Excessive Credit Supply and the Housing Boom in the US

Transcription

1 Excessive Credit Supply and the Housing Boom in the US VAHID SAADI Abstract Ever since the housing boom of the early 2000s, economists have been discussing the underlying reasons for the formation of this boom. This paper is intended to answer the question of whether excessive supply of credit can explain increased rate of price growth in the housing market or not. Using the Community Reinvestment Act as a natural experiment, my results show that indeed this act generated excessive credit supply to CRA tracts. I then use this outward shift in credit supply to estimate the semi-elasticity of housing price growth rate to mortgage supply. My results show that mortgage supply explains more than half of the jump in house price growth rates during the pre-crisis housing boom. JEL classification: G28, G21, R21, R31. Keywords: Community Reinvestment Act, Mortgage supply, House prices, Financial crisis. Goethe University Frankfurt, SAFE and IWH

2 Between 2001 and 2006 real house prices in the U.S. rose by more than 50 percent in real terms and then fell by about one third until These house price developments helped fuel enormous financial instability, large scale output losses in many countries around the world and the collapse or near collapse of numerous financial institutions. Most academic research has focused on the role of credit market conditions in this boom/bust cycle: Nominal interest rates that were too low for too long (Maddaloni and Peydró (2011)) together with or caused by a global savings glut resulted in easy credit conditions that may have boosted credit supply and housing demand (Bernanke (2005) and Caballero, Farhi, and Gourinchas (2008) ). In this paper I examine the role of U.S. government policy encouraging home ownership as a driver of an increase in mortgage credit supply and the house price boom from 1998 to I focus on amendments to the Community Reinvestment Act (CRA) enacted in The CRA was passed in order to help minorities and households in low and medium income neighborhoods to obtain home ownership by reducing discriminatory credit practices against low income neighborhoods (redlining). Although enacted in 1977, the CRA only started to bite in 1997/1998, when in order to boost the compliance rate of financial institutions, three formal criteria were introduced based on which the banks would be judged on their CRA performance. Banks in violation of these criteria would no longer be allowed to open new branches and merge with other institutions. 1 The identification strategy of the paper rests on two important institutional features of the CRA. One, the CRA classifies census tracts within a MSA as eligible whose median family income was less than 80% of the respective MSA s median family income. Hence, whether or not in a given census tract CRA rules apply depends on the income level of the MSA, not just on the income level in the census tract. Census tracts with similar median household income levels may be classified as CRA eligible or CRA ineligible, depending on the median household income of the MSA they are located in. Second, not all financial institutions are subject to CRA regulation. Hence, I have two exogenous variations for identification: I can compare the credit supply of financial institutions subject to CRA regulation to the credit supply of financial institutions not subject to CRA in CRA eligible and CRA non-eligible census tracts. CRA eligibility is defined at the census tract level. Although I observe mortgage supply at the census tract level, I only observe house price growth at the zip code level. Hence as a next step I used the approach proposed in Mayer and Pence (2008) to aggregate census tracts to zip codes. This procedure is explained in more detail below. Using census tract level data, I show that the effect of the CRA on credit supply is economically and statistically significant. Using matching to find the closest CRA-non-eligible census tracts for each CRA-eligible tract from other MSAs, I show that the amount of mortgages generated by CRA-regulated institutes increased significantly more for CRA-eligible cen- 1 Further institutional details of CRA are discussed in section I. 2

