Abstract
This research assesses the relationship between subprime lending rates among applicants to traditional and fintech mortgage lenders and metropolitan-level racial and ethnic segregation in the United States. Fintech—short for financial technology—mortgage lenders underwrite loans using all-online applications and proprietary machine learning underwriting algorithms that process unprecedented amounts of applicant data. While traditional lenders have long been associated with high rates of subprime lending in segregated metropolitan areas, it is unknown whether fintech lenders also exhibit this relationship. Using Home Mortgage Disclosure Act data from the nation’s 200 largest metropolitan areas in 2015–2017 and a series of binomial logistic regressions, I find the probability of an applicant receiving a subprime loan at both traditional and fintech lenders is positively associated with metropolitan area Black and Hispanic segregation. However, fintech lending is associated with significantly lower rates of subprime lending, relative to traditional lending, in metropolitan areas with high levels of Black segregation. This relationship holds true when analyzing both Black-white dissimilarity and Black isolation. Results related to white-Hispanic segregation are mixed. Fintech lenders are more likely than traditional lenders to originate subprime loans in metropolitan areas with high levels of white-Hispanic dissimilarity, but less likely as a metropolitan area’s Hispanic isolation increases. Findings suggest the structural forces connecting subprime lending to metropolitan segregation—especially Black segregation—have a weaker association with the fintech lending market than the traditional market, but still play a significant structural role in shaping fintech lending outcomes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
All applications missing at least one variable in the HMDA data set or missing one of the covariates described below are excluded. Additionally, in line with sampling procedures from prior research (Faber, 2018; Hwang et al., 2015) only first-lien, owner-occupied, home purchase loans for 1–4 unit residences are used. This allows applications in my sample to reflect the typical American home buyer as opposed to investors and those and refinancing existing mortgages.
Core-based Statistical Areas (CBSAs) are urbanized areas containing either Metropolitan Statistical Areas (MSAs) or Micropolitan Statistical Areas. All CBSAs included in this study are centered on Metropolitan Statistical Areas (i.e. no Micropolitan Statistical Areas are used), which are defined as urbanized areas of at least 50,000 residents and the surrounding territories with a high degree of community ties. See https://www.census.gov/topics/housing/housing-patterns/about/core-based-statistical-areas.html for additional details.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Past research (Bartlett et al., 2019; Haupert, 2020) has found little or no difference between fintech and traditional lenders in approval rates, and minimal evidence of racial disparities in approvals overall. Thus, I choose to focus on subprime lending, where applicant race, ethnicity, and spatial demographics continue to play an important role.
The prime rate is not set by the Federal Reserve Bank, but is determined by individual banks who report their “base” or “reference” rate for many types of loans, including mortgages. See https://www.federalreserve.gov/faqs/credit_12846.htm for additional details.
In line with other HMDA-based nationwide studies of racial disparities in mortgage lending (e.g., Faber, 2018; Haupert, 2020) I exclude the HMDA-reported categories of American Indian or Alaskan Native and Native Hawaiian or other Pacific Islander, as the number of respondents reporting these racial identities is extremely small and substantially reduces the study’s sample size if included.
Nonconventional loans such as those from the Veterans Administration (VA) or Fair Housing Administration (FHA) are included in order to maintain a nationally representative sample while controlling for their potential impact on interest rate assignment.
To create this instrument, I draw a 50% random sample of my data set before removing denied applications, but otherwise cleaned to the specifications described here. The 50% sample is separated to avoid circularity issues in the calculations. Using this random sample, I estimate a binary logistic regression estimating the relationship between a loan being denied due to an applicant’s poor credit history and a series of applicant, loan, and neighborhood covariates. Then, using this model’s estimates, I create a new variable comprised of the predicted probability that each loan application in the 50% random sample would have been denied for poor credit history. I take the saved parameters of the regression and estimate the probability of denial for poor credit history in the remaining 50% of observations in the master data set then combine the two 50% subsamples.
References
Anacker, K., Niedt, C., & Kwong, C. (2017). Analyzing segregation in mature and developing suburbs in the United States. Journal of Urban Affairs, 39(6), 819–832. https://doi.org/10.1080/07352166.2017.1305730
Ashton, P. (2009). An appetite for yield: The anatomy of the subprime mortgage crisis. Environment and Planning a: Economy and Space, 41(6), 1420. https://doi.org/10.1068/a40328
Bartlett, R. P., Morse, A., Stanton, R. H., et al. (2019). Consumer Lending Discrimination in the FinTech Era. NBER Working Paper. 25943, June. Cambridge, MA. Retrieved January 9, 2019 from https://www.nber.org/papers/w25943
Been, V., Chan, S., Ellen, I. G., et al. (2011). Decoding the forecolsure crisis: Causes, responses, and consequences. Journal of Policy Analysis and Management, 30(2), 388–396. https://doi.org/10.1002/pam
Been, V., Ellen, I., & Madar, J. (2009). The high cost of segregation: Exploring racial disparities in high-cost lending. Fordham Urban Law Journal, 36, 361.
