WORKING PAPER SERIES

Is There Crowd Out in Mortgage Refinance?

Nick Frazier 

Federal Deposit Insurance Corporation 

Ryan Goodstein 

Federal Deposit Insurance Corporation 

January 2023 

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The Center for Financial Research (CFR) Working Paper Series allows CFR staff and their coauthors to circulate 

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Working Paper” and should note that findings and conclusions in working papers may be preliminary and subject to 

revision. 
Is There Crowd Out in Mortgage Refinance?*

Nick Frazier†

Ryan Goodstein‡

January 2023

ABSTRACT: We examine whether supply-side capacity constraints contribute to the welldocumented “failure to refinance” among certain borrowers who would benefit financially from 

doing so. We find that, conditional on the potential financial benefits of refinancing, “marginal” 

borrowers (those with low loan balances, low incomes, or low credit scores) are substantially less 

likely to prepay their mortgage during refinance booms when markets are operating at or near full 

capacity. In contrast, borrowers with high loan balances, high incomes, or high credit scores prepay 

at higher rates during these periods. These patterns hold after controlling for a rich set of 

observable characteristics and among borrowers that are likely able to qualify for a conventional 

refinance loan. We provide suggestive evidence that marginal borrowers are crowded out from 

supply-constrained markets before making a formal application for a refinance loan. Overall, our 

results indicate that in addition to demand-side explanations for differences in refinancing activity, 

supply-side factors also play an important role.

 

* The views and opinions expressed in this paper reflect those of the authors and do not necessarily reflect those of 

the FDIC or the United States. We gratefully acknowledge helpful discussions with Lauren Lambie-Hanson, David 

Low, and Ioannis Spyridopolous as well as audiences at the AREUEA-ASSA 2022 conference, the Consumer 

Finance Round Robin, the Consumer Financial Protection Bureau 2022 Research Conference, the DC Juniors 

conference, and the FDIC for their comments. Any errors are our own.

† Senior Financial Economist, Center for Financial Research, Division of Insurance and Research, Federal Deposit 

Insurance Corporation, Washington, DC. Phone: (202) 898-6742. Email: nfrazier@fdic.gov. ‡ Senior Economist, Division of Depositor and Consumer Protection, Federal Deposit Insurance Corporation, 

Washington, DC. Phone: (202) 898-6863. Email: rgoodstein@fdic.gov. 
1 

1. Introduction

The U.S. has experienced several booms in refinance activity over the past decade. As shown in 

Figure 1, these booms are largely attributable to movements in interest rates over time. When the 

market rate falls below levels seen in preceding years (e.g., in March of 2020), new loan 

applications spike as many borrowers respond to their increased financial incentive to refinance. 

While refinance booms are characterized by widespread increases in refinancing, the relative 

magnitudes of these increases differ across borrower groups. For example, Figure 2 shows that 

refinance booms coincide with sharp declines in the share of refinance loans originated to

borrowers with lower loan amounts, lower income, or lower credit scores. This suggests there are 

systematic differences across borrowers in their willingness or ability to refinance during booms, 

and that some miss out on the financial gains from refinancing when rates are low. 

Understanding what drives these disparities is important for a broad set of policy concerns. 

For consumers, refinancing a mortgage when it is optimal to do so can result in thousands of 

dollars in savings on interest expenses over the life of the loan (e.g., Keys, Pope, and Pope 2016; 

Agarwal, Chomsisengphet, Kiefer, Kiefer, and Medina 2021). Household refinancing activity 

also has implications for the real economy. Several recent studies have highlighted how frictions 

in mortgage refinancing affect the distributional impact and effectiveness of monetary policy.1

Policymakers are actively considering ways to make refinancing easier for all homeowners who 

might benefit from doing so.2 

What can explain differences in borrowers’ refinancing activity during booms? It is well 

known that many borrowers fail to refinance when they have a financial incentive to do so (e.g., 

Keys, Pope, and Pope 2016; Gerardi et al. 2020), and demand-side factors are an important 

reason why. Prior literature (detailed in the next section) has linked such differences in 

borrowers’ propensity to refinance to differences in their financial sophistication, trust in 

financial institutions, exposure to lenders’ advertising or news media, and their social networks. 

 

1 See, for example, Di Maggio, Kermani, Keys, Piskorski, Ramcharan, Seru, and Yao (2017); Beraja, Fuster, Hurst, 

and Vavra (2018); Amromin, Bhutta, and Keys (2020); Gerardi, Willen, and Zhang (2020); Berger, Milbradt, 

Tourre, and Vavra (2021). Much of this literature highlights the inequitable distribution of efforts to ease credit and 

stimulate consumption. There are operational concerns as well if, for example, the savings from refinancing 

predominantly accrue to households with a relatively low marginal propensity to consume. 2 For example, the Consumer Financial Protection Bureau issued a Request for Information in September 2022 

seeking public comment on (among other things) ways to spur new mortgage products that facilitate refinancing, 

especially for homeowners with smaller loan balances. See consumerfinance.gov/about-us/newsroom/cfpblaunches-effort-to-spur-new-opportunities-for-homeowners-in-the-mortgage-market/.for details.
2

This paper explores a new explanation for differential patterns in refinance activity during 

booms: supply-side capacity constraints. When swamped with demand for refinance loans, a 

capacity-constrained lender may prefer to “...prioritize the processing of easier-to-complete or 

more profitable loan applications” (Duke 2013). Following this intuition, we hypothesize that 

during booms supply-constrained lenders engage in credit rationing whereby some borrowers are 

“crowded out” from the refinance loan market and unable to refinance their loan at the prevailing 

low rates. In contrast, supply-side capacity constraints should have less pronounced (if any) 

effects on refinance activity of borrowers that lenders perceive as easy-to-underwrite or more 

profitable, as lenders prioritize loan applications from such borrowers. 

To evaluate these hypotheses, we analyze data from two primary sources. The first is loanlevel administrative data from the National Mortgage Database (NMDB®). The NMDB is a 

nationally representative sample of U.S. residential mortgages, with detailed data on loan terms, 

borrower and property characteristics, and payment history. Second, we use data collected under 

the Home Mortgage Disclosure Act (HMDA) to construct county-quarter level indicators that 

proxy for whether capacity constraints are binding. These measures are based on within-county 

variation in the number of days it takes lenders to process refinance loan applications (as in, e.g., 

Fuster, Goodman, Lucca, Madar, Molloy, and Willen 2013) and reflect periods where the surge 

in demand for refinancing exceeds lenders’ ability to adjust supply.

We specify linear probability models of loan prepayment in which the key independent 

variable is the indicator for whether the borrower is located in a county with constrained supply 

in a given quarter.3 To better isolate the effects associated with refinancing (as opposed to other 

forms of prepayment), we allow the effect of being in a capacity constrained market to vary with 

the borrower’s financial incentive to refinance. Reflecting the intuition outlined above, we also 

let the effects of capacity constraints vary across borrower characteristics. We categorize 

borrowers using three alternative measures: unpaid principal balance (UPB), income, and credit 

score. The first directly relates to how profitable a new refinance loan would be to originate; the 

second and third proxy for lenders’ perceptions of whether a borrower is relatively difficult to 

underwrite or the expected profit from origination the loan. We refer to borrowers in the lowest 

 3 NMDB does not currently distinguish refinancing from other reasons for prepayment such as home sales. We show 

that the variation in prepayments observed in our data is almost entirely attributable to refinances.
3

UPB, income, and credit score groups as “marginal” borrowers; lenders may de-prioritize such 

borrowers when their resources are constrained.

A challenge for identification in our setting is that capacity constraints typically bind during 

refinance booms that coincide with recessionary periods when interest rates have dropped. 

Marginal borrowers may be disproportionately more likely to experience shocks to employment 

or income during recessions, and thus may find it harder to qualify for a new refinance loan. To 

address this issue, we focus our analysis on borrowers that are very likely able to qualify for a 

new loan – those that currently have a conventional loan and, since origination of that loan, have 

not missed a mortgage payment or had their credit score fall below 620. These sample selection 

criteria limit the scope for our estimated effects of supply-side capacity constraints on 

refinancing to suffer from downward bias due to unobserved differences in borrowers’ ability to 

qualify for a new mortgage.

A second challenge is that demand for new refinance loans increases during booms, even 

after conditioning on the potential financial gains. Borrowers are more likely to be “awake” to 

the possibility of refinancing during booms, based on a model of refinancing behavior that 

incorporates psychological and information-gathering costs and allows these costs to vary across 

borrowers and over time (Andersen, Campbell, Nielson, and Ramadorai 2020).

4 Because we do 

not observe such shifts in borrower demand, our estimates reflect the net impact on prepayment 

of two opposing factors: (1) the negative effect of supply constraints (the focus of this paper), 

and (2) the positive effect of concurrent increases in demand.

5

Our empirical results indicate that when borrowers have a financial incentive to refinance, 

the overall conditional quarterly likelihood of prepayment is about 0.7 percentage points higher 

during refinance booms when capacity constraints bind; this is about 15 percent of the mean 

prepayment rate in our sample. This positive effect is consistent with increased demand for 

refinancing during booms outweighing any dampening effect that supply constraints have on 

overall prepayment activity.

 4 Psychological and information-gathering costs may differ over time for various reasons. For example, media 

coverage of refinancing activity increases during booms, and Hu, Li, Ngo, and Sosyura (2021) show that exposure to 

such media coverage can affect refinancing behavior, particularly among lower-income and minority borrowers. 5 There may be heterogeneity across borrowers in the extent to which demand for refinances increases during booms

but, absent supply constraints, a fall in demand for any group is very unlikely. One potential concern is that cash-out 

refinancings (disproportionately taken out by marginal borrowers) may decline during booms. We show that 

changes in cash-out refinance activity are not likely to be driving our results; see the robustness section for details.
4

In contrast, we find that among marginal borrowers that lenders may prefer to de-prioritize, 

conditional prepayment rates are substantially lower when supply of credit is constrained. For 

example, among borrowers with low UPB and a financial incentive to refinance, the quarterly 

probability of prepayment is about 0.8 percentage points (or 18 percent) lower when capacity 

constraints bind. Results are qualitatively similar when categorizing borrowers by income at 

origination or by credit score. Our estimates imply the cumulative effects of capacity constraints 

are economically significant. Had capacity constraints not been binding during the most recent 

refinance boom, cumulative prepayment rates over the period 2019Q3 through 2021Q2 would 

have been roughly 10 to 15 percent higher for the most marginal borrower groups. We interpret 

these estimates as the lower bound of the “true” effect of capacity constraints, as we cannot 

account for increased demand that might otherwise increase the likelihood of prepayment.

In supplemental analyses, we examine the ways lenders may ration credit to borrowers they 

may perceive as less profitable or more difficult to underwrite. Based on analyses of HMDA 

data, we find little evidence that supply constrained lenders throttle demand by raising prices on 

loans to marginal borrowers. We find some evidence that constrained lenders de-prioritize the 

processing of loan applications from such borrowers (as reflected in higher rates of applications 

being closed or denied for incompleteness) but these effects are small in magnitude.

Instead, we find that when capacity constraints are binding, lower UPB and lower income 

borrowers in our NMDB sample are substantially less likely to successfully contact and obtain 

pricing from a mortgage lender, as proxied by the incidence of inquiries for new mortgage 

credit.6

 This suggests that much of the inhibiting effect of capacity constraints on marginal 

borrowers’ propensity to refinance occurs before a formal loan application is filed, and is 

consistent with Agarwal et al. (2021), who attribute inequality in refinancing activity to lower 

income borrowers being underrepresented in the pool of applications received during the postCOVID boom.

Taken together, our estimates indicate that in addition to demand-side factors, supply-side 

capacity constraints play an important role in explaining why some borrowers fail to refinance

during booms. That supply-side crowd out occurs primarily because marginal borrowers find it 

 6 We view a mortgage credit inquiry as a reliable indication that the borrower has obtained pricing from a lender 

because credit score is central to loan pricing and a common initial step in a potential refinancing is for the loan 

officer to request a hard credit check on the borrower(s) expected to submit an application for a new loan.
5

more difficult to connect with and obtain pricing from constrained lenders has implications for 

policy, which we discuss in the concluding section.

2. Background

In this section we review the relevant literature on mortgage prepayment and refinancing 

decisions. We then discuss existing evidence that the supply of mortgage credit is constrained 

during refinance booms, and building on this literature, describe the methodology we use to 

identify markets where such capacity constraints bind. 

A. Relevant Literature 

The conceptual framework for this analysis is the option based model of mortgage 

terminations, standard in the economic literature (e.g., Hendershott and Van Order 1987; Kau,

Keenan, Muller, and Epperson 1995). The model implies that a mortgage holder should exercise 

her option to refinance when the benefit of doing so exceeds the costs. The benefit from 

refinancing is primarily the savings on future interest payments achieved by lowering the 

contract rate. The present value of savings is a function of the borrower’s current rate relative to 

the market rate and also the expected life of the new mortgage. The latter factor may depend on, 

among other things, future interest rate movements and the borrower’s expected tenure in the 

home. Costs of refinancing include upfront fees charged at origination (e.g., discount points paid, 

appraisal fees, title fees, other lender charges) and time costs (e.g., searching for a lender and 

providing the necessary documentation).

It is well-known that many borrowers fail to exercise their refinance option when it is in their 

financial interest to do so (e.g., Keys et al. 2016). This is especially true among certain segments 

of the population including lower-income and minority households (Firestone, Van Order, and 

Zorn 2007; Goodstein 2013; Gerardi et al. 2020; Brevoort 2022).

Differences in borrowers’ propensity to refinance have been linked to a variety of demandside factors including: financial sophistication (e.g. Agarwal, Rosen, and Yao 2016; Bajo and 

Barbi 2018); consumer inattention (Byrne, Devine, King, McCarthy, and Palmer 2022); trust in 

financial institutions (Johnson, Meier, and Toubia 2019); exposure to lenders’ advertising 

(Grundl and Kim 2019); news media stories about refinancing (Hu et al. 2021); and social 

networks (Maturana and Nickerson 2019; McCartney and Shah 2022). Some of these factors 
6

(e.g., media coverage, social networks) may be especially salient when refinance activity is high. 

Consistent with this intuition, Andersen et al. (2020) find that more borrowers are “awake” to 

their option to refinance during low rate periods, based on a model of refinancing behavior that 

incorporates psychological and information-gathering costs and allows those costs to vary across 

borrowers and over time. It follows that the dynamics of who refinances during booms is driven 

not only by potential financial gains but also differences across borrowers in their responsiveness

to those gains. 

Less clear is whether differences in borrowers’ propensity to refinance might also be 

attributable to constrained credit supply. Two earlier papers find that capacity constraints affect 

the provision of credit to home purchase mortgage borrowers. Sharpe and Sherlund (2016) 

develop a theoretical model in which capacity constrained lenders prioritize higher credit score

and refinance lending (assumed to be less costly to underwrite than home purchase lending). 

They find that increased “capacity utilization” is associated with a decrease in the number of 

home purchase loans originated to less credit-worthy borrowers, based on an analysis of GSE 

lending from 2003 to 2014. Choi, Choi, and Kim (2022) find that, following the 2008 financial 

crisis, banks with limited “risk capacity” (e.g., due to capital requirements) or “operating 

capacity” shifted from home purchase to refinance lending, arguing that such banks may prefer 

safer loans or easier-to-process loan applications. Neither paper considers how supply constraints 

impact the distribution of borrowers who refinance, the focus of this paper.

