The Racial Landscape of Fintech Mortgage Lending

Little is known about racial patterns in fintech mortgage lending, despite evidence of racial disparities in the broader mortgage market. This study leverages 2015-2017 Home Mortgage Disclosure Act data to assess disparities in lending outcomes between White and non-White applicants and between neighborhoods of varying racial composition in the United States' 200 largest metropolitan areas at fintech and traditional lenders. Results of a series of binary logistic regression models suggest disparities in rates of loan approvals between White and similarly qualified non-White applicants are substantively small overall, but lower at fintech lenders relative to traditional lenders, most substantially for Latinos. Non-White applicants are more likely to receive subprime terms relative to similarly qualified White applicants at both lender types, and disparities in rates of subprime loan receipt between Black and similarly qualified White applicants are greater at fintech lenders than traditional lenders. Neighborhood racial composition has a mixed but substantively small impact on approval rates at both lender types. However, both lender types distribute subprime credit to non-White neighborhoods at significantly higher rates than to White neighborhoods. Findings suggest fintech lending contributes to racial and spatial disparities in subprime mortgage lending and warrants increased scrutiny from regulators.

Automated summary

Despite limited understanding of racial patterns in fintech mortgage lending, this study examines disparities in lending outcomes between White and non-White applicants, as well as neighborhoods of different racial compositions in the United States. The results suggest that fintech lenders have lower approval rates for non-White applicants, particularly Latinos. Additionally, both fintech and traditional lenders distribute subprime credit to non-White neighborhoods at significantly higher rates than to White neighborhoods.