3 sus tracts after I do not find this effect on the amount of loans generated by the non-cra-eligible institutions. As depicted in Figure 1, the total amount of mortgages to the CRA-eligible areas originated by CRA-regulated issuers starts to increase around 1998 relative to the growth rate of total amount of mortgages to other areas serviced by the same institutions. At the same time, this patterns is exclusively observable for mortgages generated by those institutions subject to CRA regulation. As we see again in 1, there is no significant difference between the growth rate of mortgages in CRA-eligible areas versus those in non-cra eligible areas, if we consider only mortgages issued by non-cra-regulated institutions. The tightening of rules of the CRA in the mid-90s appears to have played an important role in the dramatic increase in mortgage supply in subsequent years. [Place Figure 1 about here] Based on this evidence, I will examine whether the increased supply of mortgages can be linked to the increase in housing prices. Adelino, Schoar, and Severino (2012) argue that better access to mortgage credit may generate higher demand for housing; this higher demand for housing increases the number of households that are able to bid on houses. If housing supply is not perfectly elastic, perhaps due to local geography as in Saiz (2010), house prices would be expected to rise. In addition, non-credit-constrained buyers may bargain less hard for reductions in prices relative to credit-constrained buyers, again resulting in house price increases. Hence, I use the exogenous variation in exposure to the CRA regulation as an instrument to estimate the elasticity of house price growth to credit supply. I show that a one percent increase in mortgage supply generates an additional 60 basis points in the annual house price growth rate. This effect can explain more than half of the house price growth rates from 1998 to The evidence presented in this paper therefore supports Maggio and Kermani (2014) and Adelino et al. (2012) who argue in favor of a causal effect of excessive and/or cheap credit on house price growth. My contribution is twofold: One, I show that the CRA had a large effect on credit supply and ultimately on house price growth. To a significant extent, government policy intended to reduce discrimination in the housing market contributed to the boom in mortgages and the boom in house prices. Second, I document the link between mortgage supply and house price growth in a novel setting which permits a clean identification of causal effects running from credit supply to house prices. The setting permits a clean solution to the omitted variable and reverse causality problems that plague most attempts at estimating an elasticity of housing prices with respect to mortgage supply. My paper also contributes to the debate on behavioral versus fundamental explanations of the housing boom. Economists who argue in favor of fundamentals-based explanations put forward the global savings glut and the subsequent low interest rates as 3

4 the prime reason for increased supply of credit to the housing markets, which eventually led to high prices. For example, Himmelberg, Mayer, and Sinai (2005) argue that the very low long-term interest rates coupled with increased income growth at a time when house prices were historically low, led to high observed price growth rates in the housing market in early 2000s. Mayer and Sinai (2009) find that one of the most important factors in explaining the boom in house prices in early 2000s is the user cost of capital. They further argue that lending market efficiency directly affected housing prices through lower origination costs for higher property prices and also greater use of subprime mortgage. Khandani, Lo, and Merton (2009) argue that the synchronization of multiple effects, especially low downpayments and high mortgage approval rates generated large swings in the housing market, which triggered faster price growth in the already inflating housing market. Similarly, Favilukis, Ludvigson, and Nieuwerburgh (2013) point out the relaxation of credit constraints and also low transaction costs as drivers of increasing house prices, although they refuse the role of inflow of foreign credit into the U.S. bond market as influential. All these reasons are themselves mostly due to the agency problems associated with the rise in securitization markets, as shown in Keys, Mukherjee, Seru, and Vig (2009), Keys, Mukherjee, Seru, and Vig (2010) and Mian and Sufi (2009), among others. On the other side there are economists who give more weight to behavioral factors in explaining the rise in prices. Most notably, Shiller (2006) and Shiller (2005) believing in mass psychology, argue that the boom in the housing market was more related to behavioral biases than to fundamentals. Moreover, Glaeser, Gottlieb, and Gyourko (2010) show that low real interest rates cannot account for more than one-fifth of the boom in house prices. In a recent paper, Maggio and Kermani (2014) make use of different anti-predatory regulations for different states as a laboratory setting, and estimate the elasticity of house prices to credit supply. They find that a 10% increase in loan generation generates 3.3% increase in house price growth rates during This effect explains only one-third of the growth rate of house prices 2. Economists are still trying to understand the causes of the great fluctuation in the housing market prior to the financial crisis of Clearly the controversy between fundamental-based explanations and behavioral-based explanations of the rise in house prices are far from settled. Adelino et al. (2012) is another example of such attempts in pinning down the elasticity of house prices to credit supply, in which the authors conclude that although house prices have a significantly positive elasticity to credit supply, it is lower than what previous studies suggested. My paper will propose an accurate estimate 2 In my sample, house prices grow at a rate of about 6.4% for the period between 1997 and 2003, and 7.8% between 2004 and 2006, which implies an increase of about 22% in average yearly house price growth rate. In the same period, total mortgage grew at a rate of about 24% per year. Therefore the finding in Maggio and Kermani (2014) translates to an average growth of 7.6% increase in yearly house price growth rate. This simple calculation suggests that mortgage generation can explain almost only one-third of the increase in house price growth rate. 4

