The Pricing of Home Mortgage Loans to Minority Borrowers: How Much of the APR Differential Can We Explain?

The public releases of the 2004 and 2005 HMDA data have engendered a lively debate over the pricing of mortgage credit and its implications regarding the treatment of minority mortgage borrowers. This research uses aggregated proprietary data provided by lenders and an endogenous switching regression model to estimate the probability of taking out a subprime mortgage, and annual percentage rate (APR) conditional on getting either a subprime or prime mortgage. The findings reveal that up to 90% of the African American APR gap, and 85% of the Hispanic APR gap, is attributable to observable differences in underwriting, costing, and market factors that appropriately explain mortgage pricing differentials. Although any potential discrimination is problematic and should be addressed, the analysis suggests that little of the aggregate differences in APRs paid by minority and non-minority borrowers are appropriately attributed to differential treatment.

The CRA within a changing financial landscape

Community Reinvestment Act of 1977

A Comparison of U.S. and Canadian Residential Mortgage Markets

As financial markets move toward increased globalization, it becomes worth considering whether inherent differences in financial markets across different countries will diminish. For two countries more similar than different in terms of geography, location, government and culture, Canada and the U.S. remain strikingly different in terms of housing finance. Public policy objectives toward housing followed quite different paths over the past seventy years and fundamental differences in banking practices have led to considerably different outcomes in terms of mortgage finance instruments in the two countries. In light of that, it is particularly surprising that homeownership rates do not diverge by much, reaching 67% in the United States and 64% in Canada by year-end 2000. We examine some of the differences in policy and in competitive practices between Canada and the U.S. in an attempt to illuminate why differences in rates and terms across the two countries still exist. While a part of the difference remains due to legal constraints concerning the finance of the domestic housing sector, we do not attempt an analysis of the legal structure and focus, rather, on the economics and public policy choices that have led to the observed differences.Housing, mortgage finance, housing policy

Stratified Sample Design for Fair Lending Binary Logit Models

Logistic regressions are commonly used to assess for fair lending across groups of loan applicants. This paper considers estimation of the disparate treatment parameter when the sample is stratified jointly by loan outcome and race covariate. We use Monte Carlo analysis to investigate the finite-sample properties of two estimators of the disparate treatment parameter under six stratified sampling designs and three data generating processes; one estimator is consistent irrespective of sample design while the other is not. Unfortunately the inconsistent estimator is employed inadvertently in fair lending studies. We demonstrate the gains in using the consistent estimator as well as providing recommendations on sample design. We also study the effect of sample design on the empirical power of a test for statistical significance of the disparate treatment parameter. We recommend adopting a sample design that approximately balances by outcome and racial group, when using the estimator that adjusts for the stratification scheme. However, if the standard logit estimator is employed, then our results suggest a sample design that balances by outcome and allocates across racial groups proportionally to the population. Though our study is framed in terms of fair lending applications, our results apply generally to the estimation of logistic regressions that use stratified or choice-based sample designs.Logistic regression, design efficiency, stratified sampling, choice-based sampling, case-control studies, balanced sampling, Monte Carlo experiment, mean squared error.