rental performance and highlights the evolving landscape of the vacation rental industry in tourist-centric locations like Orlando.

This research paper conducts a comprehensive data analysis of New York City (NYC) Airbnb listings spanning from 2011 to 2023, encompassing over 40 thousand listings. Employing Python for data cleaning, type handling, and extensive visualization, the study addresses data integrity issues, delving into critical aspects of the NYC Airbnb market. Visualizations cover topics such as hosting listings distribution by room types and neighbourhood’s, price dynamics by room type and borough, spatial distribution via heatmap and geographical maps, average pricing trends, guest satisfaction based on reviews, room availability, and pricing trends over time, host activities, and frequently occurring host names in a Word Cloud. These insights serve property hosts and potential guests, promoting data-driven decisions in the dynamic NYC Airbnb market while contributing to a broader understanding of short-term rental landscape evolution over more than a decade.

We examine the impact on hotel transaction prices at the onset of the COVID-19 pandemic and the associated market volatility. The hedonic regression methodology is used to analyze a sample of 1,334 hotel transactions across 26 European countries between 2007 and 2020. For the year 2020, the index shows a statistically significant price decline of 12.2%. Hotels located in a city’s central business district (CBD) suffered a stronger impact, whereas larger hotels and full-service hotels performed significantly better. At the country-level, hotel transaction prices in the UK declined significantly more in 2020, whereas those in Germany and France performed significantly better. Regional differences are related to a country’s effectiveness in dealing with the pandemic from an economic perspective, as well as from a health perspective. Countries with a smaller drop in GDP in 2020 are associated with significantly higher hotel transaction prices, whereas there is a negative and statistically significant relation with a country’s excess mortality.

In this paper, we explore the health consequences of housing affordability on household members. We use the information about health in EU-Silc microdata from 2008 to 2020 for the European countries and identify the different impacts that lack on ‘ex-post’ housing affordability would have had at country and regional level. Based on previous studies, the paper tests whether different situations of lack of housing affordability (measured by the affordability ratio and housing stress) would have contributed to health conditions at a personal level, focusing on the different persons in the household, and paying attention to children health. Thus, the personal files of EU-Silc are used for this purpose in a panel analysis for 2008-2020. An approach of the Covid19 effect can be added to the analysis by using detailed ECV (Spanish EU-Silc) for 2021 retrieved from INE.

Research shows that people prefer not to live near negative environmental conditions, and policymakers work to reduce or eliminate such conditions for health and safety reasons. Researchers have extensively studied the effects of proximity to negative environmental externalities on home prices (Fernández-Avilés, Minguez, & Montero, 2012; Freybote & Fruits, 2015; McElveen, Brown, & Gibbons, 2017; Miller, Sah, & Sklarz, 2018; Simons, Levin, & Sementelli, 1997; Simons & Saginor, 2006; Simons, Seo, Rosenfeld, 2015; and Wyman & Mothorpe, 2018). What is little explored, however, is whether there are also economic considerations for doing so. In this study, we explore four questions of interest centering on the impact that lead water pipe replacement has on (1) the value of homes in which the pipes are replaced, (2) the value trends of the homes that previously had pipes replaced, (3) the value of homes that have not had pipes replaced, and, (4) the value of homes with no replacement relative to proximity to the home with pipes replaced. The study's results will inform our discussion of critical policy implications, including whether pipe replacement positively impacts price, how significant the marginal (monetary) effects are, and how impacts are influenced by proximity. Finally, we will discuss the broader generalization of our findings to inform policy change considerations in other U.S. cities grappling with aging water supply infrastructures.

When the Federal Open Market Committee (FOMC) makes monetary policy announcements, liquid markets tend to react immediately to both the direct change in short term rates and expectations about the future path of monetary policy. In this paper, we examine the extent to which a much less liquid market, residential housing, responds to monetary policy announcements using a novel micro dataset that covers millions of individual property transactions nationally. Rather than using monthly or quarterly aggregated data, we use the underlying microdata obtained from Zillow (“ZTRAX” data set) that includes rich information on individual transactions as well as corresponding home characteristics for each property. Methodologically, transactions-level intra-monthly data better exploits the timing of the announcements for cleaner identification, providing new insights into how monetary policy shocks affect a market that makes up a substantial portion of the economy, where interest rates are thought to play a key role. Empirically, we compare the effect of “surprise” announcements to “expected” announcements on home prices using a regression discontinuity design (RDD), finding that monetary policy surprises generally have a more potent, immediate impact on home prices. Further, we explore the effects of quantitative easing on this market, as well as geographical variation in home price responses to monetary policy more generally.

The conventional model of perfect competition is inadequate for the heterogeneous, illiquid, and decentralized housing market, which clears via multiple local time-varying equilibria. We first propose a spatial search model that caters to such market characteristics. It then provides the theoretical micro-foundations to motivate a nonlinear spatiotemporal autoregressive model with autoregressive disturbances (NLSTARAR) by
and local time-varying factors to unify the current hedonic pricing framework by uncovering the real estate market structure.

Using a unique Greater Toronto Area (GTA) dataset, the NLSTARAR model captures the effects of the local time-varying market structure
in addition to the hedonic, demographic, and policy effects on the housing market.

To address model uncertainty, we respectively apply the OLS post-SCAD(BIC) estimator and the shrinkage Mallows model averaging (SMMA) estimator to estimate the proposed real estate market structure and generate forecasts.

We find that the real estate pricing is driven by a local time-varying market structure with unexpected market shocks causing market volatility. Then, we investigate market dynamics, and the results further validate the uncovered real estate market structure.

Not only do we apply new estimation methods to predict and forecast the real estate market, but we also show that a carefully designed traditional econometric model could outperform in pricing and forecasting the housing market, even in the age of machine learning, and it turns the proverbial black-box into a white-box.

<p>The home insurance market in Florida is struggling, with premiums skyrocketing and some insurance providers no longer offering coverage in the state. We examine the role of information on this market unraveling. Specifically, we look at a policy change by Citizens Insurance which required a four-point inspection for some properties that are 30 years or older. We focus on original tile roofs and find no significant break in the likelihood of a roof replacement around age 30 prior to Hurricane Irma. However, we find tile roof homes that were under 30 years old at the time of Hurricane Irma were significantly more likely to file a claim than those over 30 years old. We do not find evidence that a four-point inspection systematically reduces the likelihood of filing a claim. Overall, our results suggest it is not the inspection itself that affects the likelihood of filing a claim, but the known change in the possibility of having an inspection that impacts claiming behavior.</p>

It has been said that to make effective real estate decisions, you need to know something about everything. Indeed, there is no aspect of history, culture, society, and contemporary affairs that does not have some derivation from real estate forces and/or inform real estate involvements, generally, and real estate decisions, specifically.

In recent years the economics discipline, concurrently with increasingly embracing advanced quantitative/mathematical approaches, has been pushed to extend the classic premise of rationality ruling all decisions to integrate behavioral factors. Notably, three leading scholars whose work emphasizes behavioral factors have been recognized with the Nobel economics prize.

Just as economics has been pushed to look beyond pure/exclusive rationality to acknowledge and integrate behavioral factors, so, too, has the psychology discipline been pressed to extend the traditional academic/research emphasis on depression/dysfunction/negativity to embrace positive psychology, in which optimism its not only recognized but favored. Strikingly, positive psychology is integral to real estate market function. Indeed, a disproportionate share of those involved in real estate, generally, and the most successful, specifically, tend to be more optimistic than the majority of people.

Concurrent with behavioral factors employing an ever more significant role in economics and the transformational shift in psychology to acknowledge the positive alongside the traditional negative orientation, information technology advances coupled with innovative data analytics applications and services have revolutionized the information available to make real estate decisions and to manage real estate involvements. Collectively, these advances challenge the traditional real estate decisions styles. Notably, those with the most powerful influence on real estate decisions may - for variety reasons - be less receptive to considering new advances in economics, psychology, and data.

Real estate and the success literature have long been linked. The aspiration to own a fabulous home is central to success motivation. Those promoting their success trainings often pose in front of impressive houses and brag on the house in which they live - as the means of establishing their success credibility and motivating their followers. Different in the 21st-century, however, is that the success literature has moved beyond exhortation and prioritizing positive attitude to reflect, embrace, and apply findings from scientific research.

This paper considers how these four phenomena – behavioral economics, positive psychology, big data, science-informed success literature – individually and interdependently, may influence and inform real estate decisions. This consideration addresses descriptive, normative, and prescriptive perspectives concerning decisions. The implications for differing aspects of real estate, generally, and different-positioned real estate decision-makers, specifically, are explored.

In an increasingly complex and digitally globalized world, the topic of risk management is becoming increasingly important for operators of large real estate portfolios. Those responsible for people, facilities or infrastructure in particular face numerous challenges when it comes to carrying out their duties responsibly while at the same time ensuring the safety and integrity of their activities. Risk assessments play a crucial role in identifying potential dangers, evaluating them and developing suitable measures to reduce risks.

This paper deals with the “development of a risk assessment for the fulfillment of operator obligations”. The focus is on how a structured approach to risk assessment can be implemented digital in companies and organizations with large real estate portfolios in order to effectively fulfill their operator obligations while ensuring a high standard of security. A look is taken at the integration of risk assessment into operator obligation management. The aim here is not to view risk assessment as an isolated process, but rather to meaningfully integrate it into existing management processes. This ensures that risk management and operator obligations are closely linked and complement each other. The development of a risk assessment with plausibility checks is intended to help deepen the understanding of the importance of a risk assessment for fulfilling operator obligations and to provide a possible guideline for those responsible for large real estate portfolios. The main goal is always to promote responsible handling of risks and to ensure the safety of people, buildings and the environment.

<p>This is the first paper in the literature to estimate risk decompositions of default, interest rate, liquidity and excess liquidity at intraday frequencies for securitized commercial real estate securities. In intervals of fifteen seconds for 572 days during the Covid pandemic we reveal stark patterns in the price formation of risks for CMBX. We then exploit links between the projected CMBX risk decomposition signals and the related REIT sector in twenty-four long-short daily trading strategies. In Year 1 of the Covid-era, 88% of our risk signalled automated trading strategies produced significant alphas, with 90% of those strategies also generating strong positive abnormal returns. These patterns of revealed mispricing of risk, and trading skill, persist across the entire sample period. In portfolio aggregations across all signals, our eigenvalue weightings perform well in- and out-of-sample compared with standard Markowitz, and equal weighted, portfolio allocations formed from the risk signalled portfolios.</p>

This study analyzes warehouse demand, supply, occupancy, rent, price by location. The original “Path of Goods Movement” (PGOM) research was created in 1994 by Mueller and Laposa, and updated in 2007 with “Warehouse Demand and the Path of Goods Movement” by Mueller & Mueller. The original POGM found that cities on major shipping routes (truck, train and ship) had higher square feet of warehouse per person than the US average of 44 square feet per person. This changed the way demand for warehouse space was analyzed as there are regional versus local warehouse markets. Regional market demand comes mainly from POGM shipments while local warehouse markets demand comes from the local population. The average has grown to 88.6 square feet per person in 2023. On-line shopping requires last-minute deliveries and additional space in local markets that was accelerated by COVID. Thus, we tier markets based upon POGM, and shifting population growth trends.