5 of the elasticity of house price growth rates to mortgage supply, which will eventually enable us to forms ideas about the size of mortgage supply effect in creating the boom. I. Institutional setting The Community Reinvestment Act of 1977 (12 U.S.C. 2901), implemented by Regulation BB (12 CFR 228), was primarily enacted by the Congress with the purpose of enforcing depository institutions to satisfy the credit needs of their local community in which they were chartered and were acquiring deposits. CRA was a reaction to concerns regarding the geographical mismatch between banks deposit-taking and lending activity. This concern was particularly more severe in disadvantaged areas, where consumers would deposit their savings in the local banks, but due to redlining 3 practices would not benefit from their local bank s credits. Therefore, CRA explicitly encourages banks to provide loans to low- and medium-income neighborhoods, while making sure of their safety and soundness. Banking institutions whose deposits are insured by the Federal Deposit Insurance Corporation (FDIC) need to comply with the CRA. These are national banks, savings associations, and state-chartered commercial and savings banks. Federal financial institution regulators are responsible for the assessment of each bank s CRA performance. The Federal financial institution regulators are: The Office of the Comptroller of the Currency 4 (OCC); the Board of Governors of the Federal Reserve System 5 (FRB) and the Federal Deposit Insurance Corporation 6 (FDIC). Furthermore, CRA does not apply to credit unions insured by the National Credit Union Share Insurance Fund or non-bank entities supervised by the Consumer Financial Protection Bureau. 7 In the early years of the CRA, compliance was measured through each bank s selfreported CRA Statemant. The CRA statements had to be publicly available and included, most importantly, a list of specific types of credit that the bank had committed to extend to its community. It was only in 1989 and 1990 when the supervisory agencies started examining the CRA statements, and conducted a four-tier grading system (i.e., outstanding, satisfactory, needs to improve, or substantial noncompliance). The grading was based on five areas of activity: (i) Determining community credit needs; (ii) marketing of the credit offered; (iii) geographic distribution and record of office locations; (iv) discrimination; (v) community development.overby (1995) 3 Redlining can be defined as the refusal of a bank to extend credit to a customer solely due to the customer s place of residence, no matter whether she is creditworthy or not fact-sheet-cra-reinvestment-act.pdf 5

