Paper on the Use of Proxies to Determine Race/Ethnicity in Assessing Fair Lending

Yan Zhang of the Office of the Comptroller of the Currency (OCC) has written Assessing Fair Lending Risks Using Race/Ethnicity Proxies, 64 Management Science (January 2018). Here is the 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.

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