This paper provides a novel empirical analysis of the intracity effect of Working From Home (WFH) on business districts. Using a unique dataset on commercial real estate in the Paris metropolitan area up to 2022, I take advantage of the Covid-19 crisis as a natural experiment to implement a difference-in-differences strategy. Findings reveal a significant rise in office vacancy due to WFH, with effects more pronounced (i) further from the city center, (ii) in areas with longer commuting distances and (iii) with respect to firm size. This suggests that aggregate demand for office space is shrinking, while firms maintain a preference for central locations, which exacerbates spatial disparities between central and suburban business districts. Furthermore, I provide evidence that WFH exposure yields a notable decrease in local retail sector employment and business numbers, underscoring the broad economic implications of teleworking.

The purpose of this research is to investigate the impact of changes in workplace visits frequency (WVF) on the housing market in the UK during the COVID-19 pandemic. Using a location-based mobility approach, we examine the mobility patterns of 126 261 individuals in England and Wales by analysing over 360 million cell phone locations and timestamp records. Our analysis reveals that the average WVF among individuals in our sample decreased from 4.0 days to 2.7 days per week between March 2020 and March 2021, and the amount of time spent at home increased from 14.9 hours to 17.1 hours per day. Using a first-difference instrumental variable (FD-IV) model at the UK Middle Layer Super Output Areas (MSOA) level, we observe that a 10% reduction in WVF is associated with a 1.6% decline in house prices. The impact of WVF on house prices is larger in areas with higher workplace density, household income, and commuting distance, and lower availability of public transportation. These findings align with both our theoretical analysis and previous research. This paper is the first to examine the impact of a change in workplace visits frequency on the housing market, thereby expanding the body of knowledge on work-from-home and COVID-related housing market research. Our study also contributes to the field of research on commuting costs and the housing market, enhances the understanding of the housing market in the post-pandemic period and provides policy implications.

Existing evidence on residential migration has focused on patterns of leaving high-density central cities and moving into peri-urban areas after COVID-19. However, the discussion on variations in these patterns between neighborhoods within the central city has been largely neglected. We investigate whether the importance of neighborhood quality to resident population turnover changes and how the attributes of movers’ origin neighborhoods affect their choice of destination neighborhoods over different COVID-19 phases. In doing so, we use the monthly information of inter-neighborhood migration from more than 43 million mobile phone users between April 2019 and October 2022 in UK, which is linked to neighborhood indicators such as the Index of Multiple Deprivation (IMD). We first report a significant but temporary increase in out-migration from central London neighborhoods after the COVID-19 outbreak. Next, we demonstrate that, neighborhoods with higher quality experience higher out-mobility than in-mobility after the COVID-19 outbreak. Finally, we find that movers from high-quality neighborhoods are more likely to move into even higher-quality neighborhoods while those from lower-quality neighborhoods show downward mobility.

Real estate are long-lasting and rather inflexible by their nature. Their ability to respond to the constantly changing needs set by real estate market participants is challenged more than ever by the uncertainty, volatility and complexity characterizing our current societies. Moreover, real estate cause significant environmental, social and economic impacts during their whole lifecycle. This means that their direct and indirect impact will be experienced widely, for better or for worse. Therefore, there is an urgent need to improve the futures resiliency of real estate market environments and ensure that the future real estate market professionals are equipped with skills which support more future oriented decisions and actions. The aim of this presentation is to showcase examples on how futures orientation can be developed and implemented into real estate education. We utilize a newly established master’s level course “Futures studies for Real Estate Economics” as an example and present its learning outcomes, contents, teaching and evaluation methods. Our presentation contributes by increasing the understanding of the importance of futures orientation especially in the field of real estate. We also offer practical examples that can be implemented in bachelor, master’s, doctoral and continuing education.

In the rapidly evolving landscape of real estate, the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) into undergraduate education is crucial for preparing future professionals. This paper explores the transformative role of AI and LLMs in developing the skill sets of students in commercial and residential real estate, appraisal, and broader market analytics. We examine how these technological advancements necessitate novel teaching methodologies that not only expect but leverage students' use of AI tools for homework and project execution. The paper highlights key areas in the real estate sector where AI is currently employed, underscoring the industry's expectation for graduates to be proficient in these technologies. Furthermore, we discuss the dual impact of AI on education: reshaping not only the methods of teaching but also the content of the real estate curriculum. This paradigm shift requires a reevaluation of pedagogical approaches to ensure that graduates are well-equipped to navigate and excel in a tech-driven real estate market.

Abstract: Mindset for Real Estate Success This presentation delves into the pivotal role of mindset in achieving success in the dynamic realm of real estate. Beginning with an exploration of mindset, it distinguishes between fixed and growth mindsets, emphasizing the profound impact on success within the industry. The real estate landscape is dissected, highlighting challenges and opportunities. The core of the presentation revolves around the importance of a growth mindset, encouraging real estate professionals to embrace challenges, learn from setbacks, and continuously improve to adapt to market fluctuations. Resilience becomes a focal point, discussing strategies to navigate market volatility and overcome obstacles. Strategic thinking is unveiled as a key element for long-term success, emphasizing the significance of goal setting and planning. Real-life success stories are interwoven to exemplify the principles discussed. The presentation also introduces mindfulness practices tailored for real estate professionals, providing tools to manage stress and maintain clarity. Building a supportive network is emphasized, emphasizing the role of professional and personal connections in fostering success. Addressing common mindset challenges, the presentation provides insights into overcoming fear and self-doubt and transforming challenges into opportunities. Concluding with actionable tips for developing a success mindset, the presentation encourages the adoption of daily habits, continuous learning, and seeking inspiration from industry leaders. Attendees are invited to discuss and pose questions, fostering an interactive and informative session on cultivating the mindset for real estate success.

This presentation focuses on the process, opportunities, and challenges of teaching integrative real estate and urban design courses in a graduate higher education setting. The dynamics of two pedagogical formats are discussed: the intensive two-week multidisciplinary design competition, and the semester-long joint real estate capstone and urban design studio. Both formats intentionally mirror the multidisciplinary process of delivering real estate projects, in which stakeholders with different skillsets and perspectives collaboratively create exciting and viable projects.

By teaching joint design and development classes, real estate and design students are deliberately challenged to step outside their comfort zone and understand the complementary side of delivering projects. Development students better understand how designers can test and add value to their work; design students better understand how developers can test and add financial viability and realism to their work. Furthermore, by introducing community and industry partners and studio sponsors, integrative courses introduce depth, purpose, and unpredictability into the pedagogical process.

However, blending the finance and process-based pedagogy of real estate education, with the design-based pedagogy of architecture and urban design education creates specific epistemological and pedagogical challenges. Epistemologically, the linear mindset of real estate development is challenged by the iterative process of design, as both students materialize their best options for a site. Pedagogically, different real estate and design studio timing, varying skillsets, varying student learning outcomes and accreditation standards, and different institutional contexts can hamper joint courses.

This presentation and paper will openly discuss the successes and challenges of the two presenters in leveraging the benefits of joint real estate and design education, while overcoming its challenges.

The expectations of customers continue to rise. The use of marketing technology, often known as MarTech, is essential to satisfying and even exceeding such needs. The use of new technology is shaking up the conventional marketing industry. The distinction between marketing and technology is becoming increasingly difficult to discern. The profession of marketing has transitioned into the digital age and is now intrinsically related to and assisted by many forms of marketing technology. The term "MarTech" refers to the software that marketers employ in order to maximize the effectiveness of their marketing activities and achieve their objectives. Technology is utilized in the planning, carrying out, and analyzing of various marketing campaigns and other initiatives. In the most fundamental sense, it can make the marketing process easier. Technology is becoming a bigger and bigger part of real estate marketing. The real estate marketing landscape is always changing because MarTech is getting smarter. This lets real estate brands keep up with the constantly changing and fragmented customer journeys better. In short, the future of modern real estate marketing depends on marketing technology. Also, marketing technology is important for real estate because it takes execution needs into account from the beginning to the end. It lets real estate marketers scale up their marketing efforts in a more complete way. MarTech helps the employees of the real estate company communicate and work with customers and coworkers better. Improving these relationships will also helps build better relationships with customers. MarTech can be classified as: Management, Social Optimization, Campaign Reach, and Insight Generation. The aim of this paper is to find out how the Real Estate companies can use MarTech for improving their sales and also being able to selling quickly. The methodology used is secondary data collection and discussion with 10 web-designers of real estate companies, followed by 5 case studies of real estate companies in Pune, India.

Rent regulation has seen increasing legislative momentum in many places, but are landlords adhering to these policies? Answering this question is critical to understand the policy's impact. We investigate non-compliance with rent caps using data from the rent stabilization policy in New York City. We uncover evidence indicative of widespread rent overcharging. Our analysis, based on panel data between 2005 and 2008, reveals that over 30% of tenants in rent-stabilized apartments without turnover were likely overcharged. Moreover, we find that minorities in rent-stabilized units are about 20% more likely to be overcharged than their White counterparts. This empirical pattern is robust after accounting for measurement errors in rent increases, alternative model specifications, and location fixed effects. Additionally, in contrast to rent-stabilized units, landlords of unregulated market units do not disproportionately increase rents for minority tenants. Furthermore, we present suggestive evidence that, compared to similar Whites, minorities who are less aware of the policy, have lower educational attainment, and are from lower-income groups, are at a greater risk of rent overcharging.

Housing prices are rising faster than incomes in many areas of the world, reducing well-being and engendering social discontent. Passivity by municipal and national governments is no longer an option. In this essay, I will describe the tradeoffs between different housing policy objectives of governments and the public. I suggest that policy goals should be made explicit, and their tradeoffs acknowledged. Due to the durable impact of real estate development, housing and land-use policies should seek broad inter-partisan consensus. To avoid pernicious general equilibrium effects and because of limited public resources, subsidies ought to be carefully targeted. I will describe the thirty major economic strategies underpinning housing policies worldwide and discuss their main advantages and caveats. Effective housing programs must skillfully deploy a combination of these basic economic strategies, as I will illustrate through several global case studies. Programs should be carefully designed to anticipate behavioral responses from individuals, firms, governments, and markets. Unideological and professional implementation is critical for their success.

The COVID-19 pandemic further extended the multi-year housing boom in advanced economies and emerging markets alike against massive monetary easing during the pandemic. In this paper, we analyze the pricing-out phenomenon in the U.S. residential housing market due to higher house prices associated with monetary easing. We first set up a stylized general equilibrium model and show that although monetary easing decreases the mortgage payment burden, it would raise house prices, lower housing affordability for first-time homebuyers, and increase housing wealth inequality between first-time and repeat homebuyers. We then use the U.S. household-level data to quantify the effect of the house price change on housing affordability relative to that of the interest rate change. We find evidence of the pricing-out effect for all homebuyers; moreover, we find that the pricing-out effect is stronger for first-time homebuyers than for repeat homebuyers. The paper highlights the importance of accounting for general equilibrium effects and distributional implications of monetary policy while assessing housing affordability. It also calls for complementing monetary easing with well-targeted policy measures that can boost housing affordability, particularly for first-time and lower-income households. Such measures are also needed during aggressive monetary tightening, given that the fall in house prices may be insufficient or too slow to fully offset the immediate adverse impact of higher rates on housing affordability.

This paper studies the value of clean air. By exploiting the cross-city variation in the implementation timing of China's clean air policy and using a panel dataset of 280 cities over 2003-2018, we find that the implementation of the clean air policy boosts housing values by 4.4%. The finding is robust to a series of potential issues, functional misspecifications, and falsification tests. We further examine whether the effect varies across different price-tier cities and changes over time, and we find evidence of such heterogeneous and dynamic effects.