6 There were two crucial issues with regards to compliance to and enforcement of the CRA. First, the grading system generated too many satisfactory cases. In essence, the regulatory supervision was not objective enough to be able to differentiate compliant banks from noncompliant banks. Second, although CRA performance had to be taken into account when a bank applied for expansion 8, banks were able to acquire the supervisors consent in almost all cases. The problems mentioned above resulted in a comprehensive revision in the CRA which was eventually approved in late April of However, the new regime were to be effective as of July 1997 for small banks (less than $250 million) and in July 1998 for larger banks (Agarwal, Benmelech, Bergman, and Seru (2012)). The most important modification under the new guidelines is the abandonment of the prior subjective, effortsbased criteria for assessing whether an institution is meeting community credit needs and the substitution of a more quantitative evaluation procedure designed to measure actual results in meeting the credit needs of the institution s assessment area (Overby (1995)). The new guideline defined three tests for each a bank would receive a clear numerical rating and ultimately its overall CRA rating. The three tests were lending, investment and service test. The lending test measures an institution s home mortgage lending, small business and small farm loans, community development lending and finally, consumer loans (only if the main business of the bank is consumer loans). The investment test similarly measures each bank s realized community development investments. Finally, the service test is focused on banks provision of retail-banking services and the extent and innovativeness of its community development services. Each test is then given a score based on a grading scale as in Table I and the final rating is calculated based on bank s performance in each test. The lending test is the most important part of the overall CRA rating, for at least three reasons. First, as we see in Table I, the lending test has the biggest weight among other tests 9. Second, banks in fact are not eligible to receive an outstanding grade on any of the other two tests unless they score outstanding on their lending test. Third, institutions must also earn at least a low satisfactory on lending to receive a satisfactory score overall. Another significant modification in 1995 amendments to the CRA is the replacement of assessment area with previously used concept of community. CRA assessment areas are the areas in which an institution operates its branches and deposit-taking ATMs and any surrounding areas in which it originates or purchases a substantial portion of its loans. 8 Such applications are (1) applications for a national bank or federal savings and loan charter; (2) applications for deposit insurance for a newly chartered state bank, savings and loan, or similar institution; (3) applications to establish a domestic branch; (4) applications to relocate a home office or a branch office; (5) applications for mergers, consolidations, asset acquisitions, or liability assumptions that otherwise require regulatory approval; and (6) applications to acquire shares in, or assets of, a regulated institution that otherwise require regulatory approval.overby (1995) 9 This grading scale only applies to large banks, i.e., banks bigger than $250 million in assets. For small banks the rules are more lenient. 6

7 The CRA tests emphasize specifically bank s CRA activities within the low- and mediumincome neighborhoods within a bank s assessment area. Low- and medium-income neighborhoods are census tracts with median income less than %80 of their respective MSAs median income. II. Empirical Strategy I present my empirical strategy in two separate sections. First, I explain the differencein-differences and matching methods that I employ in a census-tract-level analysis, to estimate the effect of the CRA regulations on the supply of mortgages. Second, I discuss the instrumental variable approach to estimate the elasticity of house price growth to mortgage supply. A. Mortgage Supply This part of the analysis is at the census tract level. Identification strategy in this section relies on arbitrariness and relativity of the 80% rule in deciding whether a census tract is or is not eligible for CRA loans. In particular, under the CRA, two identical census tracts would be classified differently if they were placed in MSAs with marginally different median family incomes. I take advantage of this arbitrary setting to isolate the effect of the CRA from other confounding factors. I employ two empirical strategies. First, I run difference-in-differences of the following form: log(mortgage it ) = β 1 After t + β 2 CRA i + β 3 (After CRA) it + ΓX it + ε it (1) separately for both mortgages generated by CRA-examined institutes and mortgages generated by other institutes. After is a dummy equal to one for the years 1998 and later and zero otherwise. CRA is also a dummy variable which is equal to one if the census tract is CRA-eligible, i.e., its median family income is less than 80% of its respective MSA s median family income. I expect to find positive and significant β 3 in the regressions where I use mortgages generated by CRA-regulated institutes, and insignificant β 3 when I consider mortgages by CRA-non-regulated institutes as the dependent variable. X it includes a vector of census tract level control variables. Most importantly, I need to control for census tracts median family income level. Note that in essence I would like to compare two census tracts with the same median family income but in different metropolitan statistical areas. Therefore, here I do not include MSA fixed effects. Second, and more importantly, I follow a matching strategy. To keep things simple, I compare pairs of census tracts with the same median family income but placed in two 7

