Assessing Fair Lending Risks Using Race/Ethnicity Proxies

Management Science, Volume 64, Issue 1, January 2018 (DOI/10.1287/mnsc.2016.2579)

45 Pages Posted: 12 Jun 2018  

Yan Zhang

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Date Written: January 27, 2016

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

Suggested Citation

Zhang, Yan, Assessing Fair Lending Risks Using Race/Ethnicity Proxies (January 27, 2016). Management Science, Volume 64, Issue 1, January 2018 (DOI/10.1287/mnsc.2016.2579). Available at SSRN: https://ssrn.com/abstract=3169831

Yan Zhang (Contact Author)

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th St. SW
Washington, DC 20219
United States
202-6495492 (Phone)

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