WORKING PAPER SERIES

Regional Variation in Transaction Costs, Mortgage 

Rate Heterogeneity, and Mortgage Refinancing 

Behavior

Leonard Kiefer

Freddie Mac

Hua Kiefer

Federal Deposit Insurance Corporation

Tom Mayock

Virginia Tech

October 2023

FDIC CFR WP 2023-03

fdic.gov/cfr

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

preliminary research findings to stimulate discussion and critical comment. Views and opinions expressed in CFR Working 

Papers reflect those of the authors and do not necessarily reflect those of the FDIC or the United States. Comments and 

suggestions are welcome and should be directed to the authors. References should cite this research as a “FDIC CFR 

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

revision.
Regional Variation in Transaction Costs, Mortgage

Rate Heterogeneity, and Mortgage Refinancing

Behavior ∗

Leonard Kiefer† Hua Kiefer‡ Tom Mayock§

October 23, 2023

Abstract

Recent work has demonstrated that the U.S. mortgage market is characterized

by significant heterogeneity in the interest rates that are offered to borrowers as

well as mortgage refinancing behavior. In this study we contribute to the mortgage heterogeneity literature by providing the first systematic analysis of regional

differences in transaction costs in the mortgage market. Using the Uniform Closing

Database—a unique repository of loan-level closing cost information—we demonstrate that there is a tremendous amount of regional variation in transaction costs

in the mortgage market, most of which is driven by differences in local mortgage

stamp taxes and recording fees.

In the second part of our paper, we take up the question of how failing to

account for such heterogeneity might affect studies of borrower behavior in the

mortgage market. We do so through the lens of the failure-to-refinance literature

on optimal refinancing activity. Accounting for rate and closing costs heterogeneity significantly reduces estimates of suboptimal refinancing behavior, particularly

among borrowers with high-risk credit profiles and those living in states with high

closing costs. Because regional variation in closing costs is driven by the state and

municipal policies, our results suggest that local governments play a role in the pass

through of monetary policy via the mortgage market that has not been previously

documented. Our findings also provide a potential mechanism above and beyond

∗The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the

views of the Federal Deposit Insurance Corporation, the United States of America, or Freddie Mac. The

authors would like to thank Michael Lacour-Little, David Zhang, and participants at the 2023 AREUEA

National Meetings and FDIC seminar for helpful comments.

†Freddie Mac, leonard_kiefer@freddiemac.com

‡Federal Deposit Insurance Corporation, hkiefer@fdic.gov

§Virginia Tech, tmayock@vt.edu

1
the home equity channel that could explain regional variation in refinancing activity

and consumer spending during recoveries.

Keywords: Mortgage Lending, Household Finance

2
1 Introduction

Recent research suggests that heterogeneity is the rule as opposed to the exception in the

mortgage market. For example, Hurst, Keys, Seru and Vavra (2016) find that default risk

varies significantly across markets, several papers have documented significant differences

in the interest rates paid by borrowers with nearly identical credit profiles (Woodward and

Hall, 2012; Gurun, Matvos and Seru, 2016; Alexandrov and Koulayev, 2018; McManus,

Liu and Yi, 2018; Bhutta, Fuster and Hizmo, 2020), and there is now evidence that refinancing behavior differs by region (Beraja, Fuster, Hurst and Vavra, 2019; Keys, Pope and

Pope, 2016), credit profile (Keys, Pope and Pope, 2016), and demographic characteristics

(Gerardi, Willen and Zhang, 2023; Andersen, Campbell, Nielsen and Ramadorai, 2020).

In this study we contribute to this mortgage heterogeneity literature by providing the first

systematic analysis of regional differences in transaction costs in the mortgage market.

Using the Uniform Closing Database (UCD), a unique repository of loan-level closing

cost information, we show that the transaction costs—or “closing costs”—associated with

refinancing a loan of a fixed amount vary tremendously across markets in the U.S.1 As we

discuss in more detail in Section 2, regional variation in closing costs is driven primarily by

interstate—and in some cases intrastate—differences in the tax treatment of new mortgage

originations. On net, these regional differences in tax policy create a sizable amount of

variance in the transaction costs associated with refinancing a mortgage. For example,

after netting out discount points, prepaid homeowner’s insurance, and prepaid escrow

fees, the median cost of refinancing a conventional 30-year fixed-rate mortgage with an

initial balance between $190,000 and $210,000 in our data ranged from a low of $1,997 in

Iowa to a high of $4,957 in New York: nearly all of this difference is driven by taxes and

government fees.2

To preview the results of the first part of our analysis, we display in Figure 1 the empirical

densities of closing costs expressed as a fraction of the initial principal balance for a subset

of loans in our data.3 The variation in transaction costs exhibited by these densities

is at odds with the assumption that closing costs scale only with loan balances that

1When a borrower takes out a new loan on a property for which the annual insurance and property

taxes will be paid via an escrow account, some fraction of these annual costs—typically referred to as

“prepaids”—must be paid into the escrow account at the time of closing. While prepaids are included in

the official Closing Disclosure document, they do not represent transaction costs in the traditional sense

of the term: the insurance premium and property tax bill must be paid regardless of whether the borrower

is taking out a new mortgage on the home. For this reason, we exclude prepaids when calculating closing

costs in our analysis. It is worth emphasizing that even if prepaids were viewed as transaction costs,

because prepaids reflect the amount of time between the loan’s origination date and the date on which

property taxes and insurance premia must be paid, prepaids are not comparable even between otherwise

identical loans that are originated at different points in time during the same calendar year.

2We focus on loans with a principal balance between $190,000 and $210,000 in this example as loans

in this value range represent a significant fraction of new originations in our data.

3The subset of loans used to produce these densities were originated in September of 2019 to borrowers

with FICO scores in excess of 740 in three different loan-to-value (LTV) ratios.

3
Figure 1: Closing Cost Densities for Refinance Loans Originated in September 2019

is frequently employed in studies of the mortgage market. This observation raises the

following question: how does failing to account for closing cost heterogeneity impact

studies of consumer behavior in the mortgage market? In the second part of our paper,

we take this question up through the lens of the failure-to-refinance (FTR) literature

pioneered by Agarwal, Driscoll and Laibson (2013) (“ADL” hereafter).4

In the context of the FTR literature, failing to account for closing cost heterogeneity could

result in estimates of suboptimal refinancing behavior (“FTR rates”) with a systematic

upward bias in markets with high closing costs and a downwards bias in markets with

low closing costs. To summarize the mechanics of this bias, in Figure 2 we display the

minimum difference between the market rate and a borrower’s existing rate (the “interest

rate threshold”) for it to be optimal for a hypothetical borrower with a $200,000 30-year

fixed-rate mortgage that expects to move in 10 years to refinance under the ADL framework.5

If our hypothetical borrower lived in a state with low closing costs such as Iowa,

4Throughout this paper we use FTR rate to measure the proportion of in-the-money borrowers who

fail to refinance.

5For example, if a borrower currently has a loan with carrying an interest rate of 4 percent, if the

ADL interest rate threshold is 1.1, the market interest rate must fall to 2.9 percent for it to be optimal

to refinance.

4
it is optimal to refinance in this example when the rate difference is roughly 100 basis

points. If this borrower lived in New York, on the other hand, interest rates would have

to fall by more than 140 basis points relative to the borrower’s existing rate to justify

refinancing. Ignoring heterogeneity in closing costs would thus lead us to systematically

overestimate suboptimal refinancing activity in New York while systematically underestimating suboptimal behavior in Iowa. By similar logic, if we fail to account for variation

in the mortgage rates offered to buyers with different credit profiles, the FTR rates would

be biased upward for low-risk borrowers who are offered below-average rates and biased

downwards for high-risk borrowers.

Figure 2: Interest Rate Threshold for Optimal Refinancing Rule

5
In our analysis of refinancing behavior, we use the UCD records and a servicing database of

residential mortgages between 2005 and 2019 to adjust the aforementioned ADL interest

rate threshold to account for heterogeneity in both closing costs and interest rates to

calculate an“adjusted FTR” rate. To get a sense of how failing to account for heterogeneity

impacts studies of optimal refinancing behavior, we benchmark the adjusted FTR against

an “unadjusted FTR” measure that does not account for variation in mortgage rates and

transaction costs.

There are three primary takeaways from this FTR analysis, which we present in Section

4. First, when we aggregate across all loans, the adjusted FTR is typically lower than the

unadjusted FTR; this finding implies that, in the aggregate, failing to account for rate and

closing cost heterogeneity will overstate the amount of suboptimal refinancing behavior

in the U.S. Second, when we study refinancing behavior in subgroups based on credit

scores and loan-to-values (LTVs), we find that accounting for mortgage rate heterogeneity

significantly reduces the estimated FTR incidence among borrowers with high LTVs and

low credit scores. The high FTR rates reported for high-risk borrowers in previous work

thus appear to be explainable, in part, by such borrowers facing higher mortgage rates that

make refinancing less attractive.6 Lastly, when we study refinancing behavior on a stateby-state basis, we find that accounting for variation in closing costs significantly lowers

the FTR rate in states with high closing costs, such as New York and Florida. Accounting

for closing cost differences across states thus appears to be particularly important when

conducting any regional comparisons of refinancing behavior.