8 different MSAs, such that one census tract is CRA-eligible and the other one is not. This means that the CRA-eligible census tract is placed in a MSA with slightly higher median family income. By matching on median family income, I compare the growth in mortgages to a CRA-census tract and compare it with the growth of mortgages to the comparable CRA-non-eligible census tract. Again, I consider both mortgages by CRAexamined institutes and other institutes, separately. This approach lets me compare two otherwise identical census tracts, where one is happened to be classified as a CRAeligible census tract only because of the way the regulators define so. This exogenous classification helps me identify the mortgage supply growth to CRA-eligible census tracts. Since I keep only census tracts just below and just above the threshold, I make sure that the differences between MSA median family incomes for the pairs of census tracts with the same median family income are not big. Therefore, in short I compare census tracts with the same median family income which are placed in MSAs with close median family incomes. I will explain the details of the sample construction in section III.F. B. Elasticity of House Price Growth to Mortgage Supply I make use of CRA as an instrument for mortgage supply in the period of study and estimate the elasticity of house price growth to mortgage supply. For the CRA dummy to be a valid instrument I need to make sure that it satisfies the two conditions of an acceptable instrument, i.e., to be correlated with the mortgage supply 10 and to be unrelated to house prices through any other channel except mortgage supply, after controlling for the observables, i.e. exclusion restriction condition. The first condition is testable. In fact, the first part of the paper is mostly intended to show that community reinvestment act led to higher mortgage supply to CRA-regulated census tracts. It is the second issue which needs more scrutiny. The main point is that house prices and mortgages both depend on unobserved zip code characteristics. The observables which I can control for, however, are income distribution within each zip code, initial house prices (as a measure of collateral value), minority share of a zip code (as it is well-known that homeownership rates of minorities were well below that of others prior to the housing boom), population and population density. Therefore, by unobservable characteristics I mean qualities like expected house prices, which drives demand for housing in each zip code. Therefore, my instrumental variable has to be independent to demand for housing. I believe that this condition holds. CRA status of a census tract is assigned based on an arbitrary rule. Census tracts can be identical, yet be classified differently under CRA regulations based on the median family income of their respective MSA. I exploit this random variation in my paper by defining a zip code as CRA-eligible if it encompasses, partly or wholly, at least one CRA-eligible census tract. Therefore, I 10 The results of the mortgage growth as designed in section II.A serve this purpose too. 8

9 will estimate the following system of equations using a 2SLS approach. The estimated logarithm of mortgages in the second stage (euqation (3)) comes from the first stage regression (equation (2)). log(mortgage ijt ) = α 0 + α 1 CRA i + α X ijt + γ j + λ t + u ijt (2) House P rice Growth ijt = β 0 + β 1 log(mortgage ijt ) + β X ijt + θ j + ξ t + ɛ ijt (3) In the two equations above, index i refers to zip codes, j refers to the county in which the zip code is placed and t runs over years, from 1998 until Note that I control for county and time effects. Therefore, my analysis in essence compares zip codes with the same observable characteristics, especially income distribution, which are located in the same county during the same year. County fixed effects capture all geographical differences which Saiz (2010) shows that can generate differential house price processes due to different elasticity of housing supply. III. Data The data that I use in this paper come from multiple sources. This section gives a detailed explanation of each one, and clarifies the sample selection procedures. A. Home Mortgage Disclosure Act Data I use the home mortgage disclosure act (HMDA) data of the universe of mortgages generated from 1991 until HMDA is at the loan application level and includes information on the applicant, the issuing institution and the loan itself. For example, it records the applicants income, sex and race, the institutions type, and the loans purpose, amount, status and etc. I restrict my sample to the loans generated for the purpose of home purchase. Next, I distinguish between the issuing institutions by their relation to the Federal Financial Institutions Examination Council (FFIEC). In fact, only institutions which are regulated by agencies such as Office of the Comptroller of the Currency (OCC), Federal Reserve System (FRS), Federal Deposit Insurance Corporation (FDIC) and Office of Thrift Supervision (OTS) have to comply with the CRA regulations. Therefore, I define a dummy variable called Regulated which is equal to 1 if the mortgage is originated by one of the above-mentioned agencies and zero otherwise. Finally, I aggregate the loans for both types of regulated and unregulated agencies up to the census tract level in each year. my tract-level data includes mortgage information for all distinct census tracts during the period 1991 until Later, I will keep only the census tracts which 9