The Paris Accords (previously named the Kyoto Protocol) is an international environmental treaty intended to reduce greenhouse gas concentrations in the atmosphere to help tackle climate change, and its associated effects such as more extreme weather events and sea-level rise. Improving energy efficiency of buildings and appliances is the most cost-effective way of reducing greenhouse gas (GHG) emissions. A report by the U.S. Climate Change Science Program estimates that homes can achieve carbon emission reductions up to 70% with current best practices (McMahon et al., 2007). New building energy codes and legislation has been introduced to address this but deal with new homes primarily. Industry too, has been pro-active with the introduction of various home rating tools. However, one of the limiting factors to achieving these efficiencies are individual behaviour change and the public policies necessary to catalyse these changes.

This paper outlines the results of research carried out in St. Augustine Beach, Florida in 2020. St. Augustine is one of many chronically flooded communities along Florida's 1,200-mile coastline. Climate change has resulted in more extreme weather events in the nation’s oldest city, such as hurricanes Matthew (2016) and Irma (2017), that caused significant damage to homes and buildings. Fortunately, the City of St Augustine Beach recently passed resolutions on sea level rise and climate change to help raise awareness of these issues. The research adopted a survey approach to help gauge the perceptions of the community toward climate change and the motivation of individuals to reduce their carbon emissions. The broad aim of the research was to examine the lifestyle choices of residents in relation to climate change and to educate them on actions they can take to reduce their carbon footprint under three categories: transportation; food production and diet, and buildings.

The survey results show that City residents are particularly concerned about flooding, sea level rise and other changes along the coastline, and hurricanes. Nearly 70% of all respondents said they are motivated to reduce their personal climate change emissions. To do this, over two-thirds (70%) said they might be willing to purchase an electric or hybrid vehicle. A similar number of respondents said they would be likely to select only locally grown produce, but just a little more than half said they would be willing to reduce their meat consumption. Most respondents say they use at least some energy-efficient light bulbs but also use energy-efficient appliances. The results help identify what behavioural and policy changes are needed to increase the uptake of energy efficiency and sustainability practices by householders.

While previous research focuses on the desirability of green buildings from the supply perspective of landlords and investors, this paper is among the first to investigate the green building premium from the tenants’ perspective (demand). We investigate whether firms re-locating to green buildings experienced superior
performance subsequent to relocation over the past two decades. We test our hypothesis empirically using corporate headquarters relocation data and green building data available from the U.S. Green Building Council (USGBC) and Energy Star for LEED-certifications and Energy Star-certifications. Our findings show that, similar to other corporate social responsibility spending, green building tenancy signals a conflict between shareholders and insiders.

Since their introduction by the UN in 2015, companies have started to report on the UN Sustainable Development Goals (SDGs). Investors increasingly use the same framework to measure, monitor and report on their impact on society. In view of the importance investors are placing on the SDGs, it may well be attractive for REITs to report on their contributions as well. I draw upon a sample of 200+ European REITs to follow the adoption process of SDGs by REITs. Furthermore, the reported contributions are compared with extant frameworks for reporting to evaluate whether the reported contributions are in line with expectations. Finally, the propensity of companies to report is analyzed using logit models to arrive at an explanation as to why REITs choose (not) to report.

Despite the importance of the connection between childhood experience and health, the pathways linking the chronicity of housing instability and childhood experience remain inadequately understood due to data limitations. This study seeks to address these gaps by investigating this phenomenon using nationally representative survey data. The data used for this research is retrieved from the National Survey of Children’s Health (NSCH). Using logistic regression, we examine whether children living in unstable housing conditions, as indicated by the total number of moves, face an elevated risk of adverse childhood experiences. The results indicate that children living in unstable homes are shown to be more likely to have exposure to each ACE factor as well as higher overall ACE scores compared to children living in stable housing.

High levels of informational uncertainty and incomplete information surround price discovery processes in real estate transactions. This leads to many potential problems for individuals making investment decisions and policy makers who rely on aggregated price data in formulating housing policy. While behavioral frameworks may provide some guidance on how this information may be processed, most behavioural research relies on experimental methods rather than noisy real-world transactions. This paper analyses whether it is possible to reframe heuristic bias as a structural characteristic of real estate markets using the anchoring and adjustment heuristic as a structural element within transaction processes allowing for quantitative models to be assessed as real-world experiments using market price data. By assessing the role limited transaction information plays on market participants, we first examine how agents take advantage of the information asymmetries present to maximize transaction prices. With widespread anchoring and adjustment bias occurring throughout the real estate marketing process as agents attempt to maximize price for their clients, market participants must deal with inaccurate information as a basis for transaction decision making. Recent changes in sale method preferences and framing should also influence anchoring and adjustment heuristics in price formation. This change in the sale process may limit the ability of buyers and sellers to use information to adjust initial anchors in the price discovery process. By further aggregating price data across sale methodologies, the potential to influence wider property market decisions becomes greater, including how price expectations may influence housing supply. Given the complexity of the development process and regulatory constraints, this finding has implications for the persistence of housing supply problems and related policies. This review suggests tight regulation can act as an anchor to supply responses in housing markets with the expectation of prolonged higher house price movements resulting. This also suggests reference dependent anchoring and adjustment heuristics present in individual prices may also carry through to both policy makers and developers in developing housing by type. The findings suggest that within a real estate context more information on price may not necessarily lead to better decision making due to pervasive reference dependent anchoring and adjustment structural elements in the marketplace.

The article deals with the development of the real estate market in the Slovak Republic after the change of the political and economic system. Emphasis is placed on the determinants that influence it, such as foreign direct investment, property-law relations, and the undersized housing sector. The fundamental issues of real estate market development undeniably include the field of education in the real estate economy, which with increasing globalization, innovations in information technology, financial instruments, a new knowledge economy, and new emerging types of professions in the context of Industry 4.0 or 5.0 requires changes in the educational processes. The authors aimed to present the situation in the real estate market in the Slovak Republic, which since 2000 has experienced more or less a boom in the construction of residential real estate. The mentioned situation reflects the establishment of new large companies, in this case automotive companies, which have a significant impact on GDP growth, but also significant demand on the availability of rental housing. The authors point to the consequences of the wrong decisions that almost liquidated the rental housing market in Slovakia after 1989.

This paper analyzes residential real estate holding period when debt and taxation are taken into account. The literature about the search for the optimal holding period in real estate portfolio management is rather recent. Real estate investment has traditionally been viewed as a rather passive process, since investor’s strategy is very often reduced to a simple buy-and-hold strategy. Such strategy avoids too large transaction cost. Since real estate cycles tend to be shorter, it is worthwhile to better analyze the concept of holding period and especially of ex-ante holding period.

In this paper, our aim is to emphasize the impact of the combination of debt and taxation on the optimal holding period, extending previous results of Baroni et al. (2007). For this purpose, we consider a risk-neutral investor that maximizes the expectation of his global wealth at maturity over a given time period. In this framework, the key parameters are the time horizon, the constraints on mortgages (mortgages tax, prepayment penalties etc.) and the various taxes such as taxes on the rent and on the capital gains. The optimization problem corresponds to the maximization of the expectation of both the free cash flows and the terminal value of the portfolio.

We first introduce various taxes and in particular several specific degression functions on the capital gains. Then, we add debt rigidities and costs with an emphasis on prepayment penalties. Both capital gains and debt inherent costs are analyzed through real options. Finally, we analyze the behaviour of the optimal time to sell for various financial parameter values and taxation levels. For this purpose, in the standard geometric Brownian framework, we derive a closed-form formula to determine the ex-ante optimal holding period of a real estate asset with taxation.

We show how the introduction of taxation and mortgage structures highly affect optimal time to sell. The optimal time to sell depends on the volatility of the real estate asset and on the rigidities of debt (e.g. prepayment penalties), since capital gains and mortgages can be viewed analysed as a combination of real options involving different call options. We provide numerical illustrations to emphasize such features and to examine the impacts of mortgage structures and taxation parameters for various markets (US and several European countries). We also compare the impact of different degression functions on the capital gains.

This article investigates a novel methodology to understand the intricacies of population migration within local governments. By employing an extensive dataset comprising 290 municipalities and 20 years of data, we analyse a critical variable: the percentage of inhabitants who continue to reside in the same municipality as they did twelve months ago. This distinctive metric functions as a dual-purpose indicator, assessing a municipality's appeal and its inhabitants' entrapment. By integrating data on migration, demographics, income levels, housing prices, labour market conditions, and crime rates, a comprehensive understanding of the determinants that impact individuals' choices to stay or relocate interregional can be attained. We provide an understanding of these variables' causal and temporal relationships by incorporating spatial autoregressive panel data analysis into our methodology. The initial results reveal trends and patterns in residential stability, underscoring the intricate interaction between demographic, social, and economic elements. This research increases the body of knowledge on urban migration and residential preference, providing policymakers and urban planners with vital information that can be used to improve the desirability and liveability of municipalities. Our objective is to differentiate between the favourable factors that enhance the attractiveness of a municipality and the unfavourable restrictions that restrict the mobility of its residents. In doing so, we hope to provide a thorough understanding of residential stability in urban environments.

The global economic trends study delves into the complex relationship between global economic trends and U.S. real estate performance, providing valuable insights for informed investment decisions. The regression analysis of data from 2005 to 2022 reveals statistically significant correlations, particularly between macroeconomic factors such as GDP growth and real estate returns. The consistent positive correlation between real GDP and real estate returns underscores the crucial role of economic expansion in driving real estate investment.

The study also distinguishes the impact of economic factors on advanced and emerging markets, highlighting the explanatory power of 35.6% for Emerging and Developing economies. In addition to macroeconomic indicators, the significance of concentration ratios in advanced economies emphasizes the relevance of localized market structures. The robust impact of fiscal policy and inflation on emerging markets further underscores the amplified role of policy in developing economies, suggesting opportunities for tailored investment strategies based on market maturity.

The study emphasizes the importance of portfolio optimization through dynamic integration of economic indicators to enhance resilience during economic cycles. It highlights diversification and strategic categorization as critical elements for managing risk. Customized strategic allocation, aligned with market-specific economic and risk factors, emerges as a potential enhancer of portfolio performance. The findings also stress the benefits of global coordination and monitoring of capital flows, which is particularly beneficial for emerging markets. Continually reassessing localized and global performance drivers is crucial, aligning with the dynamic nature of real estate investing.

Regardless of the limitations in the aggregated data, this study advances the literature on optimizing real estate portfolios in dynamic global economic environments, providing sophisticated approaches beyond macroeconomic indicators for strategic decision-making.

This paper compares real estate mutual funds and real estate exchange-traded funds (ETFs). This study reveals a trend: a steady increase in the number of real estate ETFs since 2001 and a decrease in mutual funds post their 2017 peak. Contrary to expectations, these vehicles are not substitutes but complements, with capital flows in one positively related with flows to the other. The analysis of market beta shows parallel trends for both mutual funds and ETFs, with large fluctuation in market beta over time challenging the notion of real estate funds as a reliable hedge against market risk. The study also finds that while market beta negatively impacts mutual fund flows, it does not affect ETF flows. While mutual funds flows are positively related to stock market and REIT market performance in the previous month, ETF flows are only positively correlated with REIT market performance. Expenses are inversely related to ETF flows but show no significant effect on mutual fund flows. The findings signal different clienteles and motivations for investing in real estate asset class by real estate mutual fund and ETF investors.