Regarding the existing literature, our analysis of the UCD data is most closely related

to a very small set of studies on closing costs in the U.S. mortgage market. To our

knowledge this entire literature is comprised of just four studies. Shroder (2007) uses

a small sample of loans guaranteed by the Federal Housing Administration that were

originated in 1997 to show that the “Good Faith Estimate” of total lending and title fees

that lenders were required to provide under the Real Estate Settlement and Procedures

Act is an unbiased estimate of the actual fees paid by borrowers. Woodward (2008) also

focuses on FHA lending and uses mortgages originated in 2001 to demonstrate that even in

a pool of relatively homogeneous loans, origination fees, title fees, and real estate agent fees

can vary significantly across borrowers. In a similar study, Feinburg, Kuehn, McKernan,

Wissoker and Zhang (2012) provides evidence of significant heterogeneity in title insurance

charges for FHA loans in five U.S. cities.7 Most recently, Acolin and Walter (2020) use

the closing cost variables in the 2019 Home Mortgage Disclosure Act (HMDA) data to

conduct a descriptive analysis of how mortgage origination costs vary with borrower and

6Accounting for interest rate heterogeneity from the possibility that borrowers with low credit scores

and high LTVs are charged higher interest rates in the Keys, Pope and Pope (2016) measures would

reduce the estimated failure-to-refinance rate among borrowers with low credit scores and high LTVs

while raising the FTR rate among borrowers with high credit scores and low LTVs.

7“Title charges” in Feinburg et al. (2012) refer to the cost associated with purchasing title insurance,

such as attorney fees and insurance premia. Title charges are only one component of total closing costs,

and they do not include mortgage taxes that drive the regional variation in transaction costs that we

document in the UCD data.

6
loan characteristics such as loan-to-value ratios, the presence of a government guarantee,

and a borrower’s race. Notably, none of these studies addresses the regional heterogeneity

in closing costs that is the focus of our analysis.

The papers closest to the optimal-refinancing analysis in the second part of our paper are

Keys, Pope and Pope (2016) and Andersen et al. (2020). Using a sample of U.S. mortgages

active in December 2010, Keys, Pope and Pope (2016) find that, based on the ADL

optimal-refinancing threshold, the overall FTR rate for U.S. mortgages is between 31.1

percent and 41.2 percent.8Importantly, this paper documents significant heterogeneity

in the FTR rate among subpopulations, even in a sample that is limited to low-risk

borrowers. For example, among the low-risk population, the FTR rate ranges from 12.3

percent for borrowers in the highest credit-score quartile to 20.1 percent among borrowers

in the lowest credit-score quartile.9 The incidence of suboptimal refinancing activity also

varies significantly across the LTV distribution, with the FTR ranging from 17.5 percent

in the lowest LTV quartile to 23.4 percent in the highest LTV quartile.10 Beyond credit

risk characteristics, Keys, Pope and Pope (2016) also provide evidence that the FTR rate

exhibits significant regional variation across states, ranging from 8 percent—9 percent in

Mississippi, Wisconsin, and Minnesota to roughly 30 percent in Florida, Oklahoma and

New York.

Andersen et al. (2020) also use the ADL framework to quantify the prevalence of suboptimal refinancing behavior, but their study focuses on Danish borrowers with fixed-rate

mortgages between 2009 and 2017. The authors find that while 43.7 percent of borrowers

in the sample should refinance, only 7.6 percent actually did. The implied FTR of 36.1

percent in this study is very similar to the 31.1 percent to 41.2 percent FTR range reported

in Keys, Pope and Pope (2016).11 To gain a better understanding of what is driving this

inaction in refinancing behavior, Andersen et al. (2020) estimate a modified version of

the ADL model that allows for borrower-level heterogeneity in the optimal-refinancing

threshold. The results from this exercise imply that household level observables—such as

the borrower’s age, residential location, and measures of financial literacy—play an important role in explaining variation in the propensity to refinance and thus any measures

of the failure to refinance.

Beyond the FTR literature, our findings also contribute to recent work studying the heterogeneous impact of monetary policy. Beraja et al. (2019) argue that regional variation in

8Keys, Pope and Pope’s (2016) sample is comprised of fixed-rate loans that are not guaranteed by

the Federal Housing Administration or U.S. Department of Veterans Affairs. Additionally, the sample is

limited to loans collateralized by owner-occupied single-family units that had a balance of at least $75,000

as of December 2010.

9Keys, Pope and Pope (2016) classify low-risk borrowers as those with loan-to-value ratios below 90

and credit scores in excess of 680.

10The overall FTR rate in the low-risk borrower sample in this study is 31.1 percent.

11In related work, Agarwal, Rosen and Yao (2016) find that even among borrowers who refinance, many

borrowers either wait too long to refinance or refinance at a suboptimal interest rate. Interestingly, when

they analyze a sample of borrowers who refinanced repeatedly in their data, the authors find evidence

that borrowers make smaller errors if they have previously refinanced.

7
home equity resulted in geographic heterogeneity in refinancing activity during the Federal

Reserve’s quantitative easing program. Our closing cost analysis suggests an additional

mechanism beyond the home equity channel that could drive such regional heterogeneity

in refinancing activity. Specifically, inter-state variation in the taxation of new mortgage

originations, and thus closing costs, alters the financial incentives to refinance for otherwise identical borrowers. In conjunction with Beraja et al.’s (2019) work, our results thus

imply that the tax treatment of mortgage debt by state and local governments might play

an important role in how monetary policy is transferred to the real economy. Our FTR

analysis is also related to recent work (Gerardi, Willen and Zhang, 2023) on heterogeneity

in refinancing activity across borrowers of different races. Minority borrowers typically

have lower credit scores and higher loan-to-value ratios and we document significant interest rate variation across these dimensions. Our work thus suggests that some of the racial

variation in refinancing activity may be driven by interest rate variation across credit risk

segments as well as geographic variation in the concentration of minority borrowers. We

address this possibility directly in the final portion of our analysis.

The remainder of the paper is structured as follows. Section 2 introduces the UCD

closing-cost data and provides a detailed analysis of the geographic variation in these

costs. Section 3 discusses the data and methodology that we use to account for mortgage rate heterogeneity in analysis of refinancing behavior. Section 4 introduces the FTR

framework, the results of which are reported in Section 5. Section 6 discusses the implications of our findings for quantifying FTR rates by race and ethnicity, and Section 7

concludes.

2 Closing Cost Heterogeneity

There are significant up-front costs that borrowers typically must pay to refinance their

mortgage. These costs include loan origination charges, discount points the borrower may

choose to pay to lower their interest rates, taxes and other government fees, and fees for

an assortment of services that the borrower either may or may not be permitted to shop

for.12 Additionally, borrowers may need to prepay a portion of their interest, mortgage

insurance, property taxes, and homeowner’s insurance at closing. These costs can easily

add up to thousands of dollars, an amount significant enough to tilt the financial calculus

on whether or not refinancing is optimal.

As discussed above, previous work on consumer behavior in the mortgage market has

been based on an assumption—likely driven by a lack of loan-level closing cost data—that

closing costs vary only with the mortgage balance but not with a borrower’s geography.

The first contribution of our analysis is demonstrating that this assumption does not hold

12Under current mortgage lending regulations, borrowers are permitted to shop for appraisals, property

inspections, and title insurance. Other services associated with closing the loan may be chosen by the

lender.

8
true in the context of the U.S. mortgage market. We provide evidence of this assertion

using data from the Uniform Closing Database (UCD). The UCD is a loan-level database

comprised of all loans that were purchased by Fannie Mae and Freddie Mac between

January of 2018 and May of 2021 and constitutes roughly 60 percent of the total U.S.

mortgage market during the sample period. The UCD records are incredibly rich and

report information on virtually every field that is recorded in the loan closing documents.

The first step in documenting closing cost heterogeneity requires calculating the costs a

borrower faces when refinancing a loan. Towards that end, we use the UCD’s closing

document fields to calculate a total closing cost measure, which we denote CCsq. For

each mortgage s on a property located in state q, we calculate CCsq by summing over the

following UCD fields: origination charges net of discount points; services that the borrower

did not shop for; services that the borrower did shop for; taxes and other government fees,

including transfer taxes and recording fees; and any fees listed in the “Other Fees” field.13

It is worth noting that in addition to the components that we include in CCsq, the UCD

also reports information on prepayments into escrow accounts at the time of origination

as well as discount points paid by the borrower to reduce the interest rate on the loan. If a

borrower opts to have their servicer pay property taxes and homeowner’s insurance premia

on their behalf, he or she will typically need to prepay some portion of the property tax bill

and insurance into an escrow account at the time a loan is originated.14 Because a borrower

must make tax and insurance payments regardless of whether the loan is refinanced,

prepayments into escrow accounts that are reported in the UCD are not transaction costs

in an economic sense, and for this reason, we do not include escrow prepayments in CCsq.

For similar reasons, we also do not include discount points in our origination charge

measure as borrowers are free to vary the amount of points they pay at closing to receive

higher or lower mortgage rates.15

After limiting the UCD records to conventional, 30-year fixed-rate refinance mortgages

to match the loans used elsewhere in our analysis, we were left with more than 5 million

loan-level closing-cost observations. We report key summary statistics for this sample in

13The origination fees in our closing cost measure include any application fees as well as the fee that

the lender charges for underwriting the loan. Lenders are required to allow borrowers to shop for some

services that are used in the loan closing process, such as property inspections and closing agents. If

borrowers elect to shop for these services, the cost of the services will be listed under the “Services the

Borrower Did Shop For” field. If borrowers instead elect to let the lender select the provider of these

services, any associated fees will be reported under the “Services the Borrower Did Not Shop For” field.

