Can Credit Supply Frictions Impact Consumers? Evidence from Mortgage Lending and Foreclosures. Kristoph Kleiner

Transcription

1 Can Credit Supply Frictions Impact Consumers? Evidence from Mortgage Lending and Foreclosures Kristoph Kleiner Indiana University, Kelley School of Business July 31st, 2014 Abstract We evaluate if credit supply frictions can (i) aect household borrowing and (ii) result in real economic outcomes, namely further foreclosures. Our identication strategy depends on following an exogenous balance sheet shock on a bank in one housing market to all bank branches outside the area. We nd that bank health signicantly impedes the borrowing abilities of households, especially if the loan cannot be sold to government-sponsored enterprises (GSEs) such as Fannie Mae and Freddie Mac. In addition, due to the long horizon of mortgage lending relationships, these shocks impact household renancing and modication opportunities years after the initial real estate purchase and can lead to an increase in foreclosures. We conrm these results at both the household level and county level. We nd that healthy banks (top quartile) are 6-10% more likely to approve a primary mortgage application than an unhealthy bank (bottom quartile) in 2009, while homeowners with unhealthy lenders are 1% more likely to foreclose. Together we estimate that credit supply channel explains 13% of the increase in foreclosures between Department of Finance, Indiana University, 1309 East 10th Street, Bloomington, IN KleinerK@Indiana.edu. 1

2 1 Introduction There is a general consensus that the Financial Crisis is largely the result of a decline in house prices and a rise of mortgage defaults, leading to sharp declines in the value of balance sheets of the nancial sector. However, understanding the extant that frictions on the supply of credit can lead to real economic outcomes is less clear, a topic frequently discussed as, does the health of Wall Street aect Main Street? Given the actions and policies that supported the nancial sector over the last several years the role of credit supply in the economy is an empirical topic that has received much interest from researchers and the public alike. There are two concerns in studying credit supply frictions. First, how do we distinguish between demand and supply shocks: did the economy collapse because of a deterioration in bank lending or vice versa? The second concern is identifying how a decline in credit impacts the macroeconomy: who is unable to obtain credit and why does this impact the rest of us? Understanding the signicance of the nancial sector during recessions requires an answer to both questions. The common narrative among fellow economists is that nancial institutions cut business lending leading to a spike in the unemployment rate. The literature rst documented that bank losses did indeed aect the rm nancing opportunities. For instance Santos [2010] nds that the banks that experienced the greatest nancial losses also oered the highest loan spreads to rms. Meanwhile Ivashina and Scharfstein [2010b] oer evidence that the institutions that co-syndicated the most loans with Lehman Brothers were the most vulnerable to credit-line drawdowns. More recent work has focused on tying this decline to rm employment using data at either the rm-level [Chodorow-Reich, 2014] or the local level [Greenstone and Alexandre, 2012]. This paper evaluates an alternative narrative: credit frictions on households facilitated a decline in consumer borrowing and exacerbated the rise of foreclosures. In this project we test how the health of a banks (i) aects new consumer loan applications and (ii) impacts future foreclosures through the renancing/modication channel. Using transaction and loan application data, along with bank health information, we can determine how exogenous shocks to the balance sheet of a bank impact lending to households and actually result in foreclosures. According to our preferred results we nd that healthiest 25% of banks to the least healthy were 6-10% more likely to approve a primary mortgage than banks in the lowest quartile, while 2

3 homeowners with unhealthy original lenders were 1% more likely to foreclose. Despite the importance of this issue, documenting the empirical impact of credit frictions on households-as well as the economic implications on the economy- has received little consideration 1. For instance, consider the quote by then Federal Reserve Chairman Ben Bernanke, perhaps the single strongest proponent of the credit supply-rm theory:... for the bank channel to aect economic activity, borrowers accustomed to relying on banks must be unable to turn to other lenders, at least not without some cost. For some business borrowers, particularly small business borrowers that rely on banking relationships, this scenario is plausible. But nancial innovation and deregulation imply that borrowers in the market for a mortgage or consumer credit have numerous nonbank nancing alternatives, blunting any direct impact of changes in bank lending. - Ben Bernanke, June 15th, 2007 At odds with this sentiment, we nd that consumer non-mortgage credit raises long after the decline in house prices and employment, falling only in the third quarter of 2008 at the collapse of Lehman Brothers and the height of the Financial Crisis. In comparison, corporate debt securities grow continuously throughout the Great Recession without even one negative quarter. One possible explanation for the relative lack of empirical research is due to identication concerns: since the credit supply frictions likely stem from households, it is particularly dicult to determine that declines in lending are not the result of deterioration in the nancial condition of the borrower. Isolating between these possibilities requires a source of variation in bank health uncorrelated to the household. Our research requires constructing a measure of bank health during the Financial Crisis. A simple analysis nds that the rise of foreclosures did indeed result in a large balance sheet shock to the nancial sector. The concern with this measure, however, is that the direct decline in a bank's nancial position is likely correlated with lending practices and so endogenous to the deteriorating nancials of households. For instance nancial institutions that focused on the subprime market may have experienced the greatest nancial losses; however cuts to their lending practices might also be due to the decline of unobserved characteristics of that same borrowing base. 1 One exception is the recent paper byramcharan et al. [2012b] which focuses on a narrow group of nancial institutions, credit unions. 3