We propose a cointegration-based method to assess the long-term independence of sectoral REITs in a portfolio and utilize this innovative approach to create diversified portfolios. We contend that sectors contributing less to the cointegration offer greater risk reduction benefits to the portfolio. To approximate the level of independence, we employ the reciprocal of the aggregate likelihood ratio (RAL) statistics. Based on these statistics, we introduce a new allocation strategy that assigns a higher weight to a sector with a larger RAL. By employing CRSP/Ziman REIT sector data, we demonstrate that this new strategy outperforms traditional methods and the real estate market benchmark. Our paper presents a fresh framework for REIT portfolio management and offers guidance on sector allocation.

University endowments have a key role in providing opportunities for universities beyond their normal budgets (eg: student scholarships etc). In many cases, these university endowments are significant pools of assets; eg: Harvard ($49B), Yale ($41B), Stanford ($36B), Princeton ($36B) and MIT ($25B) etc; often seeing separate investment management organizations established to manage these assets. The significance of these university endowments to university operations highlights the importance of the portfolio asset mix of these endowment portfolios. This sees real estate as a key asset in many university endowment portfolios. This paper examines the role of real estate in the portfolios of the top 100 US university endowments. A range of critical issues are assessed for these university endowment portfolios, including the level of real estate, real estate strategies used, real estate vehicles used, dynamics and risk management procedures used to achieve this real estate exposure. The ongoing strategic issues for real estate in university endowment portfolios are also highlighted.

When seeking an apartment, much like choosing a restaurant for dinner, many individuals turn to online reviews for guidance. The pivotal question arises: can positive online reviews contribute to attracting new tenants for multifamily properties? Additionally, do properties with favorable reviews demonstrate superior performance in terms of rent growth and occupancy rates? This study aims to address these questions through an analysis of over 10,000 multifamily properties across the United States, utilizing multifamily property performance data from Axiometrics and review data from Google Maps. Various data analytics and statistical techniques, including regression analysis, machine learning algorithms, and data visualizations, will be applied to extract insights.

This paper presents a methodology to develop a bargaining index that measures the relative bargaining strength of sellers and buyers over time and across markets. We use listing and transaction data from across the US to estimate regressions with the log transformation of the ratio of the sale and list prices as the dependent variable and quarterly indicator variables relating to the sale date as explanatory variables of interest along with characteristic related variables to account for heterogeneity. The resultant quarterly estimates proxy for the relative bargaining strength of sellers versus buyers that is based on aggregate market supply and demand factors that may vary over time. The indices for over 200 metropolitan areas depict substantial variation in the bargaining strength over time and across areas.

This paper examines the relationship between the monthly National Association of Realtor Hotness Score metric and the Median Listing prices for homes in 1596 counties in the U.S over the period of 2017-2022. A higher the Hotness Score indicates that houses at that county are selling faster than in other counties and that there are more viewers per property, essentially a relationship between the demand and supply for homes. Economic theory suggests that there should be a positive correlation between Market Hotness and housing prices. This research examines whether prices should increase indefinitely and is inspired by studies of the Environmental Kuznets Curve, which models if the shape of the relationship between Market Hotness and median listing prices is linear or polynomial by including a quadratic and cubic factor in the empirical model. The results illustrate that there is a N-Shaped relationship between Market Hotness and median listing prices, and that movements in the supply side (median days on market) are the ones leading the changes in prices rather than movements on the demand side (viewers per property).

While supply constraints (e.g., land availability or building codes) are known to determine how much property price appreciates in a market boom, their impact in a bust is still under debate. Empirical evidence was mixed because how housing, as a durable good, could shrink its supply was often neglected. This paper argues that construction reversibility should play an important role in shaping how ‘kinked’ a supply curve is when demand is gone: 1) When stalled construction is not allowed in a given institution, the price falls at different locations should be irrespective of supply constraints. Our evidence from Hong Kong after the Asian Financial Crisis supports this conditional prediction. 2) If construction can be stalled at affordable costs, locations with stronger supply constraints should suffer deeper price drops from the same bust. It explains the findings about the US Subprime Mortgage Crisis in the literature. This paper provides one possible modification to the well-known kinked supply curve to reconcile the seemingly mixed evidence, which is actually expected under different conditions.

Urban village renewal, which is a specific phenomenon in rapidly urbanizing areas in China, has a significant influence on the neighborhood building environment and urban sustainability, but few studies have focused on its impact on land development. Combining real option theory and optimal decision method, urban village renewal would influence the surrounding land development by reducing the developmental uncertainty of the urban lands and raising their current yields. Based on the urban land and urban village renewal data from 2008 to 2018, an empirical analysis of the impact of urban village renewal on urban land development time was conducted in Hangzhou, China. Using the difference-in-differences method, the results showed that the development time of urban lands located within 2,000 m range of urban villages decreased after the surrounding urban village renewal. Furthermore, the land scale and the surrounding number of bus stops of urban lands had positive influences on the urban land development time while the distance to the urban centers had a negative relationship. Thus, city governments could re-examine the role of urban village renewal so as to improve land use efficiency and speed up urban development in rapidly urbanizing areas.

Using REITs as a laboratory to isolate the advisory role of the board of directors, we determine that directors with executive/governance experience in finance and accounting create significant value. Adding “high-value” directors is associated with an increase in monthly returns of between 1.1% and 2%, along with a 50-basis point increase in risk-adjusted return. CARs indicate that high-value directors are added to underperforming REITs, and results hold when controlling for endogeneity. High-value board members increase capital use efficiency, sell underperforming properties, and focus future investments on outperforming submarkets, while having higher pay-to-performance sensitivity and shorter tenure than average directors.

Please refer to attached for complete description of the proposed study.

The primary purpose of this study is to analyze both long-run
and short-run interactions between securitized property markets and each country's own EPU for a number of North American, European, and Asian economies. Moreover, due to the global dominance of the US economy, and due to the fact that its economic policies are likely to have a significant influence on global financial markets, we also examine the impact of the US EPU on global securitized property markets. Additionally, since listed real estate securities share many characteristics with common stocks and are an integral part of portfolios primarily containing equities, the study compares how the stock market in each country reacts to its own country's EPU versus US EPU over both a short-term and long-term basis.

This paper debates the main impacts on the Brazilian real estate market of the growing level of inflation and interest rates during and after pandemic crisis, in order to clarify if it would be attractive for a usual real estate investor to buy office-building portfolios in Brazil. The paper highlights:

[i] – the regulatory frontiers, comparing investment securitization, using a typical American REIT structure, with the Brazilian solution, using the Fundo de Investimento Imobiliário – FII (Brazilian REIT).

[ii] – the investment quality attributes in the Brazilian market, using an office-building sample, along the pandemic period.

[iii] – the comparison of [ risk vs. yield ] investment in the Brazilian market, using a FII, benchmarked against an existing REIT (Office Sub-sector) in the USA market, considering the inflation and interest rates fluctuations along and after the pandemic period.

In the article, we are going to investigate whether investing dollars exchanged for reals (the Brazilian currency) in a FII with a triple A office-building portfolio in the São Paulo marketplace will yield an annual income and a premium return above an American REIT investment. It will take into consideration both, the IPCA (Brazilian inflation index), the American CPI, and interest rates time series of the two countries.

The impact of international diversification on REIT performance

The function of a REIT manager is to effectively and profitably manage a real estate portfolio. Among the ways to achieve this goal is to improve performance by managing market exposure through geographic diversification. Previous research recognizes the role of portfolio geographic diversification on efficiency and firm value. For example, Campbell, Petrova and Sirmans (2003) explain REIT property acquisitions produce wealth benefits when companies reconfirm their geographical focus suggesting firms benefit from more geographically concentrated portfolios. Similarly, Hartzell, Sun and Titman (2014) find that as REITs increase the geographical dispersion of their properties, firm value significantly decreases, also suggesting a REIT geographical diversification discount. More recently, Feng, Pattanapanchai, Price and Sirmans (2021) find a relationship between the level of firm transparency and the benefit of geographical diversification; that is, less transparent firms benefit from geographical concentration while more transparent firms benefit from diversification. Relatedly, Zhu and Lizieri (2022) suggest REITs with more geographically concentrated portfolios observe higher risk when the portfolio is exposed to more volatile property markets but if portfolios are geographically diversified, the effect of local market risk decreases. In sum, the evidence of whether geographical diversification improves or decreases REIT efficiency and value remains inconclusive.

In this paper, we explore the impact of geographical diversification from an international perspective. We find that although most U.S. equity REITs tend to concentrate assets in the continental United States, a non-trivial portion of the U.S. REIT market tends to diversify internationally. Therefore, we raise the question of whether the strategy of international geographical diversification is value-enhancing.

Data and Sample Description

We employ accounting and property portfolio data in annual frequency from S&P Global Market Intelligence (formally SNL Financial) for the sample period 1993 to 2019. Our sample is comprised of 3,441 REIT-year observations for 310 unique REITs. From the 3,441 REIT-year observations, we have 836 that include international (non-U.S.) properties (24.3% of the sample). The property portfolio data provides information on more than 85,000 unique assets which includes spatial location data and property characteristics.

To estimate the degree of international geographical diversification, we geocode all property locations; 98.15% of properties have longitude and latitude data or address information. To correct for missing location information, we exclude REIT-year observations where more than 50% of the properties are missing coordinate data or a property address. Then, we construct geographical diversification scores based on multiple criteria which include dispersion, distance from headquarters, and number of countries where properties are located.

Our dependent variable is, alternatively, Tobin’s Q and Firm Q (Capozza and Seguin, 2003; Eichholtz, Holtermans, Kok and Yonder, 2019; Hartzell, Sun and Titman, 2014; Beracha, Feng and Hardin, 2019a). Tobin’s Q is the ratio of the market value of equity plus the book value of debt to the book value of assets and Firm Q is the ratio of the implied market capitalization plus total assets minus the book value of equity to total assets.

Control variables include Leverage calculated as the ratio of REIT total debt to total assets. Size is measured as the natural logarithm of total market capitalization. Firm age is the natural logarithm of one plus the smaller of either the number of years since a REIT’s initial public offering or the number of years since the firm adopted REIT status. REI Growth is the growth rate of real estate investments as defined by S&P Global Market Intelligence. Self-managed is a binary variable specifying if a REIT manages the day-to-day operations of its own properties (value of 1) or if the properties are managed by a subsidiary (value of 0). Finally, Self-advised is a binary variable indicating if the company makes acquisition and management decisions internally (value of 1) or if it is externally advised (value of 0). Other controls include domestic geographical diversification, agglomeration, operational efficiency, and property-type diversification.

The market price of Low-Income Housing Tax Credits (LIHTCs) is referred to the allocated tax credit amount divided by the total equity raised from the sale of tax credits. Such price change directly affects the capital structure of the subsidized multifamily housing that provides rent restricted units towards to low-income household. It is simply because the nominal price range is between $0.8 to $1.2 per tax credit and the sale profit of tax credits are contributed to close the funding gap. Anecdotal evidence suggests that the major buyers of tax credits are banks, because they have stable expected tax bill and are subjected to certain federal regulation. A natural question raises that to how much extent the price of LIHTCs could affect the subsidized multifamily financial performance, physical characteristics, and geographical distribution.

To test the hypothesis, I firstly hand-collected a dataset which includes all applications of LIHTCs projects (both awarded or not) in the state of California during the 2012 to 2023, with detailed building attributes, geographic location, financial statements and hypothetical proforma. Furthermore, I use two empirical strategies to test the price effect. To begin with, I exploit the exogenous shock of Tax Cut and Job Add Act of 2017, which dramatically decreases the corporate tax rate and tax credit demand, subsequently reducing the nominal price of LIHTCs from $1.04 per tax credit in 2016 to $0.92 in 2017, and staying at this low level until now. This 12% price reduction is associated with a regime shift in affordable housing location, characteristics, and financial performance (i.e., subsidized multifamily have smaller rooms but more rent restricted units, and more concentrated in higher-income census tracts).

