Nonbanks and Lending Standards in Mortgage Markets.

The Spillovers from Liquidity Regulation.∗

Pedro Gete† and Michael Reher‡

This Draft: July 2018

Abstract

We show that liquidity in secondary mortgage markets affects the composition of

lenders and credit risk in the primary mortgage market. Using exogenous changes in MBS

liquidity associated with the U.S. liquidity coverage ratio (LCR), we find that greater

liquidity increased the market share and risk taking of nonbanks and originate-to-sell

lenders. This effect is driven by the FHA market because LCR risk weights dispropor-

tionately raised MBS liquidity for FHA loans. Greater MBS liquidity increased credit

supply for risky borrowers and led to higher homeownership rates. LCR can explain 26%

of nonbanks’growth in market share from 2013-2015.

Keywords: Lending Standards, LCR, Liquidity, Mortgages, Nonbanks, FHA, MBS.

JEL Classification: G12, G18, G21, G23, E32, E44.

∗We appreciate the comments of Afras Yab Sial, George Akerlof, Elliot Anenberg, Deniz Aydin, Jen-nie Bai, Greg Buchak, James Bullard, Murillo Campello, Seth Carpenter, Morris Davis, Behzad Diba,David Echeverry, Jesus Fernandez-Villaverde, Lynn Fisher, Andreas Fuster, Douglas Gale, CarlosGarriga, Lei Ge, Ed Glaeser, Adam Guren, Diana Hancock, Samuel Hanson, Stefan Jacewitz, RobertKurtzman, Mark Kutzbach, Steven Laufer, Sylvain Leduc, Fabrizio López Gallo, Doug McManus,Tim McQuade, Kurt Mitman, Patricia Mosser, Charles Nathanson, Stephen Oliner, Austin Par-enteau, Mark Palim, Donald Parsons, Wayne Passmore, Ed Pinto, Jon Pogach, William Reeder, SteveRoss, Farzad Saidi, Asani Sarkar, Amit Seru, Lynn Shibut, Jeremy Stein, Bryan Stuart, Ted Tozer,Jeff Traczynski, Skander Van den Heuvel, Larry Wall, Nancy Wallace, Susan Wachter, ChristopherWhalen, Paul Willen, Anthony Yezer, referees and the participants at the 2017 American EnterpriseInstitute Housing Conference, 2017 AREUEA-National, 2017 Basel Committee Research conference,FDIC, Freddie Mac, George Washington, 2017 HULM St. Louis Fed, 2017 Summer Macro-FinanceBecker-Friedman Institute, 2017 WashU-JFI conference and 2018 Columbia Univ. Liquidity Confer-ence.†Corresponding author. IE Business School. Maria de Molina 12, 28006 Madrid, Spain. Email:

pedro.gete@ie.edu.‡Harvard University. 1805 Cambridge Street Cambridge, MA 02138. Email: mreher@g.harvard.edu

1

1 Introduction

Non-depository institutions ("nonbanks" for short) currently play a larger role in U.S.

mortgage markets than before the crisis.1 They originate around 80% of loans insured by the

Federal Housing Administration (FHA) and more than 50% of all mortgage loans (see Section

2.1). This fact worries policymakers because most of the nonbanks that were active before

the financial crisis either defaulted or were restructured post-2008 (see for example Pinto and

Oliner 2015, Wallace 2016 or Wall Street Journal 2017). The fiscal risk borne by U.S. taxpayers

may be increasing since it is likely that the FHA will be unable to recover losses from low-

capitalized nonbanks in the next recession (Kim et al. 2018).2 Moreover, Demyanyk and

Loutskina (2016) and Huszar and Yu (2017) show that nonbanks contributed to a deterioration

of lending standards in mortgage markets. Our results provide similar evidence for the post-

crisis period.

A new literature is studying what drives the rise of nonbanks and the role of regulation in this

shift. For example, Buchak et al. (2018) study the effects of regulatory constraints on traditional

banks. Ganduri (2018) studies a shock that increased creditor protection for nonbanks’funding

intermediaries. Fuster et al. (2018) analyze the role of technological innovation. Irani et

al. (2018) show that nonbanks step in to replace less-capitalized banks facing tougher capital

requirements. Chernenko et al. (2018) show that firms are more likely to borrow from a nonbank

lender if local banks are poorly capitalized. Meursault (2017) focuses on complementarities

between banks and nonbanks.

In this paper we propose an explanation for the rise of nonbanks that operates through a

general equilibrium channel. Our explanation is new and complements other explanations for

the rise of nonbanks. Our theory has two steps: 1) Because nonbanks do not collect deposits and

instead rely on the originate-to-securitize model, they are more sensitive to secondary market

(i.e. MBS) liquidity than banks. For example, Echeverry, Stanton and Wallace (2016) discuss

how nonbanks borrow in repo markets or from a warehouse line of credit using the MBS as

collateral. They repay once the loan has been securitized and the MBS sold. 2) Higher liquidity

in MBS markets benefits nonbanks for two reasons. First, higher MBS prices increase the value

of their collateral. Moreover, faster MBS sales reduce the cost of credit lines. Thus, higher

demand for MBS reduces funding costs for nonbanks. Nonbanks can then lend more and with

1To keep the language simple, we refer to depository institutions as "banks" and non-depository institu-tions as "nonbanks", although, strictly speaking, there are lenders, such as credit unions, which are nonbankdepository institutions. However, such cases comprise less than 5% of our data.

2FHA mortgagors usually have higher default rates over the business cycle (Frame, Gerardi and Tracy 2016).When losses increase FHA tries to recover some of them from the lenders that granted credit.

2

more relaxed lending standards.

The theory we just described is related to the Hanson et al. (2015) model of shadow banks.

In Hanson et al. (2015), access to deposit insurance and capital requirements make banks

more willing to hold illiquid assets than shadow banks. In our theory, nonbanks react more to

changes in liquidity conditions in secondary mortgage markets. To our knowledge, this is the

first paper that studies MBS premia as a driver of the composition of mortgage lenders and

their lending standards.

Our identification exploits changes in liquidity generated by the U.S. Liquidity Coverage

Ratio (LCR). The LCR rule was announced in October 2013 and finalized in September

2014. It gave preferential liquidity weights to mortgage-backed securities backed by Ginnie

Mae (GNMA), relative to those backed by Fannie Mae (FNMA) or Freddie Mac (FHLMC).

Because only GNMA securities have an explicit government backstop, the liquidity weight for

GNMA-backed MBS is 1, compared to 0.85 for GSE-backed MBS.3 Moreover, there was ex-ante

uncertainty over the institutional details of LCR, since in 2011 Governor Daniel Tarullo of the

Federal Reserve suggested that the U.S. implementation of LCR would differ from international

standards, but did not indicate how.4 Thus, the announcement of LCR regulation provides an

exogenous shock that increased demand for GNMA MBS, and thus their prices and trading

liquidity.5 We provide evidence supporting these assumptions.

It is important to stress that this is not a paper centered on the effects of LCR regulation.

Such a paper should focus on banks subject to the rule versus banks non subject to it. Our

paper tests how developments in secondary mortgage markets caused by LCR helped nonbanks

to grow in primary markets. Our identification builds on pre-LCR cross-sectional differences in

lenders’funding sources.

We confirm that securitization is the key mechanism of our theory. All results that apply

to nonbanks also apply to those lenders that relied more heavily on securitization before the

LCR announcement, and also to those banks with less deposit funding or more reliance on

securitization. Focusing only on banks provides a strong robustness test since we can utilize a

rich set of balance-sheet controls.6

Our main results are: 1) after the LCR finalization, borrowers who apply to a nonbank are

3By law, only loans insured by the U.S. government (FHA, Veterans Affairs, Rural Development and Publicand Indian Housing) can be securitized into a GNMA-backed product.

4See the November 4, 2011 speech “The International Agenda for Financial Regulation", as well as Getter(2014).

5Bech and Keister (2015) and Keister (2017) predict that LCR will create regulatory premiums.6All our data sources are publicly available and straightforward to replicate.