4 To overcome this issue we use real estate exposure at the bank branch level and then examine how negative real estate shocks at a branch in one location impact the lending decisions at all other branches across the rest of the country. For instance, consider East West Bank with headquarters in Pasadena, California. East West Bank had high exposure to the housing bust since 104 of its 135 branches are located in California, principally in and near MSAs. As a result, the value of properties underlying their mortgages loans declined 28% between 2006 and In addition to the California branches, there are two branches in Massachusetts, fourteen in Texas, four in Georgia, six in New York, and four in Washington State. Our estimation technique is to test how the decline in the bank balance sheet due to exposure to the California housing market impact households in the other states without signicant house price declines. We nd large variation in bank health: the mean institution experienced only a 2.5% negative balance sheet shock from house prices during ; however banks at the 90th percentile actually saw 7% real estate price growth while the bottom 10% saw a 14% total decline. Our measure of housing exposure is also strongly correlated with the residential charge-os for each bank, especially during the crisis years. To determine the eects of lending due to these cross-sectional heterogeneity in bank health, we then compare the loan application outcomes between households living within the same county. We focus our analysis on loan approvals/denials is valuable for three reasons. First, by considering all loans- rather than just originated loans- we are able to more closely control for the possibility that credit demand is driving our results. Importantly, our data can distinguish between loans that were approved but not accepted by the consumer as compared to loans that were rejected by the nancial institution. Secondly, by focusing on the extensive margin we are better able to link our results to the possibility of macroeconomic outcomes: being unable to obtain a rst mortgage will likely have larger impact on house prices than having to pay a slightly higher interest rate. Third, mortgage loan amounts are likely decided not by the bank but the underlying real estate value. We control for observable dierences between households and also include bank xed eects to control for unobservable characteristics between banks. Between 2006 and We nd that the income of household applicants increased faster at unhealthy banks between largely due to dierences in geographic locations. We see no statistical dierence in household characteristics in any other measure. 4

5 Our results suggest that a 10% increase in bank health is responsible for a 2.8%, 1.6%, and 0.9% increase in the approval rate of primary, secondary, and renanced mortgages, respectively. In addition, we show that our results are not driven by local demand shocks or banks that securitize and sell the majority of their real estate loans. Instead we nd that balance sheet shocks are most signicant when the institution will not be able to readily securitize the loan. To identify the ease of securitization of a loan we use the Freddie Mac and Fannie Mae Conforming Loan Limits. At its simplest, loans above $417,000 are too large to be purchased by the government-sponsored enterprises (GSEs) and so are more likely to remain on the bank balance sheet. Therefore we might suspect these loans to be particularly susceptible to the credit supply of the particular bank. We nd that the approval of loans above the threshold are over two times more aected by real estate shocks than slightly smaller loans. The result highlights that the real estate shock is indeed a nancial shock to the banks. Once we have clearly identied a decline in bank-specic credit supply, we move onto to our second research question: what then are the economic impacts of on households? We test how credit supply shocks to the original lender aect homeowners years into the future. The argument here is that foreclosures are costly not only for the borrower, but also for the lender; previous research has indeed proved and quantied this exact cost. As a result lenders has particular incentives to renance relative to other banks, making the relationship sticky. Additionally, loan modications must be approved by the owner of the loan. Matching with the theory we estimate that a ten percent increase in bank health decreases foreclosures by 0.5%. With this we can explain approximately 14% of all foreclosure during Our results again appear robust to alternative explanations frequently discussed in the literature. We conrm our bank balance sheet shocks using county level of mortgage delinquencies. Even after including county and state-by-year xed eects we nd strong and positive eects on mortgage delinquencies during the crisis, but no eects prior to In comparison, exposure to the real estate market negatively impacts both automobile and credit card delinquencies during the entire time sample. Our result imply a negative externalities from credit supply frictions on households and suggest a role for government intervention in mortgage nancing that facilitate lending during times of crisis. Of current interest is the extension of the Home Aordable Renance Program. Currently the program 5

