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Technological innovation in mortgage underwriting and the growth in credit, 1985–2015

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Abstract

The application of information technology to finance, or ?fintech,? is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-to-income ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomes?in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.

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  • Christopher L. Foote & Lara Loewenstein & Paul S. Willen, 2019. "Technological innovation in mortgage underwriting and the growth in credit, 1985–2015," Working Papers 19-11, Federal Reserve Bank of Boston.
  • Handle: RePEc:fip:fedbwp:19-11
    DOI: 10.29412/res.wp.2019.11
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    Cited by:

    1. Stephen D. Oliner & Morris A. Davis & Will Larson, 2019. "Mortgage risk since 1990," AEI Economics Working Papers 1001502, American Enterprise Institute.
    2. Rojas, Alejandro, 2021. "Mortgage credit growth for lower-income borrowers during the 2000s housing boom: Evidence and implications," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 220-233.
    3. Margaret Jacobson, 2019. "Beliefs, Aggregate Risk, and the U.S. Housing Boom," 2019 Meeting Papers 1549, Society for Economic Dynamics.
    4. Egle Jakucionyte & Swapnil Singh, 2020. "Bowling Alone, Buying Alone: The Decline of Co-Borrowers in the US Mortgage Market," Bank of Lithuania Working Paper Series 78, Bank of Lithuania.
    5. Santiago Carbó-Valverde & Pedro J. Cuadros-Solas & Francisco Rodríguez-Fernández, 2022. "Entrepreneurial, institutional and financial strategies for FinTech profitability," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.
    6. Egle Jakucionyte & Swapnil Singh, 2021. "Emergence of Subprime Lending in Minority Neighborhoods," Bank of Lithuania Working Paper Series 94, Bank of Lithuania.

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    More about this item

    Keywords

    mortgage underwriting; housing cycles; technological change; credit boom;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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