3

less likely to be denied than when applying to a depository institution. This holds conditional

on the borrower’s quality, and multiple fixed effects. 2) The effects are stronger for black and

Hispanic borrowers, which are variables highly correlated with low credit scores (Bhutta and

Ringo 2018), and for borrowers with higher loan-to-income ratios. 3) When the GNMA liquidity

premium falls, nonbanks lower mortgage interest rates by more, consistent with their greater

sensitivity to MBS liquidity. This result relates to a more general finding that the sensitivity

of primary market mortgage rates to secondary market rates depends on the cross sectional

distribution of mortgage originators (e.g. Scharfstein and Sunderam 2016). 4) Since FHA loans

are the vast majority of the loans that GNMA securitizes, LCR contributed to the increase

in the FHA share of the market because it encouraged lenders to substitute from conventional

loans to FHA-insured loans. 5) In terms of market share, a back-of-envelope calculation suggests

that nonbanks would have comprised 74.5% of FHA originations in 2015 as opposed to their

actual share of 77.1%. Put differently, nonbank market share grew 9.9 percentage points from

2013 to 2015, but their share would have grown 2.6 percentage points less, or 26% less, in

the absence of the LCR policy. 6) The rise of nonbanks partially offset the recent decline in

homeownership.

We do six sets of robustness tests to support our identification: 1) lender-year-MSA fixed

effects ensure that the results are not driven by nonbanks’ability to better predict areas of

high consumer potential; for this specification to be invalid, any confounding shock would have

to not only disproportionately affect nonbanks after the LCR proposal, but it would also have

to affect nonbanks’willingness to approve FHA over conventional loans. 2) A parallel trends

analysis shows that the results are not driven by pre-trends. 3) The basic result that the

GNMA liquidity premium raises nonbank share goes through at a monthly frequency which

coincides with the LCR timing. This alleviates concern that the results reflect spurious time

series correlations. 4) We control for alternative explanations like regulatory arbitrage, changes

in the pool of borrowers, or the Fed’s large scale asset purchases. 5) We perform multiple

placebo tests. 6) We estimate the homeownership regressions at the census tract level with

county fixed effects. Thus, we only compare borrowers within the same county, with some

having more exposure to nonbanks than others. Thus, our results cannot be driven by metro

level changes such as a home price recovery.

Our paper, like the new literature on nonbanks cited previously, contributes to a growing

literature that studies the effects of post-2008 regulations on mortgage markets. For exam-

ple, Ambrose, Conklin and Yoshida (2016) suggest that regulatory changes that have essen-

tially eliminated low-doc loans would result in credit rationing against self-employed borrowers.

Bhutta and Ringo (2018) show that lowering the FHA mortgage insurance premiums in 2015

4

increased the number of loans to lower credit score and high LTV borrowers. Di Maggio, Ker-

mani, and Korgaonkar (2018) show that relaxed state regulations for OCC regulated banks are

associated with lower lending standards for both OCC and non-OCC regulated banks. Fuster,

Lo and Willen (2017) find evidence of higher regulatory costs and risks over 2008-2014. Gete

and Reher (2018) show that a credit contraction associated with regulatory shocks experienced

by lenders over the 2010-2014 period caused higher housing rents.

We connect with the literature that analyzes the effects of liquidity in MBS markets on

credit supply. For example, Cornett et al. (2011) show that during the financial crisis of 2007—

2009 banks that relied more heavily on core deposit and equity capital financing contracted

credit less than other banks. Dagher and Kazimov (2015) find that banks that were more

reliant on wholesale funding curtailed their credit significantly more than retail-funded banks

during the financial crisis. Loutskina (2011) shows that securitization increased banks’ability

to lend. Keys et al. (2010) show that securitization caused less screening effort by originators

of sub-prime mortgages. Van Bekkum, Gabarro, and Irani (2017) study how changes in ECB

collateral eligibility affect loan origination. They find that banks actively issuing RMBS with

lower-rated tranches are more likely to securitize newly originated loans, including those with

lower interest rates.

This paper also relates with papers that exploit cross-sectional variation to analyze the effect

of the Federal Reserve’s large-scale MBS purchases after the financial crisis. For example, Di

Maggio, Kermani and Palmer (2016), Chakraborty, Goldstein and MacKinlay (2017), Darmouni

and Rodnyanski (2017) and Kurtzman, Luck and Zimmermann (2017) find a positive impact

on mortgage lending. Fieldhouse, Mertens and Ravn (2018) find that MBS purchases by the

GSEs has had a positive effect on mortgage originations.

The rest of the paper is organized as follows. Section 2 documents recent dynamics of

nonbanks in mortgage markets and discusses the changes in MBS prices induced by the LCR

policies. Section 3 has the core analysis. We check the robustness of the results in Section 4.

Section 5 contains an aggregate analysis and the implications for homeownership. Section 6

concludes. The online appendix has additional results.

2 Nonbanks and Liquidity Regulation

In this section we document recent dynamics of nonbanks in mortgage markets. Then we

show that the LCR rule increased demand for GNMA MBS.

5

2.1 Nonbanks in Mortgage Markets

Since all depository institutions are subject to a federal supervisor, we use the associated

Home Mortgage Disclosure Act (HMDA) codes and identify nonbanks as lenders without a

federal supervisor (that is, lenders not under the regulatory oversight of OCC, FRS, FDIC,

NCUA, or OTS). Demyanyk and Loutskina (2016) and Huszar and Yu (2017) follow the same

criteria. We cross-checked that our sample, which comes from HMDA and covers the vast

majority of originators in the U.S. mortgage market, is consistent with Buchak et al. (2018),

who manually define nonbanks as non-depository institution and focus on the largest lenders

(50% of total originations). Table A1 in the online appendix provides a list of the top 50

nonbanks in our data based on their FHA originations in 2013 and 2014.

Figure 1 shows that nonbanks’for-purchase mortgage origination share has increased dra-

matically since the financial crisis. In the top panel, we see that nonbanks historically comprised

around 50% of the FHA market. Their share grew during the crisis, fell around 2010, and has

seen sustained rapid growth since then. The bottom panel shows how nonbanks historically

held a smaller share of the overall mortgage market, although their share grew markedly during

the boom period. Since the crisis their share has grown and now they comprise over half of all

for-purchase mortgage originations.

2.2 LCR and MBS prices

Figure 2 shows the direct effect of LCR. It plots the portfolio holdings of banks affected

by the LCR rule. Affected banks substantially increased the amount of GNMA MBS on their

balance sheets.

Figure 3 shows that higher demand increased MBS prices. Following Echeverry, Stanton,

and Wallace (2016), we focus on the To-Be-Announced (TBA) market for 30-year fixed-rate

mortgages, which is the most-commonly traded bond in terms of settlement date and coupon.7

Figure 3 shows that the prices of GNMA, FNMA, and FHLMC MBS all increased following

the LCR proposal. As expected, the price of GNMA MBS increased by more than the GSEs’.

To confirm the previous result, we estimate the following difference-in-difference specification

7Our data source is the TRACE database from FINRA. Because securities change from day to day, wesmooth the data by taking the monthly average MBS price in the TBA market. Vickery and Wright (2013) andGao, Schultz and Song (2017) discuss the TBA market.

6

with continuous treatment exposure, given by the LCR weight:

OASs,t = αs + β(PostLCRt ×Weights) + PostLCRt + γXst + us,t, (1)

where Weights denotes the LCR weight assigned to security s. PostLCRt denotes whether the

day is after the LCR proposal. Data are daily. The OAS is the spread between the yield to

maturity on a bond and the risk-free rate after accounting for the probability of prepayment due

to changing interest rates.8 We focus on the OAS because it is already adjusted for prepayment

risk, so that any additional variation comes from liquidity or credit risk. We use securities with

basically no credit risk: GNMA, FNMA, and FHLMC MBS, and AAA bonds.9 The liquidity

weights for these securities are 1, 0.85, 0.85, and 0.5 respectively. In all specifications we control

for the TED spread to capture market credit risk, as well as day-of-week fixed effects.

Equation (1) asks what effect an additional percentage point of regulatory weight has on

a security’s OAS. The results are in Table 2. Each column estimates (1) over various sample

periods of increasing window size around the announcement of the U.S. LCR (October 24, 2013).

The estimates increase as we expand the window size and they converge on a cumulative LCR

impact of 25 basis points (bps) for a security with a regulatory weight of 1. Thus, LCR raised

the liquidity premium of GNMA MBS by 25 bps, of FNMA and FHLMC MBS by 21.25 bps

(25× 0.85), and AAA corporate bonds by 12.5 bps (25× 0.5).

