The Role of Technology in Mortgage Lending

Abstract

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.

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, 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 borrower’s 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

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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 limited access to finance by some borrowers. The alternative hypothesis is that FinTech 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 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 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.

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, although we find no evidence FinTech lenders select the highest-quality borrowers (“cream skim”), which could reduce credit for other borrowers, these results could change as technology-based lending becomes more widespread. Lastly, FinTech lenders use a less personalized loan process that relies on hard information, which could reduce credit to borrower applications that rely on soft information.

Our research contributes to several strands of the literature. Although a large body of research has studied residential mortgage lending (see Campbell 2013 and Badarinza et al. 2016 for surveys), much of the recent work focuses on securitization and the lending boom prior to the U.S. financial crisis.⁴ Our paper instead focuses on how technology affects mortgage lending after the crisis. Most closely related to this paper, Buchak et al. (2018) study the recent growth in the share of nonbank mortgage lenders, including FinTech lenders. Although there is some overlap between the descriptive parts of our analyses, the two papers are otherwise strongly complementary. Buchak et al. (2018) focus on explaining the growth of nonbank lending, using reduced-form analysis and a calibrated structural model. Our paper focuses on how technology affects frictions in the mortgage origination process, such as slow processing times, capacity constraints, and slow or suboptimal refinancing.⁵

Our findings inform research on the role of mortgage markets in the transmission of monetary policy (e.g., Beraja et al. 2019; Di Maggio et al. 2017). If lenders constrain the pass-through of interest rates (Agarwal et al. 2017; Drechsler et al. 2017; Fuster et al. 2017b; Scharfstein and Sunderam 2016), or borrowers are slow to refinance (Andersen et al. 2015; Agarwal et al. 2015), changes in risk-free interest rates will not be fully reflected in mortgage rates and originations. Our findings suggest that technology may be easing these frictions, potentially improving monetary policy pass-through in mortgage markets.

Finally, our paper contributes to a growing literature on the role of technology in finance (see Philippon 2016 for an overview), and a broader literature on how new technology can lead to productivity growth (see, e.g., Syverson 2011; Collard-Wexler and De Loecker 2015). In our case, the “productivity” or “efficiency” measures we consider are processing times, supply elasticity, default and refinancing propensities, and we are the first to document that FinTech lending appears to lead to improvements along these dimensions.

1. Who Is a FinTech Lender?

1.1 Defining FinTech lenders

A central feature of our study is the distinction between FinTech mortgage originators and other lenders. While many mortgage lenders are adopting new technologies to varying degrees, it is clear that some lenders are at the forefront of using technology to fundamentally streamline and automate the mortgage origination process. The defining features of this business model are an end-to-end online mortgage application platform and centralized mortgage underwriting and processing augmented by automation.⁶

How does the FinTech business model affect the mortgage origination process in practice?⁷ Online applications mean that a borrower can be approved for a loan without talking to a loan officer or visiting a physical location. The online platform is able to directly access the borrower’s financial account statements and tax returns to electronically collect information about assets and income. Other supporting documents can be uploaded electronically, rather than by being sent piecemeal by mail, fax or email.⁸ This automates a labor-intensive process, speeds up information transfer, and can improve accuracy by, for example, eliminating transcription errors (Goodman 2016; Housing Wire 2015). The online platform also allows borrowers to customize their mortgage based on current lender underwriting standards and real-time pricing.

Supporting and complementing this online application process, FinTech mortgage lenders have developed “back-end” processes to automatically analyze the information collected during the application. For example, borrower information is compared with employment databases, property records, and marriage and divorce records; additionally, algorithms can examine whether recent bank account deposits are consistent with the borrower’s paystubs. Optical character recognition and pattern recognition software can be used to process documents uploaded by the borrower and flag missing or inconsistent data. These systems make the mortgage underwriting process more standardized and repeatable, and may help identify fraud (Goodman 2016).

This approach does not eliminate the role of human underwriters, but does make mortgage processing less labor intensive. In contrast with more hub-and-spoke loan origination operations that put loan officers and underwriters in branches, FinTech lenders centralize their processing operations, which allows for labor specialization in the underwriting process. Lenders have told us anecdotally that this makes it easier to train new workers and to adjust labor supply in response to demand shocks.

However, a more automated approach to mortgage underwriting may also come with important disadvantages. For example, poorly designed online platforms may confuse borrowers or lead to errors, and a lack of personal interaction may impede the transmission of soft information, resulting in less effective borrower screening or credit rationing.⁹ Our empirical analysis examines both the benefits and costs of the FinTech mortgage lending model.

We emphasize that automation and online applications are not entirely new.¹⁰ For example, many lenders in recent years have allowed borrowers to initiate a mortgage application online. However, the online application is often just a first step before directing applicants to speak to a loan officer who then initiates a more traditional loan application process. Similarly, although online mortgage rate comparison services, such as LendingTree and BankRate, have been a feature of the mortgage market for many years, these services simply provide information and connect borrowers and lenders; they do not automate the mortgage origination process or put it online.

The emergence of several stand-alone FinTech firms as major lenders over the last few years is a strong indicator that fundamental change is underway. These firms are at the technological frontier and focus exclusively on the new business model. In contrast, established lenders with branch-based mortgage origination processes face significant obstacles in recalibrating their operations away from branches and loan officers. For this reason, the vanguard of FinTech lenders is composed of nonbanks, which do not have access to deposit finance and therefore do not retain originated loans on balance sheet. Like other nonbanks, the vast majority of FinTech lenders sell their loans through established channels supported by government guarantee programs (FHA, VA, Fannie Mae, and Freddie Mac).

That said, a significant and growing number of mortgage lenders are at present incorporating aspects of the “FinTech model,” and the current distinction between FinTech originators and other firms, including banks, may be temporary. The current market structure presents a window of opportunity to study the impact of FinTech on mortgage origination, and to draw inferences about what is likely to happen to the mortgage industry as a whole as these technologies diffuse more broadly.

1.2 Classifying FinTech lenders

For our empirical analysis, we classify an originator as a FinTech lender if they enable a mortgage applicant to obtain a preapproval online. We believe this classification distinguishes FinTech lenders from more traditional mortgage originators that may use “online applications” for marketing purposes but still require interaction with a loan officer.