Board of Governors of the Federal Reserve. (2019). Interagency statement on the use of alternative data in credit underwriting. Washington, D.C. Retrieved from https://www.federalreserve.gov/newsevents/pressreleases/files/bcreg20191203b1.pdf
Bond, C., & Williams, R. (2007). Residential segregation and the transformation of home mortgage lending. Social Forces, 86(2), 671–698.
Buchak, G., Matvos, G., Piskorski, T., et al. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453–483. https://doi.org/10.1016/j.jfineco.2018.03.011
Chan, S., Gedal, M., Been, V., et al. (2013). The role of neighborhood characteristics in mortgage default risk: Evidence from New York City. Journal of Housing Economics, 22, 100–118. https://doi.org/10.1016/j.jhe.2013.03.003
Courchane, M. J., & Ross, S. L. (2019). Evidence and actions on mortgage market disparities: Research, fair lending enforcement, and consumer protection. Housing Policy Debate, 29(5), 769–794. https://doi.org/10.1080/10511482.2018.1524446
Diventi, T. R. (2009). Fannie Mae and Freddie Mac: Past, present, and future policy briefs. Cityscape: A Journal of Policy Development and Research. Retrieved November 10, 2018 from http://www.financialstability.gov/roadtostability/homeowner.html
Dwyer, R. E., & Phillips Lassus, L. A. (2015). The great risk shift and precarity in the U.S. Housing Market. Annals of the American Academy of Political and Social Science, 660(1), 199–216. https://doi.org/10.1177/0002716215577612
Faber, J. W. (2013). Racial dynamics of subprime mortgage lending at the peak. Housing Policy Debate, 23(2), 328–349. https://doi.org/10.1080/10511482.2013.771788
Faber, J. W. (2018). Segregation and the geography of creditworthiness: Racial inequality in a recovered mortgage market. Housing Policy Debate, 28(2), 215–247. https://doi.org/10.1080/10511482.2017.1341944
Faber, J. (2020). Contemporary echoes of segregationist policy: Spatial marking and the persistence of inequality. Urban Studies, 58(5), 1067–1086. https://doi.org/10.1177/0042098020947341
Federal Financial Institutions Examination Council. (2018). HMDA & PMIC data products. Retrieved from https://www.ffiec.gov/hmda/hmdaproducts.htm
Federal Financial Institutions Examination Council. (2019). Community Reinvestment Act. Retrieved from https://www.ffiec.gov/cra/craproducts.htm
Federal Housing Finance Agency. (2018). Housing price index datasets. Retrieved from https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx#qpo
Fourcade, M., & Healy, K. (2017). Seeing like a market. Socio-Economic Review, 15(1), 9–29. https://doi.org/10.1093/ser/mww033
Gaughan, M. (2017). FinTech and the liberation of the community reinvestment act marketplace. Cityscape: A Journal of Policy Development and Research, 19(2), 187–198. https://doi.org/10.2307/26328334
Hammel, D. J., & Nilsson, I. (2019). Mortgage foreclosures, race, and postrecession lending. The Professional Geographer, 71(3), 536–550. https://doi.org/10.1080/00330124.2019.1578975
Harras, S. (2019). States, consumer groups blast CFPB’s fintech protections. Retrieved from March 1, 2020 from https://www.rollcall.com/2019/02/21/states-consumer-groups-blast-cfpbs-fintech-protections-2/
Haupert, T. (2019). Racial patterns in mortgage lending outcomes during and after the subprime boom. Housing Policy Debate, 29(6), 947–976. https://doi.org/10.1080/10511482.2019.1636286
Haupert, T. (2020). The racial landscape of fintech mortgage lending. Housing Policy Debate. https://doi.org/10.1080/10511482.2020.1825010
Holloway, S. R. (1998). Exploring the neighborhood contingency of race discrimination in mortgage lending in Columbus, Ohio. Annals of the Association of American Geographers, 88(2), 252–276. https://doi.org/10.1111/1467-8306.00093
Hwang, J., Hankinson, M., & Steven Brown, K. (2015). Racial and spatial targeting: Segregation and subprime lending within and across metropolitan areas. Social Forces, 93(3), 1081–1108. https://doi.org/10.1093/sf/sou099
Hyra, D. S., Squires, G. D., Renner, R. N., et al. (2013). Metropolitan segregation and the subprime lending crisis. Housing Policy Debate, 23(1), 177–198. https://doi.org/10.1080/10511482.2012.697912
Jagtiani, J., & Lemieux, C. (2018). Do fintech lenders penetrate areas that are underserved by traditional banks? Journal of Economics and Business, 100, 43–54. https://doi.org/10.1016/j.jeconbus.2018.03.001
Jagtiani. J., Lambie-Hanson, L., & Lambie-Hanson, T. (2019). Fintech lending and mortgage credit access. WP 19–47.