My second identification uses a triple-difference way to test the casual effect of price change. It relies on the desirability of investing in low- and moderate-income census tracts, where the median family income below 80% of the metro level are favorable under different federal regulations. The Community Reinvestment Act (CRA) of 1977 has encouraged banks to make a variety of community development investments towards low- and moderate-income households and neighborhoods. By doing so, banks can be evaluated and considered for charters, mergers, acquisitions, and branch openings applications from Federal regulators. Such qualified investment activities of community development include debt and equity investments in LIHTCs. I introduce an exogenous shock, Opportunity Zone, which drives the tax credit demand in certain eligible tracts in 2020. The results reveal that the price of tax credit increases by 5.2% when the LIHTCs investments are treated as qualified community investment under CRA in those Opportunity Zone census tracts. Though the application of federal tax credit is competitive (success rate: 39%), I find that investments in beneficial areas have a higher probability of awarding. This shock-induced price appreciation directly crowds the subsidized units into lower-income tracts. More importantly, the awarded projects with higher tax credit price after the shock are more likely to have higher Loan-to-Value, but lower operating expense ratio, developer profit and project cost.

This paper examines the price effect of tax credits on LIHTCs, and more importantly, the real effects of the regulation change on the affordable housing market, which neither the initial design of TCJA or CRA was trying to affect. Given the increasing housing affordability issue in U.S. and the debate on the effectiveness of project-based affordable housing program (Eriksen 2009), this paper implies a possible unintended consequence of corporate tax rate reduction and federal financial regulation in the subsidized multifamily market.

Keywords: Low-Income Housing Tax Credit, Community Reinvestment Act, Tax Cut and Job Add Act, Banking Regulation, Affordable Housing

Auctions enable sellers to extract maximum value and reduce the time required for sales. When dealing with heterogeneous goods and a limited market, auctions also help in determining prices. These aspects should make real estate auctions a highly effective means of selling properties. Sellers aim for maximum value without waiting for a buyer, and the heterogeneity of properties makes pricing imperfect. Real estate auctions in the United States typically result in transaction price reductions, as they often occur under distress, leading to urgency-driven price decreases. All else being equal, there is no reason for an auctioned property to have a lower transaction price.

A growing technique in residential real estate involves agents simulating sealed bid auctions. Properties are entered into a multiple listing service with specified viewing times and deadlines for final and best offers. Buyers remain unaware of the number of bidders or their bids. Agents can show sellers that the sealed bid listing method results in transaction prices 6% greater than the list price, a 44-day reduction in days on the market, and an 18.0% increase in the probability of sale. However, these results lack controls and represent sample averages between standard and sealed bid listings.

Over a 22-year period from 2000 to 2021 across Massachusetts, 815,927 properties were sold, with 22,584 being sealed bid listings. Sealed bid listings, on average, had smaller living areas, smaller lots, and were older. This study aims to investigate the true results of this listing strategy.

Due to the joint determination of price and marketing duration, a simultaneous system of equations is used for estimation. The models are as follows:

Ln(P_i )=α+β_1 Ln(〖DOM〗_i )+γ〖SB〗_i+τX_i+〖λLD〗_i+ω+δ+ϵ_i

Ln(〖DOM〗_i )=α+β_2 Ln(P_i )+γ〖SB〗_i+τX_i+〖φMC〗_i+ω+δ+ϵ_i

where Ln(P_i) represents the logarithm of the sales price of house i and Ln(〖DOM〗_i) represents the logarithm of the days on market. The variable, X_i, is a time invariant matrix of many house attributes. LD_i and MC_i denote listing density and market competition. Temporal fixed effects, ω, are by month and year while spatial fixed effects, δ, are controlled for at the ZIP code level. ϵ_i is the residual term. The variable of interest in the study is SB which is a binary variable equal to one for a sealed bid listed property and zero otherwise.

Results of this first test display a 2.1% increase in the transaction price for properties sold with a sealed bid listing, all else being equal. Marketing duration for properties sold with a sealed bid displays a reduction of 9.1%, all else being equal. This equates to roughly $7,600 and 7.5 days based on average transaction prices and marketing duration. This transaction price premium could simply represent a winner’s curse as the explanation.

The generation of this 2.1% premium begs the question: why is this not the dominant selling strategy? Well, in recent years, it has become more common. Segmenting the sample by year reveals slow adoption. From 2000 to 2011, less than 0.3% of annual sales used sealed bids. The majority of properties that did use it in the early years were in the top quintile of transaction prices and drove the observed premiums. Starting in 2012 with the continued adoption of the strategy, there is no displayed premium. This could also be driven by high demand-low supply markets, especially in 2020 and 2021, where bidding wars are happening in standard listings. In markets that were contracting, such as those during the great recession, the shorter marketing time resulted in a lower price.

Further testing to control for both observables and unobservables is done. To test for possible observable differences, a balanced subsample is generated based on a propensity score matching. The price difference declines to less than 0.5%. As expected from auction theory, there is no profit to be made by the seller. The decline in market duration is still present and reports an 11.0% decrease. Unobservables are tested using an Inverse Mills Ratio, and the price difference is no longer statistically significant, while the marketing duration decline is 12.3%.

With proper controls and testing, there is no price benefit. So, what would drive a seller to use this method? The major benefit is displayed in the difference in the probability of sale between a standard listing and a sealed bid listing. Estimating a probit model, the probability of sale for a standard listing over the entire period is 67.6%, compared to an 85.6% probability of sale for a sealed bid listing. Across each year, the higher probability of sale was present. In more recent years with higher usage and high-demand markets, the gap is not as large.

This study examines a listing method that mimics a first-price sealed bid auction. Based on standard auction theory, no profit should be gained by the seller using an auction. The presence of such a profit would be seen as a winner's curse. Real estate auctions in the United States are not common outside situations of distress and, as such, result in discounted prices. Using a listing method that shares many traits with the sealed bid auction raises the question: does the market see it as an auction and demand a discount, or does the seller indeed get the best price, as auction theory dictates? Sellers benefit from a higher probability of sale, a decrease in marketing duration, and the ability to observe the distribution of buyer offers at a single time.

In Nigeria, evidences abound of property development failures and abandonments with the attendant social, environmental and economic consequences. Real estate development is a complex and risky business with dire economic, social, and environmental consequences in the event of development failure. Despite the fact that the development process is very crucial to the economic growth and development of a nation, there is limited substantive research that has been conducted which relates directly to real estate development. While research on risk and general risk management are well documented, there are few empirical studies on how development companies/organisations manage risks. Therefore, the aim of this doctoral thesis is to provide an in-depth study on risk management practices within the property development companies in Nigeria with a view to understanding how to enhance these practices for optimum results. The specific objectives were to: (i) identify important risk factors likely to affect property development projects in Nigeria, (ii) assess and categorise property development risk factors at the different phases of the development process in the study area, (iii) investigate the practices among Nigerian property development companies concerning risk analysis and management in the study area, (iv) evaluate factors influencing the adoption of risk management techniques in property development projects by property development companies in the study area, (v) determine if there is a relationship between property development companies demographics and risk perception indicators, and (vi) propose an actor-network theory model for the analysis of property development risks. Corresponding research questions and hypothesis were formulated in line with the objectives of the study. The explanatory sequential mixed methods design was adopted to conduct an in-depth investigation on risk management practices by property development companies in Nigeria. It involves a two-stage/two-phase data collection procedure. In the first phase, quantitative data is collected and analysed to produce results. The results are then used to plan the second stage/phase. The study population comprised all the real estate development companies registered with the Real Estate Developers Association of Nigeria (REDAN). Purposive and snowballing sampling techniques were used to select respondents for the study. Secondary data were sourced from diverse literature; while primary data were sourced through questionnaire administration. The questionnaire comprised open-ended and mostly closed-ended questions with mostly 5-points Likert scale. The questionnaire was face-validated by five experts: two academics in Estate Management, two professional real estate developers and a statistician. The reliability of the questionnaire was determined at a Cronbach’s Alpha value of 0.802 indicating a very strong reliability. Data collected was analysed using frequency, percentage, mean, standard deviation, and factor analysis (PCA). The PCA was used to test the hypotheses at 0.05 level of significance. The Statistical Package for Social Sciences (SPSS) windows version 26 software was used for the analysis. Results were presented in tables and charts. Results show that while some risk factors are high, other are low within the five broad categories of the risk factors which include social risks, political risks, economic/financial risks, technological/technical risks, and environmental risks. There were also high and low risk factors in the different stages of the development process. Risk analysis and management practices adopted by most of the companies include checklist (67.9%), interviews (56%), brainstorming (71.4%), Experience/judgement/intuition (64.3%) and avoidance (63.1%). The Chief executive officer is responsible for risk management in 36.9% of the companies studied. Whereas 17.9% reported that the operations officer handle risk management, 17.9% reported that no staff is directly responsible for risk management. The respondents agree that all the factors listed influence the adoption of risk management techniques in property development projects except “taught but difficult to understand” which has a low mean response value of 2.90, less than the criterion mean of 3. There is a significant association between characteristics of the development companies and risk management practices in the study area. There is a significant relationship between property development companies’ demographics and the risk perception indicators. An actor-network model was developed for risk management in property development based on the findings of the study.

<p>Reverse mortgages (RMs) offer eligible senior homeowners liquidity of their home equity without them moving out and repayments before loan termination. By incorporating RMs into a quantitative equilibrium life-cycle model, we assess their impacts on household decisions, the mortgage and housing market. We show that retired RM borrowers experience enhanced significant consumption smoothing. Additionally, the presence of RMs in the mortgage market enhances the perceived value of houses to households, making homeownership a more financially attractive option and stimulating demand for housing. This also leads to increased overall household welfare in our model, highlighting the positive impact of RMs.</p>

With the rapid spread of telecommuting during the COVID-19 pandemic, companies are relocating to high-end business center, enhancing the workplace experience for employees, known as the "flight to quality." However, this trend’s impact on rents and vacancy rates in individual office buildings has not been extensively studied. Our study focuses on amenities like lounges, rooftop terraces, childcare, and fitness centers, which directly influence employee lifestyles, examining their influence on rents and vacancy rates. Using propensity score-based estimation, we find that commercial properties with such modern amenities command higher rents and experience lower vacancy rates than those without. The vacancy rate impact has notably increased after 2020. However, the significance of these amenities diminishes for properties less competitive in age, size, and location. The results indicate that the "flight to quality" may further polarize the office real estate market into two: one for high-end buildings experiencing increasing demand, and another for those with modest amenities experiencing reducing demand. The findings have implications for office building owners/investors and the government, enabling them to decide whether to invest in modern amenities, joining the quality competition and encourage urban restructuring, respectively.

The literature on housing prices suggests that various factors can influence the trajectory of property prices. A housing bubble can arise due to a combination of factors such as low interest rates, easy credit availability, speculation activities, government policies, economic conditions, or other external factors. Thus, understanding of what explains the rise and fall in house prices and its relation with fundamental factors is essential for regulators and policymakers to prevent or mitigate the formation of a bubble in the housing market. The housing market in the United States has also experienced a boom over the last few years with rapid house price appreciations. Our study examines the dynamics of the U.S. housing market from 2020 to 2023. We will compare this period to the last real estate bubble period 2007-2010. The goal is to establish an objective and consistent empirical framework that provides a thorough comparison and contrast of the evidence. This will enable us to identify both the similarities and differences between these two time periods. We will analyze changes in economic fundamentals and their impact on observed real estate price movements. Additionally, we will examine the difference in means between each variable, together with the t -statistic during the past bubble, bust, and current trends in the 2020 to 2023 period to identify patterns and anomalies and develop a predictive model using historical data and economic variables to forecast possible future bust.