10 are just above or just below the 80% threshold by which CRA eligibility is decided (i.e., between 76% to 85% and similar boundaries) and compare only these census tracts with each other. Although this does not qualitatively change my results, I believe that this is a more conservative approach to take because it restricts the sample to census tracts which are much more similar on observables and therefore more comparable. B. Census 2000 According to the CRA, census tracts with a median family income of less than 80% of the median family income of their respective MSA are considered to be low and medium income tracts and are classified as CRA-eligible. I use median family income at the census tract and MSA level from the decennial data of census 2000 to find the CRA eligible tracts based on the above-mentioned criteria. FFIEC also uses the same data to identify CRA eligible tracts and bases its CRA lending tests on this classification 11. Finally, I also use minority share of population at the zip code level from census decennial data of C. Zip code-level House Price Data Since my main purpose is to study the effect of mortgage supply on house prices, I need to have a measure of house price at census tract level. Unfortunately, as far as I know, there is no such database which includes all the census tracts in the US. Therefore, I work with the house prices at the zip code level. This data are collected from Zillow.com 12. Zillow provides estimated monthly house price indexes for different kinds of homes and various geography levels in the US starting from In this paper I use single family residence price index 13 which is widely used in the academic real estate literature. The dataset includes information on distinct zip codes which are from 50 states and 1035 distinct counties in the US. This is about one-third of the total number of zip codes in the US. Using the monthly data I calculate the yearly growth rate of the single family residence house price index for each zip code. I also keep the initial level of index at the beginning of each year for each zip code. Especially, I define a new variable, initial price which is the index at the beginning of my period of study, i.e., D. Zip code-level Residential Loan Data I use the Call Report data and aggregate the amount of family residential loans generated by the financial institutions up to the zip code level. The dataset contains 11 I assume that CRA classifications do not change dramatically from year to year and it is rather stable over a period of five years. Moreover, the FFIEC also updates its list of CRA-eligible tracts every few years after the new census data is available Median estimated home value for all detached single family homes within a given region 10

11 63591 zip code-year observations from 1998 until 2006, which corresponds to more than 7000 zip codes each year 14. E. Within-zip code Income Distribution Data from IRS I collect income distribution data within each zip code from IRS income data for years 1998, 2001, 2004, 2005 and 2006 and calculate mean and standard deviation of income at the zip code level in each year. The IRS data is very comprehensive in covering almost all zip codes in the US. Each year I see around zip codes in the dataset. The only problem is that I do not have information for years 1997, 1999, 2000, 2002 and I try different approaches to interpolate the missing information for these years and I find that it does not affect my results significantly 15. Therefore, I set each missing year s information equal to the closest year s available data. For example, income data for years 1997 and 1999 will be set equal to the data in 1998 that I observe. It would also be possible to calculate income distributions within zip codes by using HMDA dataset. However, first there is a selection problem inherent in using this dataset due to the fact that I only observe income of mortgage applicants and not a random sample of the population, and second, based on Mian and Sufi (2015) there is evidence of income overstatement in low credit score and low income zip codes during the period. F. Final Census Tract-level Sample My final census-tract-level sample contains information on the amount of mortgages generated by both CRA-examined institutes and other institutes, census-tracts median family income and MSAs median family income. I keep only census tracts with a median family income ratio (as a fraction of MSA median family income) in the interval [0.76, 0.85]. This helps us to compare census tracts with similar median family income which are also placed in MSA s with close median family incomes. Therefore, I abstract from inherent differences between extremely high income and low income census tracts or similar census tracts in very different MSAs. Therefore, my final census tract-level sample to study the effect of CRA regulations on the supply of credit contains census tractyear observations for 5974 unique census tracts from 1991 until 2006, of which 2626 are CRA-eligible census tracts. The summary statistics of this sample are presented in Table II 14 Ideally I would like to aggregate the HMDA data up to the zip code level to have the yearly total amount of mortgages for each zip code. However, there is no zip code entry provided in the HMDA dataset. 15 The results are available upon request. 11