Our classification should be viewed as a proxy, since an online application platform is only one dimension of the FinTech “model.” Even so, it is an important component, and is also easily measurable in a consistent way across a large number of mortgage lenders. In practice, the set of lenders classified as FinTech by our approach matches up well with firms considered by industry observers and media to be at the frontier of technology-based mortgage lending. It also quite closely matches with the independent classification by Buchak et al. (2018).¹¹

We implement our classification by first compiling lists of the top-100 nonbank lenders for purchase loans and for refinancings over the analysis period.¹² The resultant list includes 135 lenders. We then manually initiate a mortgage application with each lender and analyze whether it is possible to obtain a preapproval online. Most lenders halt the online application prior to the preapproval and ask the borrower to directly contact a loan officer or broker. We classify the lender as a FinTech lender if we are able to continue with the online process until we get to the preapproval decision that is based on a hard credit check of the applicant’s credit score.

Our final classification is based on an analysis completed in June 2017. To construct a panel, we go back in time using a database that archives Web sites (“Wayback Machine”). Using the database from 2010 to 2017, we evaluate at which point in time a lender appears to have adopted their qualifying online lending process. We cannot always conduct a full evaluation because online application processes often rely on a technological process that evaluates information in real time. However, we can use the archived Web site to evaluate when a lender adopted an application which resembles the qualifying application in 2017. We use this information to determine the year in which a lender adopted a FinTech lending model. We corroborate our results using industry reports.¹³

FinTech lenders exhibit several other distinguishing characteristics relative to their competitors. For example, FinTech firms typically require a Social Security number and conduct a hard credit check online, unlike most traditional mortgage originators we classified. FinTech lenders also tend to orient their marketing efforts around their Web site or mobile phone app. In particular, FinTech lender advertisements emphasize the functionality and ease of use of their Web site or app, and direct borrowers to those platforms. Other lenders may include their Web site in their marketing material but do not emphasize it to the same degree, and may primarily use it for “lead generation.”

Figure 1 plots the number of FinTech lenders by year based on our classification. The number increases from two firms in 2010 to 18 lenders by 2017. In Table 1 we list the top-20 lenders in 2016, along with other FinTech lenders in the data in that year. The three largest originators identified as FinTech lenders are Quicken, LoanDepot.com, and Guaranteed Rate. All of the primary analyses in this paper use this classification, although we have verified that our main results are robust to the alternative classification of Buchak et al. (2018).¹⁴

Table 2 provides summary statistics of mortgage originations and applications, in total and by lender type, based on data collected under the Home Mortgage Disclosure Act (HMDA). HMDA data report characteristics of individual residential mortgage applications and originations from the vast majority of U.S. banks and nonbanks. Data include the identity of the lender, loan amount, property location, borrower income, race, and gender, though not credit score or loan-to-value (LTV) ratio.

Based on known local conforming loan limits, we impute whether each loan has “jumbo” status and thus cannot be securitized by Fannie Mae, Freddie Mac, or Ginnie Mae. The processing time of loan applications, one of our main outcome variables of interest, can only be computed from a restricted version of the data available to users within the Federal Reserve.¹⁵ We include loans with application dates between January 2010 and June 2016.¹⁶

In terms of basic risk characteristics, nonbank lenders originate loans to borrowers with relatively low income and a high loan amount to income (LTI) ratio relative to banks. In keeping with this fact, nonbank lenders also originate a much higher share of FHA and VA loans, although this is less pronounced for FinTech lenders than for other types of nonbanks. Nonbanks also originate fewer jumbo mortgages, which are more difficult to securitize or sell. Notably, FinTech lenders originate many more refinance loans (as opposed to loans used for a home purchase) than either banks and other nonbank lenders.¹⁷

The table also shows that FinTech lenders have shorter average processing times than either banks or other nonbank lenders. In the next section, we study whether this result persists after controlling for loan characteristics and location-time fixed effects. In Section 6 we study differences in borrower and location characteristics between FinTech and non-FinTech mortgages more systematically, building on Table 2.

2. Is FinTech Lending Faster?

Our regression includes a large number of observable loan and borrower characteristics to control for factors other than lender efficiency that may affect processing time (e.g., local laws, housing market conditions, the complexity of the loan, borrower, and property, and the speed of obtaining a property appraisal). We expect that our rich set of controls should account well for these factors. In particular, census-tract-month fixed effects control in a highly disaggregated way for common geographic and time variation in processing times. We conduct the analysis separately for home purchase mortgages and refinances because the latter do not require the homeowner to move and the application process is simpler.

2.1 Processing time results

Panel A of Table 3 presents the results for purchase mortgages. In Column 1, we find that FinTech lenders process loans 7.9 days faster than non-FinTech lenders. This effect is large, corresponding to 15% of average home purchase processing time of 52 days. The result is slightly larger in magnitude and remains statistically significant when we include loan and borrower controls (Column 2), census tract-month fixed effects (Column 3), and both (Column 4, where the estimated effect corresponds to 18% of the average processing time). The results are also robust to dropping deposit-taking banks from the sample (Column 5), which suggests that the results are not driven by regulatory factors or the different funding model of banks.

Panel B of Table 3 finds even larger effects for refinances. Across specifications, FinTech lenders process mortgages 9.2 to 14.8 days faster than other lenders. The effect corresponds to 17%-29% of the average refinance loan processing time of 51 days. Again, the result is robust to comparing FinTech lenders only to other nonbanks, which suggests that it is not driven by regulation or funding. The FinTech advantage for refinance loans might be larger because refinances offer more scope for automation than home purchase loans. For example, purchase loans always require an appraisal, which is administered locally and is not (yet) automated. This interpretation is consistent with the fact that FinTech lending growth has been larger for refinances relative to home purchase loans.¹⁹

While these regressions capture average effects, it is instructive to study the entire distribution of processing times across lender types. We do so in Figure A.1 in the Internet Appendix, where we plot the cumulative distributions of processing times for both purchase and refinance mortgages, after accounting for census-tract-month fixed effects and loan characteristics. For purchase mortgages the advantage of FinTech lenders comes primarily from the right tail (i.e., few loans have very long processing times), while for refinances the entire distribution is shifted to the left. This again suggests that efficiency gains may be easier to realize for refinances.

2.2 Additional analysis

One potential concern is that our processing time results are affected by endogenous matching between borrowers and lenders. For instance, if younger borrowers are more likely to use FinTech lenders and also tend to submit their paperwork faster, FinTech lenders would appear to process mortgages more quickly, even if they do not have an inherent technological advantage. Alternatively, FinTech lenders may attract the most complex mortgage applications, which would attenuate the estimated FinTech processing time advantage.

