Assessing Fair Lending Risks Using Race/Ethnicity Proxies

Abstract

Fair lending analysis of non-mortgage credit products often involves proxying for race/ethnicity since such information is not required to be reported. Using mortgage data, this paper evaluates a series of proxy approaches (geo, surname, geo-surname, and BISG) as compared with the race/ethnicity reported under HMDA. The BISG proxy predicts the reported race/ethnicity the best as judged by prediction bias, correlation coefficient, and discriminatory power. In assessing fair lending risks where classification of race/ethnicity is called for, we propose the BISG maximum classification, which produces a more accurate estimation of mortgage pricing disparities than the current practices. The above conclusions withhold various robustness tests. Additional analysis is performed to assess the proxies on non-mortgage credits by leveraging consumer credit bureau data.

Keywords: Fair Lending Risk, Race/Ethnicity, Proxy, BISG, Bayesian, Measurement Error, Misclassification

JEL Classification: C11, C81, D18, J15