12 G. Final zip code-level Sample The zip code-level sample in which I have both house prices and residential loans spans the years from 1998 until I have house price data for 7179 zip codes and residential loan data for 2316 distinct zip codes in each year. my dataset corresponds to a yearly average of 130 million in population which is more than 46% of the whole US population in I consider any zip code in which at least some part of the population is from a CRA-eligible census tract to be CRA-eligible zip code. To find the link between census tracts and zip codes in year 2000 I link census tracts in years 2000 and 2010 and then use the census tract to zip code link as of 2010 to assign the 2000 census tracts to their respective zip code 16. However, due to the fact that some census tracts either split or merge between 2000 and 2010, I may face some errors in linking census tracts to zip codes. Nonetheless, the only major error is the cases where I wrongly assign a CRAeligible census tract to a zip code. This is the only influential error that may happen and the reason is the fact that I may assign a merged census tract to a zip code to which in fact it did not belong to in year 2000, but it appears in my link between census tracts in 2000, census tracts in 2010 and zip codes in If the merged census tract is a CRA one, then the zip code will be wrongly coded as CRA. However, this only generates an attenuation bias which pushes the results against my findings. Finally, after I merge zip code level house prices to mortgage data from Call Reports, income data from IRS and demographic data from census, I end up with zip code-year observations. The summary statistics of this sample is presented in Table VII. IV. Results A. Differential Mortgage Growth for CRA Census Tracts I use my census tract level dataset to estimate the regression model in equation (1) and also the matching estimations. The sample consists of census tract-year observation, which includes 5974 distinct census tracts, of which 2626 are just below the 80% threshold (therefore CRA-eligible) and 3348 are just above. Table II presents the summary statistics of this sample. Regulated loans are the sum of all mortgages generated in a census tract by institutes which have to comply with the CRA regulations. NonRegulated loans is the sum of all mortgages generated by other institutes in a census tract. On average, CRA-eligible census tracts receive lower amount of mortgages by both regulated and non-regulated institutes, and also on average regulated institutes have a higher market share of mortgages. Table III shows the breakdown of loans generated by different types of institutes to different types of census tracts. On average CRA-eligible 16 This is the only link between zip codes and census tracts that I could find. 12

13 census tracts receive lower amounts of mortgages both from CRA-regulated institutes and others. Moreover, as expected, CRA-eligible census tracts are on average poorer than other census tracts. However, there is not a substantial difference between MSA median family income for CRA-eligible census tracts and non-eligible tracts. To see the differential growth rates of mortgages by different types of institutes to different census tracts, I first start by a simple unconditional triple-difference analysis. As I see in Table IV, the growth of mortgages generated by CRA-examined institutes to CRA-eligible census tracts has been significantly more than the amount of mortgages generated by the same institutes by to non-eligible census tracts. However, I also need to ckeck whether I find the same pattern for mortgages generated by non-examined institute. As one can see from the last row of Table IV this is not true. I.e., the amount of mortgages generated by other types of institutes does not statistically differ for CRAeligible census tracts versus other census tracts. Finally, these two effects are statistically significantly different as seen by the triple-difference estimate presented at the last column of the table. This number implies that CRA regulations in 1998 generated a 2.5% relative growth in mortgage supply. To control for differences in income at the census tract level, I run difference-in-differences regressions for both types of mortgages. The results in Table V support my previous findings. The first three columns of the table present the estimates for the mortgages generated by CRA-regulated institutes and the last three columns pertain to the mortgages generated by other institutes. As seen, the difference in growth of mortgages for CRA-eligible tracts versus other tracts is significant only for mortgages generated by CRA-regulated institutes. The most conservative estimate predicts a 3.76% higher increase in mortgage generation for CRA-eligible census tracts after Motivated by these preliminary results, I now proceed with a rigorous matching estimation procedure to compare the growth of mortgages in a CRA-eligible census tract with a comparable non-eligible tract which has the same median family income but is not CRA-eligible only due to the fact that is placed in a MSA with lower median family income. I find the closest non-eligible census tract for every single CRAeligible tract from a different MSA, and compare their growth of mortgages from before to after I do this for both mortgages generated by CRA-examined institutes and non-examined institutes. This will help us to difference out any effect due to MSA-level differences. In other words, when I compare CRA-eligible census tracts with comparable non-eligible census tracts in a different MSA, part of the estimated difference will come from differences between MSA level income 17. However, I can simply difference this effect out by comparing the estimations based on CRA-examined institutes mortgages to other institutes mortgages. The results are presented in Table VI. For each CRA-eligible census tract I find a Non- 17 I have already reduced this concern by limiting the sample only to census tracts with median family income just around 80% of their respective MSA median family income. 13