The online appendix contains several robustness checks that confirm that LCR caused higher

demand for GNMA MBS and this implies higher prices. Table A2 shows that the results are

robust to using TBA prices. Tables A3 and A4 show that the results hold if we employ an

intra-MBS strategy which only uses variation between GNMA and GSE MBS. This ensures

that AAA bonds do not drive our estimates. The results imply a GNMA-GSE premium of

around 10%, which is very close to the 15% gap implied by the difference in regulatory weights.

Figure A1 of the online appendix looks at the standard deviation of security-level prices in

the TBA market as a measure of market thickness and liquidity. This price volatility declined

especially for GNMA MBS after the LCR proposal, and it remained at this lower level long

after the announcement date.

Finally, Figure 4 documents an increase in securitization activity for FHA loans coinciding

with the date when the LCR rules were proposed (October 24th, 2013), an effect not seen

8Boyarchenko, Fuster, and Lucca (2015), Gabaix, Krishnamurthy and Vigneron (2007) and Diep, Eisfeldtand Richardson (2017) show that the risk of homeowner prepayment is priced in the MBS market.

9Treasuries have (essentially) zero OAS, by definition. We measure the OAS for GNMA, FNMA, andFHLMC using Standard and Poor’s MBS indices, and we measure the AAA OAS using the corresponding Bankof America Merrill Lynch indices because the Standard and Poor’s index does not cover our sample window.

7

among either conventional or jumbo loans.

Thus, to recap, all the above evidence strongly suggests that LCR was a shock increasing

liquidity for GNMA MBS. Next we study which lenders reacted the most.

3 Cross-Sectional Analysis

3.1 Data

For our core analysis, we merge bank Call Reports with Home Mortgage Disclosure Act

(HMDA) data, which contain information on the borrower and outcome of almost all mortgage

applications in the U.S. Table 1 contains summary statistics. Our core data consist of FHA

loan applications for the purchase of an owner-occupied, single-family dwelling. Moreover, we

focus on lenders which received at least 10 applications in each year, and which have a record

in HMDA from 2011 through 2015.10 This gives a sample of 396 lenders, 123 of which are

non-depository institutions.

3.2 Nonbanks and LCR

We consider how higher demand for GNMAMBS may have impacted credit supply through

lenders’incentives to lower denial rates on FHA loans. HMDA denial rates correlate strongly

with survey evidence from banks on lending standards (Driscoll, Kay and Vojtech 2016).

To support the rest of the paper, Online Appendix Figure A2 contains a parallel trend

analysis. It shows that pre-LCR the denial rates followed similar trends for banks and nonbanks.

However, after the LCR policy the denial rates decreased faster for nonbanks.

We estimate the following specification on the sample of FHA loan applications:

Yi,l,t = β(MGNMAt × Fl

)+ PostLCRt + δZl,m,t + γXi,t + αl + ui,l,t, (2)

where i, l, m and t denote borrowers, lenders, MSA and years, respectively, and PostLCRtindicates whether the LCR policy has been announced (t ≥ 2014). We choose 2014 as our shock

year because our data are at the yearly frequency, and the announcement occurred toward the

end (October) of 2013, while the finalization occurred one year later in September 2014. The

10We start in 2011 to have a balanced sample around the LCR dates. Moreover, we avoid the "structuralbreak" associated with Dodd-Frank in 2010 and discussed in Gete and Reher (2017).

8

outcomes Yi,l,t are whether the application from borrower i to lender l in year t was denied.

Below we also check originations obtaining similar results.

Fl measures lender l’s exposure to securitization. In theory, higher demand for GNMAMBS

should have a greater impact on the behavior of lenders which fund more of their mortgages

through securitization. We employ two measures of Fl. First, we use an indicator of whether

lender l is a non-depository institution (NDI). Second, we use the ratio of securitized loans to

total originations in 2011 of lender l.

MGNMAt measures the collateral and liquidity of GNMA MBS. We measure MGNMA

t using:

1) an indicator of whether t ≥ 2014, since the LCR rule was proposed in October 2013 and

finalized in September 2014, with few changes to the proposed rule; 2) the OAS spread between

FNMA and GNMA MBS; 3) same as 2) but for FHLMC MBS. To facilitate interpretation, the

units of the OAS spread are percentage points divided by 100, which are the same units as our

outcome variables. The units are not basis points, unlike Table 2.

The borrower controls in Xi,t are log income, the ratio of requested loan to income, and an

indicator of whether the borrower is black or Hispanic, which we call Minorityi,t. The lender

controls in Zl,m,t are MSA-lender fixed effects and, when considering banks, the lagged log of

total assets and the lagged ratios of net income to total assets, loss provisions to total assets,

and total equity to total assets.

We estimate (2) over the period 2012-2015. This choice of sample window ensures that

we do not confound reliance on securitization with regulatory arbitrage (e.g. Buchak et al.

2018), since the major U.S. financial regulations had already been passed in 2010. In the online

appendix we re-perform our analysis on the narrowest possible window, 2013-2014, and find

similar results.

Table 3 contains the results for mortgage denials when Fl is an indicator of whether lender

l is a non-depository institution. Table 5 redoes the exercise when Fl equals the securitization

ratio. The two tables give the same result, which is consistent with our theory, and robust across

measures of MGNMAt . Lenders with more reliance on funding from securitization responded to

the higher value of GNMA MBS by denying fewer loans.

The estimated coeffi cient on PostLCR×NDI from Table 3 suggests that, because of LCR

rules, borrowers who apply to a non-depository institution are around 0.6 percentage point less

likely to be denied. This holds conditional on the borrower’s quality, and joint lender-MSA

effects. It is also economically meaningful, given the average denial rate of 14%. Relating

this to the estimated coeffi cient on PostLCR × SecRate in Table 5 of -0.012, note that theestimated nonbank effect is the same as applying to a lender with a 50% securitization rate

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(0.06 = 0.5∗0.012). In other words, the differential response of banks versus nonbanks is about

half the differential response of a pure portfolio lender (securitization rate of 0) and a pure

originate-to-distribute lender (securitization rate of 1).

One might wonder whether nonbanks and lenders more exposed to securitization increased

denial rates in conventional loans to compensate for their laxer standards in FHA loans. Table

A5 considers this possibility by replicating our baseline analysis on the sample of conventional

loans (non-jumbo, non-FHA loans). The results suggest that, because LCR amplified the

GNMA premium relative to GSEs MBS, this encouraged lenders to substitute from conventional

loans to FHA-insured loans.

3.3 FHA vs. Conventional Loans

Our key identification assumption is that lenders more reliant on securitization did not

loosen their standards after LCR for reasons other than the policy’s effect on GNMA liquidity.

Here we explore an alternative specification with a weaker identifying assumption. Specifically,

we pool FHA and conventional loan applications together so that we can include lender-year-

MSA fixed effects. Then, we estimate

Deniedi,l,t = β(MGNMAt ×NDIl × FHAi

)+ αm,l,t + ui,l,t, (3)

where αm,l,t is a lender-year-MSA fixed effect, FHAi indicates whether the application is for

an FHA loan, and the remaining notation is the same as in Table 3. Given that LCR gave

preferential treatment to GNMA compared to GSE MBS, we now take β from (3) as our

estimate of the policy’s effect.

Any confounding shock would have to not only disproportionately affect nonbanks after the

LCR proposal, but it would also have to affect nonbanks’willingness to approve FHA over

conventional loans. Moreover, all of this would have to happen to two borrowers within the

same year, the same MSA, and applying to the same lender, given our use of lender-year-MSA

fixed effects. This is a much weaker identifying assumption than in our baseline analysis. The

results in Table 4 are consistent with the baseline ones from Table 3. That the point estimates

are of a larger magnitude reflects the joint loosening among FHA loans (Table 3) and tightening

among GSE loans (Table A5). Moreover, we obtain similar results if we instead use the 2011

securitization rate to measure a lender’s exposure to LCR.

While the location-time-lender fixed effects in (3) are a powerful identification tool we use

(2) as our baseline for the remaining analysis. This is because (2) allows us to make inferences

10

about the overall level of credit as opposed to just the distribution between FHA an conventional

loans.