We emphasize that the coefficient on FinTech lenders is robust across specifications and samples. If FinTech lenders matched with borrowers or loan types that are easier to process, then adding the control variables should attenuate the estimated coefficient; instead, the coefficient tends to get larger with additional controls. To the extent that unobservable factors that make some borrowers faster than others are also correlated with observables, this is a first piece of evidence that our results are unlikely to be driven by endogenous matching or other unobserved variables, but instead represent the direct effect of FinTech lending on processing times.

To investigate further, we test whether the FinTech processing time advantage is driven by “fast borrowers” migrating to FinTech lenders. We implement this test in two stages. In the first stage, we predict the probability that each loan is originated by a FinTech lender as a function of loan and borrower characteristics. We then include this predicted probability as an explanatory variable in a second stage analysis of processing times among non-FinTech mortgages. If non-FinTech lenders lose their faster customers to FinTech lenders, non-FinTech processing times should have increased disproportionately for borrower and loan types with high FinTech penetration (as measured by a high first-stage probability).

Table A.1 in the Internet Appendix shows the second-stage results. In our baseline specification, we find a positive effect of the predicted FinTech probability on non-FinTech processing times. This is consistent with selection, although the coefficient is not nearly large enough to explain our earlier processing time results.²⁰ In addition, the coefficient of interest flips sign once we control for lender-by-census tract fixed effects to allow for the possibility that FinTech lenders have a high market share in areas where traditional lenders are slow (Columns 2 and 4 of the table). In sum, it does not appear that selection effects could easily explain the large processing time differences we document.

Furthermore, as a direct test of whether FinTech lenders match with “fast” borrowers, we study whether FinTech originators have gained the highest market share in geographic locations where processing times were shortest ex ante, measured in 2010 prior to the growth in FinTech. Section 6 presents these results. To preview the key result, we in fact find the opposite; FinTech lenders have become popular in locations where processing times were originally slow conditional on observables. This is inconsistent with an “endogenous selection” interpretation of our processing time estimates, and in fact suggests that slow processing by traditional lenders may be a driver of the growth in FinTech lending.

Summing up, our results suggest that FinTech mortgage lenders are roughly 20% faster at processing mortgage originations than other lenders; the estimated effects range from 7.5 to 9.4 days for purchase mortgages and 9.2 to 14.7 days for refinances. Several pieces of evidence suggest that this finding is not due to endogenous borrower-lender matching or other omitted variable biases.²¹

3. Is FinTech More Efficient or Just Less Careful?

Fast FinTech processing speeds could simply be a product of less careful screening of borrowers, rather than greater efficiency.²² We test this “lax screening” hypothesis by studying the ex post performance of FinTech loans compared to similar mortgages from other lenders. We focus on FHA lending, which has been the riskiest segment of the mortgage market in recent years and where we are therefore most likely to detect differences in loan risk.²³ We use two separate sources of public data on FHA mortgage defaults: segment level data extracted from the FHA Neighborhood Watch Early Warning System (“FHA NW data”) and FHA loan-level data from Ginnie Mae (“FHA Ginnie Mae data”). To our knowledge, this is the first academic study to make systematic use of either of these data sources.²⁴

3.1 Analysis of default rates in FHA NW data

The FHA NW data report originations and default rates by lender at the national level and by state and metropolitan statistical area (MSA). The data are available for all FHA loans as well as certain subcategories including home purchase mortgages, refinances, and mortgages originated in underserved census tracts.²⁵ The data generally covers the period 2015:Q3 to 2017:Q3, although state and national data for all loans (not broken down by loan type) are available over a longer sample period from 2012:Q3 to 2017:Q3.

Default rates are calculated as the share of loans that become at least 90 days delinquent or are the subject of an FHA insurance claim within a specific time horizon after origination. The data include rates at 1-year (“1-year default”) and 2-year (“2-year default”) horizons. To control for geographic variation in default rates we scale a lender’s default rate in each location by the overall default rate in that area. As an alternative to raw default rates, the data also contain the “supplementary performance metric” (SPM), which scales a lender’s default rate by a benchmark default rate defined based on the credit score distribution of the underlying mortgages. Again, we then take the ratio of the lender’s SPM to the overall SPM in the area. The SPM is only available at the state and national level and at a 2-year horizon after origination (“mix-adjusted 2-year default”).

Our analysis focuses on the difference in default rates between FinTech lenders and other lenders. We compute the difference by taking the weighted average of FinTech relative default rates using origination volume by region and lender as weights and subtracting one. This measure yields zero if there are no differences in default rates between FinTech lenders and other lenders. We use a difference-in-means test to examine the null hypothesis that FinTech lenders’ default rates are the same as other lenders’.

Table 4 reports the results. Column 1 presents the relative difference in default rates for FinTech lenders using 1-year default as the default measure. In panel A, we find that loans originated by FinTech lenders are 35% less likely to default than comparable loans from non-FinTech lenders. The coefficient is almost unchanged when using MSA-level data instead of state-level data or using the 2-year default rate instead of the 1-year default rate (Column 2). The coefficient remains statistically significant, albeit slightly smaller (-25.5%), when using the SPM mix-adjusted default rate as the outcome variable (Column 3). We find quantitatively similar results when restricting the sample to high-market share regions (panel B), when considering home purchase loans or refinances separately (panel C), for loans to underserved neighborhoods (panel D), and when considering a longer sample period (panel E). Overall, we find no evidence that FinTech loans are risker than non-FinTech loans; in fact, they appear to default less often.