The “Other Fees” field can contain payments for services such home inspections and title insurance. If

a loan was originated without discount points, we define origination charges for that loan as the total

upfront origination charges reported in the UCD. If a borrower purchased discount points, we define

origination charges as the total upfront origination charges in the UCD less the discount points.

14The amount of funds that must be paid into escrow at origination depend on when the loan is

originated. If property taxes are not due for another year, for example, the borrower would only have to

prepay a small fraction of the total tax bill when the loan is closed. Alternatively, if the tax bill is due

shortly after closing, the required prepaid taxes into escrow would be roughly equal to the full property

tax bill.

15See Stanton and Wallace (1998) for a formal analysis of mortgage point choice.

9
Table 1: Closing Cost Summary Statistics

Cost Type

Total Origination Taxes and

Net Cost Charges Government Fees

Mean $2,844 $2,181 $265

Median $2,990 $1,510 $140

Standard Deviation $1,136 $1,905 $374

25th Percentile $2,372 $1,030 $69

75th Percentile $3,701 $2,913 $281

Observations 5,443,056 5,443,056 5,443,056

This table summarizes the net closing cost and its two main components.

The sample is comprised of conventional, 30-year, fixed-rate, refinance

mortgages reported in the UCD that closed between January 1, 2018

and May 5, 2021. Total Net Cost is defined as the sum of: origination

charges net of discount points; services that the borrower did not shop

for; services that the borrower did shop for; taxes and other government

fees, including transfer taxes and recording fees; and any fees listed in

the “other fees” field. Discount points are not included in our Origination Charges measure.

Table 1. The average and median total net closing costs in our sample are $2,844 and

$2,990, respectively. As evidenced by Figure 3, which reports average total net closing

costs, origination charges, and taxes and government fees on a state-by-state basis, pooling

loans across states obscures a considerable amount of geographic variation how closing

costs are determined for a particular loan.

An analysis of the sub-components of total closing cost measures reveals that much of

this regional heterogeneity is driven by inter-state differences in tax policies. Taxes and

government fees for refinancing are very low in much of the U.S. For example, the average

borrower in the state with the lowest closing costs in our sample (Iowa) paid only $84 in

taxes and fees. In two of the largest states by population (New York and Florida), the

fee and tax component of closing costs exceeds $1,000 on average. Florida’s high closing

costs are driven by a combination of two separate taxes: a documentary stamp tax of

35 cents per $100 of the mortgage’s outstanding balance at origination (Florida Statute

201.08, 2020) and an intangible tax of 20 cents per $100 of the mortgage balance (Florida

Statute 199.133, 2020).

The mortgage tax landscape is even more complex in New York, where the mortgage

recording tax varies by county and loan amount. The minimum recording tax in state of

New York is 1.05 percent of the mortgage balance, and borrowers in New York City could

10
Figure 3: Average Closing Costs by State for Refinance Loans

This figure reports average total net closing costs, origination charges, and taxes and government

fees on a state-by-state basis using our UCD sample.

potentially be taxed at rates as high as 1.925 percent of the loan balance.16 Borrowers who

are refinancing existing mortgage debt can, in principle, reduce these costs by petitioning

their previous lender to engage in a Consolidation, Extension and Modification Agreement

(CEMA), commonly referred to as a mortgage assignment (Prevost, 2013). Processing

CEMAs is costly (Dhen, 2021), and it is not clear how frequently New York borrowers

pursue mortgage assignments.

A full analysis of the nuances of mortgage tax policy is beyond the scope of this paper.

The foregoing details on tax policy in Florida and New York, however, suggest that the

relationship between closing costs and loan balances varies significantly across states. This

conjecture is borne out by the UCD data. Consider Figure 4, which displays the median

closing costs as well as the median of closing costs as a fraction of the unpaid principal

balance (UPB) for loans of different balance amounts for select states in our sample. In

California and Texas, closing costs rise very slowly with UPB. Because of the progressive

nature of their mortgage tax structure, however, closing costs for borrowers in Florida

and New York are much higher for borrowers with high UPBs relative to to those with

low UPBs.

Our initial analysis of the UCD data suggests when studying refinancing behavior in the

U.S. mortgage market, researchers should account for regional variation in the relationship

16In New York City, borrowers pay 1.8 percent of the loan amount if the balance is less than $500,000

and 1.925 percent if the loan exceeds $500,000.

11
Figure 4: Closing Costs by Loan Balance for Select States

This figure displays the median closing costs as well as the median of closing costs as a fraction

of the unpaid principal balance for loans of different balance amounts for select states in our

sample.

between UPB and total closing costs. That said, to estimate the closing costs that a

borrower would face when refinancing, we first use the UCD data to estimate simple

regression models of the following form on a state-by-state basis

CCsq = αq + βqUP Bsq + εsq (1)

where UP Bsq denotes the outstanding balance for loan s in state q at the time when it was

refinanced and εsq is the error term. Note that because we estimate this model on a stateby-state basis, we allow for regional variation in both the fixed costs of refinancing, as

captured by αq, and the relationship between total closing costs and UP Bsq, as captured

by βq. We summarize the results of these regressions in Figure 5, which reports the

estimated slope and intercept from Equation 1 for each state in our sample.

Our FTR analysis in Section 4 uses the estimated regression parameters summarized in

Figure 5 in conjunction with the property location and outstanding principal balance to

construct our preferred estimate of the closing costs that a borrower would face if they

opted to refinance.17

17In the context of Equation 1, Agarwal, Driscoll and Laibson (2013) and Keys, Pope and Pope (2016)

assumed that αq = 2000 and βq = 0.007905 for all loans, regardless of the state in which they were

originated. This value for βq was based on a closing-cost expression derived in Appendix A of Agarwal,

Driscoll and Laibson (2013).

12
Figure 5: Closing Cost Regression Parameters by State

This figure reports the estimated slope and intercept from Equation 1 for each state in our UCD

sample.

To get a sense of how ignoring state-level heterogeneity in closing costs might affect studies

of borrower refinancing behavior, we took the loan-month observations from our McDash

data sample 18 and estimated transaction costs in a manner identical to previous studies

in the U.S (“Unadjusted Closing Costs”).19 Then, we used the regression parameters from

Equation 1 estimated using the UCD data to estimate closing costs on the same set of

loans (“Adjusted Closing Costs”). Figure 6 displays the state-by-state comparisons of

these two closing-cost measures, with darker shades of purple denoting higher average

closing costs.

Unsurprisingly, there is little geographic heterogeneity in closing cost estimates when we

assume away regional variation in the relationship between loan balances and closing

costs: by assumption, all of the variation in the left panel of this figure is driven by

differences in loan amounts. When we account for geographic heterogeneity in closing

costs, however, a very different story emerges: the adjusted closing costs for mortgages in

some states like Florida and New York are markedly higher on average, while the adjusted

transaction costs in states like Iowa and Nebraska are significantly lower. All else equal,

this simple analysis implies that failing to account for regional differences in closing costs

could significantly overstate the amount of suboptimal refinancing behavior in states with

18The McDash data sample is the main data source underlying our FTR calculation in Section 4.

19Across all states, this sample is comprised of roughly 829 million loan-months observations. Section

4 describes the construction of the sample in detail.

13
Figure 6: Geographic Variation in Unadjusted and Adjusted Closing Costs

This figure displays the state-by-state averages of our two closing-cost measures: Unadjusted

Closing Costs and Adjusted Closing Cost, with darker shades of purple denoting higher average

closing costs. The loan sample underlying this figure is from our McDash data used for the FTR

analysis.

high taxes and fees on mortgage originations, such as Florida and New York.

3 Interest Rate Heterogeneity

Previous mortgage refinance research has assumed that all borrowers could potentially

refinance at the same interest rate, regardless of the borrower’s credit profile. This

common-rate assumption is at odds with the emerging literature on mortgage rate heterogeneity which finds that rates vary significantly both across and within borrower risk

segments (Woodward and Hall, 2012; Gurun, Matvos and Seru, 2016; Alexandrov and

Koulayev, 2018; McManus, Liu and Yi, 2018; Bhutta, Fuster and Hizmo, 2020). We use

loan-level origination records from Black Knight’s McDash servicing database to account

for this heterogeneity. In addition to reporting “dynamic” information on mortgages such

as a loan’s delinquency status, outstanding balance, and borrowers’ refreshed credit scores,

the McDash database also includes static variables on loan and borrower characteristics

as of a loan’s origination date—such as the credit score, LTV, and interest rate used to

underwrite the loan.

We use the static component of the McDash records to estimate the rate at which a

loan would likely be refinanced. The first step in this estimation process is defining loan

segments based on LTV and credit scores that align with the typical segmentation schemes

used in mortgage pricing rate sheets.20 We then limit the McDash data to fixed-rate firstlien refinance loans on single-family owner-occupied homes that satisfied the following

20The credit score ranges that we use to define our segments are: (0, 640), [640, 660), [660, 680),

[680, 700), [700, 720), [720, 740), and [740, 850). The LTV ranges that we use to define our segments

14
restrictions: the loan had a 30-year term; the loan was not a cash-out refinance; the loan

was not guaranteed by the FHA or VA; and the loan did not have exotic characteristics

such as negative amortization periods, balloon payments, or prepayment penalties. Next,

to align the origination population with the sample that we use in the FTR analysis

described in Section 4, we remove mortgages from the data with original loan balances

that were below $75,000 or greater than the relevant conforming loan limit.

