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08:40 28/3/2019 RFS-OP-REVF190019.tex Page: 1854 18541899 The Role of Technology in Mortgage Lending Andreas Fuster Swiss National Bank Matthew Plosser Federal Reserve Bank of New York Philipp Schnabl NYU Stern, NBER, and CEPR James Vickery Federal Reserve Bank of New York Technology-based (“FinTech”) lenders increased their market share of U.S. mortgage lending from 2% to 8% from 2010 to 2016. Using loan-level data on mortgage applications and originations, we show that FinTech lenders process mortgage applications 20% faster than other lenders, controlling for observable characteristics. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than do other lenders in response to exogenous mortgage demand shocks. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest. We find no evidence that FinTech lenders target borrowers with low access to finance. (JEL D14, D24, G21, G23) Received June 1, 2017; editorial decision November 5, 2018 by Editor Wei Jiang. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. The U.S. residential mortgage industry is experiencing a wave of technological innovation as both start-ups and existing lenders seek ways to automate, We thank the Editors Itay Goldstein, Wei Jiang, and Andrew Karolyi; an anonymous reviewer; Sudheer Chava, Scott Frame, Sam Kruger, Chris Mayer, John Mondragon, and Stephen Zeldes; participants at RFS FinTech conferences at Cornell and Columbia; and seminar and conference participants at the National Association of Realtors, FDIC Consumer Research Forum, Federal Reserve Bank of Dallas, NBER Summer Institute IT and Digitization meeting, WFA Day-Ahead Summer Real Estate Symposium, Fannie Mae, Notre Dame Mendoza College of Business, NYU Stern, Kellogg School of Management, University of St. Gallen, Federal Reserve Bank of Atlanta Real Estate Conference, Homer Hoyt Institute, RBA, and the University of Technology, Sydney for helpful comments. We also thank a number of anonymous mortgage industry professionals for providing information about institutional details and industry trends. Katherine di Lucido, Patrick Farrell, Eilidh Geddes, Drew Johnston, April Meehl, Akhtar Shah, Shivram Viswanathan, and Brandon Zborowski provided excellent research assistance. The views expressed in this paper are solely those of the authors and not necessarily those of the Federal Reserve Bank of New York, the Federal Reserve System, or the Swiss National Bank. Supplementary data can be found on The Review of Financial Studies Web site. Send correspondence to James Vickery, Federal Reserve Bank of New York, 33 Liberty Street, New York, NY 10045; telephone: +1(212) 720-6691. E-mail: james.vickeryny.frb. The Author(s) 2019. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: . doi:10.1093/rfs/hhz018 Downloaded from by Renmin University user on 18 December 2019 08:40 28/3/2019 RFS-OP-REVF190019.tex Page: 1855 18541899 The Role of Technology in Mortgage Lending simplify and speed up each step of the mortgage origination process. At the forefront of this development are FinTech lenders, which have a complete end- to-end online mortgage application and approval process that is supported by centralized underwriting operations, rather than the traditional network of local brokers or “bricks and mortar” branches. For example, Rocket Mortgage from Quicken Loans, introduced in 2015, provides a tool to electronically collect documentation about borrowers income, assets and credit history, allowing the lender to make approval decisions based on an online application in as little as 8 minutes. In the aftermath of the 2008 financial crisis, FinTech lenders have become an increasingly important source of mortgage credit to U.S. households. We measure “FinTech lenders” as lenders that offer an application process that can be completed entirely online. As of December 2016, all FinTech lenders are stand-alone mortgage originators that primarily securitize mortgages and operate without deposit financing or a branch network. Their lending has grown annually by 30% from $34bn of total originations in 2010 (2% of market) to $161bn in 2016 (8% of market). The growth has been particularly pronounced for refinances and for mortgages insured by the Federal Housing Administration (FHA), a segment of the market which primarily serves lower income borrowers. In this paper, we study the effects of FinTech lending on the U.S. mortgage market. Our main hypothesis is that the FinTech lending model represents a technological innovation that reduces frictions in mortgage lending, such as lengthy loan processing, capacity constraints, inefficient refinancing, and lim- ited access to finance by some borrowers. The alternative hypothesis is that Fin- Tech lending is not special on these dimensions, and that FinTech lenders offer services that are similar to traditional lenders in terms of