Over the past century, workplaces have undergone significant changes reflecting different societal phenomena. In recent decades, the transformation has been fueled by the rise of knowledge work and subsequent green and digital transformations. The COVID-19 pandemic has further accelerated this evolution, prompting questions about the role of physical working environments and their direct and indirect impacts. This paper aims to identify the past, presents and future driving forces influencing the office transformation. Employing a future-oriented research approach, a futures clinique method was applied in a workshop with 20 academic experts from the fields of real estate economics, architecture, land use planning, workplace management, and futures studies. Our findings show that the prevailing forces of change play a pivotal role in shaping the dynamics of the office market. The identified forces have diverse characteristics, ranging from widely recognized megatrends to more subtle weak signals acknowledged by only a few. Additionally, the possibility of wild cards –low probability, high significance events– was acknowledged, and several emerging drivers shaping future offices were identified. This study makes a contribution by unravelling the intrinsic link between the societal change and preferences related to spatial considerations. Additionally, our findings shed light on the future development of offices. This knowledge can assist organisations in creating strategic plans related to spatial considerations. Furthermore, our findings lay the groundwork for future research in incorporating a future oriented perspective in the real estate sector, particularly in real estate development, asset management, land use planning and workplace management.

This study is the first to examine the real estate-specific determinants of REIT bond risk premia. Using a dataset of 33,877 U.S. REIT bond yield spreads and 19 explanatory variables, we predict REIT bond yield spreads with a non-parametric artificial neural network algorithm and interpret the model’s predictions using the explainable machine learning method Accumulated Local Effect Plots (ALE). We report evidence of a direct real estate factor for U.S. REIT bond yield spreads proxied by real estate market total return and REIT property type. In addition we find a property-type diversification risk premium for REIT bonds, indicating that there is no economic benefit in the form of lower cost of bond debt for most propertytype diversification at the REIT-level. We argue that this is due to higher management and valuation complexity of diversified REIT portfolios. This study’s findings have relevant implications for REIT portfolio strategy and REIT capital structure decisions, as we show that specialized REITs generally have lower bond debt costs compared to diversified REITs. Moreover, a better understanding of the drivers influencing REIT bond risk premia helps investors to effectively manage bond portfolio risks.

In the current context of heighted market tensions driven by rising interest rates, there is vital interest for both researchers and practitioners to understand the dynamics of Real Estate Investment Trust (REIT) returns and their accompanying uncertainties. To address this concern, we examine the drivers of REIT returns and volatility in a time-varying framework, spanning the modern REIT era (1991 to 2022). Our study is the first to simultaneously forecast both REIT returns and their associated volatility using an artificial neural network. We contribute to the literature by opening the black-box character of neural networks, enabling the identification of individual feature impacts on predictions and their evolution over time.

The key focus revolves around understanding how the influence of accounting and macroeconomic variables changes during periods of financial crises compared to non-crisis periods. The results showcase superior predictive capabilities of the neural network compared to conventional regression models. We shed light on the intricate interplay of diverse variables influencing the performance of REITs. Our findings hold implications for investors, policymakers and researchers navigating the complex landscape of real estate investments in a dynamically evolving market environment.

This study investigates the impact of social media sentiment on indirect real estate market returns by utilizing a comprehensive natural language processing approach to identify relevant Twitter posts and extract sentiment from them. To handle the complex linguistic features inherent in social media messages, three different sentiment classifiers are compared. The findings suggest a significant relationship between monthly sentiment and REIT returns, which occurs in two phases: a short-term speculative reaction and a greater longer-term reaction related to actual changes in the real estate market. The study also highlights that while the conventional dictionary approach can identify this relationship, more sophisticated classifiers can achieve higher accuracy. Overall, the results demonstrate the valuable insights that can be gained from analyzing social media data and its potential impact on the real estate market.

This dissertation consists of three essays conducting empirical asset pricing research on Real Estate Investment Trusts (REITs). The first essay, titled “Aggregate Investment and Equity REIT Market Returns”, is included in Chapter 1. I examine the first-order prediction of static investment theory that aggregate corporate investment should predict market returns. I find that aggregate investment is a strong predictor of U.S. equity REIT market returns between 1972 and 2019. Equity REITs make high levels of investment before periods of low market returns. Firm investment has stable predictive power after controlling for other known predictors, including standard discount-rate proxies, market-valuation multiples, aggregate operating accruals, and equity issuance. I do not find support for a sentiment-based interpretation of the results. Instead, the fact that investment sometimes significantly negatively predicts market returns suggests efficiency and that firms rationally respond to time variation in discount rates. I show that aggregate investment is little affected by contemporaneous market sentiment; future firm earnings shocks cannot be predicted by aggregate investment in advance; higher aggregate investment is followed by higher future macroeconomic growth; and the aggregate investment prediction of future market returns survives the inclusion of future firm and macroeconomic fundamentals. The second essay, titled “Expected Investment Growth and the Cross Section of Expected REIT returns”, is included in Chapter 2. Motivated by the dynamic investment theory, I examine the cross-sectional expected REIT return implication of expected investment growth. I form cross-sectional future investment growth forecasts with Tobin’s q, gross profitability, change in return on assets, and prior 11-month returns. The resulting expected investment growth yields a significant premium in the 1998-2021 sample. I form an augmented investment-based factor model with an expected investment growth factor. Robust to alternative empirical specifications, the factor captures a new dimension of the expected REIT return variation. The augmented model is not subsumed by its competing REIT-based and common stock-based factor models and outperforms its competing models in explaining prominent REIT return patterns. I conceptually argue an alternative risk-based explanation for the expected investment growth premium, which emphasizes the role of operating and financial leverages. I show firms with high expected investment growth have high future profitability, but they have high future degree of operating and financial leverages, and future cash flows more sensitive to future economic conditions, giving rise to high discount rates. The third essay, titled “Pricing Physical Climate Risk in the Cross Section of REIT returns”, is worked in progress and will be included in Chapter 3. I test for the existence of material risk premium on multiple climate and climate-related hazards, including floods (coastal and riverine), hurricanes and cyclones, heat waves, and extreme precipitation, etc. I probe how investors can construct portfolios that hedge against the physical risks resulting from climate change. Leveraging location-specific physical climate risk exposure, I assess firm-level exposure at the property-level before a weighted-sum is calculated to create a firm-level score. I consider multiple future climate change scenarios and time periods. For each physical climate risk exposure, I examine its future returns predictability using cross-sectional regressions as well as portfolio sorts. I propose an approach to dynamically hedge climate hazard news based on factor-mimicking portfolios. I examine the ability of a hedge portfolio to profit in adverse conditions on an out-of-sample basis.

The overall research aim of the PhD project is to better understand the relationship between the physical workplace environment and employee engagement in light of a post-COVID-19 workplace ecosystem. Subsequently, the research objectives are: i) to investigate options for the future development of employee engagement metrics and industry approaches to monitoring workplace design and management, ii) to explore the interplay of employee behaviours and environmental factors for employee engagement in hybrid work practices, iii) to better understand the impact of a workplace ecosystem on employee engagement in hybrid work practices, and iv) to explore options for the development of an ‘engaging’ workplace post-pandemic. The PhD project applies a mixed-method approach: quantitative surveys, interview study and qualitative thematic analysis, and content analysis. The key findings of this PhD project are: i) traditional employee engagement metrics and industry approaches to monitoring workplace design and management do not fully reflect the recent shift to hybrid work patterns in the context of the post-pandemic workplace ecosystem (i.e., home, office, third places, and urban realm), ii) a workplace ecosystem has a positive effect on employee engagement components (i.e., vigour, dedication, and absorption) via the interplay of environmental and behavioural factors, iii) flexibility - associated with both employee behaviours and the physical workplace – is one of the main drivers of employee engagement in a workplace ecosystem, and iv) the evaluation of a workplace ecosystem needs better alignment between organisational and workplace industry metrics in the wider city context to ensure a successful transition to an ‘engaging’ workplace ecosystem post-pandemic. The PhD project found that the compilation of both a home and the office can strengthen and sustain employee engagement post-pandemic. The PhD project contributes to existing knowledge and practice by i) demonstrating the role of the physical workplace environment (indoor/outdoor) as an antecedent of vigour, dedication, and absorption (i.e., the UWES scale), ii) providing new insights on the role of a workplace ecosystem in employee engagement in knowledge-intensive organisations, iii) informing the global workplace industry regarding the future evaluation of an ‘engaging’ workplace ecosystem, and iv) delivering empirically-based research evidence on employee engagement in knowledge organisations working in a hybrid mode.

Energy efficiency in the built environment is one of the deciding factors in the transition to a carbon-neutral economy. The transition has implications for real estate values and the mortgage credit risk financial institutions face. We study the relationship between a building’s energy efficiency and mortgage interest pricing. To this end, we exploit a granular dataset on securitized residential mortgages issued in the Netherlands. Our results show that lenders cut mortgage interest margins by 3.3 bps for energy-efficient homes and charge a premium for energy-inefficient homes of 1.7 bps. The discount in interest rates is most pronounced for the early years of our sample (up to 9.8 bps). Lenders also tend to be more willing to discount interest margins on energy efficiency for higher-income borrowers. The findings of this paper are pertinent to understanding how lenders factor energy efficiency into their mortgage credit pricing and risk management.

In recent years, Berlin has been the scene of various government interventions in the local real estate market, especially in the residential sector. Alongside measures like the "Mietpreisbremse" (rent brake) and the "Mietendeckel" (rent cap), the establishment of socalled "Erhaltungsgebiete" (urban preservation areas) is one of the most dominant state interventions in the Berlin real estate market. In Germany, urban preservation area statutes are regulations that can be enacted by the municipalities based on the special urban development law according to the German Building Code (BauGB). Since 1995, Berlin has established 74 urban preservation areas to preserve the composition of the local residential population and to avoid segregation and gentrification. This study provides insights into how the designation of urban preservation areas affects local housing prices and whether the objectives pursued by the regulator are achieved. Therefore, a unique data set, including 370,000 apartment transactions between 1984 and 2020, is analyzed. In combination with spatial data, an event study approach and group time average treatment effect estimators according to Callaway and Sant’Anna (2021) are used to investigate the real estate price development before and after the implementation of the respective regulation in comparison to non-regulated properties.

This study examines the relationship between greenways, property crime, and property values in Shelby County, Tennessee, focusing on the Shelby Farms Greenline and the Wolf River Greenway. The study employs spatial models and a difference-in-differences methodology to assess the impact of greenline-related crime on adjacent and nearby residential property values and highlights the importance of carefully considering crime rates in different neighborhoods when evaluating the amenity value of greenways. Through careful analysis, we will gain valuable insight into the true impact of crime on property values adjacent to and within close proximity of greenlines. We will be able to address the perception of crime and how it affects the capitalization of amenities like greenlines when moving from one neighborhood to another. This research aims to provide valuable insights for urban planning, policy-making, and fostering community well-being.