6 guarantees renancing to homeowners that a Freddie Mae or Fannie Mac rst loan (subject to a few minor requirements), but recent discussions that suggested extending the reach to all nancial institutions. According to our results, this program would likely increase the number of households that can take advantage of current lower interest rates and reduce foreclosures. Our research relates extends several current topics in nance. First, previous work has focused on how exogenous nancial shocks through bank failures, asset values, bank runs, or government intervention aect lending supply Ashcraft [2005], Amiti and Weinstein [2013], Ivashina and Scharfstein [2010a], Santos [2010]. We dier from these papers by focusing not only credit shocks to rm but rather households. To our knowledge Ramcharan et al. [2012a] is the only other paper that tests credit friction on consumers. However, this paper explores only a subset of nancial institutions (correspondent credit unions), focuses primarily on consumer credit as opposed to mortgage credit, and considers an alternative shock to the nancial sector. A second set of research explores the consequences of credit supply shocks on rm employment Greenstone and Alexandre [2012], Jimenez et al. [2010]. Additionally, our empirical estimation on relationship lending follows the work of Chodorow-Reich [2014]. Unlike these papers we evaluate the credit supply eect on consumers and the economic implications on foreclosures. Of course there is a large literature on the implications of nancial shocks to consumers during the nancial crisis. Mian and Su [2011], Mian et al. [2013], Mian and Su [2012] evaluate eect of household balance sheet shocks on foreclosures, consumption, and employment, respectively. Relatedly, previous work has also attempted to explain the raise of foreclosures due to securitization Mian and Su [2009], relaxed lending standards Demyanyk and Van Hemert [2011], or mortgage contracts Kung [2012]. Our research considers a new and complementary channel based on lender-specic health. In addition, we oer a new potential explanation for the surprising lack of loan modications even during the nancial crisis. According to one estimate by Adelino et al. [2013b] only ten percent of all delinquent borrowers receive a loan modication within the following year. They explain this result on the basis that modications are costly due to information frictions on the nancial situation of the borrower. In comparisonpiskorski et al. [2010], Keys et al. [2009] oer evidence that low modication rates are due to the raise of securitization and misaligned servicer incentives. We introduce and quantify a new explanation based on the availability of bank capital. 6

7 Research by Chaney et al. [2012], Cvijanovic [2014], Kleiner [2014] have all studied how real estate shocks impact the investment, capital structure, and employment of rms. In this regard our paper is closest to Cuñat et al. [2013] and Chakraborty et al. [2013] who rely on a similar identication strategy to determine the eect of bank balance sheet shocks to commercial lending and bank capital structure. Our strategy to identify strategy is developed on prior empirical evidence that the foreclosure process is costly for the lender. According to one estimates foreclosures of subprime mortgages in 2007 costs senior lien holders over fty percent of the average principal balance due to legal fee, continued price declines, foreclosure discounts, and missed mortgage payments Cordell et al. [2008]. It is this cost that is the driving force behind our foreclosure results. The outline for this paper is as follows. Section 2 introduces our empirical methodology, Section 3 summarizes the data, Section 4 includes our results, and Section 5 concludes. 2 Empirical Methodology 2.1 Financial Outcomes The rst focus of this research project is to test the implications of credit supply frictions on consumer borrowing. The general strategy is to isolate balance sheet shocks to nancial institutions that aect borrower loan applications exclusively through the credit channel. More specically, we determine lender-specic shocks from one branch aect the availability of new loans in a branch in a separate location. In our paper we measure lender health by exposure to the housing market and the foreclosure crisis. Previous research has documented signicant cost due to foreclosures to both the borrower and lender. For instance, using data from a subprime mortgage servicer Cordell et al. [2008] nd that losses on foreclosures in 2007 were over fty percent of the average balance of $190,000. The breakdown of losses includes: legal fees, sales commissions, and maintenance expenses average eleven percent of the principal balance, missed mortgage payments are ten percent, decline in the real estate value and foreclosure discount represent another twenty-two percent of the loss, and roughly ten percent more for all other factors. Similarly Suisse [2008] calculates a severity loss for subprime mortgages of 55 percent during 2008 with losses of over 80 percent in Midwest states. In addition, even alternative processes that mitigate direct foreclosure expose the lender to signi- 7

8 cant cost. For instance a real estate short sale occurs when the proceeds from property sale fall short of the debt balance and the lien holders accept less than the total debt owed. Suisse [2008] reported that short sales still have a forty percent loss severity rate; the dierential is due by avoiding liquidation costs, the foreclosure discount, and fewer lost mortgage payments. Approval c = β HousingExposure b t 1 Crisis t +ρ HousingExposure b t 1+πX c +ψ b +δ t φ l +ε c (1) The primary dependent variable is a binary variable that denotes whether the loan for consumer c is approved. The consumer submits the application to bank b at time t and lives in location l. For our purposes we focus on primary residential mortgages, secondary residential mortgages, and home equity lines of credit. Intuitively, an increase in exposure to acts as a balance-sheet shock to the institution and limits the capital available for new loans. As discussed before, simply using the actual measure of net charge-os for each bank is likely endogenous to potential borrower characteristics; we next develop our exogenous measure of net chargeos. Our key variable of interest is HousingExposure, the change in the value of the bank's real estate loans. In this estimation we interact this variable with an indicator with a positive value during the nancial crisis period. The idea is that a sudden negative decline in the value of the residential property underlying the loan will make a default more likely; however, a sudden increase in the value of the property should have more minor eects and therefore we need to distinguish between these two scenarios. In addition we control for potential dierences in consumer applicants and the underlying real estate by including the term πx c. These controls include decile indicators for applicant income and for loan amount, applicant and co applicant race, applicant and co applicant sex, type of property (oneto-four family, manufactured, or multifamily), and primary or secondary residence. We also conduct the analysis independently for both primary mortgage loans, secondary loans, and renancing loans. Unfortunately due to data constraints we are not able to identify applicants in our data and so include borrower xed eects Instead we include ψ b a xed eect for each bank. Therefore we can control for unobservable dierences in the applicant pool at the nancial institution level. Secondly, we attempt to control for unobservable dierences between loan applicants- namely the 8