Kuebler, M. (2012). Lending in the modern era: Does racial composition of neighborhoods matter when individuals seek home financing? A pilot study in New England. City & Community, 11(1), 31–50. https://doi.org/10.1111/j.1540-6040.2011.01391.x
Lammi, G. G. (2019). State vs. Federal Clash Over National “Fintech Charter” Set For 2020 Appellate Showdown? Forbes, November. Retrieved from https://www.forbes.com/sites/wlf/2019/11/14/state-vs-federal-clash-over-national-fintech-charter-set-for-2020-appellate-showdown/#2d8c7995757d
Loya, J. (2021). Racial stratification among latinos in the mortgage market. Race and Social Problems. https://doi.org/10.1007/s12552-021-09326-3
MacDonald, H. I. (1998). Mortgage lending and residential integration in a hypersegregated MSA: The case of St Louis. Urban Studies, 35(11), 1971–1993. https://doi.org/10.1080/0042098983971
Masayoshi, O., & Wong, D. W. S. (2014). Capturing the two dimensions of residential segregation at the neighborhood level for health research. Frontiers in Public Health, 2, 118. https://doi.org/10.3389/fpubh.2014.00118
Massey, D. S. (2020). Still the Linchpin: Segregation and stratification in the USA. Race and Social Problems, 12, 1–12. https://doi.org/10.1007/s12552-019-09280-1
Munnell, A., Tootell, G., Browne, L., et al. (1996). Mortgage lending in boston: Interpreting HMDA data. The American Economic Review, 86(1), 25–53.
Quan, D. (2019). CFPB can do better by fintechs than a ‘policy tool’. Retrieved from https://www.americanbanker.com/opinion/cfpb-can-do-better-by-fintechs-than-a-policy-tool
Reardon, S. F., Matthews, S. A., O’sullivan, D., et al. (2008). The geographic scale of metropolitan segregation. Demography, 45(3), 489–514.
Rugh, J. S., & Massey, D. S. (2010). Racial segregation and the American foreclosure crisis. American Sociological Review, 75(5), 629–651. https://doi.org/10.1177/0003122410380868
Sanchez-Moyano, R. (2020). Geography and Hispanic homeownership: A review of the literature. Journal of Housing and the Built Environment, 36, 215–240. https://doi.org/10.1007/s10901-020-09745-5
Schindler, J. (2017). FinTech and Financial Innovation: Drivers and Depth. Finance and Economics Discussion Series 2017–081. Washington, D.C. https://doi.org/10.17016/FEDS.2017.081.
Social Explorer. (2018). American Community Surveys (5-Year Estimates). Retrieved from https://www.socialexplorer.com/explore-tables
Squires, G., Hyra, D. S., & Renner, R. N. (2009). Segregation and the subprime lending crisis. Washington, DC. Retrieved July 23, 2018 from https://www.kansascityfed.org/publicat/events/community/2009carc/Hyra.pdf
Stackhouse, J. (2019). Fintech: How technology is changing consumer and small business lending. Retrieved December 13, 2019 from https://www.stlouisfed.org/on-the-economy/2019/may/fintech-technology-changing-consumer-small-business-lending
Stuart, G. (2003). Discriminating risk: The U.S. mortgage lending industry in the 21st century. Cornell University Press.
Surden, H. (2014). Machine learning and law. Washington Law Review, 89(1), 87–115.
Tenenbaum, H. S., & Waters, N. M. (2011). Spatial patterns of subprime mortgages by local banks, nonlocal banks, and independents in the continental US. Environment and Planning a: Economy and Space, 43(8), 1761–1778. https://doi.org/10.1068/a43328
Tootell, G. M. B. (1996). Redlining in Boston: Do mortgage lenders discriminate against neighborhoods? The Quarterly Journal of Economics, 111(4), 1049–1079. https://doi.org/10.2307/2946707
Turner, M. A., & Skidmore, F. (1999). Mortgage lending discrimination: A review of existing evidence lending discrimination. Washington, D.C. Retrieved from http://webarchive.urban.org/publications/309090.html
Wyly, E. K., Atia, M., Foxcroft, H., et al. (2006). American home: Predatory mortgage capital and neighbourhood spaces of race and class exploitation in the United States. Geografiska Annaler, Series b: Human Geography, 88(1), 105–132. https://doi.org/10.1111/j.0435-3684.2006.00208.x
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No potential conflict of interest was reported by the author.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Haupert, T. New Technology, Old Patterns: Fintech Lending, Metropolitan Segregation, and Subprime Credit. Race Soc Probl 14, 293–307 (2022). https://doi.org/10.1007/s12552-021-09353-0
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1007/s12552-021-09353-0