Fintech lenders have rapidly expanded their presence in the US mortgage

market, raising the question of whether they play a role in expanding access

to mortgage credit for underserved borrowers. I use mortgage application data

to investigate the impact of fintech lenders on mortgage credit access and

costs for underserved borrowers. The findings suggest that fintech lenders are

more likely to serve underserved borrowers, such as minority borrowers and

borrowers with higher debt-to-income ratios, implying an increase in credit

access. Even though fintech lenders are charging lower interest rates than

traditional lenders, the total loan cost, which includes origination and other

fees, is higher for fintech lenders than for non-fintech lenders.

Global inequality varies, with the U.S. experiencing historical income disparity, while Denmark stands as a model of lower inequality despite an upward trend. While certain nations may witness rising inequality coinciding with growing disadvantages for the lower-income segment, a prevalent trend, particularly in impoverished regions, involves facing tangible scarcity and hardship. These challenges in poverty-stricken areas include insufficient healthcare, limited medical access, exacerbated health issues, elevated unemployment, unhealthy housing conditions, restricted nutritious food access, heightened crime and violence, social isolation, and limited financial services. The impact is heightened in the presence of visible extreme wealth, intensifying envy and unhappiness. Therefore, this study examines growing inequality in Denmark, comparing it with the U.S. and emphasizing its impact on the real estate industry. Using diverse analytical tools, the research explores causes, consequences, variations in income distributions, and policy considerations of income inequality. Essential metrics like Lorenz curves, Gini coefficients, and Quantile Share Ratios precede advanced econometric techniques such as Ordinary Least Squares (OLS), Pooled OLS, and Fixed Effects in examining income disparities. Investigating income distribution disparities between Denmark and the United States involves nuanced Counterfactual Decomposition techniques utilizing an enriched Oaxaca–Blinder decomposition method. These incorporate economic, demographic, and real estate market variables, aiming to discern specific contributions of these factors, utilizing both non-parametric reweighing and parametric regression methods. The paper identifies education, high income, and well-educated immigrants as contributors to rising inequality. Historical trends, migration patterns, and varying returns to skill, especially in finance and computing, further contribute to skill distribution imbalances and income disparities. Cities with universities experience increased income disparities and higher real estate prices, impacting low-income households.

Finally, this study endeavors to contribute valuable insights for evidence-based interventions that promote equity, inclusivity, and sustainability within the real estate industry, particularly focusing on addressing regional disparities within social policies.

My research investigates the cause and subsequent effect of the prevalence of gender norms leading to gender inequality in the real estate industry by examining its impact on high-level female participation in Botswana, South Africa, and Zambia. Gender studies within the real estate industry are gaining momentum as gender inequality and parity within different facets are becoming global focal topics.

Currently, in the real estate industry, gender parities and inequalities such as gender pay gaps, high ratios of men to women in the workforce, and fewer women in high-level positions are ubiquitous. Consequently, the research topic determines factors causing the increase in gender inequality in high-level positions and identifies ways of alleviating the situation of marginalization from psychological, socioeconomic, cultural, and societal influences. It also determines how the marginalization of the female gender and the existence of relegation have compressed the general females' participation in the real estate industry and how it affects the output and performance of a country's economy and organizations to fill the present-day situation gap.

The input factors that influence women to pursue a career path in five (5) Royal Institute of Chartered Surveyors (RICS) pathways: Commercial Real Estate, Corporate Real Estate, Property Finance, and Investment, Valuations, and Quantity Surveying pathways are identified, and their subsequent effects analyzed.

In conclusion, the research aims to uncover impactful factors influencing women's career selection and high-level participation in the real estate industry from the organizational to the macroeconomic level of the comparative case studies, with a view to developing a theoretical framework that encompasses how gender inclusivity of women at high-level positions can be achieved.

This mixed methods study explored the impact of social capital on objective and subjective career success outcomes of women and men in the commercial real estate (CRE) industry. The research focused on understanding the role of social capital in shaping career success and analyzed the structural characteristics, social resources, and benefits within the social networks of women and men. A quantitative survey assessed the connectedness of women and men to their social networks, and tested the influence of network benefits, including organizational information and resources, mentorship, and sponsorship benefits on objective career outcomes such as promotion, salary, and subjective career satisfaction. Semi-structured interviews further analyzed CRE professionals’ perceptions and experiences with mentorship and sponsorship relationships and career outcomes. The study contributes to Seibert, Kraimer, and Liden’s (2001) integrated social capital theory, offering insights into CRE network structures and the multifaceted application of social capital to career advancement. The findings hold practical value for students, CRE professionals and leaders, HR professionals, and professional industry associations aiming to enhance inclusivity and employee/member experiences. This research addressed occupational sex segregation, contributing to the broader literature on workplace inequality, to inspire organizational changes in industry norms and practices. Implications for practice and areas for further research are provided.

Understanding the effect of a macroeconomic shock on the financial system is imperative to formulating appropriate policy responses. With the oil sector highly concentrated in the western Prairie provinces of Canada, the 2015 oil price shock serves as a useful case study to shed light on the effect of a substantial terms-of-trade shock on consumers’ financial distress. As Alberta, Saskatchewan, and Newfoundland are oil-dependent provinces, shocks to these regions present an opportunity to understand how households managed their debt repayments during the oil price collapse. The oil shock treatment effect is estimated through a synthetic control method (SCM). This method is used to estimate the differences in residential mortgage arrears between the actual and proxy control outcomes. Our findings also apply to other economies relying heavily on international trade and susceptible to commodity price volatility.

Extended Abstract

Relationships formed by individuals within and across ethnic groups affect individual welfare and are sometimes reflected in housing transaction outcomes (Bertrand et al. 2000; Macpherson and Sirmans 2001; Nowak and Sayago-Gomez 2018; Bikmetova et al. 2023). Moreover, individuals' ethnic tastes are complex and non-linear: according to Wong (2013), people prefer to live within their ethnic group, but after reaching a saturation point, they tend to choose neighbors from other ethnic groups. While that study pertains to the Singapore market in which buyers have strong cultural affiliations, the evidence that even buyers with strong ethnic ties prefer greater neighborhood diversity after a point raises questions about the role of ethnic buyers in diverse neighborhoods in American housing markets with a mix of buyers with strong and weak ethnic or cultural affiliations. The question in such settings is: who values neighborhood diversity?

This paper examines how different types of buyers value neighborhood diversity. Krupka’s (2008) study of inter-neighborhood migration patterns in American housing markets concludes that diverse neighborhoods in terms of household incomes are not in a state of equilibrium but rather in a transitional phase. This suggests that residents do not explicitly prefer neighborhood income diversity. Our question is whether this pertains to ethnic diversity as well.

This study contributes to the body of empirical evidence on how buyers value neighborhood diversity. It provides several contributions to the literature. Methodologically, it addresses an empirical question by offering an evaluation of the most appropriate measure of diversity out of the several widely used alternatives. Additionally, our empirical approach controls for property, neighborhood, seller, buyer, and agent characteristics using a simultaneous systems hedonic price and liquidity model that explicitly takes into account the simultaneous nature of price and selling time. This new approach to the diversity question allows us to identify the extent to which diversity is capitalized into price and liquidity in the search market environment.

We begin by weighing alternative neighborhood diversity measures. The first is the diversity index constructed by the US Census Bureau, which essentially measures the probability that a random draw from the census tract population will be individuals with different ethnicity; we use the nearest census decade measure for each year in our sample. The second is the Herfindahl index of neighborhood ethnic/racial composition, inverted so that a higher value indicates greater diversity. Motivated by Krupka (2008), we also take into account diverse neighborhoods may be transitioning neighborhoods and the implication that the direction of transition matters; we therefore also examine diversity change measures in addition to the levels, where changes are calculated over the census decade.

The challenge lies in assessing these four alternative models. Each model is non-nested in the sense that it cannot be derived as a special case of any of the others. Therefore, we apply the non-nested hypotheses J-test (Atkinson 1970) to assess the alternative models. To do so, we employ the seemingly unrelated regression (SUR) technique to estimate the reduced form system for selling price and time on the market that explicitly takes into account the supply of competing houses for sale in the neighborhood as in Turnbull and Dombrow (2006), Zahirovic-Herbert and Turnbull (2008) and Turnbull and Zahirovic-Herbert (2012). This approach ensures asymptotically efficient error estimates considering potential cross-equation correlation in the error terms. The J-statistics for pair-wise tests of the four models do not provide unambiguous support for any single model over all others, but nonetheless provides the strongest support for the census diversity index of neighborhood ethnic/racial composition among the alternatives.

After establishing the most appropriate measure of diversity, we proceed to examine buyer-clientele effects. We use an established algorithm that infers the ethnicity of buyers, sellers, and agents based on the probability their last name belongs to a particular ethnic group (Sood and Laohaprapanon 2018). We add buyer ethnic variables (general Ethnic indicator and narrower categories Hispanic, Asian, and Black) and their interaction with the diversity variable to the models. Overall, houses in diverse neighborhoods sell at a discount. Although they generally pay less, ethnic buyers are willing to pay a higher price than non-ethnic buyers for houses in diverse communities. Probing more deeply into specific ethnicities, we find that Black and Hispanic buyers pay more while Asians pay even less than non-ethnic buyers for houses in more diverse neighborhoods.

But, is it neighborhood diversity per se or a cultural connection with the seller that the buyer values? Does the diversity discount diminish when controlling for buyers and sellers of the same ethnicity? We find the diversity discount holds even when we control for buyers and sellers of the same ethnicity by adding a Same Ethnicity Buyer-Seller interaction term to the model. Further decomposing the ethnic buyer diversity premium, the results show that it exists predominantly when the buyer and seller of the diverse neighborhood house share the same ethnicity.

We also recognize that listing and selling agents may have an influence on the buyers and sellers, and their possible cultural interactions may modify transaction outcomes (Bikmetova e al. 2023). As a robustness check, we introduce agent ethnicity into the model and find that overall, there is no additional diversity effect on the price when the agent and client share ethnicity. Extending the model to include these factors does not change the key findings.

As a final set of tests, we examine how diversity is valued in different house price segments. We divide the sample of sales into three subsamples by selling price terciles in Census tracts. The estimates show that no specific housing market segment drives diversity discount observed in the full sample. The diversity discount is significant for all terciles and, on average, increases with the selling price. For ethnic buyers, however, the discount becomes less pronounced as the selling price increases.

Drawing the results together, how buyers value neighborhood ethnic diversity varies. Diversity may offer value to specific buyers but it repels others. Hispanic and Black buyers appear to value neighborhood diversity more than non-ethnic buyers, while Asian buyers, on average, value diversity less. These differences may relate to decisions to integrate into a broader community or to fewer constraints related to household language or culture. We expect individuals who largely abandon their ethnic culture exhibit minimal differences in tastes and behaviors, while those retaining ethnic preferences may benefit from locating in their ethnic neighborhood.

References (Abstract only)

Bertrand, M., Luttmer, E.F. and Mullainathan, S., 2000. Network effects and welfare cultures. Quarterly Journal of Economics, 115(3), 1019-1055.

Bikmetova, N., Turnbull, G.K. and Zahirovic‐Herbert, V., 2023. Ethnicity in housing markets: Buyers, sellers and agents. Real Estate Economics, 51(1), 196-232.

Krupka, D.J., 2008. The stability of mixed income neighborhoods in America. IZA Discussion Paper 3370.

Nowak, A. and Sayago-Gomez, J., 2018. Homeowner preferences after September 11th, a microdata approach. Regional Science and Urban Economics, 70, 330-351.

