Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 7th Oct 2022, 03:59:16am IST

Session Overview
Amenity Valuation
Thursday, 07/July/2022:
9:00am - 10:45am

Session Chair: Amine Ouazad, HEC Montreal, Canada
Location: Room A

Room in the Arts Building, Trinity College Dublin. Exact details to be confirmed by May 31

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View and status determinants of residential vertical price gradients: A comparative study of Manhattan and Rotterdam

Nase, Ilir1; Barr, Jason2

1Oxford Brookes University, United Kingdom; 2Rutgers University, USA;

Research on vertical spatial structure of cities has gained increased attention in the recent urban economics literature. A handful of studies estimate vertical rent gradients and document their non-monotonic nature. Nevertheless, the answer to the question ‘what drives demand for height?’ remains anecdotal at best. This paper estimates vertical price gradients in residential towers and disentangles their underlying mechanisms. Using data from apartment transactions in Manhattan and Rotterdam we find that the average premium for being one floor up is respectively 1.15% and 0.53%. This difference in premiums we relate to the higher supply of height in Manhattan, deriving from the sheer differences in size of the two cities under investigation. The underlying mechanisms of height premiums are similar in both cities whereby premiums comprise of roughly 40% by the view amenity and 60% by status associated with higher vertical locations. While the size of the visible area from an apartment commands significant premiums, households show no preference for different aspect of view quality, apart from a view on Central Park in Manhattan and the Erasmus Bridge in Rotterdam. These command premiums of 17% and 4% respectively, all else being equal.

JEL Classification: R22, R30, R32

Aesthetic Preferences for Residential Architecture: Finding Ground Truth with Machine Learning Approaches

Lindenthal, Thies; Schmidt, Carolin; Wan, Wayne X.

University of Cambridge, United Kingdom;

ML-enabled classifiers are regularly criticized for being "black boxes": While their predictive power is undisputed, it is difficult to understand why the model arrived at a particular classification. The same can be said for humans classifying photos according to their aesthetic appeal. They can quickly say whether they like a photo or not—but giving justifications for such a choice is often challenging. Also, human classifiers exhibit inconsistencies and biases, adding to the black box nature of their classifications.

This paper first collects binary classifications of house pictures from a large group of participants and then trains personalized ML classifiers for each participant. Predictions from these automated yet personal classification machines shed light on biases and dynamic inconsistencies in the participants' assessment of residential real estate's visual appeal.