The Effects of Mortgage Credit Availability: Evidence from Minimum Credit Score Lending Rules

Finance and Economics Discussion Series

Divisions of Research & Statistics and Monetary Aﬀairs

Federal Reserve Board, Washington, D.C.

The Eﬀects of Mortgage Credit Availability: Evidence from

Minimum Credit Score Lending Rules

Steven Laufer and Andrew Paciorek

2016-098

Please cite this pap er as:

Laufer, Steven, and Andrew Paciorek (2016). “The Eﬀects of Mortgage Credit Availabil-

ity: Evidence from Minimum Credit Score Lending Rules,” Finance and Economics Dis-

cussion Series 2016-098. Washington: Board of Governors of the Federal Reserve System,

https://doi.org/10.17016/FEDS.2016.098.

NOTE: Staﬀ working pap e rs in the Finance and Economics Discussion Series (FEDS) are preliminary

materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth

are those of the authors and do not indicate concurrence by other members of the research staﬀ or the

Board of Governors. References in publications to the Finance and Economics Discussion Series (other than

acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

The Effects of Mortgage Credit Availability:

Evidence from Minimum Credit Score Lending Rules

∗

Steven Laufer

†

and Andrew Paciorek

‡

Board of Governors of the Federal Reserve System

December 8, 2016

Abstract

Since the housing bust and ﬁnancial crisis, mortgage lenders have introduced progres-

sively higher minimum thresholds for acceptable credit scores. Using loan-level data,

we document the introduction of these thresholds, as well as their eﬀects on the distri-

bution of newly originated mortgages. We then use the timing and nonlinearity of these

supply-side changes to credibly identify their short- and medium-run eﬀects on various

individual outcomes. Using a large panel of consumer credit data, we show that the

credit score thresholds have very large negative eﬀects on borrowing in the short run,

and that these eﬀects attenuate over time but remain sizable up to four years later. The

eﬀects are particularly concentrated among younger adults and those living in middle-

income or moderately black census tracts. In aggregate, we estimate that lenders’ use

of minimum credit scores reduced the total number of newly originated mortgages by

about 2 percent in the years following the ﬁnancial crisis. We also ﬁnd that, among

individuals who already had mortgages, retaining access to mortgage credit reduced

delinquency on both mortgage and non-mortgage debt and increased their propensity

to take out auto loans, but had little eﬀect on migration across metropolitan areas.

∗

All errors are our own. We thank Elliot Anenberg, Neil Bhutta, Paul Calem and participants at the AEI-

BoI-BGFRS-TAU-UCLA Conference on Housing Aﬀordability for helpful comments. The views we express

herein are not necessarily those of the Board of Governors or others within the Federal Reserve System.

†

E-mail: stev[email protected]

‡

E-mail: [email protected]v

1 Introduction

Since the housing bust and subsequent ﬁnancial crisis, US mortgage lenders have signiﬁcantly

tightened their lending standards. These tight lending conditions have likely contributed

to the steep decline in the homeownership rate as well as the slow recovery in residential

construction. In addition, tight mortgage credit may pose a problem for housing aﬀordability,

as the historically low interest rates over the past few years mean that mortgage-ﬁnanced

owner occupied housing would be less expensive than rental housing for many people. More

broadly, there is considerable evidence connecting the availability of household credit to

overall consumer demand (Guerrieri and Lorenzoni, 2011; DiMaggio and Kermani, 2015;

Mondragon, 2016).

While the evidence that mortgage credit conditions have tightened is fairly strong, it is

diﬃcult to quantify the magnitude of the tightening or to disentangle the eﬀects of tight

mortgage supply from low mortgage demand. Factors that prevent households from qual-

ifying for a mortgage—such as low credit scores, high debt balances, and a lack of liquid

assets—also reduce demand for owner-occupied housing. For example, the decline in mort-

gage originations to less credit-worthy borrowers over the past few years (see Bhutta (2015))

likely reﬂects more stringent lender standards, but it also likely reﬂects relatively weak labor

market conditions among such borrowers, as well as reluctance by more ﬁnancially vulnerable

households to assume housing market risk following a period of extreme volatility.

In this paper, we address this identiﬁcation challenge by focusing on lenders’ requirements

that borrowers must meet a sharply deﬁned minimum credit score threshold in order to

qualify for a loan. In some cases, these thresholds may be imposed to allow the lenders to

securitize the mortgages through government programs that specify minimum credit scores.

In other cases, they may simply reﬂect a rule-of-thumb about which mortgages are too risky

to underwrite. Importantly for our work, lenders’ use of these minimum credit scores has

varied over time in response to concerns that are likely unrelated to changes in demand from

marginal borrowers.

Focusing on the most recent time period, we show that lenders progressively tightened

their standards in the years following the ﬁnancial crisis of 2008. Much of this tightening

occurred for loans guaranteed by the Federal Housing Administration (FHA), which domi-

nated lending to borrowers with low credit scores during this time period. In particular, we

document the eﬀects of several large lenders imposing minimum credit scores of 620 on FHA

loans in the ﬁrst quarter of 2009, and then raising this threshold to 640 (on some loans) in the

second half of 2010. In the data, these minimum score thresholds manifest as discontinuities

in the distribution of credit scores on newly originated mortgages, with substantially fewer

loans made to borrowers with credit scores just below the thresholds.

We use the size of

these discontinuities as a measure of how important the thresholds are during each period.

Our empirical analysis is based on a diﬀerence-in-diﬀerences approach in which we com-

pare borrowers above and below the credit thresholds in periods where the thresholds were

more and less important in lenders’ underwriting decisions. More speciﬁcally, we calculate a

single measure of credit availability that captures the eﬀects of the changes in the thresholds

on borrowers with diﬀerent credit scores. Crucially, the nonlinear relationship between our

credit availability measure and borrowers’ credit scores allows us to separately identify its

eﬀect while still controlling for variation in mortgage demand that is also correlated with

borrowers’ credit scores. Equally important, we are able to control for this diﬀerence in

mortgage demand between high and low score borrowers even as it varies over time. In other

words, our approach lets us separate out mortgage demand from mortgage supply even as

both are simultaneously changing during our sample period.

We calculate our credit availability measure for individuals in the FRBNY Consumer

Credit Panel (CCP) and estimate its impact on various outcomes.

Starting with mortgage

attainment, we ﬁnd that for borrowers with scores below the relevant thresholds, the tighten-

ing that occurred between 2008 and 2011 reduced their probability of obtaining a mortgage

in the subsequent quarter by 0.5 percentage points, compared to an average probability of

taking out a mortgage of just under 1 percent. When we look over longer horizons of up to

16 quarters, the eﬀects shrink in magnitude relative to the average probabilities but remain

very large, indicating that credit availability (or the lack thereof) has persistent consequences

for individual borrowing behavior.

In aggregate, we estimate that lenders’ use of minimum credit scores reduced the total

number of newly originated mortgages by about 2 percent, with much larger eﬀects among

prospective borrowers with scores near the thresholds. Furthermore, we show that the eﬀects

of this tightening are largest in areas with moderate income, which feature a combination of

relatively low credit scores and relatively high housing demand. Similarly, we ﬁnd that the

eﬀects are largest for borrowers aged 34-45 and for borrowers living in census tracts with

moderate shares of black residents.

We plot this distribution for several diﬀerent years in ﬁgure 1.

The Equifax Risk Score included in the CCP is distinct from the FICO scores typically used by mortgage

lenders. We spend considerable eﬀort addressing this challenge in our analysis.

Working with data from the Home Mortgage Disclosure Act (HMDA) that contains information on

the race of individual borrowers, Bhutta and Ringo (2016) ﬁnd that tight credit conditions have had a

disproportionate eﬀect on credit access for minorities.

The fact that our approach produces any substantial estimates of the eﬀect of these

thresholds on mortgage attainment results establishes two non-trivial facts about the credit

scores in consumer credit data. First, these scores are in fact a meaningful measure of access

to mortgage credit, even though, as we discuss below, they are not the actual credit score

used for mortgage underwriting. Second, these scores are suﬃciently stable that a single

observation taken at the end of the quarter does reﬂect the individual’s ability to borrow

over the following three months. Establishing these facts is particularly important given the

wide range of studies that use these scores as a measure of individuals’ access to credit.

Our study of the eﬀects of these credit score thresholds on mortgage attainment falls

within a larger literature that has tried to identify the eﬀects of mortgage credit availabil-

ity on homeownership. Early work in this literature includes Barakova et al. (2003) and

Rosenthal (2002) who constructed measures of mortgage credit access from responses to the

Federal Reserve’s Survey of Consumer Finances (SCF). More recently, Barakova et al. (2014)

constructed a measure of mortgage credit access from the National Longitudinal Survey of

Youth and Acolin et al. (2016) use more recent waves of the SCF. Among the few papers

that have explicitly considered the eﬀect of credit score, Chomsisengphet and Elul (2006) use

credit scores merged with mortgage data to shed light on the eﬀect of personal bankruptcy

exemptions on secured lending. We conduct our analysis on a far larger data set with many

more observable outcomes and also, crucially, while controlling for the variation in demand

that is correlated with access to credit. However, like other studies based on consumer credit

data, we are unable to see income or assets and therefore unable to account for the impact

of those factors on individuals’ ability to borrow.

We also examine the implications of mortgage credit availability for other outcomes.

First, we ﬁnd that credit availability has relatively little eﬀect on mortgage or other loan

delinquency among new mortgage borrowers, but that it dramatically lowers delinquency

of both types among individuals who already had a mortgage, suggesting that the ability

to reﬁnance a mortgage is an important ﬁnancial cushion. While Keys et al. (2014) show

that lower costs of mortgage credit, in the form of ARM rate resets, lead to fewer mortgage

defaults and lower delinquent card balances, we are not aware of previous work showing

that increased access to mortgage credit reduces borrowers’ delinquency rates. In contrast,

Skiba and Tobacman (2015) show that increased access to payday lending leads to higher

bankruptcy rates, but the settings of our respective analyses are quite diﬀerent.

Next, we study the impact of credit availability on moving and migration behavior,

ﬁnding mixed eﬀects depending on the horizon and whether an individual already had a

mortgage. Perhaps most notably, our results on cross-metropolitan migration suggest that

lacking access to new mortgage credit did not “lock in” prior borrowers to their current city.

This part of our paper contributes to the discussion of whether fall-out from the housing

crisis might have hampered the economic recovery by preventing workers from relocating to

stronger labor markets. Previous research has asked whether underwater homeowners were

locked into their homes because they were unable to pay oﬀ their mortgages by selling their

homes (Schulhofer-Wohl, 2011; Ferreira et al., 2011; Farber, 2012). Our approach allows us

to answer a slightly diﬀerent question, which is whether low-score homeowners who could

no longer qualify for a new mortgage would remain in their home rather than relocate to a

new area where they would be forced to rent. We ﬁnd that this is not the case. Current

homeowners without access to mortgage credit are as likely to move as homeowners with

access to credit.

In our ﬁnal set of results, we show that mortgage credit availability seems to aﬀect auto

borrowing, positively in the case of individuals who were prior mortgage borrowers—again

pointing to the importance of reﬁnancing—and negatively in the case of prior non-borrowers,

perhaps because of substitution from houses to cars when mortgages are not available. This

last result contrasts somewhat with the conclusions of Gropp et al. (2014), who document a

reduction of consumer debt for renters in areas with larger house price declines and interpret

this ﬁnding as a response to cutbacks in the provision of mortgage credit in those areas. Our

ﬁnding relies on a diﬀerent and potentially sharper identiﬁcation of credit constraints.

More broadly, our paper is related to a growing literature that has used a variety of

identiﬁcation strategies to isolate the eﬀects of mortgage credit availability during the recent

housing cycle. Anenberg et al. (2016) characterize mortgage credit availability as the largest

mortgage that a borrower can obtain given his credit score, income and ability to make a

down payment, assuming this maximum size is determined by mortgage supply rather than

demand. The authors show that tighter credit conditions depress both house prices and

new residential construction. Gete and Reher (2016) identify local variations in mortgage

credit tightness based on the share of mortgage lending by the largest banks in diﬀerent

areas prior the crisis. They argue that these banks tightened credit more in response to new

ﬁnancial regulations and use the variation in their lending share to show that tight credit

helps explains higher residential rents. Finally, Favara and Imbs (2015) use heterogeneity in

US bank deregulation to look at the eﬀects of mortgage credit supply on house prices, while

DiMaggio and Kermani (2015) use heterogeneity in the eﬀect of predatory lending laws to

measure the eﬀect of credit supply on lending, house prices, and employment. Our paper

presents yet another way of identifying the eﬀects of mortgage credit availability by focusing

explicitly on the variation in lenders’ use of minimum credit scores. Unlike all of these other

studies, our approach us allows us to measure the eﬀects on individuals rather than just local

areas.

