Comparing racial and neighborhood disparities in fintech and traditional mortgage refinancing

Racial and spatial disparities in homeowners’ ability to build and leverage home equity contribute significantly to the racial wealth gap in the United States. Yet disparities in mortgage refinancing, a key mechanism for lowering borrowing costs or extracting equity, have received less attention than home purchase lending. This study examines whether fintech lenders, a fast-growing class of online, technology-driven firms, differ from traditional brick-and-mortar lenders in their refinance lending patterns across racial and neighborhood lines. Using a novel dataset classifying lenders as fintech or traditional from 2018 to 2020, combined with Home Mortgage Disclosure Act data and neighborhood covariates, I estimate logistic and OLS regression models comparing refinance approvals and interest rate spreads by applicant race and neighborhood racial composition. Results show that Black and Latino applicants face significant disadvantages relative to similarly qualified White applicants at traditional lenders. Fintech lenders, however, are associated with lower overall approval rates and higher interest rate spreads, while exhibiting smaller disparities in approvals and narrower gaps in rate spreads across racial groups and neighborhoods. Findings suggest that technology-driven lending is associated with smaller disparities in refinancing outcomes, while underscoring the importance of distinguishing between relative disparities and absolute levels of credit access and borrowing costs.