3.4 Risk Taking

Here we ask whether the post-LCR shift in origination behavior differed by borrower char-

acteristics that proxy for risk-taking. In Tables 6 and 7 we interact our measures of lender

l’s exposure to securitization with, respectively, an indicator of whether the applicant is black

or Hispanic, and with the borrower’s requested loan-to-income ratio. These loans are usually

associated with higher default rates.

The results suggest that LCR policies encouraged nonbanks to relax their lending standards

and increase their risk-taking among FHA loans. From a welfare perspective, it is unclear

whether this is a bad thing. For example, in Section 5.2 we will evaluate the consequences for

the homeownership rate.

4 Robustness of Identification

Our identification assumptions in Section 3 are those of a standard difference-in-difference

exercise: the LCR proposal, and subsequent increase in the relative value of GNMA MBS, did

not coincide with other shocks that would have affected the treatment group (lenders with less

funding liquidity) differently from the control group. In this section we consider reasons this

assumption might be violated, and we modify our specification accordingly. In all cases, we

are unable to find evidence that contradict the results from the previous section. Moreover, we

conduct several placebo tests.

4.1 Monthly Frequency

One might be concerned that our baseline specification lacks tight identification because

it is estimated at a yearly frequency. To address this concern, we use data from the HUD FHA

Single Family Portfolio Snap Shot to estimate a similar specification at the monthly frequency.

Relative to our core HMDA dataset, we only observe originated FHA loans in the HUD data,

as opposed to applications. Therefore we estimate

NDIi,l,t = β(OASGSEt −OASGNMA

t

)+ γXi,t + αz + ui,l,t, (4)

11

where i, l, z and t denote borrowers, lenders, zip codes and months, respectively.11 OASGNMAt

denotes the option adjusted spread for GNMA and analogously for GSEs.12 Each observation

is an originated loan. We cannot include month fixed effects because the OAS is already at the

monthly level. Borrower controls in Xi,t are the log loan size and an indicator for whether the

loan is a fixed rate mortgage.13 We estimate (4) over 2012-2015.

The parameter of interest β is the effect of the GSE-GNMA OAS spread on nonbank market

share. When the GNMA liquidity premium rises this spread is higher. Therefore the predicted

sign of β is positive: when the liquidity premium is higher, nonbanks are more willing to

originate FHA loans. The results in Table 8 confirm this intuition.

While we do not observe borrower demographics in the HUD data, we do observe the interest

rate. Therefore we ask whether nonbanks are more willing to lower the price of credit when

GNMA liquidity increases. The regression is

Ratei,l,t = β0[NDIi,l,t ×

(OASGSEt −OASGNMA

t

)]+ β1NDIi,l,t + ... (5)

...+ β2(OASGSEt −OASGNMA

t

)+ γXi,t + αz + ui,l,t, (6)

where the notation is the same as in (4), and Ratei,l,t is the loan’s interest rate.

Table 9 has the results. Focusing on the first column, a 1 percentage point (pp) increase

in the GNMA liquidity premium, measured by the GSE-GNMA OAS spread, corresponds to a

0.45 pp lower interest rate. However, it leads to a 0.27 pp lower rate among nonbanks, equal to

a 0.72 pp total reduction. In the second column, we include zip code-month fixed effects, which

addresses any concern that the GSE-GNMA OAS spread is simply picking up spurious time

series variation in nonbanks’market share. We again find a similar effect: nonbanks charge

0.17 pp lower interest rates than banks following a 1 pp increase in the liquidity premium.14

The results of this robustness exercise provide evidence that our baseline methodology is

not misidentified due to the yearly frequency. The results also suggest that nonbanks lower the

price of credit, in addition to approving more loans, following an increase in MBS liquidity.

11We classify lenders as non-depository institutions if their parent company’s name does not contain “Bank",“Credit Union", or variant spellings of these terms.12We define GSE OAS as the average of FNMA and FHLMC OAS.13We do not observe the borrower’s income in the HUD data.14We cannot control for the GSE-GNMA OAS spread due to the zip code-month fixed effects.

12

4.2 Regulatory Arbitrage

As documented by Buchak et al. (2018), regulatory arbitrage has been a key driver of

nonbanks’ increasing market share. Thus, a potential concern is that in Section 3 we cap-

ture differential costs of regulation across lenders rather than a response to regulation-induced

changes in MBS prices and liquidity. This is unlikely given our results on securitization-reliant

lenders in Table 5. However, Table A6 of the online appendix addresses this concern directly.

We make use of the fact that the major regulatory overhaul occurred in 2010 and 2011,

before the start of our sample.15 Thus, in Table A6 we re-estimate our baseline specification

over the narrow window 2013-2014.

Table A6 shows that relative to the baseline counterpart, Table 3, the results for 2013-2014

convey a similar message: the LCR policy reduced nonbanks’propensity to deny a mortgage.

In terms of magnitude, the point estimates are stronger, so that the baseline estimates from

Table 3 may be interpreted as conservative.

4.3 Net Stable Funding Ratio

Now we evaluate another potential concern with our identification. The Basel III accords

involved not only a Liquidity Coverage Ratio (LCR), but also a complementary Net Stable

Funding Ratio (NSFR). The NSFR aimed to ensure that banks "maintain suffi cient levels of

stable funding, thereby reducing liquidity risk in the banking system".16 However, the NFSR

was not proposed in the U.S. until May 2016, more than two years after the LCR proposal. It

is thus unlikely that the NSFR is affecting the results. Nonetheless, it is possible that lenders

updated their expectations following the LCR announcement, and that banks with less funding

liquidity subsequently aimed to shrink their balance sheets.

The previous logic contradicts the second two columns of Table A7 in the online appendix

which uses banks’securitization rates to capture their exposure to LCR. Specifically, Table A7

re-estimates the specification of Table 5 using only banks and a rich set of bank balance sheet

controls. Consistent with the results from Section 3, Table A7 shows that banks with greater

reliance on securitization denied fewer FHA applicants when the corresponding GSE-GNMA

spread increased.

15Dodd-Frank was passed in 2010, implemented in 2011, and 2011 was the year when bank stress tests hadthe greatest impact on real activity (Calem, Correa, and Lee 2016).16See the Federal Reserve’s press release on May 3, 2016.

13

4.4 Changing Pool of FHA Applicants

Since our core analysis is at the application level, it takes as given the distribution of

borrower quality across different loan types. If FHA loan applicants are becoming less risky,

this alone would not generate our results. One would further need that lenders with more

exposure to GNMA MBS have some cost of adjusting to the new quality of FHA borrowers.

However, Figure A3 in the online appendix shows that our two measures of credit risk (requested

loan-to-income ratio and minority status) have steadily grown at about the same rate for both

FHA and non FHA applicants.

If anything, the top panel of Figure A3 suggests that FHA applicants have become slightly

riskier, in terms of LTI, relative to non FHA applicants. Thus, it does not seem that changes

in the pool of borrowers can drive the core results of Section 3.

4.5 Changing Pool of Nonbank Applicants

Similar to the previous concern, one might wonder whether the pool of applicants to

nonbanks is changing over time. If it is, then our estimates may reflect the improving quality

of applicants to nonbanks. However, Figure A4 in the online appendix provides evidence to the

contrary.

The top panel of Figure A4 shows how the gap in the loan-to-income ratio of applicants to

banks versus nonbanks has been remarkably stable over time. Turning to the bottom panel, the

fraction of applications from minorities to banks versus nonbanks have been on parallel trends,

at least through 2014. In 2015, the minority share of bank applicants fell relative to nonbanks.

If anything, this is consistent with a relative increase in the risk pool of nonbank applicants,

making the baseline results from Section 3 conservative.

4.6 Originations, Placebo and the Ratio of Total Deposits to Total

Assets

Table A8 confirms that the result is robust when instead of denials we look at originations.

Lenders that are more sensitive to secondary mortgage markets approve and subsequently

originate more applications when the GNMA premium rises.17

17Denial rates are not the complement of origination rates because some borrowers may choose to turn downthe lender’s offer.

14

Table A9 contains a placebo test in the 2007-2010 period preceding LCR policies. The

results are insignificant for the securitization rate measure, and of the wrong sign for the

nonbank measure. Thus, we can say that our results are not driven by pre-trends.