In using credit score thresholds, our study is also related to work by Keys et al. (2009,

2010, 2012), who argue that, before the crisis, the greater ease of securitizing mortgages made

to borrowers with credit scores above 620 led to lax screening by originators because of moral

hazard. Bubb and Kaufman (2014) instead argue that the use of 620 as a threshold arose as

a lender response to a ﬁxed cost of screening potential borrowers. During the more recent

period we study, lenders’ reliance on minimum credit scores clearly does not reﬂect their

diﬃculty in securitizing these loans. As we describe below, most securitized loans issued

around the thresholds since the ﬁnancial crisis have been guaranteed by the FHA, whose

explicit credit score minimums were substantially lower than the thresholds we study. In

any case, we are less concerned with the origin of lenders’ decision to apply minimum credit

scores and more concerned with the eﬀect of these rules on individuals’ ability to obtain

mortgage credit.

The rest of the paper proceeds as follows: Section 2 describes lenders’ use of minimum

credit scores, how we observe the eﬀects of these rules in the data, and the construction of

our credit availability measure. We present our empirical results on mortgage borrowing and

other outcomes in section 3. In section 4 we examine heterogeneity in the eﬀects of credit

availability on mortgage borrowing across diﬀerent demographic and socioeconomic groups,

while in section 5 we calculate the cumulative eﬀects of the credit restrictions over various

horizons. Finally, section 6 concludes the paper and oﬀers thoughts on directions for future

research.

2 Data Sources and the Credit Availability Measure

2.1 A Recent History of Credit Score Thresholds

As noted in the introduction, since the ﬁnancial crisis, there have been signiﬁcant disconti-

nuities in the distribution of credit scores on newly originated mortgages. In ﬁgure 1, we plot

the density and cumulative distribution of credit scores for mortgages originated in 2005,

2008, 2010, and 2012.

At certain key scores, there are fewer loans originated to borrowers

The data, which come from Black Knight, are described more fully in section 2.2.

with credit scores just below those thresholds. By 2010 (the blue lines), there were very few

loans made to borrowers with credit scores below 620. By 2012 (the green lines), the most

signiﬁcant threshold score was 640.

These discontinuities are largely explained by lenders’ changing policies on issuing mort-

gages guaranteed by the Federal Housing Administration (FHA), which has dominated the

market for low-score mortgages since the crisis. In the early 2000s, the FHA’s market share

fell sharply because of competition from sub-prime lenders who oﬀered comparable mort-

gages at lower prices. However, by 2008, most of those lenders had disappeared from the

market, leaving the FHA program as a last resort for borrowers with low scores. Around

the same time, the Economic Stimulus Act of 2008 raised the maximum loan size on FHA

mortgages in a further eﬀort to increase the scope of FHA lending and thereby help stabilize

the mortgage market.

As house prices continued to decline, losses on the book of mortgages insured by the

FHA rose substantially. By the end of 2008, the 90-day delinquency rate on FHA loans

reached 6.8 percent and although payments to the owners of these loans were guaranteed

by the US government, lenders also bore some risk from these loans. These risks included

the increased cost of servicing the delinquent mortgages if they had retained the servicing

rights, as well as reputational risks in a market increasingly sensitive to the dangers of risky

mortgage lending. In February 2009, two of the nation’s largest lenders, Wells Fargo and

Taylor, Bean & Whitaker (TBW), announced that they would require credit scores of at

least 620 for newly originated loans guaranteed by the FHA and the Department of Veterans

Aﬀairs. A Wells Fargo spokesman stated, “This change is a reﬂection of our commitment

to do business with brokers and correspondents who manage to the economics and risks of

the mortgage industry” (Inside FHA/VA Lending, 2009b). Over the next six months, the

average FICO score on FHA loans climbed 30 points, from 663 in February to 692 in August

(Inside FHA/VA Lending, 2009a).

In January 2010, the Department of Housing and Urban Development (HUD) announced

its own tightening of FHA standards, including an increase in upfront and ongoing mortgage

insurance premiums, a minimum credit score of 500 on all FHA loans, and a minimum score

of 580 for borrowers seeking to make down-payments below 10 percent.

This introduction

of minimum credit scores on FHA mortgages had little impact because lenders were already

making very few loans to borrowers with such low scores. More importantly for FHA lenders,

HUD also proposed lowering the percentage of the sale price that sellers were allowed to put towards

closing costs or renovations (“seller concessions”) from 6 percent to 3 percent.

HUD announced two changes regarding its practice of terminating lenders’ eligibility to origi-

nate FHA loans. First, HUD announced that it would systematically review the performance

of each lender’s FHA mortgages and revoke the lender’s eligibility as FHA lenders if the over-

all default rate exceeded a speciﬁed threshold. Second, HUD announced that lenders would

now also be evaluated based on the performance of the loans made through third-party cor-

respondent lenders whereas previously, only mortgages originated by the lenders themselves

were used in these reviews. Both policy changes were phased in gradually over 2010.

In response to the new FHA rules, many lenders tightened their FHA lending, including

by imposing new minimum credit scores on the FHA mortgages they were willing to originate

themselves, and especially on those originated through third-party correspondents. Two of

the largest lenders, Wells Fargo and Bank of America, stopped buying FHA loans made

to borrowers with credit scores below 640, though both continued to originate loans to

lower-score borrowers through their retail channels (Bloomberg News, 2010). Other lenders

reportedly established minimum credit score thresholds as high as 660 (Inside FHA/VA

Lending, 2010).

The impact of these changes in lenders’ policies around FHA lending is apparent in the

distribution of credit scores for newly originated mortgages in ﬁgure 2, where the blue lines

in the four panels show the distribution of FICO sores for FHA mortgages in 2005, 2008,

2010 and 2012, respectively. In ﬁgure 2A, we see the low share of FHA mortgages prior

to 2008. Then ﬁgure 2B shows the dominance of FHA lending among low-FICO borrowers

during 2008 and the absence of any large discontinuities in the distribution, reﬂecting the

limited use of minimum FICO scores by lenders during this period. The announcements

by Wells Fargo and TBW in January 2009 that they would stop originating loans below

620 are apparent in ﬁgure 2C, which shows a dramatic reduction in the fraction of FHA

mortgages to borrowers with scores below 620 in 2010. The size of this reduction suggests

that many other lenders also adopted a similar practice. Finally, ﬁgure 2D shows that, by

2012, few FHA mortgages—or mortgages of any other type—were made to borrowers with

scores below 640, a situation that has remained essentially unchanged since then.

2.2 Measuring Credit Availability

Our analysis uses the discontinuities in the distribution of mortgages at particular credit

scores as indications that lenders are using these scores in their underwriting decisions and

are exhibiting some reluctance to lend to borrowers with credit scores that fall below this

value. Intuitively, if borrowers with credit scores just above the threshold have a similar

demand for mortgages compared to borrowers just below the threshold, then the diﬀerence

in the number of mortgages originated to these two groups must reﬂect pure diﬀerences in

the supply of mortgage credit. We can use these diﬀerences to identify the eﬀects of credit

supply on borrowers. From the distribution of newly originated mortgages, there appear to

be many scores that exhibit discontinuities in the number of mortgages originated. However,

in the period since the ﬁnancial crisis, the two most prominent discontinuities occur at 620

and 640 and we focus on these thresholds.

Our credit availability measure is constructed to capture the diﬀerence in the ability of

borrowers above those thresholds to obtain mortgages compared to borrowers below them.

In practice, computing this measure requires two steps. First, we need to estimate the

impact of falling above or below the threshold at each point in time. Second, we need to

determine how likely it is that each individual would fall below the threshold if she applied

for a mortgage.

2.2.1 Credit Score Thresholds in Originated Mortgages

In order to identify the use of the thresholds, we look at the distribution of credit scores

on loans originated each quarter, as captured in a data set of mortgages provided by Black

Knight Financial Services, formerly known as “LPS” and “McDash”. For each mortgage,

Black Knight reports detailed information that includes the origination date, the loan-to-

value ratio, the debt-to-income ratio, and the borrower’s credit score. Importantly for our

purposes, the credit score reported in the data is the FICO score used in the lender’s mortgage

underwriting decision, a point we return to below. As discussed above, ﬁgure 1 plots the

density and cumulative distribution of FICO scores for mortgages in the Black Knight data

originated in 2005, 2008, 2010, and 2012.

We quantify the size of the 620 and 640 thresholds by calculating the ratio of the number

of mortgages originated within ﬁve points below the threshold compared to the number

of mortgages originated within ﬁve points above the threshold. Assuming that these two

groups of borrowers have similar demand for mortgage credit, diﬀerences in the number of

new mortgages originations should reﬂect diﬀerences in lenders’ willingness to provide credit

above and below the threshold. Looking at the black line in ﬁgure 1, lenders appear to have

used 620 as a relevant threshold in their lending decisions even before the crisis.

In 2005,

for example, only 70 percent as many mortgages were originated to borrowers just below the

As discussed in the introduction, Keys et al. (2010) argue that the discontinuity existed because loans

with credit scores above 620 were easier to securitize, while Bubb and Kaufman (2014) dispute this conclusion.

thresholds compared to those just above. In contrast, the ratio around 640 was about 90

percent, suggesting that 640 was not a particularly important score in underwriting decisions

during that time period. These ratios were similar in 2008 (the red line).

By 2010 (the blue line), however, the ratio at 620 had plummeted to just 20 percent,

suggesting a dramatic tightening of mortgage credit for borrowers with credit scores under

620. By 2012 (the green line), the ratio at 640 had also fallen sharply, to about 45 percent.

These ratios have changed relatively little since 2012.

The discontinuities around these credit score thresholds could in theory emerge from

several diﬀerent kinds of restrictions by lenders. First, it may be that some lenders simply

refuse to lend at all to borrowers with credit scores below the threshold values. Low-score

borrowers who would have approached these lenders because of their geographic proximity or

other reasons would therefore not be able to get a mortgage from their preferred lender and

may face search costs that prevent them from turning to other lenders. Alternatively, it may

be that lenders impose other restrictions—on loan-to-value (LTV) or debt-to-income (DTI)

ratios, e.g.—on borrowers with credit scores below the threshold and these other restrictions

limit the demand from these less credit-worthy borrowers. This second explanation would

imply that loans originated to borrowers with scores just below the threshold should appear

less risky based on other observable characteristics. Indeed, we do ﬁnd some evidence of

this behavior. For example, DTI ratios and LTV ratios are both slightly lower on mortgages

originated just below the thresholds compared to mortgages originated just above. In the

end, the precise form of the restriction is not important for our analysis as long as the

discontinuity reﬂects diﬀerences in the supply of mortgage credit to borrowers above and

below the threshold rather than diﬀerences in demand.

One additional complication in studying mortgage underwriting decisions during this

period is lenders’ participation in the FHA’s streamline reﬁnance program, which allows

borrowers to reﬁnance FHA-guaranteed mortgages into new FHA mortgages without going

through the full underwriting process.

For example, it may be that there are actually

many low-credit score borrowers getting mortgages through this program who appear in the

data with missing FICO scores. While we can’t observe in the data which mortgages are

As the number of mortgages to borrowers with credit scores between 620 and 640 fell between 2010

and 2012, the ratio at 620 actually rose back to 40 percent, a mechanical response to the decrease in loans

to borrowers with scores just above 620, the denominator. A combined measure of the two discontinuities,

which calculates the ratio of mortgages just above 640 to the number of mortgages just below 620, shows a

clear overall tightening during this period.

In theory, the program allowed FHA mortgages to be reﬁnanced with no underwriting at all, though in

practice, many lenders did impose restrictions on which loans they would reﬁnance.

originated through the streamline reﬁnance program, we can study the pool of mortgages

with characteristics that would make them likely to part of this program: reﬁnance mortgages

guaranteed by the FHA that do not involve any equity extraction.

Reassuringly, the fraction of mortgages in this category with missing FICO scores is only

slightly higher than the overall fraction of mortgages in the data with missing scores (14

percent compared to 12 percent overall), making it unlikely that there are a large number

of low-score borrowers obtaining mortgages through the program and appearing in the data

with missing scores. In contrast, FHA reﬁnances just below the 620 threshold do exhibit

other risky characteristics that suggest they were underwritten less stringently, likely because

they were disproportionately originated through the streamline program. In particular, FHA

reﬁnances with credit scores just below the threshold have higher DTIs and are more likely

to lack full documentation of the borrower’s income. Again, however, these are supply-driven

diﬀerences that do not invalidate our identiﬁcation strategy.

