This paper compares default rates on Islamic and Western mortgages. Islamic mortgages feature a `partnership' arrangement between the bank and the borrower, which allows the borrower to recover the current market value of his accumulated equity upon default. By itself, this makes default attractive relative to Western mortgages. However, we show that if agents care about realized gains and losses, as in models based on `Realization Utility' (Barberis and Xiong (2012)), the equity recovery associated with Islamic mortgages creates a powerful disincentive to default. Using a standard real options framework, we use data from Indonesia, Malaysia, and Pakistan to show that our model can explain the relatively low default rates on Islamic mortgages.

This paper investigates the borrower response to the housing assistance scheme ‘Help to

Buy’ (HTB) in Ireland. Employing a traditional difference-in-difference estimator, we assess three possible transmission channels of the grant enhancement introduced in mid-2020: borrowers’ liquidity, indebtedness, and home purchase values. Our key findings suggest that a combination of all of the above channels is at play, but that the liquidity-enhancing effects appear to be the most economically meaningful. Our DiD coefficient suggests that out-of-pocket downpayments fall by almost the size of the increase in the subsidy value. We find a strong liquidity response across the five quintiles of the borrowers’ income distribution. However, equity-enhancing and house price-increase effects appear particularly strong among higher income borrowers. Our findings suggest that the liquidity constraints introduced by banks’ lending standard and macroprudential policies are particularly salient for mortgage borrowers.

We introduce a loan-level model of mortgage default with heterogeneity in borrower characteristics and mortgage terms, including idiosyncratic penalties for default. Borrowers’ penalties determine how closely their behavior hews to the predictions of the double-trigger or strategic models. The state space varies loan-to-loan based on all of the loan’s, borrower's, property's, and neighborhood's idiosyncratic characteristics. We test the model on a high-performance computing cluster against real data drawn from linked databases with billions of observations of hundreds of simultaneous attributes. The model predicts defaults out-of-sample, fits cross-sectional characteristics of the distribution of mortgage performance, and classifies likelihood of default with high accuracy and better than all known benchmarks.