After imposing these data restrictions, we were left with a sample of more than 6 million

distinct refinance mortgage originations. We then used the static McDash variables to

calculate the median mortgage rates for newly originated loans on a month-by-month

basis for each LTV-FICO segment.21 These median rates, which we denote as irt, serve

as our estimate of the rate that a loan in segment r as of month t would receive upon

refinancing. The loans in the McDash data are drawn from across the credit spectrum,

and in the aggregate the McDash data has covered between 52 and 70 percent of the

total U.S. market since 2005. That said, irt should thus serve as a good approximation

to the rate a borrower in a particular risk segment would expect to receive if decided to

refinance.

Consistent with previous work, we find that mortgage rates vary considerably both within

and across segments. For example, consider Figure 7, which depicts box plots for each of

the risk segments for loans originated in September of 2019 that satisfy the restrictions

discussed above.22 In this plot, within each FICO-LTV segment the upper and lower

boundaries of the rectangle represent the 25 percent and 75 percent quantiles of the rate

distribution, respectively, while the segment-level mean is depicted by a red dot and the

segment-level median is depicted by the horizontal black line.

In this particular month, for loans in the lowest risk segments with LTVs less than 75

and FICO scores in excess of 740, the median interest rate on originations was 3.625

percent. In contrast, in segments with LTVs in excess of 90 and FICO scores below 700,

all of the segment specific rates were 4 percent, with rates in the highest risk segments

exceeding 4.125 percent. The overall mean interest rate in the September 2019 sample

was 3.8 percent, and the reported rate in Freddie Mac’s Primary Mortgage Market Survey

(PMMS) in that month ranged from 3.49 percent to 3.64 percent. The variation in rates

summarized in Figure 7 suggests that if we assume that all borrowers can refinance at

either the average rate in the market or at the rate reported in the PMMS rate, we will

seriously understate the true mortgage rates facing high-risk borrowers who might consider

are: (0, 60], (60, 70], (70, 75], (75, 80], (80, 85], (85, 90], (90, 95], and (95, 100].

21For example, one segment is defined as borrowers with a credit score greater than 740 and an LTV less

than 60 taking out a 30-year fixed-rate mortgage. To construct loan pricing measures for this segment in

January of 2019, we identify all 30-year fixed-rate refinance mortgages in the McDash records that satisfy

our data restrictions that were originated in January of 2019 to borrowers with credit scores greater than

740 and LTVs less than 60. We then take the median of these interest rates to construct irt.

22Rate variation across risk segments was evident in every month in our sample. We chose to focus on

the variation in one month here to simplify the exposition. Box plots of rate variation by risk segment

for every month in our sample are available upon request.

15
refinancing, while the converse is true for very low-risk borrowers. Much like our analysis

of closing cost variation, our mortgage rate analysis implies that our FTR analysis should

account for the sizable and systematic heterogeneity in interest rates across different risk

segments.

4 Failure to Refinance Framework

Whether or not a borrower should refinance a mortgage is a complex decision that depends

on a host of variables. The starting point for all FTR analyses is adopting a framework

that maps all of these factors into a decision rule that can be used to determine when it

is in the borrower’s interest to refinance his or her mortgage. Following previous work on

the topic, we use the methodology established in Agarwal, Driscoll and Laibson (2013)

for making this determination.

Before outlining the optimal refinancing rule, we must introduce some notation. Let Mst

denote the outstanding balance on loan s as of month t, let ist denote the prevailing

nominal mortgage rate at which loan s could be refinanced as of month t, and define is

as the existing rate on loan s. If xst is defined as

xst = ist−1 − is (2)

it is financially advantageous for borrowers to refinance their mortgage when xst falls

below a critical level denoted x

∗

st. Agarwal, Driscoll and Laibson (2013) show that x

∗

st can

be approximated as

x

∗

st ≈ −r σκst

Mst(1 − τ )

p

2(ρ + λst) (3)

where σ is the annualized standard deviation of the mortgage interest rate, ρ is the

discount rate, τ is the marginal tax rate, and κst is the cost of refinancing loan s. λst

denotes the expected rate of exogenous mortgage prepayment and is defined as

λst = μ +

is

exp(isΓst) − 1

+ π (4)

where Γst is the remaining life of the existing mortgage in years for loan s as of month

t, π is the inflation rate, and μ is the exogenous relocation hazard rate. Note that λst

captures only the exogenous factors that reduce the expected value of the real mortgage

obligation, such as relocation, principle repayment, and inflation; mortgage terminations

due to refinancing are endogenous and are not reflected in λst.

16
Figure 7: Variation in Mortgage Rates by LTV-FICO Segment for September 2019 Refinance Originations

This figure summarizes the variation of mortgage rates by FICO-LTV buckets using the McDash

sample for loans originated in September of 2019. In this plot, within each FICO-LTV segment

the upper and lower boundaries of the rectangle represent the 25 percent and 75 percent quantiles

of the rate distribution, respectively, while the segment-level mean is depicted by a red dot and

the segment-level median is depicted by the horizontal black line.

17
In this framework, it is optimal to refinance a mortgage if

ist − is < −

r σκst

Mst(1 − τ )

p

2(ρ + λst) (5)

To study how heterogeneity in mortgage rates and closing costs influences FTR measures,

we construct two different estimates of the fraction of borrowers who failed to refinance

optimally in each month between January of 2005 and December of 2019. We produce

the first measure, which we refer to as the “Unadjusted FTR,” using the methodology of

Agarwal, Driscoll and Laibson (2013) under the assumption that closing costs only vary

with loan balance and that all borrowers receive the same interest rate when refinancing.

We then contrast the Unadjusted FTR measures against an “Adjusted FTR” that also uses

Agarwal, Driscoll and Laibson’s (2013) optimal-refinancing rule but allows for geographic

variation in fixed and variable closing costs as well as heterogeneity in mortgage rates

across credit risk segments.

We use the dynamic variables in the McDash data to create both the Unadjusted and

Adjusted FTR shares. To construct these measures, we first restrict the dynamic McDash records to loan-month observations for owner-occupied first-lien 30-year fixed-rate

mortgages on single-family homes that have a loan age of at least two months and have

loan balances that are at least $75,000 but less than the conforming loan limits. We also

remove from the sample loans that are guaranteed by the Federal Housing Administration (FHA) or the Department of Veterans Affairs (VA), as well as loans that have exotic

characteristics, such as negative amortization schedules, balloon payments, or prepayment

penalties. Lastly, we limit the data to loans that are reported as being current as of the

reporting month.23 These data filters align closely with those used in previous work and

will thus allow for a comparison between our benchmark Unadjusted FTR results and

those reported in previous work.24

Following the existing literature, our Unadjusted FTR measure is constructed based on

the assumption that in a particular month all loans in the sample could potentially be

refinanced at the same mortgage rate reported in the monthly PMMS survey and that the

monetary cost of refinancing at this rate is equal to 0.79 percent of the loan’s outstanding

23A loan is classified as being current as of a reporting month if the dynamic McDash indicate that the

loan is not delinquent nor is the loan in any part of the foreclosure process.

24For example. Keys, Pope and Pope (2016) limit their data to fixed-rate mortgages loans collateralized

by single-family owner-occupied homes that had an outstanding balance of at least $75,000 and were not

in foreclosure proceedings as of December of 2010. They also exclude from their sample any loans that

were guaranteed by the Federal Housing Administration or the Department of Veterans Affairs. In a

robustness check, the authors further limit the sample to loans that had a FICO score in excess of 680,

had a current LTV of less than 90, and did not have a missed payment recorded in the servicing records;

these limitations were intended to remove from the sample households that could not have refinanced

even if they wanted to because of credit conditions at the time.

18
balance plus $2,000.25 That is, in the context of Equation 2 and Equation 3, when we

construct our Unadjusted FTR measure we assume the following values for the interest

rate and closing-cost function parameters

ist = i

PM

t

κst = 2000 + 0.007905Mst

(6)

where i

PM

t

is the rate reported in the PMMS in month t.

For each of the loan-month observations that satisfy the data restrictions discussed above,

we use Equation 3 to define the optimal refinancing threshold under the unadjusted parameter assumptions - indicated by a superscript U - as26

x

U∗

st ≈ −s

σ (0.007905Mst + 2000)

Mst(1 − τ )

p

2(ρ + λst) (7)

in which case it is optimal to refinance if

i

PM

t − is < xU∗st . (8)

If Pst is an indicator variable that is equal to one if loan s prepays between month t − 1

and t, then we classify a loan as failing to refinance (F T RU

st = 1) under the unadjusted

25Agarwal, Driscoll and Laibson’s (2013) closing-cost function is defined as

κst = Fs + γsMst

where Mst is the balance of loan s in month t and Fs denotes the fixed component of closing costs. γs

captures the rate at which closing costs change with the balance of the newly originated loan and is

defined as follows

γs = fs



1 −

τ

θ + ρ + π



1 − exp(−(θ + ρ + π)N)

N

ρ + π

θ + ρ + π

+ θ



In the expression above, fs denotes the discount points paid as a percentage of the loan’s balance, θ

denotes the expected arrival rate of an event such as a move or subsequent refinancing that allows for

fully deducting the discount points, N denotes the number of years of the borrower’s new mortgage term,

and τ /ρ/π are defined the same as in Equations 3 and 4 . Agarwal, Driscoll and Laibson (2013) and

Keys, Pope and Pope (2016) assume that τ = 0.28, ρ = 0.05, π = 0.03, θ = 0.2, Fs = 2000, N = 30, and

fs = 0.01, resulting in the following closing-cost function

κst = 2000 + 0.007905Mst

26When calculating λst and xU

∗

st , we assume that τ = 0.28, ρ = 0.05, π = 0.03, and μ = 0.1. Additionally, we set σ = 0.0109 These assumed parameter values are consistent with the assumptions used to

construct the optimal financing threshold in Agarwal, Driscoll and Laibson (2013) and Keys, Pope and

Pope (2016).