processing times and scalability. Under this explanation, economic forces unrelated to technology explain the growth in FinTech lending (e.g., regulatory arbitrage or marketing). It is important to distinguish between these explanations to evaluate the impact of technological innovation on the mortgage market. If FinTech lenders do indeed offer a substantially different product from traditional lenders, they may increase consumer surplus or expand credit supply, at least for individuals who are comfortable obtaining a mortgage online. If, however, FinTech lending is driven primarily by other economic forces, there might be little benefit to consumers. FinTech lending may even increase the overall risk of the U.S. mortgage market (e.g., due to lax screening). Studying these questions in the context of the mortgage market is informative for evaluating the broader impact of technological innovation in loan markets. Mortgage lending is arguably the 1855 Downloaded from by Renmin University user on 18 December 2019 08:40 28/3/2019 RFS-OP-REVF190019.tex Page: 1856 18541899 The Review of Financial Studies / v 32 n 5 2019 market in which technology has had the largest economic impact thus far, but other loan markets may undergo similar transformations in the future. 1 Our analysis identifies several frictions in U.S. mortgage markets and examines whether FinTech lending alleviates them. We start by examining the effect of FinTech lending on loan outcomes. We focus particularly on the time it takes to originate a loan as a measure of efficiency. FinTech lenders may be faster at processing loans than traditional lenders because online processing is automated and centralized, with less scope for human error. At the same time, this more automated approach may be less effective at screening borrowers; therefore, we also examine the riskiness of FinTech loans using data on loan defaults. We find that FinTech lenders process mortgages faster than traditional lenders, measured by total days from the submission of a mortgage application until the closing. Using loan-level data on the near-universe of U.S. mortgages from 2010 to 2016, we find that FinTech lenders reduce processing time by about 10 days, or 20% of the average processing time. In our preferred specifications, this effect is larger for refinance mortgages (14.6 days) than purchase mortgages (9.2 days). The result holds even when we restrict the sample to nonbanks, indicating that it is not accounted for by differences in regulation. Faster processing by FinTech lenders does not result in riskier loans. We measure loan risk using defaults on FHA mortgages, the riskiest segment of the market in recent years. We find that FinTech default rates are about 25% lower than those for traditional lenders, even when controlling for detailed loan characteristics. Interest rates are not economically significantly different. These results speak against a “lax screening” hypothesis. We also study how FinTech lenders respond to mortgage demand shocks. Existing research documents evidence of significant capacity constraints in U.S. mortgage lending. 2 FinTech lenders may be better able to accommodate demand shocks because they collect information electronically and centralize and partially automate their underwriting operations. To empirically identify capacity constraints across lenders, we use changes in nationwide application volume as a source of exogenous variation in mortgage demand and trace out the correlation with loan processing times. We find that FinTech lenders respond more elastically to changes in mortgage demand. A doubling of the application volume raises the loan processing time by 13.5 days (or 26%) for traditional lenders, compared to only 7.5 days for 1 Many industry observers believe that technology will soon disrupt a wide range of loan markets including small business loans, leveraged loans, personal unsecured lending, and commercial real estate lending (Goldman Sachs Research 2015). 2 Fuster et al. (2017b) show that increases in aggregate application volumes are strongly associated with increases in processing times and higher interest rate margins, thereby attenuating the pass-through of lower mortgage rates to borrowers. Sharpe and Sherlund (2016) and Choi et al. (2017) also find evidence of capacity constraints, which they argue alter the mix of loan applications that lenders attract. 