B. Capacity Constraints in the Supply of Mortgage Credit

Supply of mortgage credit is not perfectly elastic. Refinance booms are characterized by 

increases in the price of financial intermediation and longer loan application processing times, 

consistent with the presence of capacity constraints (Fuster et al. 2013; Fuster, Lo, and Willen

2017). More recently, Fuster, Hizmo, Lambie-Hansen, Vickrey, and Willen (2021) find that 

capacity constraints were particularly acute after the onset of the COVID-19 pandemic because 

of related operational and labor market frictions. 

In this study, we derive a measure of supply-side capacity constraints using confidential data 

collected under the Home Mortgage Disclosure Act (HMDA). First, for all first-lien, single 

family refinance originations reported in years 2010 through 2021, we compute the number of 
7

application processing days by taking the difference of the origination date and application date.7

Figure 3 shows the 50th, 75th, and 90th percentile value of application processing days on 

refinance loan originations across all counties over our period of analysis. The figure shows that 

at each of these points in the distribution, the number of application processing days is positively 

correlated with the number of refinance applications, and increases sharply during refinance 

booms.

To proxy for whether capacity constraints are binding in a given local market and time 

period, we create an indicator variable equal to one if the 75th percentile of application 

processing days in a given county-quarter exceeds 110 percent of the average 75th percentile 

value for the county over the period of analysis.8 While this approach is admittedly arbitrary, we 

find it attractive because it reflects cross-sectional as well as time-series variation in mortgage 

lender capacity constraints, and because the measure accounts for level differences in application 

processing days across counties. 

Figure 4 shows the share of counties in our sample categorized as capacity constrained over 

our period of analysis. The figure shows that during the most pronounced refinance application 

booms over the past decade (e.g., 2012-13; 2020), supply of mortgage credit was constrained in 

nearly every county in our sample. However, during milder booms (e.g. early-2015; mid-2016) 

capacity constraints were much less widespread across markets. We note that the variation in our 

capacity constraint measures occur primarily in the time series rather than in the cross-section, 

suggesting that refinance booms are generally national in scope.

Figure 5 illustrates that when capacity constraints are binding, refinance mortgage loan credit 

may be rationed to certain types of borrowers. The figure shows the quarterly incidence of 

mortgage prepayments for selected quarters in which borrowers’ refinance option goes in the

money (ITM), separately by borrower credit score. 

 7 Publicly available HMDA data do not include the exact application and action (origination) dates. This information 

is included in the restricted-access HMDA data, generally available only to certain federal regulatory agencies. We 

are not the first to use application processing days to characterize lender capacity constraints; earlier examples 

include Fuster et al. (2013), Fuster et al. (2017), Fuster et al. (2021), and Choi et al. (2022). 8 We construct the capacity constraint indicator using application data from the three-month window around the 

month that ends quarter t, to reflect the approximate timing of when a borrower would need to submit a loan 

application in order to complete a refinancing by the end of the next quarter. For example, for a county-quarter 

observation in 2020Q1, we compute loan application processing days (and the capacity constraint indicator) using 

loan applications filed in February to April 2020. Further, to ensure we have enough data to reliably construct the 

capacity constraint measure, we limit to the 1,126 counties in which we observe at least 50 refinance loan 

originations in every quarter of our analysis period. We drop the 2,168 counties that did not meet this criterion. 
8

During periods of moderate refinance activity, quarterly prepayment rates are fairly similar 

regardless of credit score. For example, among higher credit score borrowers whose refinance 

option goes ITM in 2016Q1, about 4 percent prepaid in 2016Q2, and another 4 percent prepaid 

in Q3 and in Q4. Prepayment rates were nearly identical among lower credit score borrowers 

over the same period. (For both groups prepayments fell sharply once rates increased at year-end 

2016.) 

However, during major refinance booms when supply of credit is widely constrained, 

pronounced differences arise in prepayment rates between the credit score groups. For example, 

compare incidence of prepayment across credit score groups that went ITM in 2019Q3, when 

rates fell below their post-2017 lows (as shown in Figure 1). In the subsequent two quarters 

when capacity constraints were not widespread, prepayment rates were similar across lower and 

higher credit score groups. However, prepayment rates diverged sharply beginning in 2020 Q2 

when 9 percent of higher credit score borrowers prepaid, compared to 6 percent for those with 

lower credit score. This divergence coincided with interest rates dropping to a historically low 

level in March 2020 with the onset of the Covid-19 pandemic, resulting in a massive increase in 

refinance applications that lenders struggled to handle. 

Of course, the differences in prepayment behavior illustrated in Figure 5 might also be 

attributable to other differences in market conditions over time. For example, lower-credit score 

borrowers might have found it relatively difficult to qualify for a refinancing in late-2020 

compared to late-2016, if they disproportionately experienced higher unemployment rates or 

other adverse shocks as a result of the pandemic. Our empirical analysis attempts to control for 

such factors.

3. Empirical Model 

We specify models of the form 

PPPPbbbb(tt+1) = [cccccccccccccccccc × rrrrrrrr rrbb

2 × bb bbbb bb ]ββ + XXbbbb γγ + μ + εεbbbb ( 1 )

where b indexes borrowers, c indexes counties, and t indexes time (in quarters). We estimate the 

models using OLS, clustering errors at loan level.9

 9 We use linear probability models for computational simplicity. Results using logit (not shown) are similar. 
9

The dependent variable PPPPbbbb(tt+1) is a binary indicator equal to 1 if the mortgage is prepaid by 

the end of the next quarter (t+1), and 0 otherwise. We analyze prepayment behavior because our 

data do not distinguish between refinancing and other forms of prepayment (e.g., selling the 

home). When interpreting our estimation results we treat prepayment as synonymous with 

refinancing. We believe this approach is reasonable as the variation in mortgage refinancing over 

our period of analysis can be explained almost entirely by variation in prepayment behavior.10

Most other papers in the literature take a similar approach.11

The key independent variable is cccccccccccccccccc , an indicator of whether supply of mortgage 

credit is capacity constrained in the county-quarter. 

To isolate the effect of capacity constraints on refinancing (as opposed to other forms of 

prepayment), we include an interaction with a quadratic measure of the borrower’s economic 

incentive to refinance ( rrrrrrrr rrbb

2 ) at time t. We follow Richard and Roll (1989) in measuring the 

borrower’s option value of refinancing using the ratio of the note rate on the mortgage to the 

“adjusted” market rate (i.e. the rate the borrower could get on a new refinancing).12 This is an 

intuitively simple measure and has a strong positive correlation with the incidence of prepayment

in our sample. However, we recognize that this measure does not reflect the full range of factors 

a borrower might consider and show below that our results are robust to using more sophisticated 

methods to calculate the borrower’s refinance option value. 

To allow the effects of supply constraints to differ across borrowers, we interact the supplyconstraint measure with a categorical measure of bb bbbb bb . We estimate three alternative 

specifications of equation (1) using the following measures to categorize borrowers: current 

unpaid principal balance (UPB), income at origination, and current credit score. Each of these 

measures is a proxy for lenders’ perceptions of whether the borrower’s refinance loan application 

would be relatively less profitable or more difficult to underwrite. In particular, because a large 

portion of underwriting costs are fixed, the profitability of a new refinance loan is increasing in 

 10 To quantify this, we regressed county-quarter level counts of refinance originations (computed from HMDA) on 

mortgage prepayments (computed from NMDB) using a variety of specifications that varied by the time period 

being analyzed and by whether other control variables (e.g. county fixed effects; quarterly fixed effects) were 

included. The R2 from these regressions exceeded 0.90 in every case and in most cases was above 0.95. 11 A recent exception to this is Gerardi et al. (2020) who distinguish between prepayments due to refinancing and 

home sales. 

12 As described in the next section, we compute the “adjusted market” rate available to the borrower as of quarter t

using the average prime rate, adjusted to reflect the GSE’s loan-loan level pricing adjustment based on the 

borrower’s current credit score and LTV. 
10

the UPB on the existing loan (which for a rate and term refinancing is approximately equal to the 

new loan amount). While the borrower’s income at origination and credit score do not directly 

relate to profitability of a new loan, they may be observed or inferred early in the lenderborrower relationship and are likely correlated with other more germane characteristics like past 

credit issues, spottier work histories and amount of documentation required to qualify for the 

new loan. 

The specification also includes county-fixed effects (μ ) and a vector (XXbbbb ) of other controls 

for borrower and loan characteristics including current loan-to-value (LTV), debt-to-income ratio 

(DTI) at origination, rate spread at origination, race and ethnicity, first-time homebuyer status, 

loan purpose, the cumulative number of quarters the loan has been in the money, and the countylevel unemployment rate. (Details on the construction of these variables are in the next section.) 

The vector also includes a linear control for calendar quarter interacted with bb bbbb bb , to 

account for differences across borrowers and over time in demand and in their ability to qualify 

for a refinance loan.13

If our measure of supply-side capacity constraints, cccccccccccccccccc , is exogenous with respect to 

the borrower’s refinancing decision (conditional on the other controls), the estimates in ββ can be 

interpreted as the causal effects of capacity constraints on likelihood of prepayment. 

However, a challenge for identification in our setting is that supply-side capacity constraints 

typically bind during recessionary periods when interest rates are low. During such periods, 

marginal borrowers (those with lower UPB, income, or credit score) may be disproportionately 

likely to experience an unobserved shock to employment or income and find it more difficult to 

qualify for a refinancing.14 To address this issue, we limit our analysis to borrowers that we 

argue are very likely able to qualify: those that currently have a conventional loan and, since 

origination of that loan, have not missed a payment on the mortgage or had their credit score fall 

below 620.15 In robustness checks we show results are similar if we further limit the sample to 

 13 We control for time as a continuous variable to reflect the fact that availability of mortgage credit has gradually 

loosened since the great financial crisis (while arguably remaining tight by historical standards). We do not include 

calendar-quarter fixed effects because doing so would absorb much of the variation in our indicator of whether 

capacity constraints are binding, the key explanatory variable of interest. 

14 DeFusco and Mondragon (2020) show that mortgage refinancing activity is constrained by employment 

documentation and out-of-pocket closing cost requirements, which bind most frequently during recessions. 15 We focus on conventional loan borrowers because (as discussed below) our measure of the prevailing market 

interest rate in a given quarter is based on the prevailing rate for a conventional loan. In an analysis of Black 
11

borrowers that have never fallen seriously behind on payments on other trade lines in their credit 

record, whose current combined LTV is 80 percent or lower, and whose credit score hasn’t fallen 

below 660. We argue that with these sample selection criteria in place, there is little scope for 

our estimated effects of supply-side capacity constraints on refinancing to suffer from downward 

bias due to unobserved differences in borrowers’ ability to qualify for a new mortgage. 

A second empirical issue is that demand for new refinance loans increases during booms as 

more borrowers become aware of their refinancing option. Because we cannot fully account for 

shifts in demand, the estimates of ββ reflect the net effect of two opposing factors: the potential 

negative impact of capacity constraints, and concurrent positive increases in demand. 

Which effect is likely to dominate? We expect that among borrowers with a larger financial 

incentive to refinance and with higher UPB, income, or credit scores, the net effect will be 

positive (ββ > 0), meaning their conditional prepayment propensities will be higher during 

refinance booms even when constraints bind. We argue that supply-constrained lenders are likely 

to prioritize these borrowers such that any dampening effect of supply constraints is likely much 

smaller than the additional activity driven by increased demand. 

In contrast, among marginal borrowers (those with lower UPB, income, or credit score) that 

are more likely to be de-prioritized by constrained lenders, the net effect of capacity constraints 

and concurrent demand shifts is less clear ex-ante. If supply constraints have little effect on 

marginal borrowers’ ability to refinance in practice, we’d expect the estimated effect to be 

positive (ββ > 0), reflecting concurrent increases in demand.16 However, a negative effect (ββ <

0) would be consistent with our hypothesis that supply constraints inhibit prepayment activity, 

outweighing the positive effect of increased demand.

4. Data

 

Knight’s McDash loan-servicing dataset and property records module, we find that over our period of analysis about 

97 percent of conventional loan refinancings were refinanced into another conventional loan. We filter on borrower 

credit score below 620 because this is the minimum credit score required to qualify for a GSE-eligible loan. We 

filter on mortgage payment history because, as shown by Keys, et al. (2016), mortgage payment history is a highquality proxy for a borrower’s current creditworthiness. 16 We acknowledge there may be heterogeneity across borrowers in the extent to which demand for refinances 

increases during booms. However, we argue that absent supply constraints, a fall in demand for any group is very 

unlikely. We return to this point in the robustness section.
12 

We analyze borrower refinancing behavior using the National Mortgage Database (NMDB®), 

jointly sponsored by the Federal Housing Finance Agency (FHFA) and the Consumer Financial 

Protection Bureau (CFPB).17 The NMDB is a five percent nationally representative sample of 

closed-end first-lien residential mortgages in the U.S., with detailed administrative information 

on mortgage terms, monthly payment streams, property value and characteristics, and creditrelated information for all borrowers listed on the mortgage. 

We limit our analysis to conventional, 30-year, fixed-rate, home purchase or refinance 

mortgages on single family, site-built, owner-occupied properties. To focus our analysis on 

borrowers who are likely to qualify for a new conventional loan refinancing, we drop from the 

sample all loans with less than full documentation or that are not fully amortizing. We also drop 

all loan-quarter observations after (and including) the first instance when the borrower’s credit 

score falls below 620 or misses at least one mortgage payment. To reduce computational burden 

we use a 50 percent random sample of loans in the NMDB. We include originations from 2009 

to 2020 and observe payment behavior in years 2010 through 2021. 

We merge the county-quarter capacity constraint measures (derived from HMDA as 

described above) into our analysis sample using the county and quarter identifiers in NMDB.18

We merge in county-level unemployment rates from the Bureau of Labor Statistics (BLS), using 

the rate as of the month ending each quarter (e.g., March for Q1). Finally, to construct the 

“market” rate available to a borrower in a given quarter, we merge in data from Freddie Mac’s 

Primary Mortgage Market Survey (PMMS) and loan level pricing adjustment (LLPA) matrices 

from Fannie Mae. We define the market rate as the minimum of the weekly average prime rate

on a conventional 30-year loan (from PMMS) observed over the quarter, adjusted for the LLPA 

the borrower would have to pay based on the borrower’s current credit score and loan-to-value 

(LTV) ratio.19 

 

17 For more details on the NMDB program see: fhfa.gov/PolicyProgramsResearch/Programs/Pages/NationalMortgage-Database.aspx 18 We construct the capacity constraint indicator only for the 1,126 counties with at least 50 refinance loans in every 

quarter of our analysis period, and drop from the sample all loans in counties that did not meet this criterion. 19 The LLPA is a risk-based pricing adjustment for GSE-securitized loans based on the borrower’s credit score, 

LTV, type of product, and other factors; we use only the LLPA matrices for credit score and LTV in this analysis. 

We convert the LLPA from an upfront dollar fee (collected by the GSEs upon delivery of the loan) to an interest rate 

equivalent (i.e., collected through a monthly stream of payments) by multiplying the LLPA by 0.21. (In an analysis

of loan pricing menus, Bhutta, Fuster, and Hizmo (2020) show (in their Figure A-2) that the relationship between 

upfront discount points paid (analogous to LLPAs) and mortgage rates is nearly linear, with a slope of -0.21.) 
13

Our final analysis dataset includes 626,418 unique loans and about 7.37 million loan-quarter 

observations. Table 1 presents prepayment rates and other selected descriptive statistics for cells 

defined by refinance option value, the capacity constraint indicator, and borrower characteristics. 