Finally, in Table A10, which studies only banks, we use the ratio of total deposits to total

assets in 2011 to measure Fl. The idea is that such banks rely more on secondary markets to

finance their originations. The results are confirmed when using OAS spreads to measure MBS

premia.

4.7 Quantitative Easing

The third round of MBS purchases by the Fed overlapped with the LCR as it lasted from

2012 to 2014 (it ended October 29, 2014). The Fed bought MBS sponsored by the GSEs and by

GNMA with a tilt towards GSEs MBS (Board of Governors 2016). Figure A7 shows that since

the ratio of Fed’s purchases was weighted against GNMA MBS then these purchases cannot

account for the higher increase in GNMA MBS prices relative to GSEs that we document in

this paper.

5 Aggregate Effects

5.1 Dynamics of Nonbanks’Market Share

While the granularity of our data in Section 3 allows us to control for a rich set of factors

at the borrower level, it is diffi cult to map the estimates into an aggregate effect because our

data are at the application level. In this section, we aggregate our data to the level of the

census tract, which is the most granular unit of geography we can identify.18 We then estimate

∆ log(Originationsk,t

)= β

(MGNMAt × Fk,t

)+ PostLCRt + γXk,t + αk + uk,t, (7)

where k indexes census tracts and t indexes years. Originationsk,t denotes the number of

originated loans in census tract k and year t. We measure MGNMAt using an indicator of

whether t ≥ 2014 as in Section (3) . Fk,t is the average of lenders’exposure to securitization

(Fl) weighted by applications from census tract k in year t. We use two proxies: 1) the fraction

of applications to non-depository institutions from census tract k in year t (denoted as NDIk,t);

18Census tracts generally have a population between 1,200 and 8,000 with a target size of 4,000.

15

2) the weighted average of lenders’loan securitization rate in 2011, with weights determined by

application share in tract k and year t. Our controls in Xk,t include the change in the share of

minority applicants in the tract, the average of borrowers’requested loan-to-income ratio, and

the log of average borrower income. We also control for the change in the log of the median

house price, based on the Zillow Home Value Index.

Consistent with the borrower-level results from Section 3, Table 10 shows that census tracts

dominated by lenders more exposed to secondary mortgage markets saw greater credit growth

following the LCR policy. To interpret, the estimates for β suggest that LCR policies raised

loan origination growth by 19 percentage points in census tracts in which nonbanks are the

only lenders relative to tracts where there are no nonbanks, and 24 percentage points higher

in tracts where all lenders tend to finance originations through securitization relative to tracts

where no lenders do so.

Table 10 sheds light on the dynamics of nonbanks’market share. The estimate β in Table

10 captures the sum of growth in origination rates and applications.19 Table 10 suggests that

in the absence of the LCR policy, nonbank originations would have been 19 percentage points

less per year in census tracts where the fraction of applications to nonbanks is 100%. The

average application share is 50%, thus the average effect is 9.5 less percentage points. Thus,

given that nonbank FHA originations in 2013 were 358,394, this means that two years later,

that is, in 2015, without LCR the nonbanks would have made 68,094 fewer loans (0.095 * 2

* 358,394). This would have lowered nonbanks market share in 2015 from 77.1% to 74.5% of

FHA originations.20 Put differently, nonbank market share grew 9.9 percentage points from

2013 to 2015, but but their share would have grown 2.6 percentage points, or 26% less, in the

absence of the LCR policy.21

5.2 Homeownership Rates

In this subsection, we study whether the increasing market share of non-depository insti-

tutions can influence homeownership rates. We observe homeownership rates at the zip code

19The estimates in Table 3 are at the application level and do not capture application growth, which isimportant given the efforts that nonbanks have put into raising applications (see for example Rocket Mortgageadvertising during the Super Bowls).20In 2015, among FHA originations, 156,404 were from banks and 525,872 from nonbanks. Thus, the

77.1% share for nonbanks. Subtracting the 68,094 loans due to LCR we obtain the counterfactual 74.5%

as(

525,872−68,094525,872−68,094+156,404

).

21To arrive at these numbers, nonbanks and banks originated 358,394 and 175,044 FHA loans in 2013, re-spectively. Thus, nonbanks’market share was 67.2%. Thus, the 9.9 percentage points change between 2013 and2015, which, absent LCR policy would have been only 7.3 percentage points.

16

level in 2011 and 2015 from the American Community Survey’s 5-Year Estimates.22 Because

the 5-Year estimates are designed to study medium-to-long run changes in homeownership, we

depart from a panel specification and run a cross sectional regression. The specification is

∆ Homeownershipz,11-15 = β (Nonbankz,11 × FHAz,11) + γXz,11 + αc + uz, (8)

where z indexes zip codes. ∆Homeownershipz,11-15 denotes the change of homeownership rate

between 2011 and 2015. Nonbankz,11 and FHAz,11 are the 2011 share of mortgage applications

which are to nonbanks and which were for FHA loans. The controls in Xz,11 are the 2011 share

of mortgage applications to nonbanks, share of applications for FHA loans, homeownership

rate, average requested loan-to-income, and minority share.

The treatment group in (8) are zip codes with (i) a high initial nonbank share and (ii)

a high share of FHA applicants. Building on the core analysis in Section 3, these are the

groups most likely to experience a loosening of standards due to the effect of MBS liquidity on

nonbanks. Importantly, our controls in Xz,11 include both the initial nonbank and the initial

FHA application share, and therefore our results are not affected by unobserved features of

nonbank-prevalent or FHA-prevalent markets that correlate with homeownership. Moreover,

the county fixed effect αc means that all variation comes from within the same county. Thus,

it cannot be that the results are driven by county-level shocks such as ease of construction (e.g.

Saiz 2010). That is, we are comparing two zip codes in the same county with the same initial

nonbank and FHA exposure. Our source of identification is the fact that nonbanks loosened

standards for a particular type of loan, namely FHA loans.

The results in Table 11 suggest that nonbanks have facilitated access to homeownership in

a period when the U.S. homeownership rate has collapsed to historic lows. Thus, the welfare

evaluation of the role of nonbanks requires us to weigh the benefits from homeownership versus

the costs from higher default risks.

6 Conclusions

In this paper we have shown that changes in the cross section of MBS premia have sig-

nificant effects on credit risk by altering the composition of lenders in the primary mortgage

and their incentives to originate and securitize. Specifically, we used variation in MBS premia

induced by the Liquidity Coverage Ratio. We show that these policies, designed to prevent runs

22Zip codes are typically larger than census tracts. We merge each zip code to a census tract in our coreHMDA data using the HUD’s crosswalk file, and then we aggregate to the zip code level.

17

in secondary mortgage markets, have attracted nonbanks and originate-to-sell lenders towards

the FHA market and lowered lending standards.

Our results suggest that regulations to prevent runs in secondary mortgage markets have

increased the credit risk borne by U.S. taxpayers because LCR has made the FHA more exposed

to nonbanks. That is, by altering the composition of lenders, macroprudential regulation could

lead to negative fiscal shocks for the U.S. government. The paper highlights the importance of

general equilibrium channels stemming from new macroprudential regulations.

It remains an open question whether the risk-taking that we document is welfare enhancing

or not. For example, we show that nonbanks and LCR policies have increased homeownership

in a period when the U.S. homeownership rate has been at historic lows.

18

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21

Figures

Figure 1. Market Share of Non-depositary Institutions Among FHA and AllLoans for Home Purchases. The figure shows the percentage of FHA mortgage dollar vol-

ume (top) and of total mortgage dollar volume (bottom) originated by non-depository institutions

(nonbanks) for home purchases. Source: HMDA.

22

Figure 2. MBS Holdings of Institutions Affected by Liquidity Regulation. Thisfigure plots the holdings of GNMA backed MBS as a percent of all securities held by banks subject to

the LCR policy. Source: Call Reports (FRY-9C)

23

Figure 3. Prices of GNMA, FNMA and FHLMC MBS. The price corresponds to themonthly average of the most-commonly traded bond on a given day. The vertical line corresponds to

October 24th, 2013, when the LCR rules were proposed. Source: Trade Reporting and Compliance

Engine (TRACE).