3.2 Loan-level analysis of FHA default rates

We complement this evidence with a loan-level analysis of data on FHA mortgages securitized into Ginnie Mae MBS. The main advantage of the Ginnie Mae data relative to the FHA NW data is that they include a rich set of loan and borrower characteristics (e.g., the borrower’s credit score and the loan-to-value ratio). This allow us to investigate whether FinTech lenders target specific borrower types based on their riskiness and whether differences in default rates can be explained by differences in observable characteristics. A disadvantage of the Ginnie Mae data is that they only include the identity of the MBS issuer, not the mortgage originator. Hence, the data do not perfectly identify which loans come from FinTech lenders. However, the issuer and originator are typically the same and a comparison to HMDA suggests mismeasurement is concentrated among small lenders.²⁶

Table 5 presents the results. Column 1 controls for origination-month fixed effects only and finds that FinTech borrowers are 1.29 percentage points less likely to default than non-FinTech borrowers, equivalent to 35% of the overall default rate of 3.65%. This result is very similar to the estimates based on FHA NW data. Column 2 adds loan purpose fixed effects. The effect declines to 0.91 percentage points, or 27%, but remains statistically significant. This result reflects the fact that FinTech lenders issue more refinance mortgages than home purchase mortgages, and that over this period, refinances tend to be less risky (especially those not involving cash-out). In Column 3, we add further loan-level controls, such as FICO and the LTV ratio. This has only a small incremental effect on the coefficient of interest, implying that FinTech lenders do not originate loans that are less risky based on these observable characteristics. Columns 4 and 5 split the sample by loan purpose; the effect is slightly larger for home purchase mortgages, but is also sizable for refinances.²⁸

3.3 Interpretation and cream skimming

Our analysis of default rates finds no evidence that FinTech lenders originate riskier mortgages. In fact, in the FHA market we find the opposite result. The difference in default rates varies across specifications but is statistically significant in almost all of them and the magnitude is economically large—default rates for FinTech-originated loans are about 25% lower in Column 3 of Table 5, which includes the largest set of controls, and ranges between 10% and 40% in the other specifications. The results are robust to using two different data sets and to different sets of loan, borrower and location controls.

Our findings speak directly against the “lax screening” hypothesis. If anything, they suggest that the automated technologies used by FinTech lenders may screen borrowers more effectively than the labor-intensive methods used by other lenders (e.g., because the automated systems directly check databases of original source documents, reducing the possibility of fraud). This reasoning has been emphasized by industry experts (e.g., Goodman 2016), and to our knowledge we provide the first systematic evidence consistent with it.

That said, FinTech lenders could achieve better loan performance through several possible channels, representing a mix of treatment and selection effects. First, FinTech lenders may be able to target or attract low-risk borrowers ex ante. Our regression results indicate this is not occurring on observable dimensions of risk (e.g., FICO, LTV), although it is possible that there is advantageous selection on unobservable dimensions. Second, as discussed above, FinTech lenders may be able to better screen loan applications to correct errors or identify deliberate fraud. Third, FinTech lenders may be able to use technology to service their loans more effectively ex post (e.g., communicate more quickly with delinquent borrowers through online channels).

Selection or “cream skimming” of low-risk borrowers by FinTech lenders could in principle have negative consequences for other borrowers, or for the government. For example, cream skimming could lead to ex ante credit rationing by weakening the credit quality of the remaining borrower pool—a mechanism explored by Mayer et al. (2013) in the context of private subprime mortgage lending. Alternatively, it could shift costs to the government if private and public lenders compete for borrowers, an argument that has been made in the context of FinTech lenders like SoFi in the student loan market.²⁹

In the current context of the mortgage market, however, it is unlikely that cream skimming by FinTech lenders has economically significant effects. The reason is that during our analysis period, nearly all risky mortgages in the United States are government insured at a preset price by the FHA or other government agencies, such as the Department of Veterans Affairs. Consequently, cream skimming by FinTech lenders is unlikely to materially affect credit access for remaining borrowers, who will still qualify for government insurance.

Even so, we estimate two specifications to investigate possible cream-skimming effects. First, we test whether a higher FinTech market share in a location leads to lower overall mortgage default risk in that location, as opposed to FinTech lenders just selecting the lowest-default borrowers from a fixed pool. We also test whether the default advantage of FinTech lenders diminishes as their market share increases. If the distribution of risky borrowers is unchanged by the presence of FinTech lenders, then as their market share increases in an area their performance advantage will diminish, as they expand to riskier borrowers.

We present the results, based on the Ginnie Mae data used earlier, in Columns 6 and 7 of Table 5. Column 6 estimates the direct effect of FinTech state-level market share on default. Although the point estimate is negative, the effect is economically small and statistically insignificant, implying that that there is no discernable effect of an increased FinTech footprint on overall defaults for local borrowers. Given the large standard errors on the estimate, it would be interesting to revisit this analysis in the future when more data are available. Column 7 adds the interaction of the FinTech lender dummy and local FinTech market share. The coefficient on the interaction term is negative, which would imply that the better default performance of FinTech mortgages is more pronounced in regions where FinTech has a larger market share, but the effect is not statistically significant. On the other hand, in panel B of Table 4, we find a smaller FinTech default advantage in high market-share regions, although again the difference is not statistically significant, and even in these locations, FinTech default rates remain lower than the market as a whole.

While somewhat mixed, we find no evidence of a robust positive relation between market share and marketwide default risk, or that the lower default rate of FinTech lenders disappears in locations where their market share is high. In sum, the findings suggest that the lower default rate associated with FinTech lending is not simply due to positive selection of low-risk borrowers. More generally, although we cannot fully disentangle the channels by which FinTech lenders achieve lower default rates, our results clearly speak against the view that automation or the lack of a local physical presence has led FinTech lenders to be less effective in borrower screening and monitoring than traditional lenders.

3.4 Are FinTech lenders charging higher interest rates?

We can also use the Ginnie Mae FHA loan-level data to test whether FinTech lenders charge higher or lower mortgage interest rates conditional on observables. Table A.3 in the Internet Appendix shows the results. We find that FinTech interest rates are 2.3 bp lower overall. Splitting the sample by loan purpose, the effect is 7.5 bp for purchase mortgages and effectively zero for refinances.³⁰ While these differences are small in magnitude, the direction of the effect is consistent with the Buchak et al. (2018) estimates for FHA loans (although they are cautious in drawing inferences from their results because their FHA data set includes fewer loan-level controls than the data used here). However, it contrasts with Buchak et al. (2018)’s finding that FinTech lenders charge higher rates for GSE mortgages. One possible explanation that could account for both sets of results, and is in line with some of Buchak et al. (2018)’s other evidence, is that lower-income borrowers, who are more likely to obtain FHA loans, are more price sensitive and less willing to pay a premium for convenience.

4. Is FinTech Lending More Elastic?

Next we study whether FinTech lenders are better able to accommodate shocks to mortgage demand. Mortgage application volumes in the United States fluctuate enormously over time, primarily due to movements in interest rates that can lead to “refinancing waves.” There is also substantial cross-sectional variation in demand for new mortgages, for example, because of differential housing market trends.

