How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

Neil Bhutta, Aurel Hizmo and Daniel R. Ringo
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Daniel R. Ringo: https://www.federalreserve.gov/econres/daniel-r-ringo.htm

No 2022-067, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant- risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 percentage point denial gaps. Overall, we find that differential treatment has played a limited role in generating denial disparities in recent years.

Keywords: Discrimination; Fair lending; Automated underwriting; Credit score; Mortgage lending (search for similar items in EconPapers)
JEL-codes: G21 G28 R30 R51 (search for similar items in EconPapers)
Pages: 44 p.
Date: 2022-10-18
New Economics Papers: this item is included in nep-ban and nep-ure
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2022-67

DOI: 10.17016/FEDS.2022.067

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