Assessing Fair Lending Risks Using Race/Ethnicity Proxies

Yan Zhang ()
Additional contact information
Yan Zhang: Enterprise Risk Analysis Division, Office of the Comptroller of the Currency, Washington, DC 20219

Management Science, 2018, vol. 64, issue 1, 178-197

Abstract: Fair lending analysis of nonmortgage 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 Bayesian Improved Surname Geocoding (BISG)) as compared with the race/ethnicity reported under the Home Mortgage Disclosure Act (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 nonmortgage credits by leveraging consumer credit bureau data.

Keywords: air lending risk; race/ethnicity; proxy; BISG; Bayesian; measurement error; misclassification (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1287/mnsc.2016.2579 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:64:y:2018:i:1:p:178-197

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().