Managing volatility in mortgage applications is a key challenge for lenders. If a lender receives more applications than their underwriting process can accommodate, their processing times increase and they risk losing money (and future business) due to loans not closing in a timely manner. Figure 2, which is similar to evidence in Fuster et al. (2017b), illustrates two main points. First, as shown in panel (a), there is large variation in the level of monthly applications, with the peak level being almost 3 times as high as the trough. Application volume closely comoves with borrowers’ average incentive to refinance, here proxied by the difference between the average coupon rate on outstanding mortgages and the 10-year Treasury yield. Second, panel (b) shows that fluctuations in median processing times are sizable (from a low of 37 days to a high of 51 days), and that processing times are strongly positively correlated with total mortgage applications.³¹

By automating, centralizing and standardizing much of the underwriting process, FinTech lenders may conceivably increase the short-run elasticity of lending supply in response to demand shocks. However, testing whether capacity constraints are less binding for FinTech lenders presents a clear empirical challenge: the volume of applications a lender receives is endogenous. For example, lenders may solicit applications when processing constraints are slack and discourage applications when processing times are expected to be long. Both behaviors would attenuate the relationship between applications and processing time, and obfuscate differences across lenders.

4.1 Demand shocks and processing time

Table 6 presents the results. The first two columns consider all originated loans; Column 1 controls only for lender dummies, and Column 2 includes additional controls for loan and borrower characteristics, location, and month. We find that FinTech lenders are about half as sensitive to aggregate mortgage application volumes as other lenders. Quantitatively, a 10% rise in overall application volume increases processing time by 1.3 days for non-FinTech lenders but only 0.7 days for FinTech firms (based on Column 2). Column 3 restricts the sample to refinances, the market where FinTech lenders specialize and where interest rates matter most for demand. We find that processing times for refinances are more sensitive to aggregate volumes, but again FinTech lenders are only half as sensitive. Column 4 considers all applications, including denied and withdrawn applications; again, the results are similar. All results are statistically significant at the 1% level. Columns 5–7 repeat the prior three specifications but restrict the sample to nonbanks. The degree to which FinTech lenders are less sensitive to aggregate applications is not as large in this sample but the magnitudes are still economically meaningful. FinTech lenders are 20%–40% less sensitive to aggregate volumes relative to nonbanks; the key coefficients are statistically significant at the standard 5% and 10% levels in Columns 6 and 7, respectively, though not in Column 5 (⁠|$p$|-value = |$.19$|⁠).³²

Figure 3 visually illustrates the differential sensitivity of FinTech lender processing times to application volume. This binned scatter plot confirms that FinTech lenders have shorter processing times on average, as already shown in Section 2. More importantly, processing time for FinTech lenders is also less sensitive to demand for new mortgages compared to banks and (to a lesser extent) other nonbank lenders. This lower sensitivity is particularly apparent at the highest levels of application volume (when aggregate application volume exceeds 1.2 million mortgages per month).

4.2 Alternative demand shocks and processing time

We repeat the analysis using the weighted average coupon on the universe of fixed-rate MBS less the 10-year Treasury yield (“Refi Incentive”) as our measure of mortgage demand, instead of log application volume.³³ Our findings, presented in panel A of Table A.4 in the Internet Appendix, are similar to those discussed above. A higher refinancing incentive is significantly correlated with longer processing times across specifications, but processing times for FinTech lenders are significantly less sensitive, if anything by a larger proportion than in our main results. The consistency with our earlier findings is sensible given that we show in Figure 2 that refinancing incentives are the key determinant of mortgage application volume. The specification does, however, address any concerns that our earlier results are affected by idiosyncratic shocks to individual large lenders that are large enough to influence aggregate applications.

As a second alternative approach, we also construct a “Bartik-style” index of exposure to local fluctuations in mortgage application volume based on the geography of lender activity. The index is calculated as the log of the weighted sum of county-level mortgage applications in month |$t$|⁠, where the weights are the lender’s average market shares in each county measured over the entire sample period. The identification assumption is that application volumes in a geographic area are exogenous to any given market participant’s share. Panel B of Table A.4 in the Internet Appendix presents the results. Processing times are positively correlated with the proxy, although once again, less so for FinTech lenders when we compare to all lenders. Comparing to nonbanks only, effects are not quite significant for all originated loans and all applications (Columns 4 and 6), although coefficients are negative and of substantial magnitude, consistent with our broader findings.

In sum, results based on two alternative measures of loan demand indicate that FinTech lenders are less sensitive to exogenous demand shocks than other lenders, particularly for refinances, supporting our main findings in Table 6.

4.3 Demand shocks and application denial rates

A possible concern is that our results may reflect credit rationing. If FinTech lenders avoid capacity constraints by becoming more selective and rationing credit when total mortgage demand rises, their processing times may seem less sensitive to demand even if they are not actually more elastic. We test this hypothesis by examining whether denial rates for FinTech lenders are differentially sensitive to aggregate application volume. The regression specification is identical to Equation (3), except that the left-hand-side variable is an indicator variable equal to 1 if a loan application was denied and zero otherwise. The sample includes all applications that were either approved or denied.

Table 7 presents the results. We find that, in fact, FinTech lenders differentially reduce denial rates by 1.1% percentage points for each 10% increase in application volume (Column 1). The effect is similar when focusing on refinances (Column 2), restricting the sample to nonbanks (Column 3), and focusing on refinance mortgages among nonbanks (Column 4).³⁴ These results are inconsistent with credit rationing and, if anything, instead provide further evidence that FinTech lenders’ credit supply is more elastic than those of other lenders.³⁵

4.4 Demand shocks and origination volumes

We find no meaningful difference in origination sensitivity for FinTech lenders. As shown in Table 8, FinTech origination volume appears equally sensitive to changes in aggregate application volumes as those of all other lenders (Columns 1 and 2) and nonbanks (Columns 3 and 4). Hence, similar to our results on denials, we find no evidence that the lower sensitivity of FinTech lender processing times comes at the expense of lower originations due to credit rationing; conversely, though, we do not see an obvious increase in origination growth for FinTech lenders when application volume rises. Overall, we are cautious about drawing strong conclusions from this analysis as it is quite difficult to establish lender-type specific effects given the strong and nonlinear upward trend in the FinTech lender market share during this period.

