Fintech’s relationship with subprime lending in immigrant gateway metropolitan areas

ABSTRACT

Financial technology lending (fintech) is a subset of the mortgage industry characterized by all-online application processes and the inclusion of non-traditional applicant data in underwriting decisions. While national studies suggest that fintech lenders mimic traditional lenders and distribute subprime loans to minority borrowers and neighborhoods at higher rates than to white borrowers and neighborhoods, these studies exclude regional differences by race/ethnicity and nativity. We assess variation in neighborhood-level fintech and traditional subprime lending rates across immigrant gateway metropolitan areas. Using Home Mortgage Disclosure Act (HMDA) data, we find that immigrant gateways are associated with higher rates of subprime lending than metropolitan areas with low rates of immigration. Results suggest that neighborhood-level composition of Asian and Latinx residents mediate the relationship between subprime lending and immigrant gateways in distinct ways depending on lender type. Findings suggest metropolitan and tract-level racial and ethnic patterns remain key factors in shaping subprime lending rates in a rapidly evolving mortgage credit market.

KEYWORDS:

• Fintech
• mortgages
• subprime lending
• immigrant gateways

Acknowledgments

The authors thank the editor and anonymous reviewers for their constructive feedback on earlier versions of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. Hereinafter, white refers to non-Hispanic white. Following Crenshaw (1991), we do not capitalize white “since whites do not constitute a specific cultural group” (p. 1244).

2. These lenders also adhered to detailed descriptions of fintech lending from other research (e.g., Schindler, 2017; Stackhouse, 2019) that includes characteristics such as promotion of the use of non-traditional data sources and techniques used in the underwriting process, and an emphasis on efficiency and near-instant loan approvals.

3. To construct this measure, we estimate the monthly payment amount on each loan given the average annual prime rate in the year of origination and the total loan amount, assuming a 30-year amortization period. We then compare this amount to the borrower’s reported annual income divided into 12 monthly increments. We then aggregate the individual-level outcomes of this calculation to the census tract level.

4. Singer’s categorization of the 104 metropolitan areas with greater than 500,000 residents represents an expansion of her 2008 categorization of the 37 metropolitan areas with over 1,000,000 residents. The updated categorization is also based on more recent ACS data. We use Singer’s expanded set of metropolitan areas to capture a greater share of mortgage lending activity and to incorporate the more recent ACS.

5. We merge the major- and minor- categories for continuous and emerging gateways for ease of interpretation.

Additional information

Notes on contributors

Tyler Haupert

Tyler Haupert is an Assistant Professor of Urban Studies at NYU Shanghai. His research focuses on the social, economic, technological, and regulatory mechanisms contributing to racial segregation and exclusion in advanced economies, with particular interests in mortgage lending, housing policy, neighborhood change, and homelessness. His work has also been published in Housing Policy Debate, Housing Studies, Race and Social Problems, and Urban Studies.

C. Aujean Lee

C. Aujean Lee is an Assistant Professor in the Regional and City Planning Program at the University of Oklahoma. She focuses on residential segregation and its impacts on racial disparities in homeownership, wealth, and community institutions. Her work has also been published in Journal of the American Planning Association, Journal of Planning Education and Research, Housing Policy Debate, and Nonprofit Voluntary Sector Quarterly.