Application of Reverse Regression to Boston Federal Reserve Data Refutes Claims of Discrimination

The topic of mortgage discrimination has received renewed interest since publication of the Boston Federal Reserve Bank study based on 1990 Home Mortgage Disclosure Act data. That study used traditional direct logistic regression to assess the influence of race on the probability of mortgage loan denial and reported the parameter estimate of race to be positive and significantly different from zero across several model specifications, thereby supporting contentions of discriminatory behavior. This paper develops an alternate approach, reverse regression, a method often used in the measurement of gender discrimination in labor markets. After discussion of theoretical issues regarding model choice, results of a reverse regression on the Boston Federal Reserve Bank study dataset are reported. Contrary to results using direct methods, reverse regression does not support contentions of mortgage discrimination in the Boston mortgage market. Rather the lower overall qualifications of minority applicants are likely to account for disparities in application outcomes.