5. FinTech and Mortgage Refinancing

This section examines whether the presence of FinTech lenders affects mortgage refinancing behavior by borrowers. Prior work has shown that many borrowers do not refinance their fixed-rate mortgages optimally; they commit errors either by failing to refinance when it is in their financial interest to do so or by refinancing even though the costs of doing so exceed the benefits (e.g., Campbell 2006; Agarwal et al. 2015; Andersen et al. 2015; Keys et al. 2016). In addition to behavioral factors, intermediation frictions in the mortgage market also contribute to inefficient refinancing patterns (e.g., Agarwal et al. 2017; Bond et al. 2017; Scharfstein and Sunderam 2016). These frictions weaken the “refinancing channel” of monetary policy (e.g., Beraja et al. 2019; Di Maggio et al. 2016; Wong 2018). Examining the effect of FinTech on refinancing is thus important, since this is one channel through which technological progress in the mortgage industry may have real effects on the economy.

Industry reports and academic research indicate that mortgage-backed securities backed by FinTech loans do exhibit faster prepayment speeds than pools from other lenders, consistent with an effect of FinTech on the speed of refinancing (e.g., Goldman Sachs Research 2016; Buchak et al. 2018). However, it is unclear whether this fact reflects faster-prepaying borrowers selecting into mortgages from FinTech lenders, or whether FinTech lending directly affects the likelihood of refinancing, thereby potentially affecting aggregate refinancing behavior. If FinTech mortgage lending does affect the market-wide propensity to refinance, an important follow-up question is whether this is due to a reduction in errors of omission (meaning that more borrowers who should refinance do so), or instead reflects an increase in errors of commission (more borrowers refinance even when they should not). Below, we assess this based on the optimal refinancing decision rule of Agarwal, Driscoll, and Laibson (2013).

To measure refinancing behavior, we use data from Equifax’s Credit Risk Insight Servicing McDash (CRISM) data set, which merges McDash mortgage servicing records from Black Knight with credit bureau data from Equifax. The sample period is January 2010 through June 2016. The CRISM data set provides information on loan and borrower characteristics, such as FICO score, CLTV, interest rate, and loan term, and features borrower identifiers that allow us to track a borrower across loans and thereby identify mortgage refinancing.³⁶ We focus on the 500 largest counties by loan volume. Details on the sample construction and how we measure refinancing are provided in Section E of the Internet Appendix, where we also confirm that the average refinance propensity we measure in our data is closely aligned with variation over time in the volume of refinancing loans in HMDA.

5.1 Refinancing propensity

Table 9 shows a positive and strongly statistically significant association between refinancing propensities and FinTech market share. The estimate of 0.689 in Column 2 implies that an 8-percentage-point increase in FinTech market share (corresponding to moving from the 10th to 90th percentile of county averages in 2015) is associated with a 0.055-percentage-point increase in the refinancing propensity, about a 10% increase relative to the average monthly refinancing propensity over this time period of 0.54%. Thus, the magnitude of the effect is economically meaningful.³⁸

Figure 4 further illustrates the effect. Here, we sort the counties into fixed terciles based on their FinTech market share in the middle of our sample period (between mid-2012 to mid-2013). We then plot the average refinance propensity in each tercile over time. We see that in 2010, before the growth in FinTech lending, the tercile of counties where FinTech lenders subsequently gained the most market share has the slowest refinancing speeds. The refinancing propensity across the three terciles converge over time, however, coincident with the growth in FinTech lending. This suggests that FinTech mortgage lenders have helped “slow” refinancing counties to “catch up.”

In summary, our results show that the faster prepayment speeds on FinTech mortgages are also reflected in overall market-wide local refinancing propensities, rather than just being due to within-region selection of “fast” borrowers into FinTech loans. As a caveat on our results, we note that, although our regressions condition on county and time fixed effects, the differential growth in FinTech market share across counties may not be exogenous with respect to refinancing propensities. For instance, it is possible that FinTech lenders predicted correctly which geographic regions still had the most potential for higher refinancing volumes, and advertised and expanded most intensively there. At the least, however, our results suggest that a higher presence of FinTech lending leads to faster mortgage refinancing, perhaps by reducing the transaction or time costs of refinancing.³⁹

5.2 Refinancing optimality

Next, we examine whether the increase in refinancing speeds documented above is associated with more optimal refinancing decisions. In other words, is it driven by additional refinancing by borrowers with a high current mortgage rate relative to the available rate on new loans, who realize large savings in interest costs by refinancing (fewer errors of omission)? Or is it largely due to borrower who refinance even though the interest savings are small and outweighed by the costs of refinancing (more errors of commission)?

To determine the breakeven interest rate differential beyond which a borrower should refinance, we use the “square-root rule” of Agarwal et al. (2013), henceforth ADL.⁴⁰ For this analysis, we restrict the sample to 30-year FRMs, since the ADL optimality calculation was derived for FRMs and does not apply to adjustable-rate loans. We note that the ADL calculation embeds a number of assumptions about the costs and benefits of refinancing; among these, it does not account for other common refinancing motives, such as cashing out home equity, shortening the term of the loan, or refinancing from a mortgage type that requires mortgage insurance (such as FHA loans) into one that does not. Although the ADL benchmark is very useful, it is ultimately a simplification.

Based on the ADL rule, we find, similar to Keys et al. (2016), that at certain points over our sample period, a lot of borrowers should refinance. For instance, in late 2012, about 60% of all 30-year FRM borrowers in our sample should have refinanced according to the ADL benchmark, yet only a significantly smaller percentage did so. However, similar to Agarwal et al. (2015), we find that among refinances that do occur, more than half are executed at a rate differential that is too small when assessed against the ADL rule. Thus, at least when compared to this benchmark, there is substantial scope for enhanced refinancing efficiency.

In Table 10, we estimate loan-level regressions similar to the county-level specifications from above, but now separating outstanding mortgages into different bins depending on how “in the money” the refinancing option is. Specifically, the “refi incentive” shown in the table is equal to the difference between the outstanding mortgage rate and current market rate, minus the optimal refinancing differential according to ADL.⁴¹ For instance, if a borrower is currently paying 6.5%, the market interest rate is at 4.5%, and the optimal refinancing differential is 1.5% based on the ADL formula, the borrower would have a 0.5% positive refinancing incentive. If the market rate increases to 5.5%, the refinancing incentive would become negative. Even though a refinancing in this situation would still reduce the borrower’s monthly payments, the savings would not be sufficient to outweigh the transaction costs of refinancing and the loss of option value.