9 current economic climate- by comparing two individuals within the same location during the same time period. Therefore include the interaction δ t φ l in the estimation. ( ) ( ) RELoans HousingExposure b b t = 0 [l= Deposit bl 1]LΣ 0 Assets b t Deposit b P rice l t (2) 0 To dene our variable HousingExposure we rst approximate the exposure each bank has to a particular real estate market. Our method follows Cuñat et al. [2013] who use the percentage of deposits coming from branches in each real estate market to determine the weighting system. Our proxy is valid if the majority of real estate loans on the balance sheet come from locations with large deposits. We then multiply each weight by the local price index and sum the results to estimate price changes for each bank over time. Finally to estimate the real estate exposure of each institution we take the sum of all real estate loans at the beginning of the period over current total assets. The variable closely is similar to the variable develop in Gan [2007], and extended in Chaney et al. [2012], Cvijanovic [2014], Kleiner [2014]. We believe our focus on local house prices is quite relevant for two reasons. First, Palmer [2013]estimates that changing borrower and loan characteristics explain only 30% of the default rate in subprime mortgage between In comparison declining prices account for the other seventy percent of all foreclosures. Secondly, house prices exacerbate lender losses even after the initial default and before the REO Sale [Research, 2008]. Therefore subprime mortgage losses are often greater due to their concentration in large areas during housing bust. For instance a forty percent house price decline generally results in a 25-37% loss (depending on an LTV 0f 80 to 95, respectively); a sixty percent decline corresponds to losses of 50-58%. Recall that we have two objectives: (i) evaluate the eect of a decline in credit supply on consumers during the nancial crisis, and (ii) associate this eect with a real economic outcome. In our current analysis we can determine if a balance sheet shock to the bank impacts new loan approvals. The next step is to test how if the failure to receive loan renancing leads to an increase in the probability of foreclosure. There are two advantages to extend our current framework. First, it is possible that a denied applicant can easily receive a loan at an alternative lender, mitigating any long-term eects. If switching lenders is costless then a decline in credit supply at one institution will be matched by an increase in 9

10 credit at a comparative bank. Therefore our notion of economic outcomes depends on the notion that switching lender is costly. Secondly, loan approval is only one outcome, but loan can dier in other dimensions including interest rate, amount, xed vs. adjustable, term length, etc. To understand the aggregate eect we need a single measure of real impact. Therefore we next focus our research on foreclosure outcomes. 2.2 Foreclosure Outcomes We argue that borrowers may be aected by the nancial situation of the lender years after receiving the primary mortgage and can ultimately lead to an increased likelihood of foreclosure. This is possible since: (i) loan modications are must be approved by the lien holders, and (ii) due to the lender costs associated with foreclosures the initial lien holder has added incentives to renance 2. In both instances we argue that the current nancial capacity of the original mortgage lender may impact the ability to meet debt obligations and escape foreclosure. F oreclosure b ct = β HousingExposure b t 1 Crisis t+ρ HousingExposure b t 1+πX ct +ψ c +δ t φ l +ε ct In the second test of the paper we evaluate how balance sheet shocks to the original bank, b, impact the likelihood of foreclosure years into the future. Here we are also able to include a xed eect for each consumer since we follow the household from initial purchase to sale, foreclosure, or the end of the period. Since we do not have full homeowner characteristics at each point in time we estimate the income growth using local wage data. Other household characteristics remain static in our analysis. Similar to before we control for consumer characteristics and exclusively compare properties only in the same time period and location. Finally, our measure of net charge-os matches the discussion from earlier. 2 We note that this only one channel in which credit supply can impact the foreclosure rate. For instance it is possible that local lenders have an informational advantage in real estate lending markets and therefore it is costly to switch to lenders without a local branch. For instance Loutskina and Strahan [2011] nd that local lenders focus more in soft information market segments. Recently, Gilje et al. [2013] nd use an exogenous deposit windfall to determine that branch networks in banking continue to play a role in the mortgage market despite the raise of securitization. Alternatively, there is the notion of granular movements as in Gabaix [2011] and Amiti and Weinstein [2013] where a few banks may be so large as to eect the equilibrium supply of credit. 10