24

Figure 4. Securitization by Loan Type. This figure shows the fraction of mortgage

applications which are originated for FHA loans, conventional loans, and jumbo loans. The vertical

line corresponds to the first year after LCR rules were proposed. The announcement was on October

24th, 2013. Source: HMDA.

25

Tables

Table 1: Summary Statistics

Variable Number of Observations Mean Standard Deviation

HMDA Variables:Denied 2,809,984 0.136 0.343Minority 2,809,984 0.312 0.463Loan-to-Income 2,809,984 3.043 2.188Depository Institution 2,809,984 0.316 0.465Securitization Rate 1,980,562 0.939 0.105GNMA Securitization Rate 1,980,562 0.386 0.391

Call Report Variables:Total Deposit Ratio 639,437 0.763 0.061Liquid Asset Ratio 463,017 0.105 0.071Equity Ratio 639,437 0.113 0.02Loan Provision Ratio 639,437 0.002 0.003Net Income Ratio 639,437 0.012 0.006Rebooked GNMA Ratio 172,805 0.04 0.023log(Assets) 639,437 18.1 3.13

Note: This table contains summary statistics of the variables used in the regressions. Eachobservation corresponds to an FHA loan application for the purchased of an owner-occupiedsingle-family dwelling over the 2012-2015 period. Variables describing lenders (Depository In-sitution, Securitization Rate, GNMA Securitization Rate, and all Call Report variables) areweighted by application share. Denied indicates whether the application was denied. Minorityindicates whether the applicant is black or Hispanic. Loan-to-income is the ratio of the appli-cant’s requested loan to her reported annual income. Depository institution indicates whetherthe lender is a depository institution. Securitization rate is the fraction of originations thatthe lender sold in a given year, and GNMA Securitization Rate is the fraction of originationsthat the lender sold as a GNMA-insured security in a given year. Total Deposit Ratio, EquityRatio, Loan Provision Ratio, Net Income Ratio, and Rebooked GNMA ratios are, respectively,the ratios of total deposits, total equity, loan loss provisions, net income, and rebooked GNMAsecurities to total assets. Liquid Asset Ratio is the ratio of Treasury securities, interest andnon-interest bearing balances, and cash to total assets.

26

Table 2: Liquidity Premium and LCR Weight

OASs,tPostLCRt× Weights -3.215 -6.793 -9.843 -18.679 -25.983 -25.680

(0.026) (0.001) (0.002) (0.001) (0.000) (0.000)PostLCRt 1.939 3.085 5.222 4.751 6.983 4.615

(0.103) (0.066) (0.026) (0.194) (0.051) (0.160)Security FE Yes Yes Yes Yes Yes YesTime Controls Yes Yes Yes Yes Yes YesWindow (Days) ±10 ±20 ±40 ±70 ±100 ±130R-squared 0.221 0.526 0.539 0.563 0.630 0.649Number of Observations 84 164 320 556 796 1024

Note: P-values are in parentheses. Subscripts s and t denote security and day. Weight denotesthe LCR liquidity weight for the given security. PostLCR denotes whether the day is October 24,2013 or later. The securities are GNMA, FNMA, and FHLMC MBS and U.S. AAA CorporateBonds. OAS data are from the Bloomberg Barclays MBS indices and the Bank of AmericaAAA Corporate Bond Index. The units of OAS are basis points. Time Controls are the TEDspread and day-of-week fixed effects. Each column considers a different window length, in days,around the event date. Standard errors are Newey-West with a lag of

√T days, where T is the

window length.

27

Table 3: LCR, FHA Denials and Nonbanks.

Deniedi,l,t

MGNMAt = PostLCRt OASFNMA

t -OASGNMAt OASFHLMC

t -OASGNMAt

MGNMAt ×NDIl -0.006 -8.944 -3.337

(0.000) (0.000) (0.000)Borrower Controls Yes Yes YesLender-MSA FE Yes Yes YesPost-LCR Indicator Yes Yes YesR-squared 0.108 0.108 0.108Number of Observations 2,809,984 2,809,984 2,809,984

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values arein parentheses. Denied denotes whether the loan application was denied. PostLCR denoteswhether t ≥ 2014. OAS denotes the option-adjusted spread computed by Bloomberg. It hasunits of percentage points divided by 100. NDI indicates whether the lender is a non-depositoryinstitution. Borrower controls are requested loan-to-income ratio, log income, and an indicatorof whether the borrower is black or Hispanic. The sample consists of applications for FHAloans for the purchase of an owner-occupied single-family dwelling from 2012 through 2015.Standard errors are heteroskedasticity robust.

28

Table 4: LCR, Denials, and Nonbanks.

Deniedi,l,t

MGNMAt = PostLCRt OASFNMA

t -OASGNMAt OASFHLMC

t -OASGNMAt

MGNMAt ×NDI

l×FHAi -0.017 -14.681 -9.754

(0.000) (0.000) (0.000)Borrower Controls Yes Yes YesLender-Year-MSA FE Yes Yes YesR-squared 0.116 0.116 0.116Number of Observations 9,811,952 9,811,952 9,811,952

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values arein parentheses. Denied denotes whether the loan application was denied. PostLCR denoteswhether t ≥ 2014. OAS denotes the option-adjusted spread computed by Bloomberg. It hasunits of percentage points divided by 100. NDI indicates whether the lender is a non-depositoryinstitution. FHA indicates whether the loan application is for an FHA loan. Borrower controlsare requested loan-to-income ratio, log income, and an indicator of whether the borrower is blackor Hispanic. The sample includes all non-jumbo loans for the purchase of an owner-occupiedsingle-family dwelling from 2012-2015. Standard errors are heteroskedasticity robust.

29

Table 5: LCR, FHA Denials and Securitization Rate

Deniedi,l,t

MGNMAt = PostLCRt OASFNMA

t -OASGNMAt OASFHLMC

t -OASGNMAt

MGNMAt ×Sec Rate

l,2011-0.012 -20.287 -27.383(0.002) (0.000) (0.000)

Borrower Controls Yes Yes YesLender-MSA FE Yes Yes YesPost-LCR Indicator Yes Yes YesR-squared 0.107 0.107 0.107Number of Observations 2,777,149 2,777,149 2,777,149

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values arein parentheses. Sec Rate is the ratio of securitized loans to total originations in 2011. Theremaining notation, controls, sample, and standard errors are the same as in Table 3

30

Table 6: LCR, FHA Denials and Minority Borrowers.

Outcome: Deniedi,l,t(OASFNMA

t -OASGNMAt

)× NDIl -7.013

(0.000)(OASFNMA

t -OASGNMAt

)× NDIl × Minorityi -6.717

(0.000)Borrower Controls YesLender-MSA FE YesPost-LCR Indicator YesR-squared 0.108Number of Observations 2,809,984

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. Minority indicates whether the borrower is black or Hispanic. Sec Rate is theratio of securitized loans to total originations in 2011. The remaining notation, controls, andstandard errors are the same as in Tables 3 and 5.

31

Table 7: LCR, FHA Denials and Borrowers’Loan-to-Income.

Outcome: Deniedi,l,t(OASFNMA

t -OASGNMAt

)× NDIl -10.773

(0.000)(OASFNMA

t -OASGNMAt

)× NDIl × High LTIi,t -13.233

(0.000)Borrower Controls YesLender-MSA FE YesPost-LCR Indicator YesR-squared 0.108Number of Observations 2,809,984

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. High LTIi,t denotes whether borrower i had an above-median requested loan-to-income ratio in year t. The remaining notation, controls, and standard errors are the same asin Tables 3 and 5.

32

Table 8: Monthly Frequency

NDIi,l,t

OASGSEt -OASGNMAt 4.960

(0.000)Borrower Controls YesZip Code FE YesR-squared 0.111Number of Observations 2463793

Note: Subscripts i, l, and t denote borrower, lender, and month, respectively. P-values are inparentheses. NDI indicates whether the lender is a non-depository institution. OAS denotesthe option adjusted spread for the indicated agency MBS category. It has units of percentagepoints divided by 100. GSE OAS is the average of FNMA and FHLMC OAS. Borrower controlsare the log loan size and an indicator for whether the loan is a fixed rate mortgage. The sampleincludes all originated FHA loans for the purchase of a single-family dwelling from 2012-2015.Standard errors are heteroscedasticity robust.