Column 1 shows that for borrowers where the refinancing option is more than 1 percentage point out of the money, a higher local FinTech share has a marginally negative effect on the likelihood of refinancing. The effect of a higher FinTech share then becomes positive, and is highest for borrowers that are within 50 bps of their optimal refinancing differential (Columns 3 and 4). The effect size then decreases again somewhat for borrowers that have a large refinancing incentive according to ADL. These borrowers in many cases have suboptimally failed to refinance for a long period of time (given that the refinancing incentive is driven by market interest rates, which drift only slowly through time). In industry jargon, these borrowers are sometimes called “woodheads” (Deng and Quigley 2012).

The results imply that an increased presence of FinTech lenders is most strongly associated with higher refinancing when the borrower’s incentive to refinance is either “at the money” or just “in the money,” meaning refinancing is (at least close to) optimal. It does not appear that FinTech lenders induce an increase in grossly inefficient refinancings (if anything, the reverse is true). However, they also do not spur a large increase in refinancing for the borrowers who would gain the most from doing so, and may induce additional suboptimal refinancing among a subset of borrowers.

Table A.5 of the Internet Appendix presents additional evidence to examine refinances from 30-year FRMs to new 30-year FRMs. Consistent with Table 10, we find that a higher local FinTech share is associated with a larger fraction of refinances that are classified as optimal by the Agarwal et al. rule. A higher local FinTech share is also associated with larger average decreases in the borrower’s interest rate at the point of refinancing, and a higher fraction of cash-out refinances.

In sum, the results suggest that a higher share of FinTech lending is associated not just with faster refinancing, but also more optimal refinancing decisions, at least on average. This effect, however, appears somewhat weaker for the borrowers that would benefit most from refinancing. Such borrowers may be less financially literate; we show in the next section that proxies for financial literacy are positively correlated with FinTech mortgage borrowing. In some cases seemingly “woodhead” borrowers may face other obstacles that prevent refinancing (e.g., a significant drop in income since the original mortgage was obtained).

We note that, to the extent that future growth of FinTech in the mortgage market continues to lead to faster refinancing, it is in fact possible that borrowers who themselves are limited in their sophistication or are otherwise unable to refinance may be worse off. In equilibrium faster prepayment speeds will be priced in MBS valuations, which could feed through to higher mortgage interest rates.

6. Who Borrows from FinTech Lenders?

The market share of FinTech lenders varies significantly by geography and across segments of the mortgage market.⁴² In this section we estimate a simple model of the likelihood of borrowing from a FinTech mortgage lender as a function of loan, borrower and location characteristics. We then compare the cross-sectional patterns in the data to hypotheses about the drivers of the growth in FinTech mortgage borrowing.

We estimate this model separately for purchase mortgages and refinances, because the determinants of demand may be quite different between the two, and because the market share of FinTech lenders is significantly higher for refinances (Table 2). Since differences in borrower characteristics between FinTech lenders and banks may be driven by regulatory factors, we present each specification both including and excluding mortgages from banks. In the specifications excluding banks, FinTech lenders are compared to other nonbanks, who are regulated similarly and have the same funding model.

6.1 Results

Table 11 presents the estimates. Each continuous right-hand-side variable is normalized to have a standard deviation of one, so that magnitudes can be compared across variables.⁴³ Below, we discuss the cross-sectional patterns relative to the predictions of four sets of potential drivers of the growth in FinTech mortgage lending: (1) access to finance, (2) technology adoption and financial literacy, (3) Internet access, and (4) local mortgage and housing market conditions.

6.1.1 Access to finance

We find no strong evidence that FinTech lenders disproportionately cater to financially constrained borrowers (e.g., borrowers with low incomes or poor credit histories), particularly compared to other nonbanks. Although the FinTech market share is higher in census tracts where mortgage borrowers have lower average credit scores, the results for income and the share of FHA/VA-insured mortgages are mixed depending on the type of loan and comparison group.⁴⁴ As shown in Section 3, FinTech mortgages also have comparatively lower default rates on FHA loans, suggesting they are not targeting the riskiest borrowers. Nonbank lenders overall, including FinTech lenders, originate a significantly higher share of FHA and VA loans than banks, however (see Table 2 or the Internet Appendix). Buchak et al. (2018) attribute the low levels of bank FHA lending to regulatory and legal factors. FinTech lenders attract a higher share of female borrowers, although the share of black or hispanic borrowers is lower in most specifications.⁴⁵

Also related to access to finance, we test whether FinTech borrowing is higher in census tracts with few physical bank branches. We measure branch access as the number of bank branches within a 25 mile radius of the geographic midpoint of the census tract, based on FDIC Summary of Deposits data. Even though the FinTech business model is focused on online applications, we find that the share of FinTech borrowing is increasing in bank branch density.

6.1.2 Technology adoption and financial literacy

Early technology adoption is often concentrated in urban areas as well as among younger and more educated consumers. Indeed, we find that the FinTech market share is higher in more urban neighborhoods, measured by population density, although only for purchase mortgages (for refinances, the coefficient flips sign across specifications).

Examining education and age directly, we find that the FinTech market share is increasing in the fraction of adults with at least a bachelor’s degree. Interestingly, however, the share of FinTech borrowing is increasing with average mortgage borrower age. Although this may seem counterintuitive, we have been told by industry practitioners that first-time mortgage borrowers often prefer to interact face-to-face with a mortgage broker or loan officer, rather than applying online, because they are less familiar with the steps involved; in other words they are less financially experienced and literate (consistent with Agarwal et al. 2009, who find that financial literacy increases with age up to individuals’ mid-50’s).

6.1.3 Internet access

As online services become more ubiquitous, the “digital divide” becomes a growing concern, meaning that inequality in access to Internet services may be exacerbating income and wealth inequality. We examine whether the availability of high-speed Internet is a constraint on FinTech mortgage borrowing (where applications are generally completed online), using two data sources: first, the fraction of households with high-speed Internet access from the Census Bureau American Community Survey (available from 2013 by county); second, census-block data from the FCC and NTIA for the ten largest states by population on the fraction of households with the option to connect to fiber or cable Internet, which we aggregate by census tract.

