Consumer Lending Discrimination in the Fintech Era
USC Lusk Center for Real Estate Research Seminar Series
UC Berkeley - Haas School of Business
Lending discrimination can stem from loan officer facial biases or algorithmic scoring, especially with big data use in FinTech. Using never-before-linked mortgage data covering loan-level ethnicity, scoring variables, contract terms, and lender identifiers, we implement a treatment-based Oaxaca-Blinder discrimination estimation, based on the unique default risk setting of the GSEs. We find that African-American and Hispanic borrowers have a 2% higher loan rejection rate, especially among low-credit-score applicants. Consistent with facial biases, differences are more pronounced among smaller lenders and independent mortgage companies, not FinTech lenders. Ethnic-minority borrowers pay a slightly (0.18%) higher interest rate fairly uniformly across lenders, probably resulting from profit-taking opportunities in weaker competitive environments.
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