19
assumptions as follows

F T RU

st =

(

1 if i

PM

t − is < xU∗

st and Pst = 0

0 otherwise

(9)

If there are S active loans in the sample in month t, the Unadjusted FTR in month t,

denoted F T RU

t

, is defined as

F T RU

t =

1

S

X

S

s=1

F T RU

st (10)

When classifying loan s as failing to refinance for our Adjusted FTR measure, we first

assign the loan to a FICO-LTV segment using the updated FICO score and LTV measures

that are reported for loan s in the dynamic McDash variables in month t. If the loan is

assigned to risk segment r in month t, we assume that if the borrower were to refinance,

they would do so at the median mortgage rate of new originations in month t and segment

r in the McDash data that satisfy the data restrictions discussed in detail in Section 3;

we let irt denote this median segment-specific interest rate.27

In addition to allowing for across-segment interest-rate variation, when constructing the

adjusted optimal-refinancing threshold, in lieu of assuming that the closing-cost function

(κst) is the same for all borrowers throughout the U.S., we use the estimated state-specific

parameters from Equation 1 to construct an adjusted closing-cost measure that allows

for inter-state variation in κst. That is, for our Adjusted FTR measure, we assume the

following values for the interest rate and closing costs

ist = irt

κst = ˆαq + βˆ

qMst

(11)

where ˆαq and βˆ

q are the parameters for Equation 1 estimated using the UCD data for

loan originations in state q.

The optimal refinancing threshold under the adjusted parameter assumptions - indicated

by a superscript A - is thus

x

A∗

st ≈ −

vuut

σ



αˆq + βˆ

qMst

Mst(1 − τ )

p

2(ρ + λ) (12)

where σ, τ , ρ, and λ are set at the same values used to construct the unadjusted optimal27For a particular loan, changes in irt can be driven by migration between the credit risk segments over

times as well as by the changes in the prevailing mortgage rate within these segments.

20
refinance boundary. Under the adjusted parameter assumptions it is optimal for a borrower to refinance if

irt − is < xA∗

st (13)

and we classify a borrower as failing to refinance (F T RA

st = 1) in month t as follows

F T RA

st =

(

1 if i

A

rt − ist < xA∗

st and Pst = 0

0 otherwise

(14)

We define the Adjusted FTR in month t, denoted F T RA

t

, as

F T RA

t =

1

S

X

S

s=1

F T RA

st (15)

where again S is the number of active loans in the McDash data in month t.

28

5 Failure to Refinance Results

We report in Figure 8 the results of our FTR analysis based on four different calculation

methods on a month-by-month basis. The Adjusted FTR and Unadjusted FTR series

in this plot were calculated as described in Section 4. We calculated the “Rate-Adjusted

Only”FTR using the segment-specific mortgage rates described in Section 3 in conjunction

with the unadjusted closing-cost measures that ignore geographical heterogeneity, while

the “Cost-Adjusted Only” measure uses the state-specific closing cost estimates from Section 2 while assuming that all borrowers would refinance at the common rate reported in

the PMMS, regardless of their risk segment. For all four of the measures, we find that

the FTR series are low when interest rates are flat or rising and the series rise rapidly

when mortgage rates—as measured by the PMMS rate—begin to decline. These dynamics are to be expected as being able to refinance at a lower rate is a necessary though not

sufficient condition for being classified as failing to refinance.

28To account for the time gap between rate lock and closing, which is roughly 45 days, we lag ist and

irt by one month in our empirical analysis followed, as in (Keys, Pope and Pope, 2016).

21
Figure 8: Overall Failure to Refinance by Calculation Method

This figure displays the results of our FTR analysis using the McDash sample. The numbers

reported here are based on four different calculation methods on a month-by-month basis. The

Adjusted FTR and Unadjusted FTR series in this plot were calculated as described in Section

4. We calculated the “Rate-Adjusted Only” FTR using the segment-specific mortgage rates

described in Section 3 in conjunction with the unadjusted closing-cost measures that ignore geographical heterogeneity, while the “Cost-Adjusted Only” measure uses the state-specific closing

cost estimates from Section 2. 22
As discussed above, ignoring rate and closing cost heterogeneity could potentially impart

downwards or upward bias to FTR measures. We find that when constructed using a pool

of loans from all risk segments and geographies, the Unadjusted FTR measure consistently

overestimates the amount of suboptimal refinancing behavior. On average, the Unadjusted

FTR exceeded the Adjusted FTR by roughly 3 percentage points, and the Unadjusted

FTR exceeded the FTR for 83 percent of the months in our sample. We summarize these

findings in the first panel of Figure 8. Notably, the difference between the Unadjusted and

Adjusted FTRs in levels varies significantly over time, with the differences particularly

pronounced when the overall FTR measures are high. For example in 2012—the year

in which FTR rates peaked in our sample—the Unadjusted FTR measure exceed the

Adjusted FTR measure by between 9.3 and 19.7 percentage points in any given month.

When overall FTR rates are low, on the other hand, the absolute differences between the

Unadjusted and Adjusted series are very small. Some of this compression in the differences

in levels is mechanical in nature. When mortgage rates are rising, fewer loans cross

the optimal-refinancing boundary regardless of how mortgage rates and closing costs are

estimated. The absolute difference between the Adjusted and Unadjusted FTR measures

in a rising-rate environment will thus be small as both measures will be very close to zero.

The limited absolute difference notwithstanding, there are sizable proportionate differences

between the measures, even when overall FTR levels are low. The years 2006 and 2007

are a case in point: while the FTR measures are virtually indistinguishable in Figure 8

during this time period, the Unadjusted FTR exceeded the Adjusted FTR on average by

72 percent in these years. The sizable proportionate differences between the FTRs also

extend beyond rising-rate periods as the Unadjusted FTR exceeded the Adjusted FTR by

40 percent in the average month in our sample. We interpret these findings as evidence

that failing accounting for interest rate and transaction cost heterogeneity is important

in absolute terms when mortgage rates are falling and in relative terms regardless of the

interest rate environment.

Turning to the middle and bottom panels of Figure 8, we see that the Rate-Adjusted-Only

FTR series is virtually identical to the Adjusted FTR series in the top panel, while the

Cost-Adjusted Only series mimics the Unadjusted FTR series. The difference between

our Adjusted and Unadjusted FTR measures in the full population of mortgages in our

data is thus driven primarily by our correction for between-borrower variation in mortgage

rates, while our closing-cost correction has a very limited impact on the estimated FTR

rates in our pooled sample of loans.

While it is interesting that the Unadjusted FTR appears to be biased upward in a sample

of loans drawn from all risk segments and U.S. states, our primary hypothesis concerns

differences between the Unadjusted and Adjusted FTR in particular loan subpopulations.

Specifically, we expect the Unadjusted FTR to systematically overstate the FTR ratio for

high-risk borrowers who face interest rates higher than the PMMS rate and systematically

understate the FTR ratio for low-risk borrowers who receive loan offers below the PMMS

rate. Likewise, we expect our closing-cost correction to affect FTR measures most for

23
borrowers living in states where closing costs are significantly above or below the national

average.

We test these hypotheses by alternately partitioning the McDash data by risk segment

and geography and then comparing the Unadjusted and Adjusted FTRs. For our risksegment analysis, we first calculate the default rate for loans in each of the LTV-FICO

groups that we used to construct segment-specific interest rates.29 We then rank-order

the FICO-LTV segments by default risk and calculate the Adjusted and Unadjusted FTR

on a segment-by-segment basis. We summarize these measures in the top panel of Figure

9, where Segment 1 denotes the highest risk segment, and Segment 56 denotes the lowest

risk segment.

For both measures, the FTR rate falls in a nearly monotonic way as default risk decreases;

the exceptions to this pattern, which are depicted by large spikes in the FTR plots,

denote risk segments where the LTVs exceed 95.30 Our finding that the Unadjusted FTR

declines with credit risk is consistent with previous results in the literature (Keys, Pope

and Pope, 2016). The fact that the Adjusted FTR also declines with risk indicates that

a higher incidence of suboptimal refinancing activity among borrowers with higher LTVs

and lower credit scores cannot be explained entirely by variation in mortgage rates and

closing costs.

Consistent with our hypothesis, we find that the Unadjusted FTR measure exceeds the

Adjusted FTR for higher risk loans that would likely only be able to refinance at rates

significantly higher than that reported in the PMMS survey. Furthermore, the difference

between the Unadjusted and Adjusted FTRs generally declines with default risk, a finding

that is also consistent with the PMMS systematically overstating potential interest rate

savings for high-risk borrowers while serving as an adequate proxy for the rates facing

the average borrower.31 It is only in the lowest risk segments that the Adjusted FTR

exceeds the Unadjusted FTR; while this can be interpreted as evidence that the PMMS

understates the rates facing low-risk borrowers, the absolute magnitude of the bias in

Unadjusted FTR for the low-risk segments is significantly smaller than the bias in highrisk segments. For instance, in the highest risk segment, the Unadjusted FTR (42.47

percent) was nearly 17 percentage points higher than the Adjusted FTR (25.99 percent),

while in the lowest risk segment, the Unadjusted FTR (9.78 percent) was only 51 basis

points lower than the Adjusted FTR (10.29 percent).