33

Table 9: Interest Rate Pass-Through at Monthly Frequency

Ratei,l,t

NDIi,l,t ×(OASGSEt -OASGNMA

t

)-0.272 -0.171(0.000) (0.000)

NDIi,l,t 0.002 0.002(0.000) (0.000)

OASGSEt -OASGNMAt -0.445

(0.000)Borrower Controls Yes YesFixed Effects Zip Code Zip Code-MonthR-squared 0.172 0.593Number of Observations 2463793 2274537

Note: Subscripts i, l, and t denote borrower, lender, and month, respectively. P-values are inparentheses. Rate is the loan’s interest rate. The first column has zip code fixed effects. Thesecond column has zip code-month fixed effects. The remaining notation, borrower controls,and standard errors are the same as in Table 8.

34

Table 10: LCR, and FHA Originations at Census Tract Level.

Outcome: ∆ log(Origk,t

)∆ log

(Origk,t

)PostLCRt× NDIk,t 0.190

(0.000)PostLCRt× Sec Rate2011,k,t 0.241

(0.050)Sample All AllTract Controls Yes YesTract FE Yes YesPost-LCR Indicator Yes YesR-squared 0.031 0.028Number of Observations 117,184 114,139

Note: Subscripts k and t denote census tract and year, respectively. P-values are in parentheses.The sample includes all originated FHA loans for the purchase of an owner-occupied single-family dwelling from 2012 through 2015. PostLCR denotes whether t ≥ 2014. Origk,t denotesthe number of originated loans in census tract k and year t. NDIk,t denotes the fractionof applications to non-depository institutions from census tract k in year t. Sec Rate2011,k,tis a weighted average of lenders’ loan securitization rate in 2011, with weights determinedby application share in tract k and year t. Tract controls are the change in: the fraction ofapplicants which are minorities, the log of average borrower income, the log of average requestedloan-to-income ratio, and the log of the MSA’s median house price. Standard errors are doubleclustered by census tract and year.

35

Table 11: Nonbanks and Homeownership

∆Homeownershipz,11-15Nonbankz,11 × FHAz,11 0.030

(0.014)County FE YesZip code controls YesR-squared 0.287Number of Observations 3519

Note: Subscript z denotes zip code. P-values are in parentheses. ∆Homeownershipz,11-15 de-notes the change of homeownership rate between 2011 and 2015 in zip code z. Nonbankz,11 andFHAz,11 are the 2011 share of mortgage applications which are to nonbanks and which were forFHA loans. Zip code controls in Xz,11 are the 2011 share of mortgage applications to nonbanks,share of applications for FHA loans, homeownership rate, average requested loan-to-income,and minority share. Each observation is a zip code.

36

ONLINE APPENDIX. NOT FOR PUBLICATION.

Intra-MBS Premium

We now focus exclusively on agency MBS, and take as our baseline outcome the OAS

computed by Standard and Poor’s as well as the difference in log OAS between GNMA and

each of the GSE’s MBS.23

We estimate both a panel regression,

log(OASs,t) = αs + β1(PostLCRt ×GNMAs) + γXs,t + τt + us,t, (9)

and a purely time-series regression

log

(OASs,t

OASGNMA,t

)= α + βPostLCRt +Xt + ut, (10)

where s ∈ {GNMA,FNMA,FHLMC} denotes the type of MBS, t denotes the quarter, τt isa quarter fixed effect, PostLCRt indicates whether quarter t equals or follows 2013Q4, and

GNMAs indicates whether security s is a GNMA MBS (s = GNMA). Although the OAS

already adjusts for prepayment risk, our controls Xs,t in (9) include the effective duration of

security s at quarter t. By analogy, Xt in (10) includes GNMA effective duration and the

effective duration of the security in the numerator.

The first column of Table A3 contains the estimates from our panel specification (9). Con-

sistent with the graphical evidence discussed in Section 2, the LCR announcement reduced the

spread on GNMA MBS by 11.9% more than for the GSEs’MBS. Taking each of the GSEs sep-

arately, the estimates of (10) in the second and third columns suggest increases in the relative

spread of 5.8% for FNMA and 7.4% for FHLMC. Given the similarity of results across data

providers and the extensive use of controls, we conclude that the LCR policy reduced spreads

on GNMA MBS by around 10% compared to GSE MBS. This is very close to the difference in

regulatory weight between the two types of securities (15 percentage points), and thus consis-

tent with the estimates from Table 2. Table A2 confirms that the results are robust if instead

we use a higher frequency and MBS prices from the TBA market. Collectively, the various

exercises point to a significant increase in the value of agency MBS, especially GNMA MBS,

due to their preferential regulatory weights.

23Online appendix Table A4 produces very similar point estimates using Bloomberg’s OAS.

37

Additional Figures and Tables

Figure A1. Ratio of Intraday Standard Deviation to Price for GNMA, FNMAand FHLMC MBS. The price corresponds to the most-commonly traded bond on a given day.The vertical line corresponds to October 24th, 2013, when the LCR rules were proposed. Source:

FINRA’s TRACE database.

38

Figure A2. Average Denial Rates for Nonbanks and Depository Institutions inFHA loans. Source: HMDA

39

Figure A3. Credit Quality of FHA Applicants. The top panel plots the average loan-to-income ratio. The bottom panel plots the fraction of minorities among applicants for FHA versus

non FHA loans over our main sample period.

40

Figure A4. Credit Quality of Applicants to Banks and Nonbanks. The top panelplots the average loan-to-income ratio. The bottom panel plots fraction of minorities among applicants

to banks versus nonbanks over our main sample period.

41

Figure A5. Share of GNMA to Total Agency MBS. This figure plots the share ofGNMA in the MBS purchases by the Fed. The vertical line corresponds to October 24th, 2013, when

the LCR rules were proposed. Source: federalreserve.gov.

42

Table A1: Nonbanks in FHA

Name Number of Originations in 2013 and 2014QUICKEN LOANS 20,905GUILD MORTGAGE COMPANY 15,692PRIMARY RESIDENTIAL MORTGAGE 13,321STEARNS LENDING 12,185HOMEBRIDGE FINANCIAL SERVICES, 12,029PROSPECT MORTGAGE LLC 11,477FAIRWAY INDEPENDENT MORT CORP 10,399STONEGATE MORTGAGE CORPORATION 9,352PACIFIC UNION FINANCIAL, LLC 9,327MOVEMENT MORTGAGE, LLC 9,113CORNERSTONE HOME LENDING, INC. 8,946PLAZA HOME MORTGAGE, INC. 8,936EVERETT FINANCIAL INC 8,547FRANLKIN AMERICAN MORTGAGE CO 8,518ACADEMY MORTGAGE CORPORATION 8,187DHI MORTGAGE COMPANY LIMITED 7,984GUARANTEED RATE INC 7726UNIVERSAL AMERICAN MTG. CO.LLC 7,602PINNACLE CAPITAL MORTGAGE 7,397CALIBER HOME LOANS 7,342SECURITYNATIONAL MORTGAGE COMP 7,113UNITED SHORE FINANCIAL SERVICE 7,111PARAMOUNT RESIDENTIAL MORTGAGE 7,087LOANDEPOT.COM, LLC 6,927CARRINGTON MORTGAGE SERVICES 6,457PHH HOME LOANS 6,057NOVA HOME LOANS 5,930FREEDOM MORTGAGE CORPORATION 5,888NTFN, INC. 5,346AMERICAN PACIFIC MORTGAGE CORP 5,294SIERRA PACIFIC MORTGAGE 5,196SUN WEST MORTGAGE COMPANY, INC 4,968AMCAP MORTGAGE LTD 4,706CMG FINANCIAL, INC 4,671SWBC MORTGAGE CORPORATION 4,658W. J. BRADLEY MORTGAGE CAPITAL 4,487IMORTGAGE.COM, INC. 4,395FIRST MORTGAGE CORP 4,118MICHIGAN MUTUAL, INC. 4,053WR STARKEY MORTGAGE, LLP 3,992MORTGAGE 1 INCORPORATED 3,820RESIDENTIAL MORTGAGE SERVICES 3,654NATIONSTAR MORTGAGE LLC 3,641COBALT MORTGAGE INC 3,623NETWORK FUNDING LP 3,573BROKER SOLUTIONS, INC. 3,550CITYWIDE HOME LOANS, A UTAH CO 3,507DAS ACQUISITION COMPANY, LLC 3,360ENVOY MORTGAGE, LTD. 3,357CALIBER FUNDING LLC 3,354