11 3 Data Our paper relies on individual consumer data involving loan applications and real estate transactions, individual bank data on both nancial variables on branch location, and regional data of both real estate prices and local economic factors. Once we introduce each specic data source, we then check the validity of our identication strategy. First, we need to determine that bank exposure to real estate shocks is a valid instrument for residential foreclosures.second, we oer simple evidence that observable borrower characteristics are not correlated with our measure of bank health at both the national level and local level. Third, we estimate the importance of relationship lending in the renancing market. 3.1 Data Sources Consumer Data Loan Level Level Data Home Mortgage Disclosure Act Dataset Our individual loan information comes from the Home Mortgage Disclosure Act Data (HMDA) for the years HMDA's loan application register includes loan characteristics including the action taken on the loan, type of loan (house purchase, home equity, or renancing), senior or junior line of credit, loan amount, and in some cases the dierence between the loan's annual percentage rate (APR) and the average prime oer rate (APOR). In addition there are consumer characteristics including the applicant and co applicant sex, race, and income. so that we are able to test for declines in the applicant population for each bank. Each loan includes the location of the property down to Census Tract Level and so can compare the application decisions between two consumers with the same location, allowing us to control for demand eects. Valuable for our purposes every loan include the name and location of bank lender and what type of entity purchased the loan. The bank lender is denoted as a Respondent ID and Agency Code pair. Freddie-Mac Single Family Loan Dataset For robustness checks we also rely on data from the Freddie Mac Single Family Loan Level Dataset. The dataset focus exclusively on fully amortizing 30-year xed mortgages acquired by Freddie Mac anytime between and requires that each mortgage has veried or waived documentation. The Single Family Loan-Level Dataset includes data 11

12 on both loan-level origination and monthly loan performance such as monthly loan balance, delinquency status, and termination events. There are three particular advantages of this data for our purposes. Transaction Level Data DataQuick Dataset There are two disadvantages of the HMDA dataset: we are not able to match loans to the consumer and we are not able to follow the consumer over time. To overcome both issues we match the HMDA dataset with real estate transaction data from DataQuick for the Los Angeles area using the following matching variables: loan amount, lender name, date of transaction, and property location. With this match we can then construct the ownership history of properties purchased between 1991 and 2009 following the rules of Kung [2012]. We determine that a property is sold if a new transaction occurs where the buyer is an individual. Alternatively, determine that is a property was foreclosed if that next buyer is a bank, trust, or mortgage servicer Bank Data Bank Financials and Branch Locations FDIC Call Reports and Branch Deposits The FDIC Call Reports includes the full nancial information of every insured institution and matches the institution to the bank holding company. The bank holding company is any company that either: (i) directly or indirectly owns, controls or has power to vote 25 percent or more of a bank's or direct holding company's shares, (ii) controls in any manner the election of a majority of the directors or trustees of a bank or direct holding company or (iii) exercises a controlling inuence over the management or policies of a bank or direct holding company. The nancial information includes full balance sheet data: for our purposes we are interested in total assets, total deposits, total equity capital, residential real estate loans broken down by senior lien, home equity line of credit, and junior lien, and net-charge os from residential real estate. For our purposes we need to match the FDIC Bank Holding Company Number to the Respondent ID and Agency Code in the HMDA dataset. To overcome name dierences between the two datasets, we do not match by nancial institution name, but rather conduct a fuzzy match by zip code and address. For robustness we also conduct a similar match using the FDIC Certication Number. When a single Respondent-Agency Code pair matches to multiple FDIC Certicate Codes, we take the institution 12

13 with the largest asset total. For instance, the Wells Fargo Branch Holding Company has ve separate matching Certicate Codes; however the institution Wells Fargo Bank, National Association ID# 3511 makes up 97% of all assets and 97% of all deposits. After completion of the fuzzy match we match each Certicate Code to the location of every branch as of Our calculation of housing exposure is based on 2003 branch locations Regional Data House Prices Oce of Federal Housing Enterprise Oversight House Price Index The OFHEO House Price Index is available at the state level starting in 1975; starting in 1987 indices are also available for the majority of Metropolitan Statistical Areas (MSAs) as well as the state indices excluding MSAs. To be as detailed as possible we break our data into two sets: we use the MSA data for all counties within the MSA and use the state excluding MSA index for all remaining counties. Regional Data Bureau of Economic Analysis In our county-level analysis we include a number of control for changes in local income, population, employment, and construction. The data is available on an annual basis between for 1969 and 2012 for the majority of US counties. The data is classied by SIC Codes from and NAICS codes after Data Summary Consumer Financials Our loan application data includes loan applications from 2006 and 2009 classied as crisis years and includes a total of 3,939,910 or nearly four million records. We rst summarize the loan applications ub Table?? by year (2006 and 2009) and by type of loan (mortgage for home purchase, second lien for home improvement, and renancing mortgage). In % of loans were primary mortgage loans compared to 32% in 2009; in comparison secondary loans make up 9% of all loans in 2006 and 5% in 2009 while renancing is 45% in 2006 and a full 63% in Next, we nd the approval rates 13