29In this part of our analysis, we define a loan as being in default if it is 30 days-past-due or worse as

of the observation date.

30Borrowers with high LTVs will typically have difficulty finding a lender to refinance their loan. The

spikes in the FTR series at these points thus reflect a combination of suboptimal refinancing behavior as

well as the fact that such borrowers are credit constrained.

31Interestingly, the difference between the Unadjusted and Adjusted FTR measures also spikes in the

segments with LTVs in excess of 95. This pattern may be driven by the fact that the PMMS rate “is

based on first-lien prime conventional conforming home purchase mortgages with a loan-to-value of 80

percent” (PMMS Frequently Asked Questions, 2022), and rates on loans with LTVs in excess of 95 are

typically much higher than the rates on loans with LTVs of 80.

24
Figure 9: Failure to Refinance by Default Risk

This figure reports the Adjusted and Unadjusted FTR on a segment-by-segment basis using the

McDash sample. The FICO-LTV segments are rank-ordered by default risk with Segment 1

denoting the highest risk segment and Segment 56 denoting the lowest risk segment.

25
We also find strong support for our hypothesis when, in lieu of the LTV-FICO segments,

we segment borrowers by LTV and FICO scores separately. We report the results of these

analyses in the middle and bottom panels of Figure 9. As summarized in the middle panel,

as FICO scores increase, and thus default risk decreases: both FTR measures decline; the

Unadjusted FTR exceeds the Adjusted FTR; and the gap between the Adjusted and

Unadjusted FTR declines. When we use LTV to segment credit risk in the bottom panel,

the results are almost identical qualitatively to those from the FICO analysis.32 When

combined with the LTV-FICO segment results in the top panel, we interpret these findings

that regardless of how we quantify credit risk, the Unadjusted FTR measure is biased

upward and the magnitude of that bias increases with credit risk.

Lastly, to investigate possible heterogeneity in the impact of our closing-cost correction on

FTR measures, we calculate the Adjusted and Unadjusted FTRs on a state-by-state basis.

We report these measures in Figure 10.

33 The FTR measures and the median closing costs

for properties in a particular state as reported in the UCD data are displayed on the y-axis

and x-axis, respectively. The state abbreviation in black font denotes the Adjusted FTR,

while the state abbreviation in red denotes the Unadjusted FTR. The dashed red and

black lines represent the fitted lines from a regression of the Unadjusted and Adjusted

FTR measures on median closing costs, respectively.

On a state-by-state basis, the Unadjusted FTR exceeds the Adjusted FTR in all but three

states (Iowa, Nebraska, and Wisconsin) in which the measures were virtually identical.

Focusing on the left side of Figure 10, we see that in states with low closing costs, the

upward bias associated with the Unadjusted FTR was typically quite small. For example,

in Iowa and Wisconsin—the states with the lowest median closing costs—the Unadjusted

FTRs were 12.4 percent and 12.35 percent, respectively. These figures were little changed

after accounting for interest rate and closing cost heterogeneity, as the Adjusted FTR for

Iowa was 12.67 percent and the Adjusted FTR for Wisconsin was 12.44 percent.

As median state-level closing costs increase, so does the upward bias of the Unadjusted

FTR measure. For instance, the impact of accounting for closing cost heterogeneity was

most pronounced in Florida and New York, the states with the highest median closing

costs in the UCD data: the Unadjusted FTR in Florida (19.58 percent) and New York

(18.97 percent) fell by more than 6 percentage points to 11.06 percent and 12.17 percent

, respectively, following the heterogeneity corrections.

Figure 10 makes clear that when studying variation in FTR rates across regions, the

net impact of accounting for heterogeneity in closing costs and interest rates is most

pronounced in states where closing costs on a typical loan exceed the national average.

32Note that while credit risk is declining as we move from left to right on the x-axis in the middle panel

of Figure 9, credit risk is increasing as we move in the same direction in the bottom panel as default risk

increases monotonically with LTV.

33We calculated the FTR measures displayed in in Figure 10 by first calculating the FTRs for each

state-month combination in our data. Then, we took the average of the monthly FTR measures within

the state.

26
Figure 10: Failure to Refinance by State

This figure reports the Adjusted and Unadjusted FTRs on a state-by-state basis. The FTR

measures along the y-axis are calculated using the McDash sample, and the median closing

costs for properties in a particular state along the x-axis are reported in the UCD data. The

state abbreviation in black font denotes the Adjusted FTR, while the state abbreviation in red

denotes the Unadjusted FTR. The dashed red and black lines represent the fitted lines from a

regression of the Unadjusted and Adjusted FTR measures on median closing costs, respectively.

27
Figure 11: Failure to Refinance by State

This figure reports the Cost-Adjusted Only FTR rate over time for four states using the McDash

data: two states with the highest closing costs in our sample - Florida and New York, and their

bordering states with significantly lower transaction costs - Georgia and Pennsylvania.

To isolate the impact of the closing-cost correction on cross-region comparisons of suboptimal refinancing behavior, we calculated the Cost-Adjusted Only FTR rate over time

for the two states with the highest closing costs in our sample (Florida and New York). To

serve as a point of comparison, we also constructed the Cost-Adjusted Only FTR series

for bordering states with significantly lower transaction costs (Georgia and Pennsylvania).

We present these series in Figure 11. The Unadjusted and Cost-Adjusted Only series are

virtually identical for Georgia and Pennsylvania over the full course of our sample. In

Florida and New York, in contrast, while there is little difference between the series when

interest rates are stable or rising, the Unadjusted FTR exceeds the Cost-Adjusted FTR

when mortgage rates are declining. These results imply that—much like how ignoring

interest rate variation imparts an upward bias to FTR measures for high-risk borrowers—

failing to account for transaction-cost heterogeneity biases FTR measures upward for loans

in states where closing costs are high, but only in declining-rate environments.

28
6 Implications for FTR Measures by Race and Ethnicity

Previous work has demonstrated that Black and Hispanic borrowers are less likely than

White borrowers to refinance (Kelly, 1995; Clapp, Goldberg, Harding and LaCour-Little,

2001; Deng and Gabriel, 2006; Firestone, Van Order and Zorn, 2007; Kau, Fang and

Munneke, 2019; Gerardi, Willen and Zhang, 2023). Virtually all of these papers have

assumed—either explicitly or implicitly—that all borrowers face the same closing costs

and could refinance at the same interest rate, regardless of their location or measures of

creditworthiness.34 On average, Black and Hispanic borrowers have lower credit scores

and higher LTVs at origination relative to White borrowers. We demonstrated in Section

3 that low-FICO and high-LTV borrowers face interest rates that are significantly above

the market-wide average. In the context of our FTR analysis, this implies that using

the average mortgage rate to proxy for the rate that available to Black and Hispanic

borrowers will systematically underestimate the true rate they could expect to realize and

thus systematically overestimate the FTR rate for such borrowers. Furthermore, minority

borrowers are not uniformly distributed across U.S. states, and the higher concentration

of minorities in states like Florida with high closing costs could also bias FTR measures

upward.

These facts suggest that previously documented racial disparities in refinancing may in

part be a statistical artifact of heterogeneity in mortgage rates and closing costs. We investigate this possibility using the “core data” records from the National Mortgage Database

(NMDB). The NMDB is a restricted-access mortgage database created and maintained

jointly by the Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection Bureau (CFPB). The construction of the core NMDB files begins with a 5 percent

random sample of all closed-end first-lien mortgage flies that were outstanding in Experian’s credit data repository as of January of 1998. The performance of each of these

loans was then tracked until the mortgages terminated through prepayment, foreclosure,

or maturity. For each quarter following January of 1998, a random 5 percent sample of

mortgages that were newly reported to Experian were added to the NMDB files, with the

performance of these loans also tracked through termination for loans that paid off or

through the present time for loans that were still active as of the end of the most recent

sampling period (Avery et al., 2021). The result of this sampling process is a 5 percent

representative random sample of activity in the U.S. mortgage market that spans more

than two decades.

The fields included in the NMDB include both variables reported as of the loan’s origi34The exception is Gerardi, Willen and Zhang (2023). This paper utilizes updated credit scores and

LTVs to construct an refinance option-value measure (“Call Option V2”) that accounts for risk-based

pricing. This adjustment is similar to the rate adjustment that we make with the NMDB data in the

analysis described below. While Gerardi, Willen and Zhang (2023) do not explicitly model regional

variation in transaction costs, the impact that such variation would have on refinancing behavior is

captured in state- and zip-code-level fixed effects in some of their specifications. Even after accounting

for rate heterogeneity, they find that Black and Hispanic borrowers are less likely to refinance.

29
nation date (“static” variables) as well as variables that are updated over the life of the

loan (“dynamic” variables). Among the variables included in the dynamic portion of the

database are fields reporting the unpaid balance of the loan as of the reporting date,

updated credit scores for the borrowers associated with the loan, the loan’s delinquency

status, and information on whether the loan has been paid off; in our FTR analysis, we

use the loan payoff field to classify borrowers as prepaying the loan.

The information contained in the static portion of the database is incredibly rich and

reports the Census tract in which the mortgaged property is located, the original loan

balance, the number of borrowers on the loan, the value of the property at the time of

origination, and a host of factors used in underwriting such as mortgage contract characteristics, the original interest rate, and borrowers’ credit scores and incomes. Most

importantly for our study, unlike any other administrative mortgage servicing database,

the NMDB files report the age, gender, race, ethnicity, and veteran status for each borrower on the loan.