2.2.2 Using Credit Scores in the Consumer Credit Panel

The second, less obvious step in computing our mortgage credit availability measure is iden-

tifying whether each individual in the population has a credit score that falls above or below

the relevant threshold. In principle, all we would need to do this is to observe the individual’s

FICO score at a given point in time. In practice, there are two complications.

First, a FICO score is the output of a proprietary scoring model, which has changed

over time, applied to data reported by any one of the three credit bureaus. As a result,

there is no single “FICO score” for an individual at any given point in time. Moreover,

scores change almost continuously as new information is reported to the credit bureaus. The

scores reported in the Black Knight data, which we used to construct ﬁgure 1, are the results

of the particular scoring model and credit bureau data used by the lender at the time of

underwriting. For both these reasons, even if we observed some FICO score from around

the same time that a mortgage was originated, it would not necessarily match exactly to the

score reported in the Black Knight data. The empirical relevance of the observed 620 and

640 thresholds in a diﬀerent data set is thus something that we need to test, not something

that we can assume.

The second complication is that we do not observe any FICO scores in our main data set

for this project, which is the Equifax Consumer Credit Panel from the Federal Reserve Bank

of New York. Instead, the CCP contains an “Equifax Risk Score”, which is a similar credit

score intended to capture the probability that individual will default on any loan. In order

to relate the Risk Score in the CCP to a FICO score, we use a linked monthly panel data set

that contains both types of credit scores. Using the joint distribution of Equifax Risk Scores

and FICO scores, we predict the probability that an individual with a given Risk Score in

the CCP would have a FICO score (using the particular model and credit bureau data in the

linked data set) that exceeded the a given threshold value.

To characterize the relationship

between the Equifax Risk Score and the probability that a FICO score exceeds a threshold

we estimate logit models using data six months prior to origination. The models allow the

relationship between the two scores to vary across years.

2.3 Identiﬁcation Strategy

Our identiﬁcation strategy combines these two steps into a speciﬁcation designed to measure

the eﬀect of having a credit score above the threshold in a period when lenders are using that

threshold to make lending decisions. To identify this eﬀect, we use a diﬀerence-in-diﬀerence

approach, comparing borrowers above and below the threshold in periods where the threshold

is more or less important. For ease of exposition, we begin with a case where there is only

one credit score threshold at 620. First, as described in section 2.2.1, our measure of the

importance of the threshold in quarter t is given by the ratio of the number of mortgages

originated to borrowers just below 620 compared to the number just above:

620

(Loan Count|F ICO ≥ 615, F ICO < 620)

(Loan Count|F ICO ≥ 620, F ICO < 625)

Second, as described in section 2.2.2, our measure of whether a borrower in the consumer

credit panel has a FICO score above 620 is based on their Equifax Risk Score, P r(F ICO ≥

620|riskscore

)

a(t)

, with the relationship allowed to vary by year (a(t)).

This approach yields an estimating equation of the form

= αP r(F ICO ≥ 620|riskscore

)

a(t)

+ βP r(F ICO ≥ 620|riskscore

)

a(t)

× (1 − r

620

)

+ δ

riskscore

+ η

+ ε

(1)

The linked data contain information only on mortgage borrowers, which is why we cannot use them for

our main estimates.

Throughout the paper we calculate the probability of exceeding a FICO threshold using the Risk Score

with which an individual enters quarter t, so that the score cannot have already directly responded to the

outcome variable. Equifax captures the information in the CCP on the last day of a quarter.

where y

is an outcome variable.

The parameter of interest is β, the coeﬃcient on the

interaction between one minus the importance of the 620 threshold and the probability that

the individual’s FICO score is 620 or greater. A similar logic applies for the 640 threshold.

Equation 1 also shows the primary controls that we include in the empirical work be-

low, including 1) quarter ﬁxed eﬀects (η

), 2) the Equifax Risk Score of the individual

interacted with quarter dummies to allow the coeﬃcient (δ

) to vary over time, and 3) the

(un-interacted) probability that the individual’s FICO score is 620 or greater.

As we note

in the introduction, these controls allow us to identify the eﬀects of credit availability using

the timing and nonlinearity of the interaction term (or, in practice, our combined credit avail-

ability measure). Formally, we require that the interaction term be uncorrelated with any

other factors aﬀecting an outcome variable, conditional on the controls. Thus our identiﬁca-

tion is secure against any confounding factors that 1) vary only in the time series dimension,

2) are correlated with credit score in a linear fashion, even if that linear relationship with

credit score shifts over time, or 3) are correlated with the threshold probabilities—which are

nonlinear functions of the Risk Scores—but do not shift over time. In particular, our view

is that credit demand could be correlated over time with the level and slope of many of our

outcomes but that it is unlikely to have an eﬀect on those outcomes that happens to shift

at the precise times and in the nonlinear ways that the interaction term above does.

2.4 Combined Credit Availability Measure

To help understand how to evaluate mortgage credit availability in periods in which lenders

used both the 620 and 640 thresholds in their lending decisions, we introduce a very simple

structural model. This model also gives a structural interpretation to the ratio of mortgage

originations above around the relevant threshold scores.

To start, we imagine a mortgage market with a large number of lenders, each of whom

makes lending decisions based on based on the FICO score of a perspective borrower. All

lenders are willing to make loans to borrowers with scores of 640 or greater. A fraction ρ

640

are willing to make loans to borrowers with scores below 640 and a fraction ρ

620

of these

lenders (i.e., a fraction ρ

620

× ρ

640

of all lenders) are willing to make loans to borrowers with

scores below 620. Assume the FICO scores of individuals who would like to purchase a home

are uniformly distributed with mass M in each 5-point FICO bin. Each borrower approaches

In practice, many of our outcome variables are binary or counts, so we estimate logistic or negative

binomial regressions, rather than linear models.

Note that the quarter ﬁxed eﬀects subsume the un-interacted ratios.

a single lender, drawn at random from the distribution of lenders, and applies for a loan.

Now consider a borrower whose credit score we do not observe but for whom we can

calculate P r(F ICO ≥ 620) and P r(F ICO ≥ 640). The probability that she will be given a

loan when she approaches a random lender is

P = P r(F ICO ≥ 640)+P r(640 > F ICO ≥ 620)×ρ

640

+P r(F ICO < 620)×ρ

620

×ρ

640

(2)

Next, we discuss how we can estimate ρ

620

and ρ

640

from the data. For borrowers with

scores between 615 and 619, a fraction ρ

620

× ρ

640

of lenders they approach will make them

loans and the total number of loans to borrowers in this range will be ρ

620

× ρ

640

× M.

Similarly, the total number of loans originated to borrowers with scores between 620 and

624, and also between 635 and 639, is ρ

640

× M . Finally, all applicants with scores above 640

will be approved so the total number of loans originated to borrowers with scores between

640 and 644 is M. Therefore we can identify estimators for ρ

620

and ρ

640

(Loan Count|F ICO ≥ 635, F ICO < 640)

(Loan Count|F ICO ≥ 640, F ICO < 645)

ˆρ

640

× M

= ˆρ

640

and

(Loan Count|F ICO ≥ 615, F ICO < 620)

(Loan Count|F ICO ≥ 620, F ICO < 625)

ˆρ

620

× ˆρ

640

× M

ˆρ

640

× M

= ˆρ

620

This derivation shows that ratio of the number of mortgages just below the threshold to the

number just above it can be interpreted as the fraction of lenders who are willing to lend to

borrowers with credit scores below that threshold.

That is, r

620

= ˆρ

620

and r

640

= ˆρ

640

To operationalize equation 2 and deﬁne our credit availability measure for a given in-

dividual, we make two simple substitutions. First, we replace ρ

620

and ρ

640

in equation 2

with our estimates r

620

and r

640

. Second, we replace the notional P r(F ICO ≥ 640) with

the P r(F ICO ≥ 640|riskscore

)

a(t)

that we estimate from the linked data described above.

These substitutions yield

credavail

= P r(F ICO ≥ 640|riskscore

)

a(t)

+ P r(640 > F ICO ≥ 620|riskscore

)

a(t)

× r

640

+ P r(F ICO < 620|riskscore

)

a(t)

× r

640

× r

620

A more realistic model could relate the ratio to the number of lenders willing to lend but also the size of

those lenders and the cost to borrowers of seeking them out. A small rural lender willing to lend to borrowers

with FICO scores below 620 is not likely to be able or willing to draw enough customers to signiﬁcantly

aﬀect the measured ratio or credit supply.

or equivalently,

credavail

= P r(F ICO ≥ 640|riskscore

)

a(t)

+ P r(F ICO < 640|riskscore

)

a(t)

× r

640

+ P r(F ICO < 620|riskscore

)

a(t)

× (r

620

− 1) × r

640

To connect this derivation to the diﬀerence-in-diﬀerence approach described above, it is

instructive to consider two special cases. If ρ

640

= 1 and we estimate r

640

= 1—no lenders

use 640 as a minimum score—then 620 is the only relevant threshold and

(credavail

640

= 1) = credavail

620

≡P r(F ICO ≥ 620|riskscore

)

a(t)

+ (1 − P r(F ICO ≥ 620|riskscore

)

a(t)

) × r

620

+ P r(F ICO ≥ 620|riskscore

)

a(t)

) × (1 − r

620

Similarly, if ρ

620

= 1—no lenders use 620 as a minimum score—then 640 is the only relevant

threshold and

(credavail

620

= 1) = credavail

640

≡P r(F ICO ≥ 640|riskscore

)

a(t)

+ (1 − P r(F ICO ≥ 640|riskscore

)

a(t)

) × r

640

+ P r(F ICO ≥ 640|riskscore

)

a(t)

) × (1 − r

640

Focusing on the last line of the deﬁnition of credavail

620

, we observe that it is precisely

the same as the interaction term from equation 1, our diﬀerence-in-diﬀerence speciﬁcation,

except that it includes the additional un-interacted r

620

term. This un-interacted term is

already absorbed into our quarter ﬁxed eﬀects. As a result, if we replaced the interaction

term in equation 1 with this credit availability measure, the estimated coeﬃcient would be

the same. In other words, when only the 620 threshold is active, we can think of this credit

availability measure as simply the interaction term from the standard diﬀerence-in-diﬀerence

speciﬁcation. The same holds for the 640 threshold.

This derivation shows that our combined credit availability measure has both theoretical

motivations and eﬀectively reduces to the standard interaction term from our diﬀerence-in-

diﬀerence speciﬁcation when only one credit score threshold is active. Our ﬁnal speciﬁcation

(for a continuous outcome variable) is then

=α

620

P r(F ICO ≥ 620|riskscore

)

a(t)

+ α

640

P r(F ICO ≥ 640|riskscore

)

a(t)

+ βcredavail

+ δ

riskscore

+ γX

+ η

+ ε

(3)

where β is again the parameter of interest, capturing the combined eﬀect of the 620 and 640

thresholds. The speciﬁcation includes our predicted probabilities of having a FICO score

over 620 and 640, to strip out nonlinear, non-time-varying eﬀects of credit score on the

outcomes. It also includes the linear eﬀect of the Risk Score, which is allowed to vary over

time. Finally, to isolate the eﬀect of current credit availability, we also add as additional

controls the ﬁrst quarterly lag of credit availability for the individual, the ﬁrst lag of the

predicted threshold probabilities, and the ﬁrst lag of credit score interacted with the quarter

dummies, all contained within the vector X

Although it is easy to think of credavail

in a binary context—one either has access to

credit or one does not—in practice it is a continuous variable with outcomes ranging from 0

to 1, both because the link between Equifax Risk Score and FICO threshold is probabilistic

and because our quantiﬁcation of the importance of the threshold is never actually 0 or 1.

Figure 3 shows the evolution of the credit availability measure. The left panel shows the

time series of average credit availability for individuals with Equifax Risk Scores between

530 and 730, our estimation sample. The timing of the sharp drops in the series correspond

to the narrative provided above and the introduction of the thresholds we identiﬁed in the

Black Knight data. The three shaded regions denote periods between 2008 and 2011 in which

availability was roughly stable.

Taking a diﬀerent slice through the data, the right panel compares average credit availabil-

ity, by 10-point Risk Score bin, across those three stable periods of credit availability between

the changes in the thresholds. As should be expected, our availability measure dropped most

for individuals with low Risk Scores between 2008 (the black line) and 2009:Q2-2010:Q2 (the

red line), as the 620 FICO threshold kicked in. By 2011 (the blue line), with the introduc-

tion of the 640 threshold, availability fell a bit further for the low end of the Risk Score

range plotted here, but also fell noticeably in the middle of the range. Individuals with Risk

Scores above 700 saw essentially no change in either period, because we estimate a very low

probability of these individuals having a FICO score below 640.