14 do not dier greatly between 2006 and 2009; home purchase loans declined from 57% to 56%, home improvement loans dropped from 60% to 52%, and renancing stayed constant at 55%. The rate spread of the loan, dened as the dierence between the loan's annual percentage rate (APR) and the average prime oer rate (APOR) appears to decline between 2006 and 2009 for all loan types. For instance we nd a mean rate spread of 5.55% for home purchase mortgages in 2006 and only 3.06% in In addition, the mean loan amount declined approximately $9,000 for primary mortgages, $27,000 for home improvement loans (a full 32% drop), and $6,000 for renancing. Our estimation analysis requires that the applicant base at each bank branch is not time-varying over the sample. This is because we can include for bank xed eects and census tract -year eects, but cannot follow loan applications for a single individual. If this does not hold then it is possible that our estimation results are driven by dierences between loan applicants choosing into a particular bank. In Table??we test if applicant types appear to switch from unhealthy to healthy banks. Most noticeably, high income households are more likely to apply to unhealthy banks in 2009 as the average income is $98,550 for a household at an unhealthy bank and a full ten thousand less for a household in a healthy bank. Upon closer inspection we nd that this appears to be largely due to geographic dierences: real estate drops were largest in large metropolitan statistical areas with higher average incomes. We conrm this by considering that income growth between was quite similar for healthy and unhealthy banks ($7,600 vs. 6,060). For all other variables we nd only minor dierences in the applicant pool. For instance unhealthy banks were more likely to apply to unhealthy banks in 2009; however, this appears to be due to a corresponding increase in the number of married couples that applied for loans when compared to single applicants. So how do consumers choose the bank? Our foreclosure analysis relies on the notion that it is costly for households to switch banks; therefore we next oer evidence that homeowners do in fact return to their original bank. Using our transaction data we match homeowners to initial purchase mortgages and to later renancing loans. We nd that homeowners return to the original lender for a mortgage in 5.6% of cases in 2006 and 7.4% of cases in For the second renancing the results are actually stronger with 10% in 2006 and 12% in Finally, the relationship between the rst and second renance is 18% in 2006 and 16% in

15 We next match out loan applications to transaction data in order to follow a household from the time of the initial purchase mortgage to the property is sold, enters foreclosure, or at the end of the sample period (end of 2009). Here we include all years from due to the signicantly smaller dataset and the fact that we have borrower xed eects. We estimate that likelihood of foreclosure was only 0.1% in 2006, but 3.8% in Bank Financials We rst document the signicant growth in net charge-os between 2006 and 2009 in Figure??. Our data includes 2810 unique nancial institutions and we rst by net charge-os from residential real estate properties as a percent of total bank assets. In 2006 net charge-os composed only 0.026% of total assets with all losses stemming from senior liens. This number then escalates grows by 1000% to 0.28% in 2009 and continues to increase through In addition about one-third of all losses come from junior liens and home equity lines of credit. Our exogenous measure of net charge-os is calculated from the real estate prices in each branch location and as a result we require signicant cross-sectional price heterogeneity between US areas. For instance between 2002 and 2006 MSAs in California, Arizona, Nevada, and Florida experienced annual returns of over twenty percent, which was then quickly followed by a similar magnitude drop between In comparison, other areas, even large metropolitan areas, experienced little growth and similarly only minor subsequent price loss. We calculate that a mean Real Estate Exposure of 0.27 at the end of 2005, which then declines to 0.12 by end of When we break our exposure measure by loan type we nd that senior liens, junior liens, and home equity lines of credit dropped by at least fty percent. We also conrm that our real estate exposure is correlated with net charge-os in Table??. Specifically, we nd that during the nancial crisis a 10% decline in our real estate exposure measure results in a 1.2% increase in net-charge-os. The results remain valid after we control for bank dierences. In addition to balance sheet shocks, our analysis also depends on two assumptions: (i) banks hold at least a portion of all loan approvals on their balance sheet, and (ii) during the nancial crisis institutions experienced a simultaneous decline in demand for mortgage-backed securities. In 2006 we nd that 43%, 88%, and 56% of all loans in our sample were retained by the initial lender (broken 15

16 down by senior lien, junior lien or home improvement, and renanced loan); compare this with 34%, 89%, and 43% respectively in Therefore (i) junior liens are particularly likely to be kept on the bank balance sheet, and (ii) the rate of senior liens and renanced mortgages held by the rm declined by about ten percentage points each. Secondly,we also nd conclusive evidence of the drop in securitization between 2006 and In 2006 we estimate that 7% of home purchases were bundled into securities, along with 3% of junior liens, and 7% or renanced mortgages. In 2009 securitization composes less than 0.5% of all loan approvals. 4 Results We next evaluate the eect of bank health on (i) individual loan application approval, (ii) individual property foreclosures, and (iii) county-level delinquency rates. 4.1 Financial Results Baseline We nd that bank health is a strong factor in mortgage lending in Table??. According to the results a ten percent decline in the bank balance sheet results in signicant declines for purchase mortgages, home improvement loans, and renancing mortgages at a rate of 2.3%, 2.0%, and 2.4%, respectively. Recall that our estimation includes the interaction of census tract and year xed eects to control for unobservable local demand eects. Note that balance sheet shocks during boom years are also positively correlated with approval rates of loans. Specically, a ten percent increase in the balance sheet during the housing boom results in rms increasing lending by 1.7% to primary mortgages and 0.9% to renanced mortgages with no signicant eect on home improvement. The results dier to previous work by Chakraborty et al. [2013] that nds that nancial institutions in strong housing markets increase mortgage lending and decrease commercial lending. Instead we nd that banks increase mortgage lending in all markets, not just high growth areas. Therefore the combined eect on crisis lending is estimated to be 4.0% for home purchases, 2.0% for home improvements, and 3.3% for renancing subject to a 10% increase in bank health. In other 16