To align our FTR-by-race analysis with the McDash results discussed above, we limit the

NMDB records to conventional, 30-year, fixed-rate mortgages that were active between

the first quarter of 2005 and the fourth quarter of 2019. As in the McDash analysis, we

also exclude from the NMDB records: loans with exotic characteristics, such as balloon

payments; loans without valid values for the variables used in our analysis; loans that

were not current as of the observation date; and loans with an outstanding balance below

$75,000 as of the observation date.

We use the LTV and credit score reported in the static NMDB records to calculate riskbased mortgage rates over time in a very similar manner to the rate-estimation process

described in Section 3, with with two notable exceptions. First, the NMDB records

report a borrower’s Vantage Score, while the McDash records report FICO scores, so we

construct our NMDB risk segments using Vantage Scores. Second, the dynamic NMDB

data is reported quarterly, in contrast to the McDash records which are updated monthly,

so we define the adjusted mortgage rate for each Vantage Score-LTV segment as the

median rate of the new originations in that segment on a quarter-by-quarter basis.35

After constructing the segment-specific origination rates, we use the updated Vantage

Score and LTV fields in dynamic NMDB records to assign a risk-based mortgage rate to

each observation.36 We then use the regression parameters estimated using the UCD data

to estimate a loan’s closing costs based on the state in which the property is located.37

We construct adjusted and Unadjusted FTR rates for the overall population as well as by

race and ethnicity using the methodology described in Section 4. Lastly, to get a sense

35The Vantage Score and LTV cutoffs that we use to create the risk segments in the LTV data are

identical to those used to create the segments for the McDash data. FICO and Vantage scores are on the

same scale and range from 300 to 850.

36As in our analysis of the McDash data, when assigning a segment-level interest rate in the NMDB

analysis, we use the lagged rate in that segment from the previous quarter.

37See Section 2 for details on the closing cost estimation.

30
of the relative importance of the interest-rate and closing-cost corrections, we calculate

the FTR rate using a segment-specific interest rate but the standard closing-cost estimate

that ignores geographic heterogeneity in transaction costs (“FTR Rate-Adjusted Only”)

as well as the FTR rate where we adjust for heterogeneity in closing costs but ignore

interest rate variation (“FTR Cost-Adjusted Only”).

We summarize key variables and FTR rates by borrower race and ethnicity as well as for

the overall NMDB sample in Table 3. Consistent with expectations based on previous

research, in the NMDB sample average Vantage Scores are lower, and average LTVs are

higher, for Black and Hispanic borrowers relative to Asian and White borrowers. Accounting for regional heterogeneity reduces the average closing-cost estimates for borrowers of

all races, with the adjustment most pronounced for Asian and White borrowers. Relative

to the unadjusted PMMS rate, the average adjusted mortgage rates are higher for borrowers of all races. The difference between the adjusted and unadjusted rates was most

pronounced for Black (28 basis points) and Hispanic borrowers (25 basis points); these

borrowers also had the highest average adjusted mortgage rates in our sample.

The Unadjusted FTR rates throughout the sample for Black (25 percent) and Hispanic (20

percent) borrowers are significantly higher than those of Asian (12 percent) and White (13

percent) borrowers. While our approach to analyzing refinance behavior by race differs

from the methodologies utilized in previous work, the racial differences in FTR rates

that we document are generally consistent with previous work on racial differences in

mortgage prepayment (Kelly, 1995; Clapp et al., 2001; Deng and Gabriel, 2006; Firestone,

Van Order and Zorn, 2007; Kau, Fang and Munneke, 2019; Gerardi, Willen and Zhang,

2023). Adjusting for closing cost and interest rate heterogeneity had a limited impact on

the results for Asian and White borrowers, reducing the FTR rates for such borrowers by 2

percent and 3 percent, respectively. The differences between the Unadjusted and Adjusted

FTR rates for Black and Hispanic borrowers, in contrast, were larger: the heterogeneity

adjustment reduced the FTR rate for Black borrowers by 6 percentage points and reduced

the FTR rate for Hispanic borrowers by 4 percentage points.

The net impact of the heterogeneity adjustment is a reduction in racial differences in FTR

rates. For example, the Black-White Adjusted-FTR difference (9 percentage points) is 33

percent lower than the Black-White Unadjusted-FTR gap (12 percentage points), while

the heterogeneity adjustment reduced the Hispanic-White FTR gap by 17 percent from

7 to 6 percentage points. Our findings thus imply that failing to account for systematic

differences across borrowers in interest rates and closing costs can systematically overestimate racial gaps in refinancing behavior. Accounting for such heterogeneity, however,

does not completely eliminate racial FTR gaps, a finding that is consistent with the results

in Gerardi, Willen and Zhang (2023).38

38While similar in spirit, our methodology to quantifying racial differences in mortgage repayment

behavior is different from that of Gerardi, Willen and Zhang (2023). Our FTR measures are, in essence,

the percentage of borrowers who do not refinance conditional on the refinance option being “in the money.”

Gerardi, Willen and Zhang (2023), in contrast, model racial differences in repayment behavior conditional

on the value of the option, which may be positive or negative.

31
Table 3: Mean Values of Key Variables in NMDB Analysis

All Hispanic Non-Hispanic

Borrowers Asian Black White White

Vantage Score at Origination 742.16 751.22 706.74 724.08 745.45

Updated Vantage Score 735.99 748.86 698.71 716.26 739.30

Updated LTV 67.53 63.30 73.75 70.12 67.17

Unadjusted Closing Costs 3514.88 3892.20 3398.27 3503.27 3498.96

Adjusted Closing Costs 3072.92 3161.81 3154.16 3179.83 3052.40

Unadjusted Mortgage Rate 4.66 4.54 4.71 4.66 4.67

Adjusted Mortgage Rate 4.87 4.73 4.99 4.91 4.87

Unadjusted FTR 0.14 0.12 0.25 0.20 0.13

Adjusted FTR 0.11 0.10 0.19 0.16 0.10

FTR Rate-Adjusted Only 0.10 0.09 0.19 0.15 0.09

FTR Cost-Adjusted Only 0.15 0.13 0.26 0.21 0.14

Observations 37,223,789 1,973,045 1,937,798 2,753,259 30,559,687

This table summarizes key variables of our NMDB FTR sample, which is comprised of conventional,

30-year, fixed-rate mortgages reported in the NMDB that were active between the first quarter of 2005

and the fourth quarter of 2019. The sample excludes: loans with exotic characteristics, such as balloon

payments; loans without valid values for the variables used in our analysis; loans that were not current

as of the observation date; and loans with an outstanding balance below $75,000 as of the observation

date. These data restrictions are identical to those used to conduct the FTR analysis with the McDash data. The calculation of the unadjusted closing costs follows the existing literature and does not

directly account for rate and closing cost heterogeneity; while the calculation of the adjusted closing

costs adjusted for the rate and closing cost heterogeneity. The Unadjusted Mortgage Rate is rate reported in the PMMS. The Adjusted Mortgage Rate is the average rate on newly originated fixed-rate

30-year refinance loans in the pricing bin that corresponds to the borrower’s updated Vantage score

and updated LTV.

Comparing the Unadjusted FTR measures with the“FTR Rate-Adjusted Only”and“FTR

Cost-Adjusted Only” measures in Table 3, we see that the impact of the closing costadjustment in isolation raises the FTR rates by about 1 percentage point. The decline in

racial gaps under the Adjusted FTR measure discussed above is thus driven by the interest

rate correction. This finding is consistent with the analysis summarized in Section 5 where

the closing-cost correction had little impact on FTR measures constructed using a pooled

sample of loans from all U.S. states.39

In addition to constructing the FTR measures aggregated over the full course of our sample, we also calculated FTR rates on a quarterly basis. Figure 12 displays the Unadjusted,

Adjusted, Rate-Adjusted Only, and Cost-Adjusted Only series for borrowers of all races,

39As discussed in Section 5, accounting for closing-cost heterogeneity had an important impact on FTR

measures across states. It could thus be the case that the cost correction plays a more important role

when loans are pooled by borrower race-state combinations.

32
and Figure 13 displays the Adjusted and Unadjusted FTR series by borrower race and

ethnicity. The FTR dynamics summarized in Figure 12, which displays the FTR series

for the NMDB sample, are very similar to the FTR series based on the McDash records

shown in Figure 8. In both samples the heterogeneity adjustments typically reduce FTR

measures, and the difference between the Adjusted and Unadjusted FTR series—which

is driven primarily by the interest rate correction— is greatest when interest rates are

declining.40

The FTR series by race and ethnicity displayed in Figure 12 largely follow the same pattern

as those of the overall sample. For borrowers of all races, FTR rates are very low when

interest rates are flat or rising and rise significantly when rates begin to decline. While

the Adjusted FTRs are almost always lower than the Unadjusted FTRs for borrowers

of all races throughout the sample, the impact of the heterogeneity adjustment is most

pronounced for Black and Hispanic borrowers when interest rates are declining rapidly. For

example, when rates were declining rapidly in the third quarter of 2011, the Unadjusted

FTR for Black and Hispanic borrowers exceeded the Adjusted FTR by more than 18

percentage points. In that same quarter, the heterogeneity adjustments were significantly

smaller for Asian and White borrowers, reducing the Asian FTR rate by 8 percentage

points and the White FTR rate by 10 percentage points.41 Our findings imply that while

failing to account for the higher interest rates faced by Black and Hispanic borrowers

imparts upward bias to FTR rates for such borrowers in all time periods, this bias is

particularly acute when interest rates are declining rapidly.