A brief discussion of the estimated coeﬃcients on lagged credit availability is presented in section 3.3.

2.5 Estimation Sample

We estimate the eﬀects of our credit availability measure using the Equifax/FRBNY CCP,

which consists of a 5 percent random sample of individuals who have a credit ﬁle. For our

main results, we use a random sample containing 50 percent of the individuals in the panel,

or a 2.5 percent sample of the population. We used a disjoint smaller subset of the CCP as a

training sample for the initial data analysis for this paper, in part for ease of computation and

in part to avoid reporting results from the same data as our training sample. This approach

likely helped us avoid reading too much into results that happened to be economically large

or statistically signiﬁcant in our initial analysis.

We restrict our estimation sample to the years 2008-2011, a period when we can clearly

identify changes in credit availability, as discussed above. Ending our sample in 2011 has

the further advantage that we are able to observe everyone in our sample through 2015, a

full four years after the end of the estimation period, allowing us to estimate longer-term

eﬀects of our credit availability measure.

We also restrict our analysis to borrowers within a relatively narrow range of Risk Scores

around the thresholds at 620 and 640 that we identiﬁed above. This restriction has two

motivations. First, borrowers with credit scores far from the threshold values are much less

likely to be aﬀected by lender’s use of these thresholds in making lending decisions. Results

suggesting that such borrowers are signiﬁcantly aﬀected by these mortgage thresholds are

thus more likely to be spurious. Second, the relationship between credit score and mortgage

demand is likely nonlinear. However, within a narrow band of scores, a linear function of

credit score should be a reasonable control for demand. Our baseline speciﬁcation uses a

sample of borrowers with scores between 530 and 730, but we perform robustness checks

around the size of the window in section 3.4.

3 Results

Having constructed a measure of mortgage credit availability for each member of the con-

sumer credit panel, we next explore the relationship between this measure and various out-

comes. Depending on the outcome, we use linear regressions, logit models in the case of

probabilities, or negative binomial models in the case of count variables. For each outcome

We drop individuals identiﬁed in the CCP as dead, those who are reported to be younger than 16 or older

than 120, and those whose address is reported as something other than a “street address” or “high-rise”.

These restrictions removed less than 10 percent of the observations in the CCP.

variable, we consider horizons of 4, 8, 12, and 16 quarters to assess both the short-term

and longer-term eﬀects of restrictions on mortgage credit. As laid out above, our baseline

speciﬁcation includes dummy variables for the quarter of observation and also an interaction

of this quarter dummy with Risk Score.

In the table for each speciﬁcation, we report results using the entire sample and also

separately for those who had a mortgage in the previous quarter and those who did not.

In determining whether someone has a mortgage, we use total outstanding balance on all

mortgages appearing on her credit report and say an individual has a mortgage if the total

is greater than zero. Because our sample is concentrated towards the bottom of the credit

score distribution, the sub-sample of people with no mortgage balance makes up about 85

percent of our estimation sample.

Finally, it is worth noting that the coeﬃcients on our mortgage credit availability measure

capture the diﬀerences between a borrower with a credit availability of one, meaning she is

unaﬀected by minimum credit scores, and a hypothetical borrower with credit availability

of zero, meaning both that she falls below the credit score threshold with certainty and

that we observe no mortgages to borrowers with credit scores just below this threshold. In

practice, we always estimate some positive probability of an individual with a low Risk Score

being above a FICO threshold, and we always see some mortgages issued below the FICO

thresholds in the Black Knight data. As a result, our credit availability measure is never less

than about 0.2. As shown in ﬁgure 3B, for borrowers with scores toward the bottom of our

range, credit availability fell from about 0.7 to 0.2 between 2008 and 2011, so the net eﬀect

for the most aﬀected group is about half as large as the reported eﬀect.

3.1 Mortgage Borrowing

Our ﬁrst set of models is intended in part to conﬁrm that our measure of mortgage credit

availability actually captures borrowers’ ability to obtain a mortgage. In these models,

the dependent variable is whether the person takes out one or more new mortgages within

the speciﬁed horizon and we use a logit speciﬁcation. We use the CCP’s trade-line data on

individual mortgages to determine the date on which the mortgage was opened.

In addition

We also note that we are cautious about using our measure to compare people with very high credit

scores to those with very low credit scores, as our identiﬁcation comes largely from the curvature in our

measure around the credit score thresholds at 620 and 640.

This is a subtle but important step. Many of the aggregate variables in the CCP only update with a

lag as the information is reported to Equifax. For example, a change in an individual’s reported mortgage

balance will typically occur in the data one or two quarters after they actually take out a mortgage. By

using the dates from the trade lines, we are able to precisely measure the timing of the mortgage origination.

to considering longer horizons, this ﬁrst set of regressions also includes speciﬁcations in which

the outcome variable is whether the individual takes out a mortgage in the current quarter.

We can get a good sense of the data by examining plots of the relationship between

credit score and the probability of taking out a mortgage. Figure 4 shows the contempora-

neous probability of mortgage attainment by credit score, across the three stable periods of

availability in our data. The plot shows that the probability of taking out a new mortgage

declined most sharply for those at the bottom of the credit distribution between the 2008

(the black line) and 2009:Q2-2010:Q2 periods (the red line). After lenders began using the

640 threshold, we see that the 2012 probabilities (the blue line) show evidence of a further

decline in mortgage originations in the middle of our sample. These patterns mirror the

evolution of our credit availability measure, as discussed above and shown in ﬁgure 3B.

More formally, our ﬁrst main result is shown in the ﬁrst column of panel A of table 1.

Even after including the various controls, we estimate that the average marginal eﬀect of

our credit availability measure on the probability of taking out a new ﬁrst mortgage in the

current quarter is 1 percentage point, with a standard error of just 0.1 percentage point.

This estimate is also very large compared to the average probability in our sample of taking

out a new mortgage (“Dep. Var. Mean”), which is just 0.9 percent.

This result conﬁrms both the importance of these credit score thresholds in determining

who receives mortgages and also the ability of our credit availability measure to capture

these threshold eﬀects. Although this result may be unsurprising given the patterns in the

Black Knight data, it is not trivial, for at least two reasons. First, the translation from

Equifax Risk Scores to predicted FICO scores could wash out the eﬀect, especially given the

controls we include. Second, there are various behaviors that could imply the patterns we

observe in the loan-level data without implying similar patterns in the individual data. For

example, credit scores could be suﬃciently variable from day to day that individuals can

easily get a mortgage tomorrow even if their score falls below the threshold today.

The fact that we do ﬁnd eﬀects using our credit availability measure suggests neither

concern is valid. Apparently, the Equifax Risk Score is suﬃciently correlated with the FICO

scores used in mortgage underwriting that that they are able to capture changes in lenders’

reactions to borrowers’ FICO scores. Also, these scores appear suﬃciently stable that a single

observation taken at the end of the quarter does aﬀect the individual’s ability to borrow over

the following three months. As we wrote in the introduction, establishing these facts seems

Our analysis focuses on ﬁrst mortgages, which made up the vast majority of mortgages during this

period. Nevertheless, all of our results are similar if we include second mortgages as well.

particularly important given the large number of studies that have interpreted these scores

as a meaningful measure of individual’s access to mortgage credit.

Looking at longer horizons, panel A of table 1 shows the cumulative eﬀect of our credit

availability measure on mortgage originations over the subsequent 4, 8, 12 and 16 quarters. In

columns 2 through 5, we see that the coeﬃcient on our credit availability measure increases

in magnitude through columns 2 and 3 (0-3 quarters and 0-7 quarters, respectively) and

then levels oﬀ at about 3 to 3.5 percentage points. However, the mean of the dependent

variable increases steadily from left to right, suggesting that our measure of mortgage credit

access becomes less important over time compared to other factors that determine whether

people take out new mortgages. Considering whether people take out any new mortgages

up to three quarters ahead, the average marginal eﬀect of our measure is about 3 percentage

points, while overall, 3.5 percent of people in the sample take out a mortgage within this

period. At a 15-quarter horizon, the average marginal eﬀect of our measure is still about 3

percentage points, but the average probability of taking out a mortgage is 13 percent. While

attenuated in relative terms compared to the short run, these eﬀects are still very large,

suggesting that the eﬀects of credit availability are quite persistent.

In panel B, we repeat our analysis on the sub-sample of people who have no previous

mortgage balance. Because this sub-sample makes up about 85 percent of our estimation

sample, the average marginal eﬀects for this group are similar to the eﬀects for the sample

as a whole, although they are somewhat larger relative to the average probability of taking

out mortgages. For people who already have a mortgage (panel C), the average probability

of taking out a new mortgage is considerably higher. Many of these individuals are likely

reﬁnancing an existing mortgage during this period of falling interest rates. Moreover, al-

ready being homeowners suggests a preference (and ﬁnancial capacity) for homeownership.

For this group, the estimated marginal eﬀects are also much larger, indicating that credit

availability boosts mortgage originations by more in percentage point terms. However, as a

ratio to their average probabilities, the eﬀects are similar in size those of the entire sample.

In table 2, we consider an alternative measure of new mortgage borrowing, namely the

change in the total mortgage balance on an individual’s credit record, relative to the quarter

prior to that in which we estimate credit availability. Qualitatively, the results are similar to

those in table 1. In panel A of table 2, credit availability increased an individual’s mortgage

balance by about $3,000 over four quarters and $6,600 over 16 quarters, where the average

increases in mortgage debt over the entire sample are close to zero. The eﬀects are noticeably

smaller for those who did not have mortgages previously (panel B) and larger for those who

did (panel C). Interestingly,those who did have mortgages previously had $56,000 less in

mortgage debt after 16 quarters, on average, either through paying it down or discharging

debt through foreclosure or other means. Even if we halve the coeﬃcient on credit availability,

to match the actual change in our credit availability measure for low-score borrowers, this

result suggests that credit availability attenuates the decline in mortgage balance, perhaps

because it allows homeowners to reﬁnance and either take out cash or avoid default.

3.2 Additional Outcomes

Aside from the direct question of whether restrictions on mortgage credit are preventing

individuals from obtaining mortgages and how these eﬀects attenuate over time, we are

also interested in understanding the broader relevance of credit supply. The richness of the

consumer credit panel allows us to explore several additional outcomes.

3.2.1 Mortgage Delinquency

We next consider whether access to mortgage credit can allow individuals to avoid negative

credit events. In table 3, we show the results of logit models in which the dependent variable

is whether individuals have had at least one mortgage delinquency of 60 days or more. For

the full sample in panel A, we ﬁnd large negative eﬀects: At a horizon of four quarters

(column 1), the average probability of being delinquent in at least one quarter is 4.5 percent,

while having credit available reduces the probability of delinquency by 2.2 percentage points.

The eﬀects are larger at longer horizons, although they are somewhat smaller relative to the

increasing average probabilities.

We get can get a better sense of the mechanism at work by looking at panels B and C.

In panel B, among people with no prior mortgage balance, we see that credit availability

has much more modest eﬀects on delinquency, both in absolute terms and relative to the

(smaller) average probabilities of delinquency in this group. In contrast, for those who

already have a mortgage balance (panel C), we ﬁnd that continued access to mortgage credit

lowers their probability of being delinquent within four quarters by 7 percentage points, half

of the dependent variable mean. The eﬀect is even larger in percentage point terms at longer

horizons. These results strongly suggest that having access to credit allows homeowners

to avoid delinquency through lowering their mortgage payments by reﬁnancing at a lower

interest rate. Since credit availability was declining during this period, it is likely that

many homeowners became delinquent because they were unable to reﬁnance in the new

environment.

In addition to mortgage delinquency, it also interesting to examine whether mortgage

credit availability aﬀects delinquency on other types of loans. Importantly, we do not think

that lenders tightened other forms of credit at the same times and at the same credit score

thresholds, so our credit availability measure should cleanly identify the spillover eﬀects of

having access to mortgage credit speciﬁcally. Panel A in table 4 shows that the overall eﬀect

of having access to mortgage credit is zero at a horizon of four quarters. At a horizon of 16

quarters, there is a meaningful negative eﬀect (-3.5 percentage points), on an average delin-

quency probability that reached 52 percent for our sample during this turbulent economic

period

As in the previous results, we can better understand the mechanism by separately con-

sidering the impact on individuals who did and did not already have a mortgage. Similar to

our results for mortgage delinquency, the eﬀects of credit availability on non-mortgage delin-

quency are uniformly negative for borrowers who already have mortgages (panel C), pointing

to the importance of reﬁnancing in avoiding negative credit events. In partial contrast to

our results for mortgage delinquency, the eﬀects are also clearly negative for borrowers who

did not previously have a mortgage (apart from the short-run eﬀect, which is very close to

zero). This pattern suggests that access to mortgage credit for new borrowers ultimately

helps avoid delinquency on non-mortgage loans, but that the eﬀect takes time to kick in.