Panel A shows that overall, 4.5 percent of loan-quarter observations in our sample are prepaid by 

the end of the next quarter. The borrower’s refinance option value is in the money (ITM) on 

about 53 percent of loan-quarter observations, meaning the ratio of the interest rate on the loan to 

the adjusted market rate is at least 1.1. As expected, the prepayment rate is higher (6.6 percent) 

on loan-quarter observations that are ITM (i.e., the “ITM sample”). 

We focus on the ITM sample in the remainder of Table 1 because, for non-ITM observations, 

prepayments are less likely to be refinancings. Panel B shows that prepayment rates are 

increasing in UPB, with the level and gradient increasing during refinance booms when supply 

constraints are binding. Among ITM borrowers in constrained markets, 4.3 percent of those in 

the lowest UPB bin (<$90k) prepay, compared to 10.0 percent in the highest UPB bin ($150k+). 

This is not likely to be driven by the potential benefits from refinancing, as all borrowers in these 

cells are substantially ITM. Further, while unemployment rates are generally higher when 

capacity constraints bind, they don’t vary much across UPB groups, indicating that lower UPB

borrowers don’t disproportionately reside in counties that suffer greater economic shocks during 

recessions. 

Panel C shows, for the ITM sample, differences in prepayment rates and other selected 

descriptive statistics by borrower income at origination.

20 Patterns are generally similar to those 

in Panel A, in that prepayment rates increase with borrower income, particularly in supplyconstrained markets. Patterns are also similar by borrower credit score, as shown in panel D.21

Table 2 summarizes distributions of the other control variables used in the analysis using the 

last quarterly observation for each loan. We focus first on the variables we use to indicate 

 20 Borrower income at origination is calculated relative to the area median family income (MFI): Low income 

borrowers are those with income below 50 percent of area MFI; Moderate income borrowers between 50 and 80 

percent; Middle-High income borrowers 80 percent or more. Area MFI estimates are based on the MSA or state 

non-MSA where the residence is located. (These estimates, based on Census’ American Community Survey, are 

tabulated by the Federal Financial Institutions Examination Council and are included in the NMDB.) 21 In cases where there is more than one borrower on the loan, we use the minimum credit score, consistent with 

GSE underwriting guidelines. A limitation of our analysis is that the credit score observed in NMDB is Experian’s

VantageScore 3.0, not the FICO score typically used in mortgage underwriting. It is not possible to transform the 

VantageScore 3.0 directly to a FICO score for the borrowers in our sample. However, our analyses of HMDA data 

(in years 2018 forward) suggests that, at least on aggregate, the distributions of these scores are similar.
14

whether a borrower is marginal from the lender’s perspective (i.e., relatively more difficult to 

underwrite or less profitable). About 9 percent of borrowers in the sample have an UPB below 

$90k and 20 percent have UPB of $90-150k; 7 percent of borrowers are Low income and another 

19 percent are Moderate income; and 7 percent of borrowers have credit score between 620-659, 

and 10 percent between 660-669. 

Other control variables include the current loan-to-value (LTV), equal to the current UPB 

divided by the mark-to-market property value.22 About 84 percent of loans in the sample have 

LTV of less than 80 percent as of the last quarterly observation. Following earlier literature, we 

include a control for spread at origination, equal to the difference between the note rate on the 

mortgage and the average prime rate (from PMMS) at origination. As noted by Gerardi et al. 

(2020), spread at origination may proxy for unobserved constraints that may prevent a borrower 

from being able to refinance at the prime rate. Around 17 percent of the loans in our sample had 

a spread at origination of 50 basis points or more. About half of the borrowers in our sample 

have home purchase loans, and of these, nearly half (or 23.5 percent of all loans) are first-time 

homebuyers. Finally, we control for the total number of quarters the borrower’s refinance option 

value has been ITM through quarter t, as a proxy for borrower awareness of the refinance option.

5. Results 

A. Effects of Capacity Constraints on Prepayment

Our primary analysis examines how constraints on credit supply affect borrowers’ quarterly 

probability of prepayment. We hypothesize that when capacity constraints bind, lenders deprioritize loan applications they perceive as being more costly or less profitable to underwrite

and thereby reducing their likelihood of prepayment. We employ three alternative specifications 

of equation (1) using the following measures to categorize borrowers: (A) current unpaid 

principal balance (UPB); (B) relative income at origination; and (C) current credit score. In the 

following discussion we focus on how the impacts of capacity constraints vary across borrower 

types and with the financial incentive to refinance. Coefficient estimates of other covariates are 

available in Appendix Table A-1. 

 22 We compute mark-to-market property value using the smoothed FHFA house price index included in the NMDB. 

For loan-quarter observations in calendar year 2013 and going forward, NMDB provides loan balances on junior 

liens secured by the same property as the first-lien. We use these data to compute current combined LTV (current 

CLTV), which we incorporate in robustness checks as detailed below. 
15

Figure 6 plots selected conditional predicted prepayment probabilities generated from OLS 

estimates of equation 1 where effects of capacity constraints are allowed to vary by the 

borrower’s current UPB. As expected, a borrower’s probability of prepayment increases sharply 

with the value of the refinance option. For example, panel A shows that when capacity 

constraints aren’t binding, the overall predicted quarterly prepayment rate is about 2.9 percent

when the refinance option value is not in the money (“not ITM”; rrrrrrrr rr=1.0), but increases to 

5.5 percent when the option value is in the money (“ITM”; rrrrrrrr rr=1.2) and 6.8 percent when 

the option value is very in the money (“very ITM”; rrrrrrrr rr=1.3).23

This same gradient in predicted prepayment probability steepens for borrowers in supplyconstrained markets. For borrowers that are not ITM, the predicted probability of prepayment is 

2.3 percent, or about 0.6 percentage points lower than being in an unconstrained market.24 For 

very ITM borrowers, the predicted prepayment incidence rises to 7.8, or a full percentage point 

higher than in unconstrained markets. As a result, for the average borrower the direction of the 

capacity constraint effect turns positive as the value of the refinance option increases. This 

positive effect is consistent with increased demand for refinancing during booms outweighing 

any dampening effect that supply-constrained lenders may have on overall prepayment activity. 

However, panel B of Figure 6 illustrates that among borrowers with a financial incentive to 

refinance, the net effect of being in a supply-constrained market differs substantially by UPB. 

For example, among ITM borrowers with higher UPB ($150k or more), predicted prepayment 

increases from 6.3 percent when constraints are not binding to 7.6 percent when constraints are 

binding. In contrast, among ITM borrowers in the lowest UPB group (<$90k), the predicted 

prepayment rate is 3.8 percent when constraints are not binding and 3.0 percent when binding. 

Thus, the marginal effect of being in a capacity-constrained market is -0.8 percentage points or 

about 18 percent of the mean prepayment rate in the sample. Among ITM borrowers in the 

 23 As discussed above, in our main specifications we measure the borrower’s refinance option value rrrrrrrr rrbb using 

the ratio of the note rate to the borrower’s market rate. Throughout the paper we define “not ITM” as a ratio of 1.0, 

“barely ITM” as a ratio of 1.1, “ITM” as a ratio of 1.2, and “very ITM” as a ratio of 1.3. These definitions are 

similar to in practice to how Firestone et al. (2007) categorize the refinance option value. 

24 The negative effect of capacity constraints when the refinance option is not ITM likely reflects changes in the 

demand for other types of prepayment which are not a focus of this paper. The decline in prepayment is consistent 

with a recession-driven reduction in home sales that coincides with a refinance boom and with the findings of 

Sharpe and Sherlund (2016) and Choi et al. (2022) who show refinance booms crowd out availability of home 

purchase mortgage credit.
16

middle UPB group ($90-150k), predicted prepayment decreases from 4.0 to 3.7 percent, a 

marginal effect of -0.3 percentage points, or 7 percent of the sample mean. 

Results are similar when we categorize borrowers by income at origination or current credit 

score, as shown in Table 3. The table presents estimated marginal effects of capacity constraints 

on prepayment likelihood evaluated at specific values of rrrrrrrr rr, for each of the alternative 

specifications. (The estimates in Table 3 are illustrated graphically in Appendix Figure A-1.) In 

panel A the effects are allowed to vary by UPB, and correspond to the predicted probabilities 

illustrated in Figure 6 and discussed in the previous paragraph.

25 In panel B, effects of capacity 

constraints are allowed to vary by borrower income. The estimates indicate that when the 

refinance option value is ITM, residing in a capacity constrained market increases the probability 

of prepayment by 1.0 percentage points among middle-to-high income borrowers, but reduces 

the probability of prepayment by about 0.6 percentage points (13 percent) among low-income 

borrowers and by 0.2 percentage points (5 percent) among moderate-income borrowers. The 

estimates are similar when allowing effects to vary by current credit score, as shown in panel C.

Overall, the estimates in Table 3 support our hypothesis that capacity-constrained lenders 

ration credit to certain borrowers that they may perceive as less profitable or more difficult or 

costly to underwrite. Looking across the alternative specifications, the economic impact of this 

credit rationing is to reduce the quarterly likelihood of prepayment by about 15 percent for the 

“most marginal” borrowers (UPB < $90k; Low income; credit score < 620) and by about 5 

percent for those in the middle groups (UPB of $90-150k; Moderate income; credit score 620-

659). As a result of supply-side constraints, these marginal borrowers will disproportionately 

experience a longer wait for or may even miss out on the gains from refinancing. 

We consider these to be lower-bound estimates of the true effect of supply-constraints. As 

discussed above, these estimates reflect the net impact of two opposing factors: the inhibiting 

effect that supply-constrained lenders have on refinancing activity (the main focus of this paper),

and the concurrent positive effect of increased demand for refinancing during booms. We do not 

account for (unrealized) increased demand, and absent supply constraints, we’d expect 

prepayment propensities to increase for all borrowers during booms.

B. Cumulative Effects of Capacity Constraints Over an In-the-Money Spell

 25 Specifically, each of the marginal effects in Table 3 panel A is equal to the difference in the corresponding 

predicted prepayment probabilities between constrained and unconstrained markets presented in Figure 6.
17

To shed light on how much capacity constraints reduce the overall incidence of prepayment 

among marginal borrowers, we present results from an alternative analysis focusing on 

borrowers’ prepayment behavior during periods when their refinance option value is in the

money (ITM). 

Specifically, we restructure our dataset as follows. For each loan in our sample we retain the 

first quarter where it goes ITM ( rrrrrrrr rr ≥ 1.1) and each subsequent quarter until it either 

leaves the sample (e.g., due to prepayment) or goes out of the money. We refer to this as the 

loan’s first “ITM spell.” We do a similar exercise for any subsequent ITM spells the loan may 

have, so that all loan-quarter observations in the sample are ITM and a loan can have multiple 

ITM spells. (See the second column of Table 1, panel A for selected descriptive statistics for the 

ITM sample.) 

We estimate equation (2) using this sample; this specification resembles equation (1) except 

that we no longer interact the quadratic in refinance option value with the capacity constraint and 

borrower type variables.26

PPPPbbbb(tt+1) = [cccccccccccccccccc × bb bbbb bb ]ββ + rrrrrrrr rrbb

2 δδ + XXbbbb γγ + μμ + εεbbbb (2)

Table 4 presents the estimation results from this alternative specification and sample, which are 

qualitatively quite similar to the main results (in Table 3). In panel A the capacity constraint 

effect is allowed to vary by current UPB. For the highest UPB group ($150k+), the quarterly 

likelihood of prepayment is 1.3 percentage points higher when the borrower is in a constrained 

market, or about 20 percent of the sample mean prepayment rate of 6.6 percent. In contrast, for 

the lowest UPB group (<$90k), the quarterly likelihood of prepayment is 1.6 percentage points

(or 23 percent) lower when constraints are binding, and about 0.5 percentage points (or 7 

percent) lower for the middle UPB group ($90-150k). Panels B and C of Table 4 show that 

patterns are similar if we allow effects to vary by borrower income or credit score. Marginal 

borrowers are less likely to prepay their mortgage when lenders are supply constrained.

We now evaluate how capacity constraints affect the quarterly and cumulative probability of 

prepayment, using the estimates from Table 4. We calculate predicted quarterly prepayment 

 26 Because all loan-quarter observations are ITM, we are less concerned about non-refinance related prepayments 

affecting our estimates of the capacity constraint effect. Thus we retain the quadratic in rrrrrrrr rrbb

2 in equation (2) 

but no longer interact this term with the capacity constraint and borrower type indicators. Results are qualitatively 

similar if we retain the interaction with refinance option value. Results are also similar if we limit the sample to 

loan-quarter observations with rrrrrrrr rr ≥ 1.2.
18

activity through 2021Q2 under two scenarios: (1) “Actual”, i.e., using the observed values for 

cccccccccccccccccc ; and (2) a “Counterfactual” scenario in which we set cccccccccccccccccc = 0 for all 

observations. In both cases all other variables in the model take on their actual values. 

As an illustrative example, we focus on prepayment activity by borrower UPB over the most 

recent refinance boom, specifically the set of loans that were active and ITM as of the end of the 

third quarter of 2019.27 Panel A of Figure 7 shows that among borrowers in the lowest UPB 

group (<$90k), we predict that 3.8 percent prepay by the end of the next quarter (2019Q4) in 

both the actual and counterfactual scenarios. Predicted prepayments are very similar in the two 

scenarios because capacity constraints were not yet widespread. However, once capacity 

constraints become prevalent in 2020, predicted quarterly prepayment rates differ sharply in the 

actual and counterfactual scenarios. For example, among loans that are active in 2020Q3, 

predicted prepayment by 2020Q4 is about 6.2 percent in the actual scenario, about 1.4

percentage points lower than the counterfactual scenario in which supply constraints don’t bind. 

Panel B of Figure 7 presents predicted cumulative prepayment probabilities calculated from 

the predicted quarterly prepayment rates in panel A. In the counterfactual scenario where 

cccccccccccccccccc is set to zero (i.e., supply constraints don’t bind), among low UPB borrowers that 

are ITM and active in 2019Q3, we predict that 36 percent prepay by 2021Q2, about 3 percentage 

points (or 9 percent) higher than the prediction based on actual values of cc . As 

discussed above, this estimate may understate the “true” dampening effect on refinancing that 

capacity constraints might have had, as the estimate does not reflect increased (but unmet) 

demand for refinancing among these borrowers. If we assume that borrowers in the low UPB 

group had the same proportional increase in demand for refinancing during booms as we observe 

for higher UPB groups, predicted cumulative prepayments by 2021Q2 would have been about 37 

percent, or 13 percent higher than the prediction based on actual values.28

C. Robustness

 27 As of 2019Q3, interest rates were relatively low (as shown in Figure 1) and about 70 percent of all active loans 

were ITM. Capacity constraints were not widespread until after the onset of the pandemic in March 2020 when rates 

fell further, as shown in Figure 4. For brevity, we focus this discussion on differences by UPB. Results are 

qualitatively similar if we allow effects to vary by borrower income or credit score. 28 Panel B of Figure 7 shows that among borrowers with UPB of $150k or more, the predicted cumulative 

probability of prepayment by 2021Q2 was about 53 percent in the counterfactual scenario and 55 percent (or 4 

percent higher) in the actual scenario that reflects concurrent increases in demand. Multiplying the predicted 

counterfactual cumulative prepayment rate among low UPB borrowers by 104 percent yields a cumulative 

prepayment rate of 37 percent, or 13 percent higher than the predicted value of 33 percent based on actual values. 
19

We now discuss the robustness of our main results presented in Table 3. First, we revisit the

potential concern that refinance booms typically coincide with economic recessions, and that the 

negative effects of capacity constraints might be attributable to unobserved adverse shocks

disproportionately experienced by marginal borrowers. Recall that in our main analysis we 

address this by focusing on a sample of borrowers that are arguably very likely to qualify for a 

new loan: conventional loan borrowers with credit scores above 620 that have never missed a 

mortgage payment. Here we expand on this strategy, exploiting data on borrowers’ other credit 

trade lines available in the NMDB beginning in 2013. 

