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

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Abstract

We assess racial discrimination in mortgage approvals using confidential 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 raceblind 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 more limited role in generating denial disparities in recent years than suggested by previous research.

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  • Neil Bhutta & Aurel Hizmo & Daniel R. Ringo, 2024. "How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions," Working Papers 24-09, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:97985
    DOI: 10.21799/frbp.wp.2024.09
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    More about this item

    Keywords

    mortgage; mortgage approval; discrimination; mortgage lender; automated underwriting; credit score; fair lending;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R51 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Finance in Urban and Rural Economies

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