This may be because the ﬁnancial beneﬁts of being a homeowner, such as the ability to

withdraw equity or to borrow more cheaply if rates decline, are only realized some time after

becoming a homeowner.

3.2.2 Moving and Migration

Because we observe the mailing address of an individual in the CCP down to the Census

block, we can also examine the eﬀects of credit availability on moving and migration decisions.

The address data in the CCP tend to be unstable because they reﬂect the most recent

address reported to Equifax, which can ﬂuctuate back and forth if that person is receiving

bills at more than one address. To try to isolate actual moves, we limit the sample to those

individuals whom we can observe in a single location for at least four quarters before we

measure their credit availability and who appear to remain in a location for four quarters

after the end of whatever horizon we use. As a consequence of this approach, the samples

Although the Home Aﬀordable Reﬁnance Program allowed borrowers, regardless of credit score, to

reﬁnance if their mortgage balance was larger than the value of their home, many lenders reportedly imposed

minimum-score overlays at the 620 or 640 thresholds.

are smaller. Also, we can only show eﬀects out through 12 quarters, because we cannot

establish four-quarter address stability for those who are 16 quarters out from 2011, as our

data end in mid-2016.

Table 5 shows the eﬀects on the individual’s probability of moving across Census blocks.

In panel A, we see small positive eﬀects at short horizons and no eﬀects at longer horizons.

As before, however, these estimates mask heterogeneity between those who do and do not

already have a mortgage balance. For those who do not (panel B), we see somewhat larger

positive eﬀects at shorter horizons. The positive eﬀect for this group is sensible, since non-

homeowners who have credit available to them usually have to move to buy a home, which

we observed them doing in table 1.

For those who do have a mortgage balance (panel C),

the eﬀects start small and grow more negative over time, suggesting that having the option

to reﬁnance leads some of these homeowners to remain in their homes for longer.

The next table (6) looks at the eﬀects on moving across metropolitan areas.

As with

moving, panels A and B indicate that credit availability has positive eﬀects on migration

behavior, both for the full sample and for those without a previous mortgage balance, al-

though the eﬀects attenuate at longer horizons. The eﬀects in column 1 appear small in

percentage point terms, with mortgage credit availability associated with a 1 percent rise in

changing CBSAs. However, these estimates are actually fairly large relative to the average

probabilities of moving across metro areas, which are under 3 percent.

Arguably the most interesting results in table 6 are in panel C, where we ﬁnd no signiﬁcant

eﬀects of credit availability on migration among those who did previously have a mortgage,

at least of a size that we can detect given our standard errors. Some articles in the popular

press have suggested that homeowners could have been “locked in” to their current properties

or local areas because they were unable to get a new mortgage, either because they were

under water, or wanted to hold on to their current rate, or had credit scores that were too

low.

Our results suggest that, at least along this last dimension, there is no evidence of

this phenomenon: Among prior homeowners, lack of mortgage credit increases moving and

has no eﬀect on migration. Therefore our analysis provides no support for the hypothesis

that the economic recovery was slowed because frictions from the housing market prevented

Of course, individuals without previous mortgage balances can be homeowners, but most people who

did not have a balance and then took out a mortgage seem likely to be purchasing and moving to a new

home.

Formally, these are known as core-based statistical areas, or CBSAs. We use the 2013 CBSA deﬁnitions,

merged into the CCP by county of residence.

See the introduction for citations of the academic literature on the impact on migration of being under-

water.

unemployed workers from relocating to areas with stronger labor markets.

3.2.3 Auto Loans

Finally, we explore whether we can observe interactions between mortgage borrowing and

other kinds of consumer credit. In particular, we consider whether our measure of mortgage

credit availability has implications for consumers’ use of auto loans. Results from this exercise

are shown in table 7, where the dependent variable is the change in the number of auto loans

on the individuals credit record, and table 8, where we use the change in the total auto loan

balance.

We have no strong prior as to either the sign or magnitude of the eﬀect. On the one

hand, individuals who cannot buy a house because they are denied mortgage credit could

substitute into cars, while those who get mortgages may substitute away from cars. On

the other hand, auto borrowing could be positively correlated with mortgage borrowing

because of complementarities between driving and purchasing a home, or because reﬁnancing

one’s mortgage lowers interest payments and relieves liquidity constraints. On net, looking

across both tables, the eﬀects among those who did not previously have a mortgage balance

(panel B) are mostly negative, suggesting that the substitution channel dominates. For prior

mortgage borrowers (panel C), the eﬀects are uniformly positive, suggesting that reﬁnancing

enables some homeowners to purchase cars.

3.3 Lagged Credit Availability

In all of the speciﬁcations described above, we include among the controls an individual’s

credit availability from the previous quarter. Doing so allows us to isolate the eﬀect of

having credit availability at a particular point in time, given that credit scores (and thus

credit availability) are likely to be highly correlated over time.

The lagged eﬀects may be

of interest in their own right, however, which is why we included them in our tables.

While the magnitudes vary substantially, the signs of the eﬀects on lagged credit avail-

ability are generally the same as on the current measure, likely because credit availability

has persistent eﬀects on some outcomes, as we showed above. In addition, to the extent that

our current measure of credit availability is noisy, the lagged measure may also pick up some

of the eﬀect on the outcome.

The abrupt changes in the credit score thresholds during the 2008-2011 period mean that current and

lagged availability may not have been as correlated as during other periods.

In some cases, however, we see diﬀerent signs on the two coeﬃcients when we focus the

analysis on people who do not have a mortgage. In particular, those who appeared to have

greater access to mortgage credit in the previous quarter but did not become homeowners

subsequently experience less growth in both mortgage balances and higher growth in auto

debt (as shown in panel B of tables 2 and 7). This may reﬂect a selection eﬀect, whereby those

who could have obtained a mortgage but chose not to have lower demand for homeownership,

and possibly more demand for cars instead.

More generally, we might have expected those who were excluded from the mortgage

market in the previous period to display an increased demand for mortgages the following

period, reﬂecting pent-up demand. This eﬀect would have appeared as a negative eﬀect of

lagged credit availability in the speciﬁcations with mortgage originations as the dependent

variables. However, this is not what we ﬁnd, suggesting that if there is pent-up demand of

this form, it is oﬀset by the persistence of the positive eﬀects of availability.

3.4 Robustness Checks

We next examine a series of alternative speciﬁcations to some of our main results, to ensure

that they are robust. Table 9 shows diﬀerent estimates of the eﬀect of credit availability on

the contemporaneous probability of taking out a mortgage, across all borrowers. Column 1

repeats our preferred estimate from column 1 of panel A in table 1. In the second column,

we add linear and quadratic terms for the age of the individual, interacted with quarter.

Age is an attractive control because it is highly correlated with credit score, as we show in

ﬁgure 5A (discussed below). Moreover, because age evolves deterministically, it may be a

more stable proxy for current and past credit scores. In any event, including age does not

change the estimated eﬀect of credit availability.

Next we consider the possibility of changing how we control for the past evolution of

credit availability and credit score, via a more direct route than controlling for age. Column

3 shows the result of including the second through fourth lags of credit availability, the

second through fourth lags of the predicted threshold probabilities, as well as the second

through fourth lags of credit score interacted with quarter dummies. The eﬀects of credit

availability and the ﬁrst lag are nearly unchanged. Similarly, the eﬀect of credit availability

is also unchanged in column 4 when we drop all lags, including the ﬁrst, from the right-hand

side.

Finally, columns 5 and 6 show the results of changing the credit score window to include

a larger or smaller sample. Our preferred speciﬁcation in column 1 includes individuals with

scores between 530 and 730. We selected that window because scores above 730 or below

530 are very unlikely to be aﬀected by changes in lenders’ use of a 620 or 640 threshold.

Moreover, we wanted to use a narrow enough window that the linear credit score controls

could plausibly pick up variation in mortgage demand by score, since a wider window makes

it more likely that the relationship between score and demand would be nonlinear.

Column 5 considerably expands the sample by including all individuals with scores be-

tween 500 and 830.

The estimated average marginal eﬀect of credit availability is slightly

smaller in magnitude, but the mean of the dependent variable is larger, because high-score

individuals are so much more likely to take out mortgages. Column 6 does the opposite,

narrowing the window to include only individuals with scores between 580 and 680. In this

case the mean of the dependent variable is about the same, but the average marginal eﬀect

is about half as large as in column 1 and is no longer statistically signiﬁcantly diﬀerent than

zero. Intuitively, with a more narrow range of scores, nonlinearities play a smaller role and

the linear credit score interacted with the time dummy picks up most of the variation. In

other words, as the range narrows, it becomes more diﬃcult to separate out the eﬀect of

being more likely to be above the credit score threshold from the eﬀect of simply having a

higher credit score.

In table 10, we apply the same alternative speciﬁcations to the longer-run probability of

taking out a mortgage, speciﬁcally the model for one to 16 quarters ahead from column 5 of

panel A in table 1. Again, column 1 repeats our main result. The next three columns are

essentially the same as column 1, the same pattern as in table 9. Unlike in table 9, the “wide

range” estimate in column 5 is larger than the baseline, but the “narrow range” estimate

in column 6 is again smaller than the baseline and not statistically signiﬁcant. We ﬁnd it

somewhat comforting that the point estimates in the ﬁnal columns of both tables 10 and 10

remain positive and large in economic terms. Nevertheless, the size of the standard errors

makes clear that we do not have enough power to pin down the magnitude of the eﬀect using

a very narrow Risk Score window, given our large set of controls and the concomitant loss

of identifying variation.

This expanded sample still drops the roughly 5 percent of individuals who have extremely high or low

scores.

4 Heterogeneity

Most of this paper focuses on average eﬀects of credit availability among the total population

with Equifax Risk Scores around the 620 and 640 thresholds. However, the importance of

the thresholds should vary across demographic and socioeconomic groups, for two reasons.

First, credit scores are highly correlated with characteristics like age, race, and income. As

a result, some groups—for example, younger adults—are more likely to have scores near the

thresholds than others. Second, the estimated eﬀects of credit availability—the salience of

the thresholds—can also diﬀer across groups. For example, individuals who do not want to

buy a home or cannot aﬀord it should be little aﬀected by the availability of mortgages.

4.1 Heterogeneity by Age

Figure 5 examines heterogeneity in the eﬀects of credit availability by age, focusing on the

sample of individuals with no mortgage balance in the prior quarter. The top left panel

(5A) shows that credit scores are highly correlated with age: Individuals younger than 35

(the two left-most bins) have an average Equifax Risk Score of around 650, while those 75

and older (the right-most bin) have an average score of around 770. The three lines in the

panel, which correspond to the three stable periods of credit availability discussed above, are

essentially on top of each other, indicating that scores in each age group were little changed

over our sample period, on average.

Moving on to the question of how these diﬀerences in credit scores translate into mortgage

credit access, the top right panel (5B) shows the average of our credit availability measure

in each age bin, across stable periods. Because of the strong correlation between age and

credit score, this panel looks fairly similar to ﬁgure 3B, where we plotted credit availability

against credit score. Credit availability fell dramatically among the younger groups between

the ﬁrst (black) and second (red) periods, as lenders started using the 620 threshold, and

then fell somewhat further by 2011 (the blue line), as they moved to a 640 threshold.

Next we consider the possibility that, like the average credit availability measure that we

calculate, the salience of credit availability could also diﬀer across age groups. The bottom

left panel (5C) shows the average marginal eﬀects of credit availability on the contempo-

raneous probability of taking out a mortgage, estimated separately for each age bin using

a speciﬁcation otherwise identical to the pooled estimate in column 1 of panel B in table

1. The solid line in the ﬁgure gives the point estimates, while the dotted lines indicate a

range of two standard errors on either side of that estimate. The impact of mortgage credit

availability is low for the youngest group, highest for the 25-34 bin, and thereafter shrinks

monotonically with age. This pattern is fairly intuitive: Relatively younger adults are more

likely to be constrained by mortgage credit availability, but the very youngest are less likely

to want to purchase a home in the ﬁrst place.