Results are summarized in Table 5; for brevity we focus on effects evaluated at rrrrrrrr rr=1.2, 

i.e., for ITM borrowers. The first column reproduces our results from Table 3 to facilitate 

comparison with the second column where the sample is restricted to payment activity from 2013

to 2021. This change in sample period is not innocuous – while the estimated capacity constraint 

effects are similar in magnitude among the more marginal borrower groups, they decrease by 

about 15 to 30 percent for the less marginal groups. The third column replaces the first-lien 

based LTV with the combined LTV (based on all credit obligations associated with the 

property), and restricts the sample to borrowers that did not miss any payments on any trade line 

reported to the credit bureaus. Across the three panels the point estimates are fairly similar to 

those in column 2. The fourth column further restricts the sample to borrowers with a combined 

LTV of no more than 80 percent, and the fifth column further restricts the sample to borrowers 

with a credit score of at least 660.29 Results are qualitatively similar in both cases. Taken 

together, these results indicate that marginal borrowers’ lower propensity to prepay during 

capacity-constrained periods does not simply reflect concurrent difficulty in qualifying for a new 

refinance. 

Next, we explore the role of borrower demand. Recall that our estimates reflect the net 

impact on prepayment of two opposing factors: (1) the negative effect of supply constraints (the 

focus of this paper), and (2) the positive effect of increased awareness among borrowers of their 

option to refinance during booms. While we don’t directly observe borrower awareness, we 

 29 We filter out loans with CLTV above 80 percent because, above this threshold, GSE guidelines generally require 

mortgage insurance which increases the borrower’s cost of refinancing and may make it more difficult for the lender 

to process and underwrite the loan application. We filter out credit scores below 660 because, in practice, some 

lenders may have credit score overlays that are more stringent than the minimum 620 score specified in GSE 

underwriting guidelines. 
20

examine whether our estimates are sensitive to filtering the sample to borrowers that are arguably 

more likely to be similar in their awareness of the refinance option, both during boom and nonboom periods. Specifically, in Table 6 we limit the sample to borrowers that currently have a 

refinance loan (column 2), live in a high-income census tract (column 3), or that live in a county 

with above-median “Economic Connectedness” (column 4).

30 The pattern of results in these 

columns are qualitatively quite similar to those from our main estimates (column 1). This 

suggests that differences in the predicted refinancing propensity primarily reflect heterogeneity 

across borrowers in the inhibiting effects of supply constraints rather than heterogeneity in 

increased awareness of refinancing during booms.

31

A related concern is the role of cash-out refinancing, a loan product that is disproportionately 

taken up by borrowers with lower incomes and credit scores (Goodstein 2013; Amromin, Bhutta, 

and Keys 2020). We note that, other things equal, a borrower’s incentive to take out a cash-out 

refinance loan increases when market rates are low, much in the same way as a rate-and-term 

refinancing.32 However, a borrower’s ability to execute a cash-out refinance also requires that 

she has sufficient equity in the home. If marginal borrowers are disproportionately less likely to 

have sufficient equity in their home during refinance booms, a preference for cash-out 

refinancing might dampen their overall incidence of prepayments. To assess whether this is an 

important concern in practice, we limit the sample to borrowers with LTV less than or equal to 

70 percent, i.e., borrowers that likely have sufficient equity to take out a cash-out refinance. 

Estimates for this subsample (in Table 6, column 5) are qualitatively similar to our main results, 

suggesting that the negative sign on the effect of capacity constraints among marginal borrowers 

is not likely due to a decline in cash-out refinancings.33

 30 We use county-level measures of Economic Connectedness constructed by Chetty et al. (2022) using privacyprotected data from Facebook, and available for download via socialcapital.org. Conceptually, Economic 

Connectedness is a measure of the degree to which low-income and high-income people are friends with each other. 31 In additional analyses (not shown), we linked the administrative NMDB data to the National Survey of Mortgage 

Originations (NSMO), which contains additional information on the characteristics of borrowers in the sample. We 

find that results are qualitatively similar (but less precisely estimated) if we limit the sample to borrowers in the 

NSMO with characteristics that are likely correlated with financial sophistication or awareness, such as those with 

higher education level or ownership of other financial assets. 32 HMDA data show that over the period 2018 to 2021, the number of cash-out refinance loan originations began 

increasing in 2019 and especially 2020 as market rates decreased. However, because the increase in rate-and-term 

refinancings was much more pronounced, cash-out refinance originations measured as a share of all originations fell 

over the same period. 

33 We conducted additional analyses using McDash loan servicing data linked to property records data, which allows 

us to distinguish between prepayment types (rate-and-term refinance; cash-out refinance; home sale). Focusing on 
21

In other robustness checks, we explored the sensitivity of our results to using alternative 

measures of the borrower’s financial incentive to refinance. As discussed above, our main 

analysis uses the ratio of the borrower’s note rate to the “adjusted” market rate, which while 

closely correlated with prepayment activity in our empirical data, does not reflect the full range 

of factors a borrower might consider when deciding whether to refinance. In Appendix Table A2, we show our main estimates of interest are generally robust to using: (1) the “Call Option” 

measure (Deng et al. 2000) which compares the present value of remaining mortgage payments 

discounted at the note rate vs. the market rate; and (2) the closed form refinancing model of 

Agarwal et al. (2013) that accounts for interest rate volatility, closing costs, inflation, and 

mobility (among other things). 

Finally, our main estimation results are robust to using alternative measures to proxy for 

whether capacity constraints bind in a given county-quarter. As shown in columns 1 and 3 of 

Appendix Table A-3, results are similar if we construct the capacity constraint indicator based on 

the 50th or 90th percentile of application processing days for completed loan applications within a 

county-quarter, instead of the 75th percentile as in our main analysis. Results are also robust to 

measuring capacity constraints using the share of applications that are “incomplete” as of each 

quarter end, following Choi et al. (2022). In column 4 of Appendix Table A-3, the capacity 

constraint indicator is equal to 1 if the share of incomplete applications in the county-quarter 

exceeds the 75th percentile value over all county-quarter observations in the sample, and in 

column 5 the capacity constraint indicator is equal to 1 if the share of incomplete applications in 

the county quarter exceeds 110 percent of the average share within the county over our sample 

period.34

 

rate-and-term refinancing as the outcome variable, results (available upon request) were quite similar to those

presented in this paper. This is further evidence our main finding – that among marginal borrowers the likelihood of 

prepayment declines during supply-constrained periods – is not attributable to a decline in demand for cash-out 

refinancing.

34 Following Choi et al. (2022), we use restricted-access HMDA data to compute the share of applications that are 

“incomplete” in a county-quarter, defined as follows: the number of applications filed in the county quarter on 

which no action has been taken by quarter-end, divided by the total number of applications filed in the countyquarter. Choi et al. (2022) define constrained markets based on the 75th percentile value over all observations (as in 

Appendix Table A-3, column 3), and in this paper we define constrained markets based on within-county deviations 

(as in Appendix Table A-3, column 4). In an additional robustness check, results (not shown) are similar if we 

define a county-quarter observation as constrained if the 75th percentile value of application processing days exceeds 

the 75th (or 50th, or 90th) percentile value of application processing days over all county-quarter observations in the 

sample.
22

6. Examining Mechanisms

We find that borrowers with lower UPB, lower income, and lower credit scores are less 

likely to refinance their mortgages during refinance booms when capacity constraints bind. This 

result is consistent with supply-constrained lenders rationing credit to marginal borrowers they 

perceive as less profitable or more difficult to underwrite. 

By what mechanisms might such credit rationing occur? Based on anecdotal information and 

prior literature (detailed below), we speculate that supply-constrained lenders might affect 

borrowers’ propensity to refinance in one of the following ways: (A) reduce the likelihood that 

marginal borrowers will obtain a price quote; (B) increase prices on loans to marginal borrowers 

in an effort to “throttle” demand; or (C) de-prioritize processing and underwriting of loan 

applications from marginal borrowers. In this section we present supplemental analyses to assess 

the quantitative importance of these channels during recent refinance booms. 

A. Do Supply Constraints Reduce Marginal Borrowers’ Likelihood of Obtaining a Price Quote? 

When a borrower becomes aware that a refinancing might be financially worthwhile, the next 

step in the refinancing process is to contact one or more lenders to learn about the pricing on a 

new refinance loan. Once the borrower makes contact and provides some basic information (e.g., 

remaining balance on the current loan, the property location and an estimate of its value, income, 

authorization to run a credit check) the lender can provide an initial price quote on a new 

refinance loan. 

Supply-constraints might affect this process in two ways. First, when operating at or near 

capacity, lenders might adjust their marketing strategies, for example by reducing advertisements 

targeted to borrowers with loans they perceive as less profitable to refinance. This may reduce 

marginal borrowers’ likelihood of contacting a lender during booms, to the extent that lender 

marketing affects such borrowers’ awareness of their refinance option.

35 Second, lenders facing 

constraints due to surplus demand during booms may triage borrowers’ requests for a price 

quote, for example by screening calls and prioritizing requests from repeat customers. Anecdotal 

 35 As discussed above, borrowers’ awareness of their refinance option may be influenced by several factors, some of 

which (e.g., news media coverage, social networks) may be particularly salient during refinance booms. Other than 

lender advertising and marketing, the positive influence of these factors is not likely to be inhibited by lenders being 

supply-constrained. Thus, we argue that, absent supply constraints, demand for refinancing would increase during 

booms. 
23

evidence suggests that some lenders engaged in such behavior during the most recent refinance 

boom, disproportionately affecting marginal borrowers.36

Due to data limitations, we are not able to separately evaluate the extent to which supplyconstrained lenders might be adjusting their marketing strategies or engaging in triage of 

borrower requests for price quotes. 

Instead, we examine whether capacity constraints affect the overall likelihood that borrowers 

contact and obtain a price quote from a lender, making use of NMDB data on the incidence of 

inquiries for new mortgage credit. We view a mortgage credit inquiry as a reliable indication that 

the borrower has obtained pricing from a lender because credit score is central to loan pricing 

and a common initial step in a potential refinancing is for the loan officer to request a hard credit 

check on borrower(s) looking to submit an application for a new loan. 

Specifically, we modify equations (1) and (2) so that the dependent variable is an indicator 

equal to one if any of the borrowers on the current loan had at least one mortgage-related credit 

inquiry in the current quarter (t), and equal to zero otherwise.37 We estimate the models using the 

Full and in-the-money (ITM) samples respectively, with an additional sample restriction of 

dropping loan-quarter observations subsequent to the first instance of a mortgage credit 

inquiry.38

The first column of Table 7 presents marginal effects of capacity constraints on the 

probability of a mortgage credit inquiry for the full sample, evaluated when the refinance option 

is ITM ( rrrrrrrr rr=1.2). In panel A the effects are allowed to vary by current UPB. Patterns are 

qualitatively similar to the main results in Table 3. Among higher UPB borrowers, the likelihood 

 36 For example, a recent Seattle Times (March 14, 2020) article highlights some of the ways lenders might respond 

to a surplus of demand: “With rates near historic lows as the coronavirus roils markets, lenders are swamped... 

[t]hey’re raising rates to discourage customers, pumping the brakes on marketing campaigns and capping the 

amount loan officers can lend. Good luck getting someone on the phone — especially if you’re not courteous. ‘If 

you’re difficult, a negotiator, or a grinder, they’re probably not going to call you back,’ said Brian Koss, executive 

vice president at Massachusetts-based Mortgage Network [...]. ‘We’re sorting calls by who are my best customers, 

who’s on top of it, engaged, and giving me all their documents up front.’ ” 37 Unlike our main specification in which the dependent variable is an indicator for prepayment in the next quarter 

(t+1), here we focus on mortgage credit inquiries in the current period (t) because the time it takes to complete a 

mortgage credit inquiry is not nearly as long as the time it takes to complete a refinancing. 38 The Full sample refers to the sample used for the main regression results in Table 3, and the ITM sample refers to 

the sample of loan-quarter observations where the refinance option value is in the money, used for the regression 

results in Table 4. In some cases we observe a new mortgage inquiry in the quarter that the current loan was 

originated; we drop these loan-quarters from the sample because we cannot rule out that the inquiry was related to 

the origination of the current loan.
24

of a mortgage credit inquiry is about 1.8 percentage points higher when constraints are binding, 

or about 38 percent of the sample mean inquiry rate of 4.8 percent. In contrast, among borrowers 

in the lowest and middle UPB groups, capacity constraints lower the likelihood of a mortgage 

inquiry by 0.6 percentage points and 0.2 percentage points (or 12 and 4 percent), respectively. 

Differences across borrower income groups are similar, as shown in Panel B. Patterns by credit 

score (panel C) are somewhat different, in that for the lowest credit score group the effect is not 

statistically different from zero and is positive for the middle credit score group. Nonetheless, in 

both cases the effect is smaller in magnitude than the large and positive effect for higher credit 

score borrowers.39 

Column 2 of Table 7 presents regression results using the modified specification (2) and the 

ITM sample; using the ITM sample focuses our analysis on mortgage credit inquiries most likely 

to be related to a refinancing.40 The pattern of results across the three panels of column 2 is 

similar to the results in column 1, with slightly larger effect sizes relative to the mean rate of 

mortgage inquiry for the ITM sample. 

In the remaining columns of Table 7, we return to the role of borrower awareness. Following 

the approach presented in Table 6, we filter the sample to borrowers that currently have a 

refinance loan (column 3), live in a high-income census tract (column 4), or live in a county with 

above-median “Economic Connectedness” (column 5). Borrowers that meet these criteria are 

arguably more likely to be aware of their option to refinance. The pattern of results in these 

columns is quite similar to those from the ITM sample in column 2. This argues for the 

proposition that supply constraints, rather than differences in awareness across borrowers, are 

driving the heterogeneity in effects of capacity constraints on the likelihood of having a new 

mortgage inquiry. 

Overall, the results in Table 7 indicate that lower UPB and lower income borrowers are less 

likely to successfully contact and obtain pricing from a mortgage lender when capacity 

constraints are binding, as proxied by the incidence of inquiries for new mortgage credit. 

Relative to the unconditional quarterly incidence of an inquiry (e.g., 6.9 percent in column 2), the 

 39 The different pattern of results by credit score may be attributable in part to a measurement issue, as credit scores 

are measured at quarter end and could be negatively affected by credit inquiries occurring during the quarter. 40 Mortgage inquiries may occur for reasons other than a potential refinancing in response to financial incentives, 

e.g., a potential new home purchase related to an upcoming move. Dropping loan-quarter observations that are not 

ITM reduces the potential influence of such inquiries on our results. 
25

magnitudes of the capacity constraint effects on mortgage inquiries are similar in magnitude to

our main estimates of the overall effects of capacity constraints on prepayment. This suggests 

that much of the inhibiting effect of capacity constraints on marginal borrowers’ propensity to 

refinance occurs early in the process, i.e., prior to their receiving pricing on a potential new 

refinancing and before a formal loan application is filed. 

B. Do Supply-Constrained Lenders Price Out Marginal Borrowers?

During refinance booms, lenders respond to excess demand in part by increasing prices, as 

reflected in the spread between primary and secondary market rates and in the “gain-on-sale” 

associated with selling the loan in the secondary market (e.g., Fuster et al. 2017; Fuster et al. 

2021). It seems plausible that constrained lenders might disproportionately raise prices for loans 

they perceive as less profitable or more difficult to originate, in an attempt to “throttle” demand 

from such borrowers. If marginal borrowers face relatively high prices, their financial incentive 

to refinance will be reduced and in cases where higher prices push their refinance option value 

entirely out of the money, they’ll be effectively excluded from the refinance market.