7 Conclusion

In this paper we provide the first systematic investigation of regional heterogeneity in

transaction costs in the U.S. mortgage market. The primary takeaway from this analysis

is that there is a tremendous amount of variation across U.S. states in the relationship

between loan amounts and closing costs. We also demonstrate that nearly all this variation

is attributable to differences in mortgage taxation and government fees. The heterogeneity

in closing costs that we document is thus driven by the decisions of state and in some

cases municipal policymakers.

The magnitude of the regional heterogeneity in closing costs is economically significant.

Based on the estimated parameters from Equation 1, it costs $3,708 more to take out a

$500,000 mortgage in Florida ($5,780), the state with the highest closing costs, than it

40The readers is reminded that the McDash data is reported on a monthly basis while the NMDB

data is reported on a quarterly basis. The FTR levels in Figure 12 and Figure 8 are thus not directly

comparable.

41In the third quarter of 2011, the Unadjusted (Adjusted) FTR rates for Black and Hispanic borrowers

were 36.31 percent (17.02 percent) and 30.92 percent (12.91 percent). For Asian and White borrowers,

the Unadjusted (Adjusted) rates in this quarter were 16.40 percent (8.26 percent) and 18.04 percent (8.23

percent), respectively.

33
Figure 12: FTR Measures for All NMDB Borrowers

This figure displays the Unadjusted, Adjusted, Rate-Adjusted Only, and Cost-Adjusted Only

FTR rates for borrowers of all races using the NMDB sample.

34
Figure 13: FTR Measures by Race and Ethnicity

This figure displays the Unadjusted, Adjusted, Rate-Adjusted Only, and Cost-Adjusted Only FTR rates by borrower race and ethnicityusing the NMDB sample.

35
costs to take out an identical loan in Iowa ($2,071), which has the lowest closing costs. To

put context around the magnitude of this difference, in their study of regional subsidies

in the mortgage market, Hurst et al. (2016, p. 3009) produce estimates of the value of the

transfers that occur between low- and high-risk states because of mispricing in the mortgage market: the 90th percentile of this transfer distribution was $780. The Florida-Iowa

difference in transaction costs is thus nearly five times larger than the implicit mortgagerate subsidy to borrowers in the highest-risk markets in the U.S.42 Given the magnitude

of the regional differences in transaction costs, our results provide a mechanism above

and beyond the home equity channel discussed in Beraja et al. (2019) that could explain

the documented geographic variation in refinancing activity and consumer spending during recoveries. Because variation in closing costs is largely attributable to differences in

mortgage stamp and recording fees, our results suggest that state and local governments

may be able to speed economic recovery when interest rates are declining by lowering such

taxes.

After establishing these stylized facts about closing costs, we investigate how ignoring

heterogeneity—in both closing costs as well as interest rates—could potentially impact

studies of refinancing behavior using the failure-to-refinance (FTR) framework of Agarwal,

Driscoll and Laibson (2013). This analysis reveals that failing to account for rate and cost

heterogeneity can bias estimates of suboptimal behavior in the mortgage market in important ways. Furthermore, the nature of this bias depends on the borrower subpopulations

under consideration. For example, when studying refinancing behavior by a borrower’s

race, ethnicity, and risk profile, we find that ignoring mortgage rate heterogeneity creates

a significant upward bias in the estimated incidence of suboptimal behavior for minority

borrowers as well as borrowers with high-risk credit characteristics, such as high LTVs

and low credit scores. Accounting for closing cost heterogeneity, on the other hand, has

the most significant impact when studying regional differences in refinancing behavior.

In light of the growing evidence of heterogeneity in both the mortgage market as well

as the larger consumer credit market, these findings should serve as a warning that researchers must think carefully about how assuming away such heterogeneity might impact

their results, especially when the focus of a study concerns between-group differences in

behavior.

42Interestingly, Hurst et al. (2016) find that many Florida borrowers receive large implicit transfers via

low interest rates. One way of interpreting our findings is that Florida recaptures some of that subsidy

via tax policies.

36
References

Acolin, A. and R. Walter. 2020. “Loan Costs: Magnitude and Drivers of Variations.”

Working Paper .

Agarwal, S., J. Driscoll and D. Laibson. 2013. “Optimal Mortgage Refinancing: A ClosedForm Solution.” Journal of Money, Credit and Banking 45(4):591–622.

Agarwal, S., R. Rosen and V. Yao. 2016. “Why Do Borrowers Make Mortgage Refinancing

Mistakes?” Management Science 62(12):3494–3509.

Alexandrov, A. and S. Koulayev. 2018. “No Shopping in the U.S. Mortgage Market: Direct and Strategic Effects of Providing Information.” Consumer Financial Protection

Bureau Office of Research Working Paper .

Andersen, S., J. Campbell, K. Nielsen and T. Ramadorai. 2020. “Sources of Inaction in

Household Finance: Evidence from the Danish Mortgage Market.” American Economic Review 110(10):3184–3230.

Avery, R., M. Bilinski, A. Clement, T. Critchfield, I. Keith, I. Mohamed, S. Patrabansh,

C. Redmer, J. Schultz and R. Sullivan. 2021. “National Survey of Mortgage Originations Technical Documentation.” Federal Housing Finance Agency NMDB Technical

Report 1.

Beraja, M., A. Fuster, E. Hurst and J. Vavra. 2019. “Regional Heterogeneity and the

Refinancing Channel of Monetary Policy.” The Quarterly Journal of Economics

134(1):109–183.

Bhutta, N., A. Fuster and A. Hizmo. 2020. “Paying Too Much? Price Dispersion in the

U.S. Mortgage Market.” Federal Reserve Board Working Paper 2020-062 .

Clapp, J., G. Goldberg, J. Harding and M. LaCour-Little. 2001. “Movers and Shuckers:

Interdependent Prepayment Decisions.” Real Estate Economics 29(3):411–450.

Deng, Y. and S. Gabriel. 2006. “Risk-Based Pricing and the Enhancement of Mortgage

Credit Availability Among Underserved and Higher Credit-Risk Populations.” Journal of Money, Credit and Banking 38(6):1431–1460.

Dhen, A. 2021. “CEMA Loans: Everything You Need to Know.” Rocket Mortgage .

https://www.rocketmortgage.com/learn/cema Accessed 6/30/2021.

Feinburg, R., D. Kuehn, S. McKernan, D. Wissoker and S. Zhang. 2012. “What Explains

Variation in Title Charges? A Study of Five Large Markets.” U.S. Department of

Housing and Urban Development Office of Policy Development and Research Report.

Firestone, S., R. Van Order and P. Zorn. 2007. “The Performance of Low-Income and

Minority Mortgages.” Real Estate Economics 35(4):479–504.

37
Florida Statute 199.133. 2020. The 2020 Florida Statutes. http://www.leg.state.

fl.us/Statutes/index.cfm?App_mode=Display_Statute&Search_String=&URL=

0100-0199/0199/Sections/0199.133.html Accessed 6/30/2021.

Florida Statute 201.08. 2020. The 2020 Florida Statutes. http://www.leg.state.

fl.us/statutes/index.cfm?App_mode=Display_Statute&URL=0200-0299/0201/

Sections/0201.08.html#1 Accessed 6/30/2021.

Gerardi, K., P. Willen and David H. Zhang. 2023. “Mortgage Prepayment, Race, and

Monetary Policy.” Journal of Financial Economics 147(3):498–524.

Gurun, U., G. Matvos and A. Seru. 2016. “Advertising Expensive Mortgages.”The Journal

of Finance 71(5):2371–2416.

Hurst, E., B. Keys, A. Seru and J. Vavra. 2016. “Regional Redistribution through the

U.S. Mortgage Market.” American Economic Review 106(10):2982–3028.

Kau, J., L. Fang and H. Munneke. 2019. “An Unintended Consequence of Mortgage

Financing Regulation—A Racial Disparity.” The Journal of Real Estate Finance and

Economics 59:549–588.

Kelly, A. 1995. “Racial and Ethnic Disparities in Mortgage Prepayment.” Journal of

Housing Economics 4(4):350–372.

Keys, B., D. Pope and J. Pope. 2016. “Failure to Refinance.” Journal of Financial Economics 122(3):482–499.

McManus, D., L. Liu and Mingzhe. Yi. 2018. “Why Are Consumers Leaving Money

on the Table?” Freddie Mac Insight Report. http://www.freddiemac.com/

fmac-resources/research/pdf/201804-Insight.pdf Accessed 1/19/2021.

PMMS Frequently Asked Questions. 2022. The Freddie Mac Primary Mortgage Market

Survey. https://www.freddiemac.com/pmms/about-pmms Accessed 10/27/2022.

Prevost, L. 2013. “Reducing Refinancing Expenses.”The New York Times . https://www.

nytimes.com/2013/05/26/realestate/reducing-refinancing-expenses.html

Accessed 6/30/2021.

Shroder, M. 2007. “The Value of the Sunshine Cure: The Efficacy of the Real Estate

Settlement Procedures Act Disclosure Strategy.” Cityscape 9(1):73–91.

Stanton, R. and N. Wallace. 1998. “Mortgage Choice: What’s the Point?” Real Estate

Economics 26(2):173–205.

Woodward, S. 2008. “A Study of Closing Costs for FHA Mortgages.” U.S. Department of

Housing and Urban Development Report.

38
Woodward, S. E and R. Hall. 2012. “Diagnosing Consumer Confusion and Sub-Optimal

Shopping Effort: Theory and Mortgage-Market Evidence.” American Economic Review 102(7):3249–76.

39