Finally, we combine the information in the previous panels to show how the tightening

of mortgage credit over time has diﬀerentially impacted diﬀerent age groups. To isolate

these eﬀects, we look at the individuals in 2008:Q1, the ﬁrst quarter of our sample. Holding

constant the credit scores of this group at their 2008:Q1 values, we ﬁrst recompute how our

credit availability measure would have changed for them as lending conditions changed over

time, as implied by the changes in the ratios around the 620 and 640 thresholds. Then,

holding constant all of the other characteristics and the estimated time ﬁxed eﬀects at their

2008:Q1 values, we use our age bin-speciﬁc logit models to predict mortgage attainment for

the sample in each quarter through 2012.

In the bottom right panel (5D), we aggregate these predictions to calculate the expected

mortgage attainment over each of the three periods deﬁned above. The black line shows

the average predicted probabilities given credit availability in 2008, while the red and blue

lines show the averages predicted given credit availability in the later stable periods. Not

surprisingly given the previous panels, we ﬁnd the largest shift down in predicted mortgage

attainment among younger and middle-aged individuals. Among the oldest adults, the

aggregate eﬀects are small, both because they have higher credit scores and credit availability

contracted less, and because their average marginal eﬀects are smaller. The aggregate eﬀects

are also smaller among the very youngest adults represented in our panel, because while their

access to credit contracted considerably, their estimated marginal eﬀects are smaller than

for somewhat older groups, as noted above.

4.2 Heterogeneity by Local Racial Distribution and Income

While we would like to repeat this exercise to measure heterogeneity by race, we do not

observe race in the credit panel. We do, however, observe mailing addresses, so we break down

the sample into four groups according to the percentage of black residents in an individual’s

Individuals under 25 are not fully represented in the consumer credit panel, because not all of them have

a credit report. This discrepancy between the CCP sample and the population shrinks with age, so there

are few 18-year-olds in the panel, but most 25-year-olds are included. As a result, the point estimate shown

for the youngest bin is likely an overestimate of the true eﬀect for this age group, because individuals who

do not even have a credit card are presumably the least likely to take out mortgages and buy homes.

In a linear model, the bottom right panel could be derived simply by multiplying the marginal eﬀects—

which would be the model coeﬃcients themselves—by the changes in the credit availability measure.

census tract in the 2000 Census.

Results from this exercise are shown in ﬁgure 6. The top

left panel (6A) shows that race, like age, is highly correlated with credit score, and that the

relationship changed little during our sample period. Accordingly, the top right panel (6B)

shows that credit availability declined most for those tracts with the largest share of black

residents.

As we did for diﬀerent age groups, we next estimate the average marginal eﬀects of credit

availability on the probability of taking out a mortgage for residents of these four groups of

census tracts. The lower left panel (6C) indicates that the marginal eﬀect is constant across

the ﬁrst three bins, containing all tracts with less than half black residents, but it is about

half as large in the right-most bin, which contains individuals in tracts with more than half

black residents. As a result, the bottom right panel (6D) indicates that the overall implied

eﬀect of lower credit availability was somewhat larger in the middle two bins than in the

other two. In sum, people living in census tracts with 10 percent or fewer black residents were

aﬀected less because they suﬀered smaller declines in credit availability, while those in areas

containing half or more black residents were less aﬀected because of their lower marginal

eﬀects, again presumably reﬂecting a lower demand for homeownership.

Of course, the

severity of the eﬀects of changing credit availability might diﬀer across these bins in other

ways that we do not observe.

A very similar pattern is evident when we group individuals with no prior mortgage

balance by quartiles of tract median income. Figure 7 shows that Equifax Risk Scores are

positively correlated with tract income (7A) and that credit availability declined most for

the lowest income tracts (7B). However, the lowest income tracts have noticeably smaller

marginal eﬀects than the other three quartiles (7C). In the end, panel 7D shows that our mod-

els predict that changes in credit availability had larger net eﬀects on mortgage attainment

for the middle two quartiles than for the top income quartile (for whom credit availability

declined least) and the bottom income quartile (who have smaller marginal eﬀects).

The four groups are tracts with 0-10 percent, 10-20 percent, 20-50 percent, and over 50 percent. We

selected the bins based on a visual inspection of the distribution, which is highly skewed, so the bins do not

contain equal numbers of observations.

In contrast, when we perform a similar analysis for tracts based on their shares of Hispanic residents

(not shown), we ﬁnd oﬀsetting diﬀerences in the marginal eﬀects and the reduction in credit availability

across the four bins. As a result, there is a parallel downward shift across all four bins in the overall implied

eﬀect of lower credit availability over this period.

5 Counterfactual

As a ﬁnal exercise, we attempt to compute the aggregate direct impact of the 620 and 640

FICO score thresholds on the total number of mortgages originated in the years following

the ﬁnancial crisis. To perform this calculation, we run a simple counterfactual experiment,

using estimates of the eﬀects of our credit availability measure on the total number of ﬁrst

mortgages taken out by individuals in the CCP sample. We do so using counts of mort-

gages taken out by each individual, over various horizons, and estimate negative binomial

models to relate these counts to the credit availability measure and our controls. Because

many mortgages are taken out jointly by couples, we estimate separate models for joint and

individual mortgages, so that we can properly aggregate and avoid double-counting. Impor-

tantly, these calculations reﬂect only the direct eﬀects of the thresholds, and not any other

constraints on mortgage credit availability since the ﬁnancial crisis.

Table 11 shows the eﬀects of credit availability on joint mortgages, while table 12 shows

them for individual mortgages. We see large positive eﬀects throughout both tables, with

uniformly larger eﬀects for joint mortgages, which have larger average probabilities as well.

Comparing panel C from each table to the other makes clear that individuals who already

have a mortgage balance on their credit record are much more likely to take out joint mort-

gages than individual mortgages, and that the eﬀects of credit availability scale up with the

average probabilities.

Next we use these models to predict the number of mortgages that would have been

originated if the credit availability measure had remained at its level in the ﬁrst quarter

of 2008. Speciﬁcally, we take every individual in our sample and recalculate our credit

availability measure using her actual Risk Score at each point in time and the 620 and 640

threshold ratios from 2008:Q1, holding all else constant. Then, using the speciﬁcations in

column 2, panel A, of tables 11 and 12, we predict the number of each type of mortgage

that would have been originated zero to three quarters ahead. We divide the number of joint

mortgages by two, and then add the two predictions together.

Starting from the ﬁrst quarter of 2011, when the full eﬀect of the 640 threshold had

kicked in, we ﬁnd that the imposition of the thresholds lowered mortgage originations in

our estimation sample—people with Risk Scores between 530 and 730—by about 260,000 in

2011. Comparing this ﬁgure to the 1.65 million mortgages that were actually originated in

our sample, we conclude that mortgage originations would have been about 16 percent higher

without the thresholds. For a broader comparison, we note that ﬁrst mortgage originations

in 2011 to people of all credit scores totaled 7 million, according to data collected under

the Home Mortgage Disclosure Act (HMDA). Assuming, somewhat conservatively, that the

thresholds had no eﬀect one people with scores outside of the 530 to 730 range, we conclude

that total mortgage originations would have been about 3.5 percent higher without the

thresholds.

We can take a longer view by doing essentially the same exercise with the speciﬁcations

in column 5, panel A, of both tables and predicting the number of joint and individual ﬁrst

mortgages that would have been originated zero to 15 quarters ahead. Again starting from

the ﬁrst quarter of 2011, we ﬁnd that the imposition of the thresholds lowered originations

in our sample by about 580,000 between 2011 and 2014. Comparing this ﬁgure to the 8.4

million mortgages actually taken out by individuals in our sample indicates that originations

would have been about 7 percent higher. Comparing the 580,000 ﬁgure to 31 million, the

total number of ﬁrst mortgages originated to all individuals from 2011 to 2014, implies that

there would have been about 2 percent more mortgages originated over this period.

6 Conclusion

The question of how tight mortgage credit should be is an important one that forces policy

makers to balance concerns in both directions. On the one hand, tight mortgage credit

prevents marginal borrowers from realizing the beneﬁts of homeownership and, on a larger

scale, reduces economic activity in the housing sector. On the other hand, the recent ﬁnancial

crisis demonstrates the risk of mortgage credit that is too loose: Banks’ losses on defaulting

mortgages can cause instability in the ﬁnancial sector, borrowers may take out loans they

are unable to repay, and an excess supply of credit can potentially contribute to a bubble in

housing prices.

Our paper aims to shed additional light on one of these issues, the eﬀect of mortgage credit

on individual borrowers. Exploiting the timing and nonlinear eﬀects of lenders’ introduction

of minimum credit score thresholds, we ﬁnd that these thresholds have very large negative

eﬀects on borrowing. In other words, borrowers are not able to avoid the thresholds in

the short run. We also ﬁnd that borrowers without current access to mortgage credit are

more likely to become delinquent on mortgages they had previously taken out, as well as

on other forms of debt. Although these eﬀects attenuate somewhat over time in relative

terms, we ﬁnd that they persist for at least several years, suggesting that the impact of these

policies on the welfare of constrained individuals could be quite large. Further research is

necessary to study these eﬀects and the other consequences of tight mortgage credit, in order

to give policy makers a better understanding of the total eﬀects of policies aﬀecting credit

availability.

References

Acolin, A, J Bricker, PS Calem, and SM Wachter, “Borrowing Constraints and

Homeownership,” American Economic Review: Papers and Proceedings, 2016, 5.

Anenberg, Elliot, Aurel Hizmo, Edward Kung, and Raven Molloy, “The Eﬀect of

Mortgage Credit Availability on House Prices and Construction: Evidence from a Frontier

Estimation Approach,” May 2016. Working Paper.

Barakova, Irina, Paul S Calem, and Susan M Wachter, “Borrowing Constraints

During the Housing Bubble,” Journal of Housing Economics, 2014, 24, 4–20.

, Raphael W Bostic, Paul S Calem, and Susan M Wachter, “Does Credit Quality

Matter for Homeownership,” Journal of Housing Economics, 2003, 12 (4), 318–336.

Bhutta, Neil, “The Ins and Outs of Mortgage Debt During the Housing Boom and Bust,”

Journal of Monetary Economics, November 2015, 76.

and Daniel Ringo, “Credit Availability and the Decline in Mortgage Lending to Mi-

norities after the Housing Boom,” 2016. FEDS Notes 2016-09-29.

Bloomberg News, “Home Ownership Gets Harder for Americans as Lenders Re-

strict FHA Mortgages,” http://www.bloomberg.com/news/articles/2010-11-17/

home-ownership-gets-harder-for-americans-as-lenders-restrict-fha-mortgages

2010.

Bubb, Ryan and Alex Kaufman, “Securitization and Moral Hazard: Evidence from

Credit Score Cutoﬀ Rules,” Journal of Monetary Economics, 2014, 63, 1–18.

Chomsisengphet, Souphala and Ronel Elul, “Bankruptcy Exemptions, Credit History,

and the Mortgage Market,” Journal of Urban Economics, 2006, 59, 171–188.

DiMaggio, Marco and Amir Kermani, “Credit-induced Boom and Bust,” 2015.

Columbia Business School Research Paper No 14-23.

Farber, Henry, “Unemployment in the Great Recession: Did the Housing Market Crisis

Prevent the Unemployed from Moving to Take Jobs?,” American Economic Review, 2012,

102 (3), 520–525.

Favara, Giovanni and Jean Imbs, “Credit Supply and the Price of Housing,” American

Economic Review, March 2015, 105.

Ferreira, Fernando, Josheph Gyourko, and Joseph Tracy, “Housing Busts and House-

hold Mobility: An Update,” 2011. NBER Working Paper No. 17405.

Gete, Pedro and Michael Reher, “Systemic Banks, Mortgage Supply and Housing

Rents,” 2016. Working Paper.

Gropp, Reint, John Krainer, and Elizabeth Laderman, “Did Consumers Want Less

Debt? Consumer Credit Demand versus Supply in the Wake of the 2008-2009 Financial

Crisis,” January 2014. SAFE Working Paper No. 42.

Guerrieri, Veronica and Guido Lorenzoni, “Credit Crises, Precautionary Savings, and

the Liquidity Trap,” 2011. NBER Working Paper No. 17583.

Inside FHA/VA Lending, “FHA Average FICO Score Nears 700,” 2009.

, “Major Lenders Tighten FHA Loan Requirements,” 2009.

, “January Applications Fall From Tighter Standards,” 2010.

Keys, Benjamin J., Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, “Financial

Regulation and Securitization: Evidence from Subprime Loans,” Journal of Monetary

Economics, 2009, 56 (5), 700–720.

, , , and , “Did Securitization Lead to Lax Screening? Evidence from Subprime

Loans,” Quarterly Journal of Economics, 2010, 125 (1), 307–362.