To explore whether supply-constrained lenders use price as a mechanism to ration credit, we 

analyze HMDA data on refinance loan originations from January 2018 to September 2021.41 We 

limit the analysis to these years because interest rates and origination costs are not observed in 

HMDA prior to 2018. 

Appendix Figure A-2 plots the average interest rate on refinance loan originations by loan 

amount, borrower income, and credit score in panels A, B, and C, respectively. While not 

conclusive, these figures suggest that on average, prices did not increase for marginal borrowers 

relative to non-marginal borrowers beginning in the second quarter of 2020 when supply 

constraints were widely binding (as shown in Figures 3 and 4).42

We formalize the analysis by specifying regression equation (3). The dependent variable is 

the interest rate on the loan to borrower b made by lender l in county c and month t. 

 41 Specifically, we analyze first lien, closed-end, conventional conforming, 30-year term, fixed-rate, fully 

amortizing, non-cash-out refinance loan originations on owner occupied, site-built, single family properties from 

January 2018 to September 2021. We omit originations in the 4th quarter of 2021 to deal with right censoring in our 

computation of application processing days used to categorize lenders as supply-constrained. See the Appendix for 

more details on the construction of the HMDA sample used in the analyses presented in this section. 42 In practice, mortgage pricing depends on both the interest rate and upfront costs (discount points). The trends 

illustrated in Appendix Figure A-2 and regression results described below are very similar if we account for discount 

points and other loan costs. 
26

PPbbbbbb = [cccccccccccccccccc × bb bbbb bb]ββ + XXbbγγ + μμ + ρρ + ττtt + εεbbbbbb (3)

The key independent variable is an indicator of whether the lender is capacity-constrained in a 

given month. Analogously to the county-quarter level indicator of capacity constraints used in 

our main analysis, we categorize a lender as constrained in a given month when the 75th

percentile value of processing days on applications received by the lender over the prior three 

months exceeds 110 percent of the average for the lender over our period of analysis. (See the 

Appendix for details on the construction of the lender-level capacity constraint indicator and the 

data used in this analysis.) 

We allow the effect of lender-level capacity constraints to differ across borrower types by 

interacting this indicator with alternative categorical measures of borrower type: (A) loan 

amount; (B) borrower income; and (C) credit score. The specification includes controls for other 

loan and borrower characteristics XXbbbb (loan amount, relative borrower income, credit score, 

combined LTV, DTI, race/ethnicity, and net points paid at closing), as well as fixed effects for 

lender, county, and application month.43

Regression results are presented in Table 8. The estimates in panel A indicate that, 

conditional on lender, application month, and other controls, interest rates are about 3 bps higher 

for loans originated in months when the lender is supply-constrained. This is a small effect, and 

further, rates are no higher on loans to borrowers with lower loan amounts (e.g., <$90k) in 

lender-months affected by supply-constraints. Results are similar using alternative measures of 

pricing including rate spread (the difference between the rate on the loan and the corresponding 

average prime offer rate (APOR)) in Column 2, or rate spread expressed as a percentage of 

APOR in Column 3. Results are also similar when allowing effects to vary over other borrower 

characteristics such as income (panel B) or credit score (panel C). 

Overall, the results in Table 8 do not support the hypothesis that, when faced with 

constraints, lenders raise prices to marginal borrowers in an effort to throttle demand. This 

suggests that most if not all ITM borrowers (calculated using our measure of “adjusted market 

rate”) will find it worthwhile to file a formal application for a new refinance loan once they 

receive pricing from a lender. 

 43 Following Brevoort (2022), we define net points at closing as the difference between discount points paid and 

lender credits, expressed as a percentage of the loan amount.
27 

An important caveat to this analysis is that, in the HMDA data we analyze, we observe loan 

pricing only for applications that result in an origination. Thus, we may understate the extent to 

which capacity constraints lead to increased prices offered by lenders, if marginal borrowers 

offered a relatively high price are less likely to complete a new refinancing. That said, our results 

are consistent with Fuster et al. (2021), who show using Optimal Blue data on mortgage rate 

offers that the spread in conventional loan mortgage rates between lower and higher credit score 

borrowers did not increase during the post-pandemic refinance boom.44 

C. Do Supply-Constrained Lenders De-Prioritize Applications from Marginal Borrowers? 

Once the borrower submits a loan application, the lender processes the application and makes 

an underwriting decision (to approve or deny the application). Processing the application 

typically requires the lender to obtain detailed documentation from the borrower including 

current income and assets, employment status and history. In making an underwriting decision, 

the lender must consider these and other factors such as borrowers’ credit history, monthly 

housing expenses, and monthly payment-to-income and debt-to-income ratios.45 

We hypothesize that when facing excess demand, lenders may de-prioritize or deny loan 

applications submitted by borrowers they perceive as less profitable or more difficult to 

underwrite. We use HMDA data on refinance loan applications reported in years 2018 to 2021 to 

assess the extent to which this occurs in practice.46 Specifically, we examine whether capacityconstrained lenders are more likely to: (1) close or deny applications from marginal borrowers 

due to incomplete information;47 (2) deny applications from marginal borrowers for underwriting 

 

44 In contrast to the conventional loan market, Fuster et al. (2021) show that for FHA loans, mortgage offer rates 

increased for lower credit score borrowers relative to higher credit score borrowers during the recent refinance 

boom. The authors attribute this difference to the fact that lenders are exposed to substantially more default risk in 

the FHA loan market compared to the conventional loan market. 

45 The Consumer Financial Protection Bureau outlines specific requirements that lenders must follow in Regulation 

Z (Ability to Repay).

46 For this analysis, we limit the dataset to years 2018 and later for a few reasons. First, beginning in 2018, lenders 

are generally required to report the reason for denial (on loan applications that are denied). Prior to 2018, many 

lenders were not required to report denial reason. Second, beginning in 2018, most loan application records include 

data on borrower credit score and other important variables that lenders consider when deciding whether to approve 

the loan application. However, credit score is not observed in HMDA for loan applications that are closed for 

incompleteness. See the Appendix for additional details about the dataset used in this analysis. 47 Under the Equal Credit Opportunity Act (ECOA), a creditor may close or deny the application for 

“incompleteness” for various reasons, generally related to the applicant not providing additional information 

requested by the lender in a timely fashion. For more details on ECOA (Regulation B), see 

consumerfinance.gov/rules-policy/regulations/1002/9/#b-2. 
28

reasons; or (3) exhibit increased application processing days for applications from marginal 

borrowers. 

We speculate that when faced with excess demand, constrained lenders may put less effort 

into following up with borrowers to obtain needed documentation, especially those they might 

perceive as less profitable or more difficult to underwrite. Consistent with this intuition, 

Appendix Figure A-3 shows that when refinance applications spiked in the 2nd and 3rd quarters of 

2020, the share of applications ending as incomplete (i.e., closed or denied for incompleteness) 

increased disproportionately for applicants with a lower loan amount (panel A) and especially for 

those with Low-income (panel B). In contrast, Appendix Figure A-4 suggests that denial rates for 

reasons related to underwriting did not disproportionately increase for applications with lower 

loan amounts or from lower income borrowers. Finally, Appendix Figure A-5 shows that during 

the 2020 refinance boom, the number of application processing days increased for all groups but 

especially so for borrowers with lower loan amounts, lower income, and lower credit scores. 

To examine whether these patterns hold up after accounting for other observable factors, we 

estimate alternative specifications of equation (3) on the HMDA sample of refinance loan 

applications described in the previous subsection. Table 9 presents the results. In the first column 

the dependent variable is a binary indicator that the application ended as incomplete. As shown 

in Panel A, applications for a loan amount under $90k are about 1.3pp more likely to end as 

incomplete in months the lender is capacity-constrained, or 11 percent of the incidence of 

incomplete applications for the sample. Panel B shows that applications from Low-income 

borrowers are about 2.1pp (18 percent) more likely to end as incomplete.48

The second column of Table 9 presents regression results where the dependent variable is an 

indicator the loan application was denied for underwriting-related reasons.49 The estimates 

indicate that applicants in the lowest loan amount and credit score groups are denied at higher 

rates when the application is filed in months the lender is constrained, but the opposite is true for 

those in the Low-income group. 

 48 We omit the analysis allowing effects to vary by credit score (panel C) because credit score is not reported in 

HMDA for applications the lender designates as incomplete. 49 Specifically, we consider the application to be “denied for underwriting” if the lender indicates the denial was due 

to any of the following reasons: (1) Debt-to-income ratio; (2) Employment history; (3) Credit history; (4) Collateral; 

(5) Insufficient cash for down payment or closing costs; or (6) Unverifiable information.
29

Consistent with constrained lenders de-prioritizing certain applications, the third column of 

Table 8 shows that lender-level constraints are associated with disproportionately longer 

application processing days for marginal borrowers. For example, panel A shows that among 

applicants in the highest loan amount group (the omitted category), capacity constraints are 

associated with about a 10 day increase in processing days which amounts to around 20 percent 

of the sample mean of 52 days.50 Applicants in the lowest loan amount group (<$90k) see a 

further increase of 3 days to the expected processing time. Applicants in the Low-income and 

low credit score groups (panels B and C) also see relative increases in processing days, although 

effects are smaller in magnitude. 

Overall, the results in Table 9 suggest that supply constrained lenders are more likely to deprioritize loan applications from marginal borrowers, as reflected in higher rates of applications 

being closed or denied for incompleteness and in longer application processing times. However, 

the effect sizes are small in magnitude, and suggest the economic impact of lenders’ deprioritizing applications from marginal borrowers on their propensity to refinance is of secondary 

importance. 

As a back-of-the envelope illustration of this point, consider that the estimates in Table 9

indicate that when capacity constraints are binding, the likelihood that the lender accepts (i.e., 

fully processes and does not deny) a loan application from a borrower in the lowest loan amount 

group declines by about (1.25+0.91=) 2.2 percentage points. This is a 3 percent reduction 

relative to the sample mean acceptance rate of (100-11.6-8.3=) 80.1 percent, or an order of 

magnitude smaller than the effect of capacity constraints on the likelihood that a low-UPB 

borrower will successfully contact and obtain pricing from a lender.51 

Our finding that capacity constraints have at most a minor effect on lenders’ processing of 

applications from marginal borrowers is qualitatively consistent with Agarwal et al. (2021), 

though there are some quantitative differences. Specifically, we show that when constraints are 

binding, applications from marginal borrowers are slightly more likely to end in an incomplete or 

 50 We note that this main effect is tautological, as the capacity constraint indicator is equal to one in lender-months 

with high processing days relative to the lender’s average over the period of analysis. However, the fact that 

processing days increases disproportionately for marginal borrowers is not an artifact of how the capacity-constraint 

indicator is constructed.

51 As discussed above, we use inquiries for new mortgage credit as a proxy for the borrower contacting and 

receiving a rate quote from a lender. From Table 6, borrowers in the lowest UPB group are about 1.3pp less likely to 

have a new mortgage inquiry, about 19 percent of the sample mean inquiry rate. 
30

be denied for underwriting reasons (though our results are mixed on the latter measure), and that 

application processing days increase slightly for such borrowers. In contrast, Agarwal et al. 

(2021) use proprietary data from Freddie Mac to show that “funding” rates and processing times 

on refinancing applications from lower income borrowers were not differentially affected during 

the pandemic-related refinance boom.52 Instead the authors attribute inequality in refinancing 

activity to lower income borrowers being underrepresented in the pool of applications received 

in this period. This conclusion is consistent with our results in Tables 7, 8, and 9 which 

cumulatively suggest that, when supply-side constraints are binding, the decline in marginal 

borrowers’ propensity to refinance largely occurs before borrowers submit a formal application 

for credit. 

7. Conclusion

This paper presents new evidence on how supply-side capacity constraints affect borrowers’ 

refinancing activity. We find that, conditional on the financial benefit of refinancing and a rich 

set of borrower and loan characteristics, “marginal” borrowers (those that lenders may perceive 

as more difficult to underwrite or less profitable) are less likely to prepay their mortgages during 

periods when lenders’ supply of credit is constrained. In particular, borrowers with lower 

remaining loan balances, lower income, and lower credit scores are about 0.5 percentage points 

less likely to prepay their mortgage in a given quarter, which amounts to 15 percent of the mean 

quarterly prepayment rate in our sample. Our estimates imply that the cumulative effect of 

capacity constraints over the post-COVID refinancing boom was to reduce prepayment activity 

of the most marginal borrowers in our sample by about 3 percentage points, or a 9 percent effect 

relative to a counterfactual scenario in which lenders are not supply constrained. We view these

as lower-bound estimates of the true effect of capacity constraints, as we cannot account for 

unobserved increases in demand (after conditioning on financial incentives) during booms that 

otherwise lead to higher prepayment rates. 

 52 Reconciling our results with Agarawal et al. (2021) is not straightforward, due to differences in empirical 

methodology and the data being analyzed. Agarwal et al. (2021) use proprietary data from Freddie Mac to compute 

“funding rates”, i.e., the share of loans run through Freddie Mac’s automated underwriting software that are 

ultimately funded by Freddie Mac. According to the authors, about 18 percent of all new loans in the market are run 

through their underwriting software. 
31

Overall, our findings suggest some scope for public policy to help alleviate the effects of 

supply-side constraints marginal borrowers’ propensity to refinance. About 7 to 9 percent of 

mortgage-holders in our sample are in the lowest loan balance, income, and credit score groups 

that are most affected by capacity constraints, and these borrowers are disproportionately likely 

to be minorities and first-time homebuyers. 

One implication of our work is that programs that make it less costly for these marginal 

borrowers to apply and be underwritten could increase their credit supply, even during refinance 

booms. A salient example of such a policy is the program the Federal Housing Finance Agency 

announced in April 2021, where Fannie Mae and Freddie Mac waived the adverse market 

refinance fee and provided the lender with a credit of up to $500 for an appraisal provided that 

the borrower has a low to moderate income and the loan meets minimum standards on LTV, 

credit score, payment history, and other criteria.

53 Another example is a streamlined refinancing 

program that would waive income and employment documentation requirements for borrowers 

with existing loans insured by the federal government (Gerardi, Loewenstein, and Willen 2021). 

By lowering lenders’ cost of processing and underwriting their loan applications, these types of 

interventions could help borrowers with lower loan amounts, lower income, and lower credit 

scores take advantage of lower rates and substantially reduce their mortgage payments. 

However, we caution that the effectiveness of such policies may be limited, to the extent they 

don’t address frictions that inhibit borrowers from initiating a new refinancing. One way to 

eliminate these frictions would be to add an automatic refinancing feature to the mortgage 

contract (e.g., Bhagat 2021). Another approach to mitigating demand-side frictions would be to 

require mortgage servicers to proactively contact in-the-money borrowers to make them aware of 

the potential financial gains from refinancing (Alexandrov, Goodman, and Tozer 2022). Recent 

evidence from field experiments conducted in Chile and Ireland suggests that such informational 

interventions may be quite effective in increasing borrowers’ refinancing activity (Bhattacharya, 

Cuesta, Illanes, Montoya, Undurraga, and Covarrubias 2022; Byrne et al. 2022). 

