An Analysis of Alternative Methodologies and Interpretations of Mortgage Discrimination Research Using Simulated Data

By Brown, Christopher L.; Simpson, W. Gary | Academy of Banking Studies Journal, Annual 2010 | Go to article overview
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An Analysis of Alternative Methodologies and Interpretations of Mortgage Discrimination Research Using Simulated Data


Brown, Christopher L., Simpson, W. Gary, Academy of Banking Studies Journal


INTRODUCTION

For the past several years, researchers have focused on investigating discrimination in the residential mortgage market. New data and methodologies have been employed and new theoretical lending models have been developed to explain why loan approval rates are higher for non-minority applicants than for minority applicants. There has been intense debate on the significance of default rates in identifying or ruling out discriminatory lending and on the appropriate methodology to use in testing for discrimination. This paper reviews the debate and uses simulated data to provide conclusive evidence on the merits of the alternative theories and methodologies. Understanding the relationship of default rates as potential indicators of discrimination and assuring that the methodology used in these studies is appropriate is very important because the findings of discrimination studies may be used to influence public policy.

The mortgage discrimination debate intensified with the release of a study conducted by researchers at the Federal Reserve Bank of Boston (Munnell, Tootell, Browne and McEneaney (1996)). The study employs the most comprehensive loan application information of any of the recent discrimination studies. The authors use a logit regression equation that includes all of the variables that should be relevant to the loan decision. The race of the applicant is included as an additional explanatory variable. If the coefficient on race is significant, it is interpreted as evidence of discrimination.

Munnell et. al. (1996) find minority applicants, on average, have greater debt burdens, higher loan-to-value ratios, and weaker credit histories than non-minorities. Furthermore, denied minorities have lower income and wealth, higher obligation and loan-to-value ratios, and worse credit histories than denied non-minorities. Despite these facts, the authors find evidence of discrimination against minorities. They find that minority applicants are rejected 60 percent more often than non-minority applicants when financial, employment, and neighborhood characteristics are held constant.

The findings of Munnell et. al. (1996) have received a great deal of attention from policymakers and academic researchers. Most of the debate focuses on perceived shortcomings in the study. Criticisms of the Munnell et. al. (1996) study include problems with the integrity of the data (Horne (1994), Liebowitz (1993), and Carr and Megbolugbe (1993)), the authors' failure to consider default rates (Becker (1993), England (1993), and Brimelow and Spencer (1993)), and problems with the use of a single-equation logit regression model of the probability of loan approval to detect discrimination.

More recently, Blank et al (2005) investigated racial discrimination in mortgage lending in Washington, DC. Using three different methodologies, a dissimilarity index approach, a three-way crosstabulation approach and a logistic regression. The adjusted dissimilarity index approach is based on the theory that, after considering for differences in neighborhood factors and using variables on the loan applicants that are available through HMDA, approval rates should be approximately the same across census tracts. Blank et al (2005) find there is a disparity between census tracts. They conclude that 10.64 percent of loans that should have gone to underserved census tracts were denied. That amounts to 1,315 loans. The crosstabulation approach simply evaluates whether there is a disparity in lending based on only income and race. After considering income, they find a significant difference in the proportion of loans denied between minorities and non-minorities across all income levels. The third approach is a logistic regression model. The model includes the race of the applicant along with neighborhood characteristics. They again find that minorities are less likely to receive loans, after accounting for the factors in the model.

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