, , , and , “Lender Screening and the Role of Securitization: Evidence from Prime

and Subprime Mortgage Markets,” Review of Financial Studies, 2012, 25 (7), 2071–2108.

, Tomasz Piskorski, Amit Seru, and Vincent Yao, “Mortgage Rates, Household

Balance Sheets, and the Real Economy,” October 2014. NBER Working Paper No. 20561.

Mondragon, John, “Household Credit and Employment in the Great Recession,” 2016.

Unpublished Manuscript.

Rosenthal, Stuart S., “Eliminating Credit Barriers: How Far Can We Go,” Low-Income

Homewonership, 2002, pp. 111–145.

Schulhofer-Wohl, Sam, “Negative Equity Does Not Reduce Homeowners’ Mobility,” 2011.

NBER Working Paper No. 16701.

Skiba, Paige Marta and Jeremy Tobacman, “Do Payday Loans Cause Bankruptcy?,”

2015. Working Paper.

550 600 650 700

0 1 2 3 4 5

FICO score bin

Percent

●

620 640

●

2005

2008

2010

2012

Panel A. Densities

550 600 650 700

0 10 20 30 40 50 60

FICO score bin

Percent

●

620 640

●

2005

2008

2010

2012

Panel B. Cumulative Distributions

Fig. 1.—Mortgages by FICO Score. This ﬁgure plots the densities and cumulative distributions of newly originated ﬁrst mortgages by

10-point FICO score bin in the Black Knight data set, across four years.

550 600 650 700

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FICO score bin

Percent

●

620 640

●

Conventional (GSE)

Convent. (Non−GSE)

FHA

Other

Panel A. 2005

550 600 650 700

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FICO score bin

Percent

●

620 640

●

Conventional (GSE)

Convent. (Non−GSE)

FHA

Other

Panel B. 2008

550 600 650 700

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FICO score bin

Percent

●

620 640

●

Conventional (GSE)

Convent. (Non−GSE)

FHA

Other

Panel C. 2010

550 600 650 700

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FICO score bin

Percent

●

620 640

●

Conventional (GSE)

Convent. (Non−GSE)

FHA

Other

Panel D. 2012

Fig. 2.—Mortgage Densities, across Types. This ﬁgure plots the densities of newly orig-

inated ﬁrst mortgages by 10-point FICO score bin in the Black Knight data set, across

types of loans. Each panel shows the data from a separate year.

2008 2010 2012 2014

0.65 0.70 0.75 0.80 0.85 0.90 0.95

Time

Credit Availability Measure

●

Aggregate

Using 620 Threshold

Using 640 Threshold

Panel A. Time Series

550 600 650 700

0.2 0.4 0.6 0.8 1.0

Equifax Risk Score Bin

Credit Availability Measure

●

2008

2009:Q2−2010:Q2

2011

Panel B. Cross-Section Across Periods

Fig. 3.—Credit Availability. This ﬁgure shows the evolution of the credit availability measure in two diﬀerent ways. The left panel plots the

time series of average credit availability for all individuals with Equifax Risk Scores between 530 and 730. The three shaded regions denote

periods between 2008 and 2011 in which availability was roughly stable. The right panel compares average credit availability, by 10-point

Equifax Risk Score bin, across the three stable periods of credit availability.

550 600 650 700

0.0 0.5 1.0 1.5 2.0

Equifax Risk Score Bin

Percent

●

2008

2009:Q2−2010:Q2

2011

Fig. 4.—Contemporaneous Mortgage Origination Probability. This ﬁgure compares the probability of taking

out at least one mortgage in the contemporaneous quarter, by 10-point Equifax Risk Score bin, across the

three stable periods of credit availability.

20 30 40 50 60 70 80

640 660 680 700 720 740 760

Age Bin

Risk Score

●

2008

2009:Q2−2010:Q2

2011

Panel A. Credit Score

20 30 40 50 60 70 80

0.65 0.70 0.75 0.80 0.85 0.90 0.95

Age Bin

Credit Availability Measure (0−1)

●

2008

2009:Q2−2010:Q2

2011

Panel B. Credit Availability

20 30 40 50 60 70 80

0.0 0.5 1.0 1.5 2.0 2.5

Age Bin

Percentage Points

Panel C. Marginal Eﬀects

20 30 40 50 60 70 80

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Age Bin

Percent

●

2008

2009:Q2−2010:Q2

2011

Panel D. Implied Eﬀects

Fig. 5.—Age Heterogeneity. This ﬁgure traces out the eﬀects of credit availability by age,

for individuals with no initial mortgage balance. Bins are 10 years wide and centered on

the bin midpoint, except for 20 and 80, which include all individuals under 25 and over

74, respectively. Panel A shows the average Equifax Risk Score in each bin, across the

three stable periods of credit availability. Panel B shows average credit availability in each

bin, across periods. Panel C shows the estimated marginal eﬀects of credit availability on

the contemporaneous probability of mortgage attainment for each bin (which are assumed

to be constant across periods), plus or minus two standard errors. Panel D shows the

model-derived contemporaneous probability of mortgage attainment for each bin, allowing

credit availability to change over time but holding all else constant at 2008:Q1 levels. See

text for details.

650 660 670 680 690 700

Tract Black Share

Risk Score

0−10 10−20 20−50 50−100

●

2008

2009:Q2−2010:Q2

2011

Panel A. Credit Score

0.60 0.65 0.70 0.75 0.80 0.85 0.90

Tract Black Share

Credit Availability Measure (0−1)

0−10 10−20 20−50 50−100

●

2008

2009:Q2−2010:Q2

2011

Panel B. Credit Availability

0.0 0.5 1.0 1.5

Tract Black Share

Percentage Points

0−10 10−20 20−50 50−100

Panel C. Marginal Eﬀects

0.9 1.0 1.1 1.2 1.3 1.4

Tract Black Share

Percent

0−10 10−20 20−50 50−100

●

2008

2009:Q2−2010:Q2

2011

Panel D. Implied Eﬀects

Fig. 6.—Race Heterogeneity. This ﬁgure traces out the eﬀects of credit availability by the

black share of tract population, for individuals with no initial mortgage balance. Panel

A shows the average Equifax Risk Score in each bin, across the three stable periods of

credit availability. Panel B shows average credit availability in each bin, across periods.

Panel C shows the estimated marginal eﬀects of credit availability on the contemporaneous

probability of mortgage attainment for each bin (which are assumed to be constant across

periods), plus or minus two standard errors. Panel D shows the model-derived contem-

poraneous probability of mortgage attainment for each bin, allowing credit availability to

change over time but holding all else constant at 2008:Q1 levels. See text for details.

670 680 690 700 710 720

Tract Median Household Income

Risk Score

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

●

2008

2009:Q2−2010:Q2

2011

Panel A. Credit Score

0.70 0.75 0.80 0.85 0.90 0.95

Tract Median Household Income

Credit Availability Measure (0−1)

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

●

2008

2009:Q2−2010:Q2

2011

Panel B. Credit Availability

0.5 1.0 1.5

Tract Median Household Income

Percentage Points

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

Panel C. Marginal Eﬀects

0.8 1.0 1.2 1.4 1.6

Tract Median Household Income

Percent

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

●

2008

2009:Q2−2010:Q2

2011

Panel D. Implied Eﬀects

Fig. 7.—Income Heterogeneity. This ﬁgure traces out the eﬀects of credit availability by

tract median household income quartiles, for individuals with no initial mortgage balance.

Panel A shows the average Equifax Risk Score in each quartile, across the three stable

periods of credit availability. Panel B shows average credit availability in each quartile,

across periods. Panel C shows the estimated marginal eﬀects of credit availability on the

contemporaneous probability of mortgage attainment for each quartile (which are assumed

to be constant across periods), plus or minus two standard errors. Panel D shows the model-

derived contemporaneous probability of mortgage attainment for each quartile, allowing

credit availability to change over time but holding all else constant at 2008:Q1 levels. See

text for details.

TABLE 1

Effects on Probability of Taking Out a First Mortgage

(1) (2) (3) (4) (5)

Horizon in Quarters: 0 0-3 0-7 0-11 0-15

Panel A: Entire Sample

Credit Availability 0.010 0.028 0.035 0.034 0.028

(0.001) (0.003) (0.004) (0.006) (0.007)

Lagged Availability 0.001 0.003 0.007 0.005 0.006

(0.001) (0.003) (0.005) (0.007) (0.009)

Dep. Var. Mean 0.009 0.035 0.068 0.100 0.130

Observations 32,521,878 31,978,664 31,397,303 30,895,003 30,392,255

Panel B: No Initial Mortgage Balance

Credit Availability 0.010 0.026 0.033 0.036 0.034

(0.001) (0.003) (0.004) (0.005) (0.006)

Lagged Availability 0.000 0.003 0.009 0.010 0.009

(0.001) (0.003) (0.005) (0.007) (0.008)

Dep. Var. Mean 0.007 0.027 0.053 0.079 0.100

Observations 27,692,800 27,203,296 26,676,170 26,217,051 25,754,177

Panel C: Positive Initial Mortgage Balance

Credit Availability 0.021 0.080 0.110 0.092 0.072

(0.004) (0.007) (0.010) (0.011) (0.011)

Lagged Availability 0.016 0.041 0.047 0.037 0.039

(0.004) (0.007) (0.010) (0.011) (0.011)

Dep. Var. Mean 0.021 0.079 0.150 0.220 0.280

Observations 4,829,078 4,775,368 4,721,133 4,677,952 4,638,078

Note.—Logit estimates of eﬀect of credit availability on the cumulative probability of taking out a mort-

gage, over various horizons. Average marginal eﬀects, with standard errors clustered at quarter-riskscore

level in parentheses. Models are estimated separately on the whole sample (panel A) and on samples split

by whether the individual has a positive mortgage balance at t=-1 (panels B and C). All models include

predicted probablities of having a score over 620 and 640, lagged predicted probability of having a score

over 620 and 640, quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted with linear riskscore term, and

quarter ﬁxed eﬀects interacted with lagged linear riskscore term.

TABLE 2

Effects on Change in First Mortgage Balance

(1) (2) (3) (4)

Horizon in Quarters: 4 8 12 16

Panel A: Entire Sample

Credit Availability 3340 5293 6809 6623

(765) (1100) (1318) (1473)

Lagged Availability -589 239 1353 0

(898) (1366) (1676) (1908)

Dep. Var. Mean -31 -564 -737 -522

Observations 32,037,646 31,612,415 31,292,893 31,002,021

Panel B: No Initial Mortgage Balance

Credit Availability 1235 1298 2359 1460

(432) (527) (635) (733)

Lagged Availability -2762 -2428 -2697 -3554

(533) (620) (743) (858)

Dep. Var. Mean 3208 5364 7405 9472

Observations 27,252,549 26,858,940 26,558,765 26,282,176

Panel C: Positive Initial Mortgage Balance

Credit Availability 16973 31038 35237 35188

(2762) (3834) (4364) (4730)

Lagged Availability 16506 24956 28714 26019

(3052) (4510) (5311) (5959)

Dep. Var. Mean -18478 -34057 -46415 -56173

Observations 4,785,097 4,753,475 4,734,128 4,719,845

Note.—Linear regression estimates of eﬀect of credit availability on the change in an in-

dividual’s mortgage balance, over various horizons. Standard errors clustered at quarter-

riskscore level in parentheses. Models are estimated separately on the whole sample (panel

A) and on samples split by whether the individual has a positive mortgage balance at t=-

1 (panels B and C). All models include predicted probablities of having a score over 620

and 640, lagged predicted probability of having a score over 620 and 640, quarter ﬁxed ef-

fects, quarter ﬁxed eﬀects interacted with linear riskscore term, and quarter ﬁxed eﬀects

interacted with lagged linear riskscore term.