43

Table A2: MBS Prices for TBA Market and Alternative Sample Periods

Outcome: log(Ps,t) log(Ps,t) log(PGN ,tPFN ,t

) log(PGN ,tPFH ,t

)

PostLCRt 0.018 0.013 0.006(0.000) (0.000) (0.001)

PostLCRt× GNMAs 0.007 0.007(0.031) (0.003)

Agency FE Yes Yes No NoMonth FE No Yes No NoSample Oct 12 - Oct 14 Jan 12 - Apr 15 Oct 12 - Oct 14 Oct 12 - Oct 14Prepayment Controls Yes Yes Yes YesR-squared 0.717 0.896 0.556 0.281Number of Observations 75 120 25 25

Note: Subscript s denotes whether the MBS corresponds to GNMA, FNMA, or FHLMC, and tdenotes the month. P-values are in parentheses. Ps,t denotes the price of the monthly averageof the most commonly traded bond on the TBA market, based on TRACE data. PostLCRtdenotes whether the month is or follows October 2013, when the LCR rules were proposed.GNMAs denotes whether the security is backed by GNMA. In columns 1 and 2, our sampleincludes GNMA, FNMA, and FHLMC securities. Columns 3 and 4 consider relative pricesas the outcome. Column 2 is based on a longer sample and so includes month fixed effectsinstead of the PostLCRt indicator. The prepayment controls are the duration of security s, ascomputed by Standard & Poor’s for its corresponding MBS index using a model to estimateprepayment risk; columns 3 and 4 also control for the effective duration of FNMA and FHLMCMBS. Standard errors are Newey-West with a lag of 9 months.

44

Table A3: Liquidity Premium and the LCR Announcement

Outcome: log(OASs,t) log(OASFN ,tOASGN ,t

) log(OASFH ,tOASGN ,t

)

PostLCRt× GNMAs -0.119(0.000)

PostLCRt 0.058 0.074(0.020) (0.099)

Agency FE Yes No NoQuarter FE Yes No NoPrepayment Controls Yes Yes YesR-squared 0.991 0.865 0.675Number of Observations 21 7 7

Note: Subscript s denotes whether the MBS corresponds to GNMA, FNMA, or FHLMC, andt denotes the quarter. P-values are in parentheses. OASs,t denotes the average quarterlyoption-adjusted spread for security s, as computed by Standard and Poor’s. PostLCRt denoteswhether the quarter coincides with or follows October 24, 2013, when the LCR rules wereproposed. GNMAs denotes whether the security is backed by GNMA. In column 1 our sampleincludes GNMA, FNMA, and FHLMC securities. Columns 2 and 3 consider relative prices asthe outcome. The sample period is 2012Q4 through 2014Q2. The prepayment controls are theeffective duration of security s, as computed by Standard and Poor’s for its corresponding MBSindex using a model to estimate prepayment risk; columns 2 and 3 also control for the durationof FNMA and FHLMC MBS. Standard errors are Newey-West with a lag of 3 quarters.

45

Table A4: Liquidity Premium and the LCR Announcement (Alternative Data Source)

Outcome: log(OASs,t) log(OASFN ,tOASGN ,t

) log(OASFH ,tOASGN ,t

)

PostLCRt× GNMAs -0.128(0.000)

PostLCRt 0.085 0.114(0.000) (0.007)

Agency FE Yes No NoQuarter FE Yes No NoPrepayment Controls Yes Yes YesR-squared 0.996 0.974 0.894Number of Observations 21 7 7

Note: P-values are in parentheses. The notation, sample period, controls and standard errorsare the same as in Table A3. The difference is that the OASs,t and effective duration data nowcome from Bloomberg.

46

Table A5: LCR and Conventional Loan Denials

Outcome: Deniedi,l,t Deniedi,l,tPostLCRt× NDIl 0.011

(0.000)PostLCRt× Sec Ratel,2011 0.007

(0.000)Sample All AllBorrower Controls Yes YesLender-MSA FE Yes YesPost-LCR Indicator Yes YesR-squared 0.095 0.094Number of Observations 6,982,398 6,891,243

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. The sample consists of applications for conventional (non-FHA, non-jumbo) loansfor the purchase of an owner-occupied single-family dwelling from 2012 through 2015. Thenotation, controls, and standard errors are the same as in Tables 3 and 5.

47

Table A6: Robustness: LCR, FHA Denials and Nonbanks 2013 to 2014

Deniedi,l,tPostLCRt×NDIl -0.059

(0.048)Borrower Controls YesLender-MSA FE YesPost-LCR Indicator YesR-squared 0.024Number of Observations 1,387,277

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values arein parentheses. Denied denotes whether the loan application was denied. PostLCR denoteswhether t = 2014. NDI indicates whether the lender is a non-depository institution. Bor-rower controls are requested loan-to-income ratio, log income, and an indicator of whether theborrower is black or Hispanic. The sample consists of applications for FHA loans for the pur-chase of an owner-occupied single-family dwelling from 2013 through 2014. Standard errors areheteroskedasticity robust.

48

Table A7: Robustness: LCR, FHA Denials and Securitization Rate Among Depository Insti-tutions

Deniedi,l,t

MGNMAt = PostLCRt OASFNMA

t -OASGNMAt OASFHLMC

t -OASGNMAt

MGNMAt ×Sec Rate

l,20110.018 -33.447 -45.966(0.033) (0.000) (0.000)

Borrower Controls Yes Yes YesBank Controls Yes Yes YesLender-MSA FE Yes Yes YesPost-LCR Indicator Yes Yes YesR-squared 0.089 0.089 0.089Number of Observations 617,221 617,221 617,221

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. Sec Rate is the ratio of securitized loans to total originations in 2011. BankControls are the lagged log of total assets and the lagged ratios of net income to total assets,loss provisions to total assets, and total equity to total assets. The remaining notation, controls,sample, and standard errors are the same as in Table 3. The sample only includes depositoryinstitutions.

49

Table A8: Robustness: LCR and FHA Originations

Originationsi,l,tPostLCRt× NDIl 0.017

(0.000)PostLCRt×Sec Ratel,2011 0.031

(0.000)Sample All AllBorrower Controls Yes YesBank Controls No NoLender-MSA FE Yes YesPost-LCR Indicator Yes YesR-squared 0.086 0.084Number of Observations 2,809,984 2,777,149

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. The notation, controls, sample, and standard errors are the same as in Tables 3and 5.

50

Table A9: Robustness: Placebo: 2007-2010

Deniedi,l,tPost2009t× NDIl 0.004

(0.013)Post2009t×Sec Ratel,2011 -0.006

(0.232)Borrower Controls Yes YesLender-MSA FE Yes YesPost-2009 Indicator Yes YesR-squared 0.133 0.132Number of Observations 1,143,124 1,137,327

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. The sample period is 2007-2010. Post2009 denotes whether t ≥ 2009. NDIindicates whether the lender is a non-depository institution. Sec rate denotes the fractionof originated loans that a lender subsequently securitized and sold in 2011. The remainingnotation, controls, sample, and standard errors are the same as in Tables 3 and 5.

51

Table A10: Robustness: LCR, FHA Denials and Deposit Ratio

Deniedi,l,t

MGNMAt = PostLCRt OASFNMA

t -OASGNMAt OASFHLMC

t -OASGNMAt

MGNMAt × (1 - Dep Ratio

l,2011) -0.004 -87.889 -81.384

(0.790) (0.000) (0.000)Borrower Controls Yes Yes YesBank Controls Yes Yes YesLender-MSA FE Yes Yes YesPost-LCR Indicator Yes Yes YesR-squared 0.088 0.088 0.088Number of Observations 589,199 589,199 589,199

Note: Subscripts i, l, and t denote borrower, lender, and year, respectively. P-values are inparentheses. Dep Ratio is the ratio of total deposits to total assets in 2011. Bank controls arethe lagged log of total assets and the lagged ratios of: net income to total assets, loss provisionsto total assets, and total equity to total assets. The remaining notation, controls, sample, andstandard errors are the same as in Table 3.

52