1856 Downloaded from by Renmin University user on 18 December 2019 08:40 28/3/2019 RFS-OP-REVF190019.tex Page: 1857 18541899 The Role of Technology in Mortgage Lending FinTech lenders. The result is robust to including a large number of loan and borrower observables, restricting the sample to nonbanks, or using an interest rate refinancing incentive or a Bartik-style instrument to measure demand shocks. The estimated effect is larger for refinances, where FinTech lenders are particularly active. We also document that FinTech lenders reduce denial rates relative to other lenders when application volumes rise, suggesting that their faster processing is not simply due to credit rationing during peak periods. Given that FinTech lenders particularly focus on mortgage refinances, we next study their effect on household refinancing behavior. Prior literature has shown that many U.S. households refinance too little or at the wrong times (e.g., Campbell 2006; Keys et al. 2016). FinTech lending may encourage efficient refinancing by offering a faster, less cumbersome loan process. We examine this possibility by studying the relationship between the FinTech lender market share and refinancing propensities across U.S. counties. We find that borrowers are more likely to refinance in counties with a larger FinTech lender presence (controlling for county and time effects). An 8-percentage-point increase in the lagged market share of FinTech lenders (which corresponds to moving from the 10th percentile to the 90th percentile in 2015) raises the likelihood of refinancing by about 10% of the average. This increase in refinancing is more pronounced among borrowers estimated to benefit from refinancing based on the optimal refinancing rule of Agarwal, Driscoll, and Laibson (2013). Our findings suggest that FinTech lending, by reducing refinancing frictions, increases the pass-through of market interest rates to households. We conduct a range of analyses to examine whether our main results reflect the endogenous matching of specific borrowers to FinTech lenders. The main identification concern is one of selection; for example, perhaps sophisticated borrowers seek out FinTech lenders, and also submit paperwork faster or are less likely to default? We conduct several auxiliary tests which in general speak against this interpretation. First, we show that our processing time and default results are robust to including or excluding a large set of borrower, loan, and geographic controls, indicating that they are not driven by selection on observables. We also find no robust evidence that FinTech penetration leads to slower processing speeds or higher defaults for other lenders, as would be expected if the pool of unobservably “fast” or low-default borrowers had simply migrated away to FinTech. Furthermore, we show that FinTech has grown most quickly in regions where mortgage processing times were previously unusually slow, again at odds with an explanation that FinTech lenders target “fast” borrowers. Related, in terms of our refinancing results we find that the FinTech market share is higher in geographic regions with previously slow refinancing rates; these regions have subsequently “caught up” as the FinTech share has grown. We cannot fully rule out selection effects, because our setting does not have a perfect natural experiment; however, these additional results suggest selection does not drive our key findings. 1857 Downloaded from by Renmin University user on 18 December 2019 08:40 28/3/2019 RFS-OP-REVF190019.tex Page: 1858 18541899 The Review of Financial Studies / v 32 n 5 2019 We also analyze cross-sectional patterns in who borrows from FinTech lenders. We find that FinTech borrowing is higher among more educated populations, and surprisingly among older borrowers, presumably because older borrowers are familiar with the process of obtaining a mortgage and thus more willing to borrow online. We find little evidence that FinTech lenders disproportionately target marginal borrowers with low access to finance. We find no consistent correlation between FinTech lending and local Internet usage or speed; similarly, using the entry of Google Fiber in Kansas City as a natural experiment, we find no evidence that improved Internet access increases FinTech mortgage take-up. These results mitigate concerns about a digital divide in mortgage lending. Taken together, our results suggest that recent technological innovations are improving the efficiency of the U.S. mortgage market. We find that FinTech lenders process mortgages more quickly without increasing loan risk, respond more elastically to demand shocks, and increase the propensity to refinance, especially among borrowers that are likely to benefit from it. We find, however, little evidence that FinTech lending is more effective at allocating credit to otherwise constrained borrowers. Our results do not necessarily predict how FinTech lending will evolve in the future. FinTech lenders are nonbanks who securitize most of their mortgages their growth could be affected by regulatory changes or reforms to the housing finance system. Some uncertainty surrounds how the increased popularity of machine learning techniques, which FinTech lenders may be using more intensely, will influence the quantity and distribution of credit. 3 Related to this issue, alth
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