On the supply-side, this paper shows that capacity constraints inhibit marginal borrowers’ 

ability to refinance during booms, primarily because they are less likely to successfully contact 

and obtain pricing from a lender when constraints are widespread. Data limitations prevent us 

 53The adverse market fee was a 50 basis point surcharge that applied to most mortgage transactions backed by 

Fannie Mae or Freddie Mac. The fee took effect on December 1, 2020 and was eliminated effective August 1, 2021.
32

from examining exactly why this is the case. One possibility is that, when constraints are 

binding, lenders may adjust their marketing strategies in ways that de-prioritize reaching 

consumers they perceive as less profitable. Alternatively, when faced with excess demand for 

new refinance loans, some lenders may triage borrowers’ requests for a price quote, for example 

by screening calls and prioritizing requests from certain customers, disproportionately affecting 

marginal borrowers. Distinguishing between these mechanisms is an important topic for future 

research, with clear implications for the design of policy interventions meant to remove barriers 

to taking advantage of refinancing opportunities.
33 

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Figure 1: Weekly Refinance Mortgage Applications, 2010 to 2021

Notes: Refinance mortgage application data are from Mortgage Bankers Association’s (MBA) weekly applications 

survey. Index is equal to 100 for the week of March 16, 1990. Interest rate data are from Freddie Mac’s Primary 

Mortgage Market Survey (PMMS), and reflect the average rate on a 30-year fixed-rate prime conventional conforming 

mortgage.

Figure 2: Share (%) of refinance originations with selected loan and borrower characteristics, by 

origination month

Notes: Authors’ calculations using Home Mortgage Disclosure Act (HMDA) data. A low- or moderate-income (LMI) 

borrower has family income less than 80 percent of the median family income (MFI) in the metropolitan statistical area

(MSA) or state non-MSA in which it is located. Borrower credit scores are not observed in HMDA for originations 

prior to 2018. 
37

Figure 3: Days to process a refinance mortgage application, by application quarter 

Notes: Authors calculations using HMDA data. Application processing days is the number of days between the date of 

the application and the date of the origination. The lines reflect the 50th, 75th, and 90th percentile values of application 

processing days over all first lien refinance loan applications on 1-4 family properties in the quarter that resulted in an 

origination. 

Figure 4: Share (%) of counties that are capacity constrained by quarter

Note: Authors’ calculations using HMDA data. A county-quarter is categorized as “capacity constrained” if the 50th

(75th; 90th) percentile value of application processing days in that county-quarter exceeds 110 percent of the average 

50th (75th; 90th) percentile value for the county over the period of analysis. Share (%) of counties capacity constrained 

in a given quarter is weighted by the county-level number of refinance applications in the quarter.
38

Figure 5: Incidence of prepayment after going ITM, by credit score

Note: Authors’ calculations using data from NMDB and HMDA. This figure illustrates quarterly prepayment 

incidence for loans on which the refinance option value went in the money (ITM) at selected quarters, separately by 

whether their credit score in that quarter was above or below 700. We selected these quarters to highlight the observed 

difference in prepayment that occurs when the majority of counties are capacity constrained (i.e., during refinance 

booms). Share (%) of counties that are capacity constrained is lagged one quarter (t-1) to reflect the length of time it 

typically takes to process a refinance application.

Figure 6: Predicted probability of prepayment by capacity constraint, refinance option value, and 

current unpaid principal balance

A. All borrowers B. By current unpaid principal balance

 Note: This figure plots quarterly predicted prepayment probabilities by refinance option value, the loan’s remaining 

unpaid principal balance (UPB), and whether the county is capacity constrained this quarter. The predictions are 

evaluated at refinance option values of 1.0 (“Not ITM”), 1.1 (“Barely ITM”), 1.2 (“ITM”), and 1.3 (“Very ITM”). The 

predictions are calculated using the coefficient estimates from specification (1), reported in Table 3 and Appendix 

Table A-1.
39

Figure 7: Predicted effects of capacity constraints on prepayment for loans that are in the money 

(ITM) as of 2019Q3, by UPB

A. Quarterly prepayment hazard (%) B. Cumulative prepayment hazard (%)

Notes: Figure A presents predicted quarterly prepayment hazards for loans that are active and ITM as of 2019Q3, 

separately by UPB. The predictions are calculated using estimates from the ITM model (specification (2) with results 

in Table 4), under two scenarios. First (“Using actual data”), the variable cccccccccccccccccc and all other covariates take on 

their actual values. Second (“Set capCnstr=0”), the variable cccccccccccccccccc is set to zero for all observations, and all 

other covariates take on their actual values. Figure B presents the corresponding cumulative prepayment hazards 

through 2021Q2. Predictions for the middle UPB category ($90-150k) are omitted for readability.
40 

Table 1: Selected descriptive statistics

Notes: Current UPB (unpaid principal balance) and credit score are based on the observed value as of quarter t. Borrower income at 

origination is expressed relative to the area median family income (AMFI); Low income means the borrower’s income is less than 

50% of AMFI (Moderate: [50-80%); Middle-High: 80% or more). “Ratio of note to adj. mkt rate” is the note rate divided by the 

current market rate, where the market rate is the average prime rate on a 30-year conventional mortgage, adjusted to reflect Fannie 

Mae’s loan level pricing adjustment based on the borrower's current credit score and LTV. The “ITM sample” includes observations 

where the ratio of the note rate to adjusted market rate is 1.1 or higher, i.e., where the refinance option value is in the money. 

“Constrained market” includes observations in county-quarters categorized as capacity-constrained. County loan processing days is 

the 75th percentile value of the number of days between the origination date and application date for first-lien single family refinance 

loan applications in the county-quarter, computed from restricted-access HMDA data. 

Prepaid by next quarter (%) 4.5 6.6 2.3

Ratio of note to adj. mkt rate (mean) 1.14 1.29 0.97

% with ratio < 1.1 47.3 100.0

% with ratio [1.1, 1.3) 33.3 63.2

% with ratio >= 1.3 19.4 36.8

County loan processing days (mean) 62.1 65.3 58.6

County unemployment rate (%) 5.6 6.1 5.07

Loan-quarter observations 7,369,942 3,880,400 3,489,542

as % of all loan-quarter obs 100.0 52.7 47.3

< $90k $90-150k $150k+ < $90k $90-150k $150k+

Prepaid by next quarter (%) 3.7 4.2 7.1 4.3 5.6 10.0

Ratio of note to adj. mkt rate (mean) 1.3 1.3 1.2 1.4 1.4 1.3

% with ratio [1.1, 1.3) 56.6 66.2 75.1 28.9 39.0 51.3

% with ratio >= 1.3 43.4 33.8 24.9 71.1 61.0 48.7

County loan processing days (mean) 59.6 60.2 61.8 74.4 74.7 76.3

County unemployment rate (%) 5.5 5.5 5.5 7.5 7.5 7.9

Loan-quarter observations 373,969 712,313 1,673,147 130,154 273,943 716,874

as % of ITM loan-quarter obs 9.6 18.4 43.1 3.4 7.1 18.5

Low Moderate Mid-High Low Moderate Mid-High

Prepaid by next quarter (%) 3.4 4.6 6.6 4.3 6.2 9.3

Ratio of note to adj. mkt rate (mean) 1.3 1.3 1.3 1.4 1.4 1.3

% with ratio [1.1, 1.3) 62.9 66.9 72.3 36.3 41.2 48.0

% with ratio >= 1.3 37.1 33.1 27.7 63.7 58.8 52.0

County loan processing days (mean) 59.9 60.4 61.5 74.6 75.1 76.0

County unemployment rate (%) 5.3 5.3 5.5 7.5 7.6 7.8

Loan-quarter observations 246,214 589,431 1,923,784 91,114 229,484 800,373

as % of ITM loan-quarter obs 6.3 15.2 49.6 2.3 5.9 20.6

620-659 660-699 700+ 620-659 660-699 700+

Prepaid by next quarter (%) 5.8 5.5 5.9 6.0 6.4 8.5

Ratio of note to adj. mkt rate (mean) 1.2 1.3 1.3 1.3 1.3 1.4

% with ratio [1.1, 1.3) 77.5 74.1 69.7 63.0 54.3 44.3

% with ratio >= 1.3 22.5 25.9 30.3 37.0 45.7 55.7

County loan processing days (mean) 61.1 61.0 61.1 75.7 75.4 75.7

County unemployment rate (%) 5.4 5.5 5.5 7.7 7.7 7.7

Loan-quarter observations 78,926 220,217 2,460,286 33,740 87,718 999,513

as % of ITM loan-quarter obs 2.0 5.7 63.4 0.9 2.3 25.8

Constrained Market

Constrained Market

Constrained Market

Not ITM 

sample

D. ITM sample by capConstr and 

current credit score

Unconstrained Market

Unconstrained Market

Unconstrained Market

B. ITM sample by capConstr and 

current UPB

C. ITM sample by capConstr and 

borrower income at origination

A. Full sample by refinance option 

value

All ITM 

sample
41 

Table 2: Sample means for other control variables (loan-level)

Notes: This table presents loan-level sample means of other selected variables, using the last quarterly observation for each loan, 

whether it was still active, prepaid, or terminated for another reason. Variables labeled with "(t)" may vary across loan quarters, while 

"(t=0)" indicates the variable is observed only in (and does not vary subsequently to) the quarter of origination. The count of unique 

loans in the second and third columns does not sum to the total in column 1 because many loans are active in at least one capacityconstrained and unconstrained quarter. Borrower income at origination is expressed relative to the area median family income 

(AMFI); Low income means the borrower’s income is less than 50% of AMFI (Moderate: [50-80%); Middle-High: 80% or more). 

Spread at origination is the difference between the note rate and the average prime rate available when the loan was originated.

All Yes No

Count of unique loans 626,418 441,422 610,834

Unpaid principal balance (t)

< $90k 9.2 9.3 9.2

$90-150k 19.7 20.8 19.8

$150k+ 71.2 69.8 71.0

Borrower income (t=0)

Low 6.5 6.9 6.5

Moderate 18.5 19.2 18.6

Middle-High 75.0 73.9 74.9

Credit Score (t)

620-659 6.6 4.6 6.5

660-699 9.6 8.8 9.7

700+ 83.8 86.5 83.8

Loan purpose (t=0)

Purchase 48.8 46.2 49.1

Refi - not cash out 44.9 46.7 44.7

Refi - cash out 6.4 7.1 6.3

Loan-to-value (t)

< 70 62.5 57.9 62.2

70-79 21.5 22.1 21.5

80-89 11.4 14.2 11.7

90-97 3.1 3.3 3.3

97+ 1.4 2.4 1.3

Debt-to-income (t=0)

< 34 47.0 49.3 47.0

34-38 16.6 16.2 16.6

39-43 17.7 16.7 17.7

44-48 12.4 11.5 12.4

49+ 5.9 6.0 5.9

unknown 0.3 0.3 0.3

Spread (bps) at origination (t=0)

50+ 16.7 15.4 16.6

25 to 49 24.1 23.0 24.1

-25 to 24 51.2 52.4 51.2

< -25 8.1 9.2 8.1

Race and Ethnicity (t=0)

Black 3.8 3.8 3.8

Hispanic (non-black) 8.5 7.8 8.5

Asian (non-Hispanic) 6.9 6.5 6.8

Other (non-Hispanic) 1.6 1.5 1.5

White (non-Hispanic) 79.2 80.4 79.2

First-time homebuyer (t=0) 23.1 22.3 23.3

Cumulative quarters ITM (t) 6.2 5.9 6.0

Capacity Constraint
42 

Table 3: Effects of capacity constraints on probability of prepayment, by refinance option value 

and borrower type

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed 

from OLS regressions of Equation 1, with errors clustered at loan-level. The estimation sample includes 

7,369,942 loan-quarter observations and 626,418 unique loans. Each panel presents results from a separate 

regression in which the capacity constraint indicator is interacted with a quadratic of the current refinance option 

value and a categorical variable for: (A) Current unpaid principal balance; (B) borrower income at origination; 

and (C) Current credit score. In columns (1) to (4) the capacity constraint effect is evaluated at a refinance option 

value of 1.0 ("Not ITM"), 1.1 ("Barely ITM"), 1.2 ("ITM"), and 1.3 (“Very ITM”), respectively, where ITM 

stands for “in the money”. Borrower income at origination is expressed relative to the area median family income 

(AMFI); Low income means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 

80% or more). Coefficient estimates on all other control variables are provided in Table A-1.

Not ITM Barely ITM ITM Very ITM

(1) (2) (3) (4)

A. Current unpaid principal balance

< $90k -0.997*** -0.909*** -0.836*** -0.777***

(0.105) (0.078) (0.064) (0.063)

$90-150k -1.045*** -0.625*** -0.272*** 0.013

(0.065) (0.047) (0.044) (0.051)

$150k+ -0.310*** 0.569*** 1.234*** 1.685***

(0.045) (0.034) (0.039) (0.046)

B. Borrower income at origination

Low -0.983*** -0.776*** -0.613*** -0.494***

(0.098) (0.074) (0.068) (0.074)

Moderate -0.995*** -0.587*** -0.248*** 0.020

(0.074) (0.050) (0.052) (0.062)

Middle-High -0.093 0.527*** 0.997*** 1.316***

(0.067) (0.040) (0.046) (0.053)

C. Current credit score

620-659 -0.832*** -0.688*** -0.594*** -0.550***

(0.096) (0.103) (0.126) (0.154)

660-699 -0.501*** -0.347*** -0.231*** -0.151

(0.084) (0.069) (0.083) (0.102)

700+ -0.110* 0.387*** 0.764*** 1.021***

(0.062) (0.037) (0.038) (0.044)

Refinance Option Value
43 

Table 4: Effects of capacity constraints on the probability of prepayment by borrower type; limit 

sample to “in-the-money spells”

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed 

from OLS regressions of Equation 2, with errors clustered at loan-level. The estimation sample includes 

3,880,400 loan-quarter observations and 498,740 unique loans. Each panel presents results from a separate 

regression in which the capacity constraint indicator is interacted with a categorical variable for: (A) current 

unpaid principal balance; (B) borrower income at origination; and (C) current credit score. Borrower income at 

origination is expressed relative to the area median family income (AMFI); Low income means the borrower’s 

income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). The specifications include

the full set of control variables detailed in Table A-1; coefficient estimates available upon request. 

Dependent variable 1[Prepay]

A. Current unpaid principal balance

< $90k -1.545***

(0.068)

$90-150k -0.487***

(0.057)

$150k+ 1.321***

(0.050)

B. Borrower income at origination

Low -1.081***

(0.081)

Moderate -0.365***

(0.063)

Middle-High 0.941***

(0.047)

C. Current credit score

620-659 -0.949***

(0.157)

660-699 -0.484***

(0.098)

700+ 0.654***

(0.043)
44 

Table 5: Effects of capacity constraints on probability of prepayment by borrower type, additional 

sample restrictions filtering to borrowers most likely able to qualify for a new refinance mortgage 

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed 

from OLS regressions of Equation 1, estimated on increasingly restrictive sample filters to retain borrowers very 

likely to qualify for a refinancing. All effects are evaluated at refiVal = 1.2 (i.e., in the money). Estimates of other 

controls are omitted for brevity. Errors are clustered at loan-level. The first column reproduces the estimates in 

Table 3, column (3) to facilitate comparison. In the second column we restrict the sample to prepayments made 

between 2013 and 2021; data on borrowers’ other credit obligations are available in the NMDB in years 2013+. In 

column (3) we include a control for combined LTV (in place of first-lien LTV) and drop any borrower reported as 

more than 30 days delinquent on any trade line reported to the credit bureaus. In column (4) we further remove 

loan quarters where the borrower’s combined LTV exceeds 80 percent, and in column (5) we filter out loan 

quarters after (and including) the first quarter in that the borrower’s credit score falls below 660, if any. Borrower 

income at origination is expressed relative to the area median family income (AMFI); Low income means the 

borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). 