17 words, a consumer that completes an application at one of the top 25% healthy banks-compared to a bank in the bottom 25%- is automatically 15% more likely to receive the mortgage. We control for bank xed eects as each lender may dier in the applicant base in ways not characterized by the data. The results remain similar: a 10% decline in bank health during the nancial crisis results in a strong negative decline in approval rates for all mortgage lending. Controlling for consumer characteristics we nd that income is a positively associated with bank lending. We focus on the period from 2006 to 2009, but note that our results would be larger if we extend the time sample since real estate prices continue to decline through 2011 for most areas of the country. We nd that bank health (as measured by real estate exposure) declined by 17% for the median rm with a value of 28.5% at the 25th percentile (which we dene as an unhealthy bank) and 7.9% at the 75th percentile (a healthy bank).using our baseline results without bank xed eects we nd that a 2009 mortgage application is 9.7% more likely to be approved at a healthy bank than an unhealthy bank. Similarly, a secondary loan for home improvement and a renancing loan show similar eects at 5.4% and 6.8%, respectively. The results drop when we include bank xed eects: primary mortgage approvals decline by 5.8%, secondary mortgages decline by 3.3%, and renance mortgage decline by 1.8%. Now, since we are dealing with house price data, we also distinguish between two potential eects: a nancial eect and a local demand eect.for our analysis we hope to isolate a nancial shock, namely that a sudden decline in the value of the underlying real estate increases the likelihood of foreclosures. However, it is also possible that application denials are correlated with our variable HousingExposure since local price indices are correlated with local demand shocks. We should note that out analysis does not necessary require this level of distinction. For instance, local demand through the bank channel is still a valid source of of variation in the analysis as long as loan applicants do not sort on this knowledge. However, our strategy allows us to isolate a unique shock from the bank balance sheet separate from all other sources and we focus on this source through the remainder of the paper. Approval c = β HousingExposure b t 1 Crisis t + ρ HousingExposure b t 1 +γ P rice b t 1 Crisis t + χ P rice b t 1 + πx c + ψ b + δ t φ l + ε c (3) 17

18 ( ) P rice b t = l= [ Deposit bl 0 1]LΣ Deposit b P rice l t (4) 0 To control for local demand shocks we also include P rice in the estimation 3. Therefore our estimation compares two nancial institutions within the same local area where only one rm has real estate loans on the balance sheet. To be consistent we also interact P rice with the crisis indicator Crisis in the understanding that local demand shocks may by asymmetric during recessions and booms. Using the specication in 3 do not change signicantly. Therefore the housing variable is driving approval rates through the balance sheet channel and not through the demand channel Robustness We next include several robustness checks to conrm that our results are not driven by alternative explanations in Table??.First, we exclude our data to include only rms that retain at least half of all securitized loans. The reason is that our measure of real estate exposure assumes that branch deposits are proportional to the real estate exposure from each branch. Therefore if the majority of loans are sold to other institutions then we will incorrectly measure the exposure to real estate market. The results hold when we focus exclusively on rms that retain the majority of loans; a ten percent increase in real estate exposure results in a 5% increase in application approval for primary mortgage loans. Next, we test how the number of branch locations eects results. We need to conrm that balance sheet shocks are from outside locations and so local demand shocks are not driving the results. This is possible if the bank has only a few branches near the applicant. Therefore we rerun our estimates excluding all banks in fewer than ten counties. As an alternative test we also make sure that bank losses are not from nearby locations to again exclude local demand. Therefore we include applicant from healthy counties dened as counties with mortgage delinquency rates in all years The estimates are again similar to the baseline. We recognize that the branch locations are an endogenous choice of the rm. This may bias our results if banks open new branches due to the local housing market. To control for this we include bank xed eects in the analysis. Secondly, we hold the location of branches constant throughout the sample; we also use branch locations as of 1994, the rst year available and our results still hold. 3 Our method to distinguish between local demand and nancial eects is similar to Cvijanovic [2014], Cuñat et al. [2013], Chaney et al. [2012]. 18

