Technological Innovation in Mortgage Underwriting and the Growth in Credit: 1985–2015

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 loanprocessing 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-toincome 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.

Information and Financialization: Credit Markets as a New Source of Inequality

“…We consider these findings and conclusions by Foote et al (2019) as evidence in favor of our assertion that the beginning of 2008 marks a discontinuity at which lenders were strongly incentivized to seek more and better information on mortgage applications. As argued, we expect an increase in the spread of interest rates at this discontinuity.…”

“…After housing prices fell, both Fannie and Freddie increased their repurchase requests to originators that had incorrectly underwritten loans. This prompted originators to follow GSE policies more carefully, which likely lengthened origination timelines” (Foote et al, 2019, p. 14).…”

“…But there are good reasons to believe that the financial industry has been continuously improving its information both before and after 2008. It is a frontrunner in adopting new ICT technologies for that purpose (Foote et al, 2019), and even with the increased role of Ginnie Mae (which reduces industry exposure to bad risks), there is a clear upward trend in the spread. This is consistent with the price of information falling over time, which is theoretically predicted to have the same effect on lender behavior as a rise in the cost of defaults.…”

“…They report “average processing time [in days] by year after stripping out any variation explained by the size of the lender, the borrower’s race and gender, whether the borrower has a coapplicant, and the concurrent monthly application volume. The processing times are calculated as of the year of application and include both closed loans and denials” (Foote et al, 2019, p. 37 (note for Figure 7)).…”

Fintech Lending and Mortgage Credit Access

“…The similarity of loan and borrower characteristics for loans originated through 2017 suggests that the main innovation of fintech firms in mortgages had been online applications, streamlined data collection, and automated, faster underwriting decisions. This builds on prior technological innovations in the mortgage industry, such as the advent of automated underwriting in the 1990s, which has been shown to have increased homeownership rates, especially among those who previously were excluded from the market because of high debt-to-income ratios (Foote, Loewenstein, and Willen, 2018). As we discuss in the conclusion, the GSEs have shown recent signs of willingness to accept alternative data, which may enable fintech firms to become greater disruptors in the mortgage market.…”