Specification (1) (2) (3) (4) (5)

A. Current unpaid principal balance

< $90k -0.836*** -0.893*** -0.943*** -1.046*** -1.058***

(0.064) (0.081) (0.097) (0.110) (0.114)

$90-150k -0.272*** -0.264*** -0.251*** -0.233*** -0.213**

(0.044) (0.059) (0.068) (0.081) (0.084)

$150k+ 1.234*** 1.054*** 1.107*** 1.312*** 1.419***

(0.039) (0.053) (0.061) (0.072) (0.076)

B. Borrower income at origination

Low -0.613*** -0.609*** -0.593*** -0.587*** -0.605***

(0.068) (0.093) (0.112) (0.128) (0.132)

Moderate -0.248*** -0.407*** -0.348*** -0.355*** -0.265***

(0.052) (0.070) (0.083) (0.095) (0.101)

Middle-High 0.997*** 0.766*** 0.813*** 0.868*** 0.943***

(0.046) (0.060) (0.074) (0.070) (0.075)

C. Current credit score

620-659 -0.594*** -0.686*** -0.775*** -1.166***

(0.126) (0.179) (0.275) (0.366)

660-699 -0.231*** -0.440*** -0.447*** -0.657*** -0.584**

(0.083) (0.121) (0.169) (0.213) (0.249)

700+ 0.764*** 0.517*** 0.553*** 0.608*** 0.626***

(0.038) (0.050) (0.060) (0.056) (0.059)

Number of observations 7,369,942 6,139,362 4,796,096 3,785,200 3,467,169

Sample restrictions

Analysis period 2010-2021 2013 - 2021 2013 - 2021 2013 - 2021 2013 - 2021

LTV measure First-lien First-lien Combined Combined Combined

Drop if ever DQ on any tradeline N N Y Y Y

Drop if CLTV > 80 N N N Y Y

Drop if credit score < 660 N N N N Y
45

Table 6: Effects of capacity constraints on probability of prepayment by borrower type, additional 

sample restrictions to explore the role of borrower awareness

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed 

from OLS regressions of Equation 1, estimated on various subsamples of borrowers with (arguably) similar 

awareness of their option to refinance. All effects are evaluated at refiVal = 1.2 (i.e., in the money). Estimates of 

other controls are omitted for brevity. Errors are clustered at loan-level. The first column reproduces the estimates 

in Table 3, column (3) to facilitate comparison. In column (2) we restrict our sample to borrowers whose current 

loan is a refinance. In column (3) we restrict our sample to borrowers whose property is in a high income tract 

(see definition below). In column (4) we filter the sample to counties with above median “Economic 

Connectedness”, as measured by Chetty et al. (2022). In column (5) we restrict the sample to quarters where the 

borrower’s LTV is no more 70 percent as a proxy for being able to obtain a cash-out refinance if desired. 

Borrower income at origination is expressed relative to the area median family income (AMFI); Low income 

means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more).
46 

Table 7: Effects of capacity constraints on inquiries for new mortgage credit, by borrower type

 Notes: This table presents marginal effects of capacity constraints on the probability of having an inquiry for new 

mortgage credit (i.e. hard credit checks), computed from OLS regressions with errors clustered at loan-level. 

Column (1) shows the effects calculated from estimating equation (1) on the Full NMDB sample, evaluated at 

refiVal = 1.2 (i.e., in the money). Columns (2) to (5) show effects calculated from estimating equation (2) on 

different cuts of the ITM sample as described in the “Restrictions” row. In column (5) we filter the sample to 

counties with above median “Economic Connectedness”, as measured by Chetty et al. (2022). In all cases we drop 

loan-quarter observations subsequent to the first instance of a mortgage credit inquiry. Borrower income at 

origination is expressed relative to the area median family income (AMFI); Low income means the borrower’s 

income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). Estimates of the coefficients 

on the other control variables are omitted for brevity. 

Dependent Variable Credit Inquiry

(1)

Credit Inquiry

(2)

Credit Inquiry

(3)

Credit Inquiry

(4)

Credit Inquiry

(5)

A. Current unpaid principal balance

< $90k -0.611*** -1.327*** -1.223*** -1.358*** -1.25***

(0.071) (0.071) (0.094) (0.078) (0.104)

$90-150k -0.180*** -0.431*** -0.521*** -0.519*** -0.426***

(0.053) (0.063) (0.078) (0.067) (0.083)

$150k+ 1.816*** 1.856*** 1.794*** 1.836*** 1.793***

(0.045) (0.055) (0.067) (0.058) (0.065)

B. Borrower income at origination

Low -0.514*** -0.979*** -0.968*** -1.101*** -0.988***

(0.084) (0.092) (0.119) (0.103) (0.120)

Moderate -0.050 -0.153** -0.188** -0.216*** -0.062

(0.061) (0.070) (0.091) (0.076) (0.090)

Middle-High 1.545*** 1.408*** 1.380*** 1.382*** 1.54***

(0.044) (0.051) (0.061) (0.054) (0.063)

C. Current credit score

620-659 0.245 -0.172 0.048 -0.009 -0.253

(0.166) (0.204) (0.267) (0.221) (0.278)

660-699 0.464*** 0.131 0.182 0.094 0.226

(0.106) (0.124) (0.162) (0.133) (0.169)

700+ 1.154*** 0.983*** 0.987*** 0.981*** 1.079***

(0.038) (0.046) (0.054) (0.049) (0.055)

Number of observations 5,824,762 3,148,498 1,696,925 2,781,916 1,784,450

Mean, Outcome Variable 4.8 6.9 6.7 6.8 7.3

Sample Full ITM ITM ITM ITM

Restrictions None None Refis Only Middle-Upper 

Income Tracts 

only

High Economic 

Connectedness
47 

Table 8: Effects of capacity constraints on loan pricing, by borrower type 

Notes: Analysis of HMDA data on first lien, closed-end, conventional conforming, 30-year term, fixed-rate, fully 

amortizing, non-cash out refinance loan applications on owner occupied, site-built 1-4 family properties from 

March 2018 to September 2021 that resulted in an origination; 4,982,388 observations in total. The capacity 

constraint indicator is computed at lender-month level; see the data appendix for details. In panels A, B, and C the 

effects of capacity constraints are allowed to vary by loan amount, income, and credit score, respectively. In 

column (1) the dependent variable is the interest rate on the loan, in basis points (bps). In column (2) the 

dependent variable is the difference between the rate on the loan and the average prime offer rate (APOR). In 

column (3) the dependent variable is the rate spread expressed as a percentage of the APOR. Borrower income is 

expressed relative to the area median family income (AMFI); Low income means the borrower’s income is less 

than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). All specifications include controls for net 

points paid at closing, loan amount, borrower income, credit score, combined LTV, DTI, race/ethnicity, and fixed 

effects for county, application month, and lender.

Dependent variable

Interest

rate (bps)

Rate spread 

(bps)

Rate spread as 

% of APOR

(1) (2) (3)

A. Loan amount (omitted category: >= $150k)

1(capConstr) * 1(< $90k) -6.175*** -7.016*** -0.411***

(0.155) (0.160) (0.052)

1(capConstr) * 1($90-150k) -1.969*** -2.044*** 0.021

(0.071) (0.073) (0.024)

1(capConstr) 2.843*** 3.408*** 1.005***

(0.031) (0.032) (0.010)

1(< $90k) 20.636*** 31.208*** 9.177***

(0.121) (0.125) (0.041)

1($90-150k) 9.388*** 14.117*** 4.276***

(0.058) (0.060) (0.020)

B. Borrower income (omitted category: Middle-High)

1(capConstr) * 1(Low income) -1.630*** -2.011*** -0.189***

(0.092) (0.095) (0.031)

1(capConstr) * 1(Moderate income) -0.492*** -0.514*** 0.100***

(0.054) (0.056) (0.018)

1(capConstr) 2.720*** 3.288*** 0.992***

(0.032) (0.033) (0.011)

1(Low income) 0.679*** 3.523*** 0.938***

(0.075) (0.078) (0.025)

1(Moderate income) 0.367*** 2.061*** 0.554***

(0.043) (0.044) (0.014)

C. Credit score (omitted category: 700+)

1(capConstr) * 1(620 to 659) -3.869*** -4.160*** 0.487***

(0.134) (0.138) (0.045)

1(capConstr) * 1(660 to 699) -2.490*** -3.017*** 0.144***

(0.072) (0.075) (0.024)

1(capConstr) 2.884*** 3.485*** 0.973***

(0.031) (0.032) (0.010)

1(620 to 659) 35.550*** 39.512*** 11.962***

(0.096) (0.099) (0.032)

1(660 to 699) 20.245*** 23.857*** 7.296***

(0.053) (0.055) (0.018)

Mean of dependent variable 322.1 11.6 3.8
48 

Table 9: Effects of capacity constraints on lenders’ processing of refinance loan applications by borrower 

type

Notes: Analysis of HMDA data on first lien, closed-end, conventional conforming, 30-year term, fixed-rate, fully amortizing, noncash out refinance loan applications on owner occupied, site-built 1-4 family properties from March 2018 to September 2021. 

Capacity constraints are measured at lender-month level; see data appendix for details. In panels A, B, and C the effects of capacity 

constraints are allowed to vary by loan amount, income, and credit score, respectively. Column (1) shows the estimated effects of 

capacity constraints on the probability that an application is closed or denied for incompleteness. Borrower credit scores are not 

reported for loan applications that the lender designates as incomplete. Column (2) shows the effects on the probability that the 

application is denied for underwriting reasons. Column (3) shows the effects on the number of application processing days, for the 

sample of loan applications that resulted in an origination. Borrower income is expressed relative to the area median family income 

(AMFI); Low income means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). All 

specifications include controls for loan amount, borrower income, race/ethnicity, and fixed effects for county, application month, and 

lender. The specification in column 3 also includes controls for net points paid at closing, combined LTV, and DTI.

Dependent variable 1[ Incomplete ] 1[ Denied for

underwriting]

Application 

processing days

(1) (2) (3)

A. Loan Amount (omitted category: >= $150k)

1(capConstr) * 1(< $90k) 1.250*** 0.908*** 3.337***

(0.153) (0.139) (0.200)

1(capConstr) * 1($90-150k) 0.661*** 0.142** 1.775***

(0.076) (0.069) (0.090)

1(capConstr) 1.280*** 0.384*** 9.624***

(0.037) (0.034) (0.042)

1(< $90k) 1.256*** -0.662*** -0.157

(0.101) (0.092) (0.131)

1($90-150k) 0.880*** -2.691*** -0.689***

(0.053) (0.048) (0.062)

B. Borrower income (omitted category: Middle-High)

1(capConstr) * 1(Low income) 2.143*** -0.976*** 0.832***

(0.088) (0.080) (0.117)

1(capConstr) * 1(Moderate income) -0.262*** 0.151*** 0.479***

(0.061) (0.055) (0.069)

1(capConstr) 1.263*** 0.476*** 9.735***

(0.039) (0.035) (0.043)

1(Low income) 1.795*** 18.541*** -0.532***

(0.061) (0.055) (0.080)

1(Moderate income) 0.686*** 4.989*** -1.048***

(0.039) (0.036) (0.045)

C. Borrower credit score (omitted category: 700+)

1(capConstr) * 1(620 to 659) 6.219*** 1.373***

(0.102) (0.181)

1(capConstr) * 1(660 to 699) 1.308*** 0.768***

(0.073) (0.096)

1(capConstr) -0.150*** 9.774***

(0.034) (0.042)

1(620 to 659) 30.846*** 4.502***

(0.063) (0.104)

1(660 to 699) 7.667*** 3.575***

(0.044) (0.057)

Sample All applications All applications Originations

Number of observations 6,288,436 6,288,436 4,982,388

Mean of dependent variable 11.6 8.3 52.4
49

DATA APPENDIX

In supplemental analyses of whether capacity constraints affect lenders’ loan pricing and loan 

processing and underwriting behavior (described in section 6.B and 6.C), we use data collected 

under the Home Mortgage Disclosure Act (HMDA) and reported in years 2018 through 2021. 

We construct our lender-month level indicator of capacity constraints as follows. First, we 

filter the HMDA data to first-lien conventional refinance loan originations on owner-occupied, 

1-4 family, site-built properties. We further limit the sample to loan applications filed between 

December 2017 and September 2021 and for each of these applications, we compute the number 

of processing days by taking the difference (in days) of the loan origination date and loan 

application date.57 For each lender-month in the data, we compute the 75th percentile of loan 

application processing days using applications received over the preceding three months.58 We 

then construct a binary indicator for each lender-month equal to one if the 75th percentile value 

of application processing days exceeds 110 percent of the lenders’ average 75th percentile value 

of application processing days over the sample. Appendix Figure A-6 summarizes the share of 

lenders in our sample categorized as capacity constrained, by application month. The figure 

shows that, following the spike in refinance applications beginning in March of 2020, the share 

of lenders categorized as capacity constrained increases to over 80 percent, compared to less than 

10 percent over the 2018-19 period.

We merge our lender-month level indicators of capacity constraints into loan-level HMDA 

data on first lien, closed-end, conventional conforming, 30-year term, fixed-rate, fully 

amortizing, non-cash-out refinance loan applications on owner occupied, site-built 1-4 family 

properties. We limit the sample to applications filed from March 2018 through September 2021 

(the months we observe our lender-level capacity constraint indicator). For the loan pricing 

analysis presented in Table 7, we limit the data to loan originations, as loan pricing is not 

observed on applications that did not result in an origination. For our analysis of loan 

 57 We drop loan applications filed in months after September 2021 to deal with potential right-censoring in HMDA 

reporting when computing the number of processing days. For each reporting year in HMDA, lenders report all loan 

applications on which the action taken (e.g., loan originated; application denied) occurs within the same calendar 

year. Thus, loan applications filed in October 2021 or later and still in-process as of year-end 2021 are not included 

in the 2021 HMDA reporting year. We exclude loan applications filed prior to December 2017 for the analogous 

left-censoring issue. 58 For example, for a given lender in September 2021, we compute the 75th percentile of application processing days 

using applications received by that lender between June and August 2021 (and that resulted in an origination). We 

drop lender-months with less than 30 applications received over the preceding three months.
50

applications being closed for incompleteness or denied for underwriting reasons (Table 8, 

columns 1 and 2), we limit the sample to loan applications on which the lender made an 

underwriting determination or closed the loan for incompleteness.59 For our analysis of 

application processing days (Table 8, column 3), we limit the sample to applications that resulted 

in an origination. 

Appendix Table A-4 presents sample means for the variables included in the 4.98 million 

observations used in our pricing analysis and the 6.29 million observations used in our analysis 

of loan processing and underwriting decisions. Certain fields are not observed in HMDA for loan 

applications that don’t result in an origination, namely credit score, combined loan-to-value, 

debt-to-income ratio, interest rate, and closing costs (used to calculate net points at origination). 

These fields are not included as control variables in the specifications we use to analyze loan 

processing and underwriting outcomes. 