Empirically, these two variables have opposite signs, and the coefficients are generally small in magnitude. We conclude that lack of access to adequate Internet does not appear to be a significant constraint on the diffusion of technology-based mortgage lending. This interpretation is also consistent with more detailed analysis described below about the staggered rollout of Google Fiber in one local market.

6.1.4 Local mortgage and housing market conditions

As FinTech lending becomes more widely available, it may be particularly beneficial in areas with long processing times. Indeed, we find a higher share of FinTech mortgage borrowing in census tracts where mortgage processing times were slow ex ante, measured in 2010 prior to the growth in FinTech lending.⁴⁶ This result suggests that borrowers have turned to FinTech lenders in part to alleviate origination bottlenecks associated with “traditional” mortgage lenders.⁴⁷

We also examine whether FinTech mortgage borrowing is more prevalent in “hot” real estate markets where prices are rising rapidly and quick closing may be more important. Anecdotal evidence suggests that borrowers may be particularly attracted to the convenience and fast processing speeds of FinTech lenders in such markets.⁴⁸ We find no empirical support for this hypothesis; in fact, home price growth is negatively correlated with the market share of FinTech lending for purchase mortgages (which are the relevant group to consider for this hypothesis).

Finally, we test whether the FinTech share is lower in neighborhoods with high average home prices (measured in 2010). This hypothesis is motivated by the fact that FinTech lenders rely on securitization for funding mortgages; as a result these lenders originate few jumbo loans, which are difficult to securitize. This in turn means that FinTech lenders may advertise less in high-home-price markets where jumbo mortgages predominate. There also may be less social learning about the FinTech mortgage lending model in such markets. We do indeed find that the likelihood of borrowing from a FinTech lender is lower in high home price areas, conditional on loan size and other observables.

6.2 Interpretation

We interpret our evidence as supporting the view that FinTech mortgage borrowers are attracted to the faster processing times and greater convenience involved with online applications and partial automation of mortgage underwriting. This is consistent with the faster growth of FinTech in census tracts with previously long mortgage processing cycle times and the higher incomes and education of FinTech borrowers. It is also consistent with the high share of refinances for FinTech lenders.⁴⁹ We find no empirical support for the hypothesis that FinTech lenders have grown by disproportionately targeting risky, marginal borrowers. Despite the emphasis of the FinTech lending model on online applications and interactions, we also find no evidence that younger borrowers or borrowers located in census tracts with better Internet access are more likely to borrow from FinTech lenders.

We emphasize that we do not attach a strong causal interpretation to our results, given the reduced-form nature of our analysis. Yet, we believe that the empirical relationships documented here are a useful benchmark for further analysis.⁵⁰

6.3 Evidence from Google Fiber rollout

In addition to our reduced-form analysis, we also conduct an in-depth empirical analysis of the potential causal effect of improved Internet access on FinTech lending. We exploit the staggered entry of a new high-speed Internet provider in a single market, namely the entry of Google Fiber in Kansas City starting in late 2012. Google Fiber is a large-scale initiative by Google to establish a new Internet service provider. Kansas City was the first metro area that Google Fiber entered.⁵¹ A key factor behind the selection of Kansas City was that households had poor access to high-speed Internet prior to the entry of Google Fiber. There was subsequently a rapid expansion in high-speed Internet access from 2012 to 2015 as Google Fiber became widely available and as competitors improved their own services.

Using HMDA data, we analyze how the market share of FinTech lenders across different neighborhoods in the Kansas City MSA evolved over this period, exploiting the staggered rollout of Google Fiber across census tracts and controlling for time and census-tract fixed effects. Internet Appendix H presents the results. Our main finding is that the discrete improvements in Internet access generated by the entry of Google Fiber did not induce a higher share of FinTech borrowing. The results are often not statistically significant, and in the cases where they are significant they have the opposite sign to that predicted, due to a migration in mortgage lending from nonbanks to banks.

Consistent with our earlier evidence, these results indicate that adequate Internet access is unlikely to be a significant constraint on the diffusion of online mortgage lending, mitigating concerns about the “digital divide” in this setting.

7. Conclusion

This paper presents new evidence on how technology is reshaping the U.S. mortgage market by studying the vanguard of technology-based lenders. We find that FinTech lenders offer a faster origination process that is less sensitive to capacity constraints. FinTech lending is also associated with an increase in the propensity to refinance, especially among borrowers that are likely to benefit from it. We find no evidence that FinTech lenders originate riskier loans or screen borrowers less effectively; in fact, FHA mortgages from FinTech lenders have lower default rates than do similar loans from other originators.

Going forward, we expect that banks and other lenders will seek to replicate the “FinTech model” characterized by an online application process and centralized, semiautomated underwriting and processing. Indeed, online mortgage lending has continued to expand rapidly since the end of our sample period in 2016. In the long run, it is unclear whether technology-based lending will remain dominated by nonbanks or whether commercial banks will be able to use technology to regain market share in the mortgage market. Banks are likely to be less nimble than specialist nonbank mortgage lenders, because they are more highly regulated and organizationally complex, and often have complex legacy processes and information systems. On the other hand, banks also have significant competitive advantages, including access to low-cost deposit funding, cross-selling opportunities, and the availability of branches for those borrowers preferring a mix of online and face-to-face interactions.

From an industrial organization perspective, the shift to online lending raises particular challenges for smaller lenders, given the economies of scale associated with developing and maintaining an online lending platform. The end result could be a more concentrated mortgage market dominated by those firms that can afford to innovate and invest in technology.

From a consumer perspective, we believe our results shed light on how mortgage credit supply is likely to evolve in the future. Specifically, our results suggest that technological diffusion will speed up the mortgage origination process and reduce the influence of capacity constraints during periods of peak demand, thereby strengthening the transmission of monetary policy to households via the mortgage market. Our findings also imply that technological diffusion may lead to more efficient refinancing decisions, which could have significant benefits to U.S. households.

Our results must be considered in the prevailing institutional context of the U.S. mortgage market. Specifically, at the time of our study FinTech lenders are nonbanks that securitize their mortgages and do not take deposits. It remains to be seen whether we find the benefits of FinTech lending prevail as the model spreads to deposit-taking banks and their borrowers. Changes in regulation or the housing finance system may significantly affect FinTech lending going forward. Furthermore, the benefits we document stem from innovations that rely on hard information; as these innovations spread, they may affect access to credit for borrowers with applications that involve soft information or that require face-to-face communication with a loan officer. We leave these issues for future research.

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.

Appendix

Footnotes

References