TABLE 3

Effects on Probability of Having a Delinquent Mortgage

(1) (2) (3) (4)

Horizon in Quarters: 0-3 0-7 0-11 0-15

Panel A: Entire Sample

Credit Availability -0.022 -0.031 -0.036 -0.034

(0.006) (0.008) (0.009) (0.009)

Lagged Availability -0.023 -0.035 -0.039 -0.039

(0.009) (0.012) (0.013) (0.013)

Dep. Var. Mean 0.045 0.068 0.085 0.098

Observations 30,558,656 29,138,671 27,932,190 26,860,384

Panel B: No Initial Mortgage Balance

Credit Availability -0.005 -0.010 -0.012 -0.009

(0.004) (0.005) (0.006) (0.006)

Lagged Availability -0.016 -0.021 -0.022 -0.020

(0.006) (0.007) (0.008) (0.009)

Dep. Var. Mean 0.028 0.041 0.052 0.061

Observations 25,790,250 24,431,303 23,282,082 22,267,892

Panel C: Positive Initial Mortgage Balance

Credit Availability -0.070 -0.089 -0.096 -0.100

(0.010) (0.011) (0.012) (0.012)

Lagged Availability -0.087 -0.120 -0.120 -0.120

(0.013) (0.015) (0.016) (0.016)

Dep. Var. Mean 0.140 0.210 0.250 0.280

Observations 4,768,406 4,707,368 4,650,108 4,592,492

Note.—Logit estimates of eﬀect of credit availability on the cumulative probability of hav-

ing a mortgage delinquent by 60 or more days. Average marginal eﬀects, with standard

errors clustered at quarter-riskscore level in parentheses. Models are estimated separately

on the whole sample (panel A) and on samples split by whether the individual has a posi-

tive mortgage balance at t=-1 (panels B and C). All models include predicted probablities

of having a score over 620 and 640, lagged predicted probability of having a score over 620

and 640, quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted with linear riskscore term,

and quarter ﬁxed eﬀects interacted with lagged linear riskscore term.

TABLE 4

Effects on Probability of Having a Delinquent Non-Mortgage Loan

(1) (2) (3) (4)

Horizon in Quarters: 0-3 0-7 0-11 0-15

Panel A: Entire Sample

Credit Availability 0.000 -0.034 -0.050 -0.035

(0.009) (0.008) (0.008) (0.008)

Lagged Availability -0.037 -0.035 -0.011 0.002

(0.010) (0.009) (0.009) (0.009)

Dep. Var. Mean 0.31 0.40 0.47 0.52

Observations 31,978,664 31,397,303 30,895,003 30,392,255

Panel B: No Initial Mortgage Balance

Credit Availability 0.006 -0.029 -0.048 -0.033

(0.010) (0.009) (0.009) (0.009)

Lagged Availability -0.024 -0.025 0.001 0.015

(0.012) (0.011) (0.010) (0.011)

Dep. Var. Mean 0.32 0.41 0.48 0.54

Observations 27,203,296 26,676,170 26,217,051 25,754,177

Panel C: Positive Initial Mortgage Balance

Credit Availability -0.033 -0.050 -0.042 -0.026

(0.011) (0.012) (0.012) (0.013)

Lagged Availability -0.092 -0.085 -0.072 -0.062

(0.012) (0.013) (0.014) (0.014)

Dep. Var. Mean 0.24 0.33 0.38 0.43

Observations 4,775,368 4,721,133 4,677,952 4,638,078

Note.—Logit estimates of eﬀect of credit availability on the cumulative probability of

having a non-mortgage loan delinquent by 60 or more days. Average marginal eﬀects, with

standard errors clustered at quarter-riskscore level in parentheses. Models are estimated

separately on the whole sample (panel A) and on samples split by whether the individual

has a positive mortgage balance at t=-1 (panels B and C). All models include predicted

probablities of having a score over 620 and 640, lagged predicted probability of having a

score over 620 and 640, quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted with linear

riskscore term, and quarter ﬁxed eﬀects interacted with lagged linear riskscore term.

TABLE 5

Effects on Moving to Different Census Block

(1) (2) (3)

Horizon in Quarters: 4 8 12

Panel A: Entire Sample

Credit Availability 0.013 -0.001 0.004

(0.005) (0.006) (0.007)

Lagged Availability 0.002 0.009 0.011

(0.005) (0.007) (0.008)

Dep. Var. Mean 0.10 0.19 0.25

Observations 20,414,501 20,379,761 20,257,793

Panel B: No Init. Mort. Bal.

Credit Availability 0.018 0.003 0.011

(0.006) (0.007) (0.008)

Lagged Availability 0.002 0.009 0.012

(0.007) (0.008) (0.009)

Dep. Var. Mean 0.11 0.20 0.27

Observations 17,003,743 16,962,731 16,842,345

Panel C: Pos. Init. Mort. Bal.

Credit Availability -0.003 -0.016 -0.034

(0.007) (0.009) (0.010)

Lagged Availability -0.009 -0.017 -0.028

(0.007) (0.009) (0.010)

Dep. Var. Mean 0.05 0.10 0.14

Observations 3,410,758 3,417,030 3,415,448

Note.—Logit estimates of eﬀect of credit availability on the probability of

moving to a diﬀerent census block, at various horizons. Average marginal

eﬀects, with standard errors clustered at quarter-riskscore level in paren-

theses. Models are estimated separately on the whole sample (panel A) and

on samples split by whether the individual has a positive mortgage balance

at t=-1 (panels B and C). All models include predicted probablities of hav-

ing a score over 620 and 640, lagged predicted probability of having a score

over 620 and 640, quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted with

linear riskscore term, and quarter ﬁxed eﬀects interacted with lagged linear

riskscore term.

TABLE 6

Effects on Moving to Different CBSA

(1) (2) (3)

Horizon in Quarters: 4 8 12

Panel A: Entire Sample

Credit Availability 0.009 0.006 0.003

(0.002) (0.003) (0.003)

Lagged Availability 0.002 0.001 0.005

(0.002) (0.003) (0.004)

Dep. Var. Mean 0.026 0.048 0.067

Observations 24,467,862 24,175,627 23,901,752

Panel B: No Init. Mort. Bal.

Credit Availability 0.010 0.006 0.003

(0.002) (0.003) (0.004)

Lagged Availability 0.001 0.001 0.004

(0.003) (0.004) (0.004)

Dep. Var. Mean 0.029 0.052 0.073

Observations 20,601,775 20,333,093 20,076,915

Panel C: Pos. Init. Mort. Bal.

Credit Availability 0.001 -0.001 -0.007

(0.003) (0.004) (0.005)

Lagged Availability 0.000 -0.004 0.000

(0.003) (0.004) (0.005)

Dep. Var. Mean 0.012 0.023 0.034

Observations 3,866,087 3,842,534 3,824,837

Note.—Logit estimates of eﬀect of credit availability on the probability of

moving to a diﬀerent core-based statistial area (CBSA), at various horizons.

Average marginal eﬀects, with standard errors clustered at quarter-riskscore

level in parentheses. Models are estimated separately on the whole sample

(panel A) and on samples split by whether the individual has a positive

mortgage balance at t=-1 (panels B and C). All models include predicted

probablities of having a score over 620 and 640, lagged predicted probabil-

ity of having a score over 620 and 640, quarter ﬁxed eﬀects, quarter ﬁxed

eﬀects interacted with linear riskscore term, and quarter ﬁxed eﬀects inter-

acted with lagged linear riskscore term.

TABLE 7

Effects on Change in Number of Auto Loans

(1) (2) (3) (4)

Horizon in Quarters: 4 8 12 16

Panel A: Entire Sample

Credit Availability -0.009 -0.021 0.008 0.010

(0.006) (0.009) (0.011) (0.013)

Lagged Availability 0.004 0.024 0.041 0.021

(0.007) (0.010) (0.013) (0.015)

Dep. Var. Mean -0.001 0.000 0.008 0.026

Observations 30,664,032 29,587,609 28,787,215 28,143,066

Panel B: No Initial Mortgage Balance

Credit Availability -0.013 -0.031 -0.004 -0.002

(0.007) (0.009) (0.011) (0.014)

Lagged Availability 0.001 0.020 0.039 0.016

(0.007) (0.010) (0.013) (0.015)

Dep. Var. Mean 0.002 0.007 0.019 0.042

Observations 25,886,884 24,852,642 24,089,800 23,481,558

Panel C: Positive Initial Mortgage Balance

Credit Availability 0.023 0.035 0.056 0.050

(0.013) (0.017) (0.021) (0.022)

Lagged Availability 0.036 0.062 0.066 0.049

(0.013) (0.018) (0.021) (0.023)

Dep. Var. Mean -0.017 -0.037 -0.050 -0.052

Observations 4,777,148 4,734,967 4,697,415 4,661,508

Note.—Linear regression estimates of eﬀect of credit availability on the change in the

number of auto loans on an individual’s credit record, over various horizons. Standard er-

rors clustered at quarter-riskscore level in parentheses. Models are estimated separately on

the whole sample (panel A) and on samples split by whether the individual has a positive

mortgage balance at t=-1 (panels B and C). All models include quarter ﬁxed eﬀects, quar-

ter ﬁxed eﬀects interacted with linear riskscore term, and quarter ﬁxed eﬀects interacted

with lagged linear riskscore term.

TABLE 8

Effects on Change in Auto Loan Balance

(1) (2) (3) (4)

Horizon in Quarters: 4 8 12 16

Panel A: Entire Sample

Credit Availability -113 -149 -46 -13

(69) (97) (117) (127)

Lagged Availability 46 -12 -31 -188

(76) (110) (127) (136)

Dep. Var. Mean -28 -21 71 232

Observations 32,037,646 31,612,415 31,292,893 31,002,021

Panel B: No Initial Mortgage Balance

Credit Availability -151 -304 -279 -221

(62) (88) (106) (117)

Lagged Availability -49 -105 -123 -245

(66) (97) (112) (122)

Dep. Var. Mean 10 61 186 371

Observations 27,252,549 26,858,940 26,558,765 26,282,176

Panel C: Positive Initial Mortgage Balance

Credit Availability 317 732 985 645

(213) (288) (328) (359)

Lagged Availability 903 906 695 205

(212) (289) (324) (354)

Dep. Var. Mean -245 -482 -571 -541

Observations 4,785,097 4,753,475 4,734,128 4,719,845

Note.—Linear regression estimates of eﬀect of credit availability on the change in an in-

dividual’s auto loan balance, over various horizons. Standard errors clustered at quarter-

riskscore level in parentheses. Models are estimated separately on the whole sample (panel

A) and on samples split by whether the individual has a positive mortgage balance at t=-1

(panels B and C). All models include quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted

with linear riskscore term, and quarter ﬁxed eﬀects interacted with lagged linear riskscore

term.

TABLE 9

Robustness Checks: Effects on Contemporaneous Quarter Probability of Taking Out a Mortgage

(1) (2) (3) (4) (5) (6)

Speciﬁcation: Baseline Age Controls More Lags No Lags Wide Range Narrow Range

Credit Availability 0.010 0.010 0.009 0.010 0.008 0.004

(0.001) (0.001) (0.001) (0.001) (0.002) (0.003)

1Q Lagged Availability 0.001 0.001 0.001 0.006 -0.001

(0.001) (0.001) (0.001) (0.001) (0.004)

Dep. Var. Mean 0.009 0.009 0.009 0.009 0.015 0.008

Score Range 530-730 530-730 530-730 530-730 500-830 580-680

Observations 32,521,878 32,521,878 28,886,923 32,521,878 72,849,732 15,108,112

Note.—Robustness checks for the logit estimate of the eﬀect of credit availability on the cumulative probability of taking out a mort-

gage in the contemporaneous quarter. Average marginal eﬀects, with standard errors clustered at quarter-riskscore level in parenthe-

ses. All models include predicted probablities of having a score over 620 and 640, lagged predicted probability of having a score over

620 and 640, quarter ﬁxed eﬀects, quarter ﬁxed eﬀects interacted with linear riskscore term, and quarter ﬁxed eﬀects interacted with

lagged linear riskscore term. Column 1 (“Baseline”) is the estimate from Panel A of column 5 of table 1. Column 2 (“Age Controls”)

includes linear and quadratic age terms interacted with quarter. Column 3 (“More Lags”) includes the second through fourth lags of

credit availability, the second through fourth lags of probabability of having a score over 620 and 640, as well as the second through

fourth lags of the riskscore interacted with quarter. Column 4 (“No Lags”) includes no lags. Column 5 (“Wide Range”) includes all

observations with riskscores between 500 and 830. Column 6 (“Narrow Range”) includes only observations with current and lagged

riskscores between 580 and 680.

TABLE 10

Robustness Checks: Effects on 0-15 Quarter Probability of Taking Out a Mortgage

(1) (2) (3) (4) (5) (6)

Speciﬁcation: Baseline Age Controls More Lags No Lags Wide Range Narrow Range

Credit Availability 0.028 0.030 0.029 0.031 0.052 0.018

(0.007) (0.007) (0.007) (0.005) (0.011) (0.020)

1Q Lagged Availability 0.006 0.012 -0.011 0.026 -0.027

(0.009) (0.008) (0.011) (0.031) (0.000)

Dep. Var. Mean 0.13 0.13 0.13 0.13 0.20 0.12

Score Range 530-730 530-730 530-730 530-730 500-830 580-680

Observations 30,392,255 30,392,255 27,136,860 30,392,255 68,818,853 14,093,101