 59 Specifically, for the loan processing and underwriting analysis we retain loan applications with action codes: (1) 

loan originated; (2) approved but not accepted; (3) denied; and (5) file closed for incompleteness. We categorize an 

application as closed for incompleteness if the action code is equal to 5 or if the application is denied for reason code 

7 (Credit application incomplete). 
51

Appendix Figure A-1: Predicted marginal effects of capacity constraints on prepayment

A. Current unpaid principal balance B. Borrower income at origination

C. Current credit score

Notes: These figures provide a graphical depiction of the estimates presented in Table 3. The bars illustrate the estimated 

marginal effects of capacity constraints on the quarterly probability of prepayment (with 95 percent confidence intervals), 

separately by borrower characteristics and evaluated at different values of the borrower’s refinance option. The refinance 

option value is set to 1.0 for "Not ITM", 1.1 for "Barely ITM", 1.2 for "ITM", and 1.3 for “Very ITM”, respectively, 

where ITM stands for “in the money”. Borrower income at origination is expressed relative to the area median family 

income (AMFI); Low income means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 

80% or more). See Table 3 for additional notes.
52 

Appendix Figure A-2: Average interest rate on refinance loan originations, by application month and 

borrower type

A. Loan Amount B. Borrower Income 

C. Credit Score 

Notes: Analysis of HMDA data on first lien, closed-end, conventional conforming, 30-year term, fixed-rate, fully 

amortizing, non-cash out refinance loan originations on owner occupied, site-built 1-4 family properties from March 2018 

to September 2021 that resulted in an origination; 4,982,388 observations in total. These figures show how various 

measure of pricing vary by borrower group and with the amount of refinancing activity. Borrower income is expressed 

relative to the area median family income (AMFI); Low income means the borrower’s income is less than 50% of AMFI

(moderate: [50-80%); middle-high: 80% or more). 
53 

Appendix Figure A-3: Share (%) of refinance loan applications closed or denied for incompleteness, by 

application month and borrower type

A. Loan amount B. Borrower income 

Notes: Authors’ calculations using HMDA data on first lien, closed-end, conventional conforming, 30-year term, noncash out refinance loan applications on owner occupied, site-built properties from March 2018 to September 2021. The 

figure shows the share of loan applications the lender “Closed for incompleteness” (action code = 5) or denied for reason 

“Credit application incomplete” (denial reason code = 7). Borrowers are categorized by loan amount and income in panels 

A and B, respectively. Borrower credit scores are not reported for loan applications the lender designates as incomplete. 

Borrower income is expressed relative to the area median family income (AMFI); Low income means the borrower’s 

income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). 
54 

Appendix Figure A-4: Share (%) of completed refinance loan applications denied for underwriting 

reasons, by application month and borrower type 

A. Loan amount B. Borrower income 

C. Credit score 

Notes: Authors’ calculations using HMDA data on first lien, closed-end, conventional conforming, 30-year term, noncash out refinance loan applications on owner occupied, site-built properties from March 2018 to September 2021. This 

figure shows the share of loan applications that are denied by the lender for any of the following reasons: (1) Debt-toincome ratio; (2) Employment history; (3) Credit history; (4) Collateral; (5) Insufficient cash (downpayment, closing 

costs); or (6) “Unverifable information”. Borrowers are categorized by loan amount, income, and credit score in panels A, 

B, and C, respectively. Borrower income is expressed relative to the area median family income (AMFI); Low income 

means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or more). 
55 

Appendix Figure A-5: Number of days to process refinance loan applications by application month and 

borrower type

A. Loan amount B. Borrower income 

C. Credit score 

Notes: Authors’ calculations using HMDA data on first-lien conventional loan originations on owner occupied, 1-4 family 

site-built properties. This set of figures shows the 75th percentile of application processing days (defined as the number of 

days between the origination date and application date) by application month, for borrowers categorized by loan amount, 

income, and credit score in panels A, B, and C, respectively. Borrower income is expressed relative to the area median 

family income (AMFI); Low income means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); 

middle-high: 80% or more). 
56

Appendix Figure A-6: Share of lenders categorized as capacity constrained by application month

Notes: Authors’ calculations using HMDA data on first-lien conventional refinance loan originations on owner-occupied, 

1-4 family, site-built properties. See the Data Appendix for details on how we construct our lender-month level indicator 

of capacity constraints.
57 

Table A-1: OLS Estimates of Main Specification, control variable estimates 

Borrower Group Current Unpaid 

Principal Balance

Borrower Income 

at Origination

Current 

Credit Score

(1) (2) (3)

Credit score: 620-659 1.152*** 1.114*** 4.602***

(0.036) (0.037) (1.155)

Credit score: 660-699 0.406*** 0.392*** 4.066***

(0.026) (0.026) (0.934)

Low income -1.785*** 14.933*** -1.786***

(0.031) (1.046) (0.032)

Moderate income -1.144*** 8.326*** -1.120***

(0.021) (0.981) (0.021)

UPB: <$90k 19.602*** -1.401*** -1.447***

(0.504) (0.032) (0.032)

UPB: $90k-$150k 15.750*** -1.345*** -1.307***

(0.417) (0.021) (0.021)

refiVal 29.178*** 27.800*** 26.514***

(0.551) (1.543) (1.271)

refiVal^2 -5.849*** -5.887*** -5.737***

(0.249) (0.659) (0.533)

Cumulative Qtrs ITM -0.003 -0.010*** -0.015***

(0.002) (0.002) (0.002)

Loan-to-value: 70 to 79 0.501*** 0.528*** 0.497***

(0.021) (0.021) (0.021)

Loan-to-value: 80 to 89 -0.194*** -0.144*** -0.180***

(0.025) (0.025) (0.025)

Loan-to-value: 90 to 97 -1.068*** -0.999*** -1.022***

(0.032) (0.031) (0.031)

Loan-to-value: 97+ -2.220*** -2.138*** -2.115***

(0.040) (0.039) (0.039)

Debt-to-income: 34 to 38 0.298*** 0.291*** 0.295***

(0.022) (0.022) (0.022)

Debt-to-income: 39 to 43 0.309*** 0.305*** 0.316***

(0.023) (0.023) (0.023)

Debt-to-income: 44 to 48 0.341*** 0.360*** 0.373***

(0.027) (0.027) (0.027)

Debt-to-income: 49+ -0.053 0.025 0.036

(0.035) (0.034) (0.034)

Debt-to-income: unknown -0.364*** -0.317*** -0.285**

(0.112) (0.107) (0.111)
58 

Table A-1 [continued]

Notes: This table presents estimates of the control variables from OLS regressions of Equation 1, with errors clustered at 

loan-level. The estimation sample includes 7,369,942 loan-quarter observations and 626,418 unique loans. Each column 

presents results from a different specification, where the capacity constraint indicator is interacted with the categorical 

borrower group indicated in the column title. In every case the dependent variable is an indicator that the loan was prepaid 

by the end of the next quarter. Borrower income at origination is expressed relative to the area median family income 

(AMFI); Low income means the borrower’s income is less than 50% of AMFI (moderate: [50-80%); middle-high: 80% or 

more). The variable refiVal is the ratio of the note rate to the borrower-specific market rate. See Table 3 for additional 

notes. 

Borrower Group Current Unpaid 

Principal Balance

Borrower Income 

at Origination

Current 

Credit Score

(1) (2) (3)

Spread at origination: 50bps+ -2.145*** -2.103*** -2.041***

(0.036) (0.036) (0.036)

Spread at origination: 25 to 49bps -1.200*** -1.141*** -1.123***

(0.032) (0.033) (0.033)

Spread at origination: -25 to 25bps -0.717*** -0.694*** -0.683***

(0.029) (0.029) (0.029)

County unemployment 0.333*** 0.349*** 0.349***

(0.005) (0.005) (0.005)

Purpose: Refi - not cash out -0.366*** -0.355*** -0.349***

(0.021) (0.021) (0.021)

Purpose: Refi - cash out 0.023 0.020 0.011

(0.035) (0.035) (0.035)

Black -1.105*** -1.093*** -1.084***

(0.038) (0.038) (0.038)

Hispanic (non-black) -0.746*** -0.734*** -0.735***

(0.030) (0.030) (0.030)

Asian (non-hispanic) 0.506*** 0.472*** 0.461***

(0.037) (0.036) (0.036)

Other (non-hispanic) 0.459*** 0.482*** 0.490***

(0.081) (0.080) (0.080)

First-time homebuyer -0.398*** -0.394*** -0.384***

(0.023) (0.023) (0.023)

Loan-quarter 0.128*** 0.123*** 0.117***

(0.001) (0.001) (0.001)

Loan age 0.449*** 0.461*** 0.470***

(0.005) (0.005) (0.005)

(Loan age)^2 -0.220*** -0.217*** -0.220***

(0.003) (0.004) (0.004)

(Loan age)^3 0.024*** 0.022*** 0.022***

(0.001) (0.001) (0.001)

Constant -32.162*** -30.365*** -28.655***

(0.326) (0.911) (0.765)

R-squared 0.029 0.028 0.027
59 

Table A-2: Effect of capacity constraint on probability of prepayment when varying the measure of the 

borrower’s financial incentive to refinance

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed from OLS 

regressions of Equation 1 on the main analysis sample (7,369,942 observations), with errors are clustered at the loan-level. 

Estimates of other controls are omitted for brevity. The panels are analogous to those in Table 3. The columns report 

results from separate specifications that use alternative measures of the borrower’s incentive to refinance. The first 

column reproduces the main results from column 3 of Table 3, using the ratio of the note rate to the adjusted market rate 

and evaluating the marginal effects at refiVal = 1.2. The second column uses the ratio of the note rate to the current prime

rate without adjusting for the borrower-specific GSE loan level pricing adjustment (LLPA), evaluating effects at refiVal = 

1.2. The third column uses the call option measure from Deng, Quigley, and Van Order (2000) with the refiVal evaluated 

at a value of 6. The fourth column uses the refinance threshold of Agarwal, Driscoll, and Laibson (2013), calculated using 

their “square-root rule” and suggested parameter values (including a discount rate of 5 percent, a 28 percent marginal tax 

rate, and a 10 percent probability of moving each year), and with the refiVal evaluated at -0.2. In both the third and fourth 

columns we use the LLPA-adjusted market rate when calculating the refinance incentive. 

Measure of Refinance Incentive Int. Rate Ratio - 

LLPA Adj

Int. Rate Ratio - 

Not Adj Call Option Refi Threshold

(1) (2) (3) (4)

A. Current unpaid principal balance ($1000s)

<90 -0.713*** -0.836*** -0.811*** 0.581***

(0.067) (0.064) (0.069) (0.065)

[90,150) -0.370*** -0.272*** -0.221*** 0.089*

(0.044) (0.044) (0.047) (0.046)

120 or more 0.863*** 1.234*** 1.439*** 1.504***

(0.034) (0.039) (0.037) (0.039)

B. Borrower income (as % of area median income)

Low (< 50) -0.596*** -0.613*** -0.529*** -0.157**

(0.070) (0.068) (0.072) (0.071)

Moderate [50,80) -0.389*** -0.248*** -0.134*** 0.164***

(0.049) (0.052) (0.052) (0.060)

Middle (80 or more) 0.733*** 0.997*** 1.222*** 1.294***

(0.042) (0.046) (0.034) (0.034)

C. Current credit score

620-659 -0.916*** -0.594*** -0.475*** -0.363***

(0.098) (0.126) (0.113) (0.123)

660-699 -0.425*** -0.231*** -0.008 0.070

(0.071) (0.083) (0.078) (0.095)

700 or more 0.573*** 0.764*** 0.960*** 1.161***

(0.036) (0.038) (0.031) (0.032)
60 

Appendix Table A-3: Effect of capacity constraint on probability of prepayment when varying the 

capacity constraint measure

Notes: This table presents marginal effects of capacity constraints on the probability of prepayment computed from OLS 

regressions of Equation 1 on the main analysis sample (7,369,942 loan-quarter observations), with errors clustered at loanlevel. The panels are analogous to those in Table 3. The columns report results from separate specifications that use 

alternative county-quarter level measures of whether constraints are binding. The first column reproduces the main results 

from column 3, Table 3 where the indicator equals one when the 75th percentile of application days for refinance loans in 

the county-quarter lies above 110% of that county's average over the period of analysis (see Background section for more 

details). The indicator is computed similarly in columns 2 and 3, but using the 50th and 90th percentile value of application 

processing days, respectively. In the fourth column we follow Choi et al. (2022) and categorize a county-quarter as 

capacity constrained if the share of applications filed in the quarter that are “incomplete” (meaning no action has been 

taken) by quarter-end exceeds the 75th percentile value of “share of apps incomplete” over all county-quarter observations 

in the sample. The indicator in Column 5 is also based on the “share of apps incomplete” measure, but is set equal to 1 if 

the county-quarter value exceeds 110 percent of that county’s average over the sample period.

Capacity Constraint Measure 75th Pctl of App 

Processing Days

50th Pctl of App 

Processing Days

90th Pctl of App 

Processing Days

High Share of 

Incomplete Apps

75th Pctl of 

Incomplete Apps

(1) (2) (3) (4) (5)

A. Current unpaid principal balance ($1000s)

<90 -0.836*** -0.723*** -0.790*** -0.393*** -0.268***

(0.064) (0.061) (0.063) (0.067) (0.063)

[90,150) -0.272*** -0.291*** -0.204*** 0.120*** 0.356***

(0.044) (0.042) (0.044) (0.047) (0.049)

120 or more 1.234*** 1.096*** 1.432*** 2.411*** 2.066***

(0.039) (0.038) (0.039) (0.038) (0.040)

B. Borrower income (as % of area median income)

Low (< 50) -0.613*** -0.548*** -0.532*** -0.358*** -0.489***

(0.068) (0.065) (0.067) (0.066) (0.059)

Moderate [50,80) -0.248*** -0.258*** -0.146*** 0.411*** 0.129***

(0.052) (0.049) (0.051) (0.051) (0.043)

Middle (80 or more) 0.997*** 0.862*** 1.189*** 2.159*** 2.486***

(0.046) (0.046) (0.046) (0.040) (0.038)

C. Current credit score

620-659 -0.594*** -0.338*** -0.542*** 0.185 -0.041

(0.126) (0.124) (0.125) (0.132) (0.125)

660-699 -0.231*** -0.256*** -0.214*** 0.491*** 0.479***

(0.083) (0.080) (0.083) (0.082) (0.078)

700 or more 0.764*** 0.626*** 0.951*** 1.796*** 1.712***

(0.038) (0.038) (0.038) (0.032) (0.032)

Number of observations 7,369,942 7,369,942 7,369,942 7,369,942 7,369,942

Uses Application Processing Days Y Y Y N N

Uses "Share of Apps Incomplete" N N N Y Y
61 

Appendix Table A-4: Sample means, HMDA-based dataset used in supplemental analyses 

Notes: This table presents sample means of the variables used in supplemental analyses of HMDA data described in 

Section 6 of the paper. See the Data Appendix for additional details. 

Sample Originations Applications

(1) (2)

Interest rate (bps) 322.1

Rate spread (bps) 11.6

Spread as % of APOR 3.8

% Incomplete 11.6

% Denied for underwriting 8.3

Application processing days 52.4

% capacity-constrained lender 38.3 39.8

Loan amount

< $90k 1.8 2.5

$90-150k 9.5 10.6

$150k+ 88.7 87.0

Borrower income

Low 5.5 7.7

Moderate 18.1 18.9

Middle-High 76.5 73.5

Credit score

[620, 660) 2.4 4.5

[660, 700) 8.8 9.2

700+ 88.8 76.4

Unknown 0.0 9.9

Race and ethnicity

Black 3.4 4.2

Hispanic 6.7 7.2

White non-Hispanic 63.0 59.5

Other 26.9 29.1

Combined loan-to-value

<= 60 29.0

(60, 70] 18.0

(70, 75] 15.9

(75, 80] 17.4

(80, 85] 7.3

(85, 90] 7.2

(90, 95] 4.0

95+ 1.1

Debt-to-income

<= 33 48.2

(33, 38] 16.4

(38, 43] 16.3

(43, 48] 13.6

48+ 5.4

Net points 0.1

Action taken

Loan originated 100.0 76.6

Approved but not accepted 2.6

Application denied 12.2

Closed for incompleteness 8.6

Number of Observations 4,982,388 6,288,436