Sood, G. and Laohaprapanon, S., 2018. Predicting race and ethnicity from the sequence of characters in a name. arXiv preprint arXiv:1805.02109.

Turnbull, G.K., & Dombrow, J., 2006. Spatial competition and shopping externalities: Evidence from the housing market, Journal of Real Estate Finance and Economics, 32, 391-408

Turnbull, G.K., & Zahirovic-Herbert, V., 2012. The transitory and legacy effects of the rental externality on house price and liquidity. Journal of Real Estate Finance and Economics, 44, 275-297

Turnbull, G.K. and Zahirovic-Herbert, V., 2019. Housing market microstructure: What is a competing house? Journal of Housing Research, 28(1), 1-22.

Wong, M., 2013. Estimating ethnic preferences using ethnic housing quotas in Singapore. Review of Economic Studies, 80(3), 1178-1214.

Zahirovic-Herbert, V., Turnbull, G.K., 2008. School quality, house prices, and liquidity. Journal of Real Estate Finance and Economics, 37, 113-130.

Residential segregation is considered a major social problem in some states in Nigeria, while little or no anti-segregation initiatives have been launched to decrease its marginal effect till date. Thus, the study examined (a) the socio-economic characteristics of the rental housing residents in Osogbo, Ile-Ife, and Ede, Osun State, Nigeria; (b) the characteristics of segregation in the rental housing market in the study area; (c) the effect of segregation on the operation of the rental housing market in the study area; and (d) the factors influencing segregation in the rental housing market in the study area. These were with a view to providing information that could enhance property market efficiency.

Primary data were used. Data sourced with the aid of questionnaire administered to residents and estate agents in the study area. A multi-stage sampling procedure was employed. Using Google Earth along with physical inspections, a total numbers of 2673 occupied residential buildings were identified in the selected electoral wards, representing the sample frame for the study. Using the systematic sampling technique, 267 residential buildings were selected, representing the sample size for the study. Questionnaires were administered to household heads or their representatives in each of the 267 residential buildings selected. Forty-Eight (48) estate agents were found and all surveyed in the area. The data collected were analysed using frequency counts, percentages, mean ranking, factor analysis, ANOVA, and chi-square.

The result showed that the socio-economic characteristics of respondents were a distribution of 72.8% male and 28.9% female, 85.3% married and 14.7% unmarried, 50.4% Islam, and 49.6% non-Islam. The characteristics of segregation in the study area showed religion-based segregation (63.6%) in the Ede community, income-based segregation (46.7%) in Osogbo, and ethnic-based segregation (67.8%) in Ile-Ife. The effects of residential segregation in the study area were “increase in rental value” (3.93), “increase in search duration” (3.89), "increase in high vacancy rate” (3.84), and “uneven infrastructural development” (3.82), among others. The result of the factor analysis showed that the key factors contributing to residential segregation in the rental housing market in the study area were ethnicity (4.11), religion (4.16), individual preference (4.16), income (4.16), and conflict (4.11). The factors that contributed to segregation in the study area were found to be socio-economic characteristics (18.27), environmental attributes (32.41), psychological attributes (42.75), and external attributes (60.73).

The study concluded that segregation in the rental housing market disrupts real estate market operations and found housing integration to be a means of promoting real estate market operation.

The vast opportunities that the metaverse offers, have become increasingly relevant in the real estate industry due to the growing complexity of digitizing property transactions and market dynamics. As the metaverse continues to evolve, understanding the required skillsets will be crucial for professionals offering meta property consultancy services in this digital real estate practice. This study therefore explores the evolving skillsets essential for meta property consultants in digital real estate and provides a comprehensive analysis of existing research. International scientific publications related to the subject area from 2013 to 2023 in the Web of Science and Scopus database were collected for data analysis. CiteSpace software was employed to analysis the existing research articles. The review highlights the need for property consultant to develop a range of skillsets, such as virtual reality expertise, 3D modelling, blockchain knowledge, marketing and data management, to effectively navigate the metaverse. Additionally, the meta property consultancy service sector is poised for growth, offering guidance on metaverse opportunities such as 3D tours of properties, brand promotion, virtual shopping experiences, virtual modelling of data centre operations among others. Moreover, this study identifies key research gaps and points towards future research directions, including the development of best practices and standardized educational programs, legal and ethical considerations, and the integration of emerging technologies in metaverse property consultancy. This study provides a valuable foundation for both academia and industry as they navigate the ever-expanding metaverse landscape. This review therefore underscores the importance of developing practical frameworks and training programs to enhance the pivotal role that meta consultancy services will play in shaping the future of digital real estate practice.

Blockchain technology in real estate markets has led to numerous innovative developments. A novel advancement is the introduction of digital real estate, known as virtual land, a digital equivalent of physical real estate. Through wavelet analysis, we analyze the dynamic correlation between these markets and consider the distinction between short- and long-term investment horizons. Our findings provide initial empirical evidence that returns in the virtual land markets are largely independent of returns in the real estate markets. However, during crisis periods, the co-movement between the two markets increases significantly. Furthermore, our analysis reveals that short-term correlation is influenced by macroeconomic surroundings and the crypto market's valuation. On the contrary, long-term correlation is not driven by these factors, suggesting that virtual land serves as a stable source of diversification for long-term investors. In summary, virtual land can be regarded as a new asset class.

Twitter is established as a major platform for sharing information and opinions online. This research explores the impact of the sentiment expressed on Twitter on the indirect U.S. real estate market, particularly focusing on financial metrics such as returns and volatility. It analyzes how Twitter sentiment correlates with the overall indirect market and additionally focuses on the corporate level, investigating if general findings are also applicable on an individual company basis. Given by the nature of Twitter messages, comprehensive natural language processing is applied to clean and identify relevant posts and to provide the foundation for extracting the sentiment. The complex linguistic features of the given informal language are handled by using three different approaches for classification including traditional and advanced machine-learning methods. Preliminary results suggest that social media sentiment holds predictive value for both market trends and corporate-level changes. Moreover, they indicate towards changing dynamics in the impact of market sentiment on performance metrics during a crisis, exemplified by the 2020 COVID-19 pandemic. This research additionally highlights the effectiveness of classical dictionary-based approaches for sentiment analysis but also shows the capabilities of more sophisticated classifiers.

We study how photovoltaic (PV) systems externally affect the prices of owner occupied houses and rents of residential dwellings. By creating a three-dimensional topographical model of Switzerland, we model each building’s view at surrounding PV installations and merge this data with housing price observations. In our hedonic difference-in-differences regressions, we provide evidence of how this view (impaired or unimpaired) on a PV system is associated with lower residential rents. This effect is stronger for the view at multiple PV systems rather than at a single one as well as in situations where seeing is more likely. However, price penalties are attenuated if rental dwellings have their own PV system or if neighboring properties have a view at comparably large PV systems which may benefit surrounding tenants in terms of electricity provision. Furthermore, by using municipal voting results on the Swiss Energy Act 2017 and the Swiss CO2 Act in 2021, we show how the attitude towards sustainability is driving the external effects of PV systems on rents. In contrast, we cannot document significant impacts of view on prices for owner-occupied housing.

Background:

In post-pandemic Australia, it is unclear how building occupants have changed their behaviour in their interaction with buildings and other occupants towards three domains of indoor respiratory transmission routes: (1) fomite: handshaking and hand hygiene behaviours; (2) airborne: individual interventions to indoor air quality including face masking and window openings; (3) droplets: social distancing and reducing working hours in the workplace. This paper explores Occupant Behaviour Change (OBC) and its determinants in this context.

Methods:

From March to April 2023, 2298 survey responses were collected from occupants working in 90 different buildings in Melbourne. This paper analyses a sub-set of 530 occupant responses from buildings with operable windows to effectively capture OBC on window openings. Uni and multi-variable regressions were conducted on potential determinants or OBCs.

Findings:

Regressions show that OBC towards different transmission routes share the same primary determinants, including risk perception and co-worker’s behaviour change, while the influence from personal factors and Indoor Environmental Quality (IEQ) factors differs significantly towards the three routes: (1) Older age significantly influences air-related OBC, while fomite-related OBC was more associated with the gender factor. By contrast, the personal factor was negligible for droplet-related OBC. (2) Droplet-related OBCs were more affected by perceived occupant density in the workplace, while the influence of perceived IAQ and occupant density on air-related OBCs was limited. Fomite-related OBC was negatively correlated with perceived environmental cleanliness.

Implications:

This study informs modellers about the significant change in occupants’ behaviour towards all the respiratory transmission routes, such as increased window opening operations, the improvement of occupant respiratory hygiene and hand hygiene, and the reduced frequency of occupant social contacts, to build robust building energy and respiratory infection risk assessment models. The findings also inform building designers of occupant preference for touchless public interface and more individual space in the post-pandemic workplace. The paper contributes to the knowledge of Human-Building Interaction (HBI) and Occupant Behaviour.

Today, the real estate business worldwide prioritizes sustainability, sustainable growth, and environmental protection. The combination of sustainability and real estate has created a new framework that relies on BIM, GIS, AI, Drones, etc.

This article discusses environmental, social, and economic issues that ensure the sustainability of Indian real estate developments. Using literature review papers and a survey in Ahmedabad, India, this study examines how scientific advances and multi-criteria decision-making (MCDM) theories might improve sustainable real estate development decision-making. The surveying process involved selecting and prioritizing relevant sustainability parameters, while constructing the index entailed the application of multi-criteria decision-making techniques.

From a pool of 212 literature papers spanning from 2012 to 2022, 16 parameters were identified using MCDM techniques. These 16 parameters were then ranked in priority. Subsequently, the same set of 16 parameters was included in a survey administered to 65 respondents, including builders, AEC consultants, and local government authorities in the Ahmedabad region. Among these respondents, 40 qualified responses were obtained. Using MCDM, these 16 parameters were once again prioritised based on their significance and relevance to the Indian real estate sector for Ahmedabad city.

The primary parameters derived from the literature comprised Energy, Expenditure, Life Cycle Cost, Safety, and Security, while the questionnaire survey emphasised Energy, Quality, Expenditure, Life Cycle Cost, and Waste as the key factors.

The review discusses the uses with pros and cons of sustainability parameter-technology relationships and how the main parameters identified in the literature and survey results improve environmental, economic, and social sustainability in real estate construction.

This result enables the researcher to advance further work towards creating sustainable real estate projects while considering the advantages and disadvantages of using various technologies in this endeavour.

Air pollution is one of the most significant threats worldwide, causing substantial environmental and social problems, particularly in megacities where its impacts are most acutely felt. This paper addresses these issues by analyzing the influence of air quality on rents in the residential market of Berlin, a major urban area. Within a spatial-temporal model of air pollutants, three interpolation techniques are applied to evaluate the processed data from 2018 and 2021, resulting in detailed seasonal pollution maps. These maps display concentrations of particulate matter and nitrogen dioxide per 1,000 sqm within the city's administrative boundaries. A semi-parametric regression analysis is then conducted to assess the impact of these pollutants on residential rents. After considering socioeconomic, spatial, and hedonic variables, the findings reveal a marginal willingness-to-pay for low particulate matter levels and good air quality conditions. This preference is quantified as a 3% increase in rent for cleaner air environments. This trend is not consistently observed for nitrogen dioxide concentrations. Therefore, the findings suggest a shifting influence of air quality on the rental market, possibly influenced by the perceptibility of particles. In principle, the significant economic impact of air quality in urban housing markets shows that better environmental conditions not only promote public health, but also have a positive impact on the real estate sector, providing valuable insights for both tenants and investors.