19 4.1.3 Securitization The eects above suggest that our results are robust to alternative explanations. Therefore we are now interested to determine that we are indeed isolating the eects of a balance sheet shock on nancial institutions. Specically, we need to identify which consumer loans are most at risk of our housing exposure shock.we achieve this by considering the role of the secondary mortgage market in our results. Consider two loans: one can be easily sold on the secondary market while the second cannot and so must be kept on the bank balance sheet. In this scenario unhealthy banks may be particularly hesitant to approve a loan that cannot be sold. The concern, however, is that the decision to securitize is generally an endogenous choice of the lender. To overcome this issue we focus explicitly on loans right below and above the conforming loan limit (CLL) as designated by the Government Sponsored Agencies (GSEs), Freddie Mac and Fannie Mae. Loans with amounts below the CLL can be purchased by the GSEs assuming other nancial characteristics-such as the loan-to-value ratio- are met. However, loans above the CLL are considered too large and will not be purchased; instead the bank must retain the loan, sell to a non-government institution, or securitize the loan. Therefore loans above the CLL generally command a higher interest rate spread. Given the sudden decline in the purchase of securitized loans as shown in Table?? and the increase in the loans purchased by the GSEs during the nancial crisis this dierence is likely particularly important during the time sample. Using data on the local Conforming Loan Limit in each county (higher house price counties have a higher CLL during the crisis), we separate loans into two groups: loans with $10,000 below the CLL and loans $10,000 above the CLL. We nd that loans above the CLL are indeed particularly susceptible to bank health. A 10% increase in the balance sheet shock increases the primary mortgage rate 4% for loans below the limit. Loans above the limit see a 9% increase in approval rates. The results continue to hold for renance loans Consumer Characteristics Finally, we test the impact of consumer characteristics on our analysis by separating loan applicants into quartiles based on income. The results have implications for which consumers are most at risk of 19

20 credit supply eects and can impact optimal polices to overcome these frictions. Interestingly, we nd strong evidence that the eects of credit supply increase with applicant income. A 10% real estate increases the approval rate by 1.7% for the lowest income applicants, but by nearly 4% for the highest earners. We nd the strongest eect for home equity loans. In the bottom quartile a 10% real estate shock increases the acceptance rate by 1.8%, while the eect is 6% for the wealthiest applicants. 4.2 Foreclosure Results Baseline In our baseline results we attempt to link the likelihood of foreclosure to the balance sheet of the original mortgage lender, which is reasonable if it is costly to work with a new lender for a renance or a loan modication. Recall from our earlier results that homeowners are likely to return to the original lender for a renanced mortgage, especially during the nancial crisis. We note that our data does not include data on loan modications. We believe our results remain valid for two reasons. First, foreclosure is in many ways a more complete picture or credit supply outcomes. Once controlling for household dierences and local factors, we can evaluate the combined bank health eect through all possible channels- modication/renancing approval, interest rate change, length of new loan, etc. Secondly, while we do not have direct data on loan modications, our results thus far have conrmed that bank health does impact primary mortgage loans, secondary loans, and renancing loans. Therefore it appears likely that alternative loan decisions, including modications, will also be aected. We present the results in Table??. In the rst row we exclude homeowner xed eects and nd that housing exposure is negatively correlated with expected foreclosure. This is particularly true during the crisis year when the result escalates by a factor of four. We next include household xed eects to control for household xed eects. We now nd no signicant eect of housing exposure on households during the boom, but a strong result during the nancial crisis. A ten percent decline in the housing exposure variable leads to a 0.5% increase in foreclosures. From our results we estimate the decline in median bank health between can explain 20

21 13.5% of the total decline in foreclosures. We calculate this from RealtyTrac data: US foreclosures totaled 2.2 million in 2006, 3.0 million in 2007, and 3.5 million in 2009 for a combined total of 8.7 million foreclosures or 6.35% total foreclosures between the end of 2006 and the end of Robustness There are a number of explanations that might be driving the foreclosure results. Similar to our application results we rst test that mismeasurement is not driving the results by restricting the results to institutions that retain the majority of approved home loans. We also exclude banks with branches in fewer than ten counties. The results hold in both cases as shown in Table??. In addition, we also attempt to isolate the bank health channel from two alternative explanations: the servicer eect and the information eect. One potential concern that may be driving the results is the role of servicers. The servicer receives the monthly mortgage payments from the borrower and conducts mortgage workouts, modications, and the foreclosure process, and might be distinct from the lender. The concern here is that if a servicer is distinct from the lender, the servicer does not internalize the costs of defaults and foreclosure. However, they do internalize the time costs of loan renances and so are less inclined to oer support. Previous research has had mixed support of the servicer eect. For instance Piskorski et al. [2010], Agarwal et al. [2011] oering evidence that securitized loans are more likely to fall into foreclosure. However, Adelino et al. [2013a] suggest that securitization is an endogenous choice driven by softinformation not available to buyers on the secondary market. Using a new instrument to control for the selection they nd no eect of securitization. To control for this possibility we rst focus our results exclusively on non-securitized mortgages since all securitized mortgages have separate servicers. We nd no signicant dierence in the estimation. Additionally, non-securitized loans may still be serviced by an outside party. Unfortunately, in our dataset we cannot directly tell whether the loan was serviced by the original lender. Instead we use data from the Freddie-Mac Single Family Loan Dataset to determine which lenders also service their loans. There is wide variation in the data as about 40% of rms service all loans, 35% service none, and about a quarter service only a portion. Again our results are not impacted. Next we move test the role of the information eect in our estimation. As discussed in Adelino et al. [2013b], Wang et al. [2002] lenders face both benets and costs when considering to renance a 21