14 CRA census tract with the same median family income (with at most a difference of $1 18 ), which ought to be from a different MSA (otherwise it would also be a CRA-eligible census tract). I report the analytical heteroskedasticity-robust standard errors proposed by Abadie and Imbens (2006) and also the results of a bootstrap exercise. Both sets of results confirm that CRA-examined institutes increased their lending to CRA-eligible census tracts relative to a comparable non-eligible census tract. Moreover, mortgages generated by institutes which are not examined by CRA regulations also increased relatively more for CRA-eligible census tracts, but it is statistically and economically much lower than the change in mortgages supplied by CRA-examined institutes. In fact, the finding regarding to mortgages by non-regulated institutes may be due to differences across MSAs and it makes more sense to net out this effect from my finding for mortgages generated by CRA-regulated institutes. The difference (which is similar to a triple-difference estimate as I performed before) is presented at the last column of Table VI, and implies a 5.8% relative increase in total mortgage supply due to CRA regulations. In sum, my results in this section support the hypothesis that the increased enforceability in the Community Reinvestment Act which took place in 1998 led to a significant increase in supply of mortgages to CRA-eligible census tracts. In the next section I take advantage of this exogenous shock in the supply of mortgages and estimate the elasticity of house price growth rates to mortgage supply. B. Semi-elasticity of House Prices to Mortgage Supply Here I will use CRA as an instrument for mortgage supply in a model to estimate the elasticity of house price growth rate to mortgage supply. Note that this section is on a zip code level. As mentioned earlier, zip codes which encompass any parts of a CRA-eligible census tract will be coded as CRA-eligible zip codes. Therefore, my sample includes 1317 unique CRA-eligible zip codes and 984 unique non-eligible zip codes from 1998 until The results of the regressions in (2) and (3) are presented in Table VIII and Table IX. Table VIII refers to the first state regression results and Table IX to the second stage. I consider several different specifications. In all specification I control for countylevel fixed effects. Therefore, as explained in Section II.B, this is a within county, hence within-msa, estimation. I report the standard errors which are clustered at the MSA level. As I expected from my results in Section IV.A, the test of weak instrument is rejected for all specifications. More specifically, the first-stage F-statistic is higher than the critical value proposed by Stock and Yogo (2005) 19. The results of the second-stage regressions are shown in Table IX. This result implies 18 The results are robust to the choice of this caliper. 19 This critical value for my case is

15 that one percent increase in yearly mortgage supply to an average zip code generates an extra 60 basis points higher yearly house price growth rate. In my sample, house price growth rate for an average zip code was 3.8% in year 1997 (the only year for which I have this data), and 6.4% for the whole period from 1998 until This equals to 2.6 percentage points increase in yearly house price growth rate for the average zip code. Considering my estimates, this would require 433% increase in total yearly mortgage to the average zip code from pre-1998 to post-1998 to be completely explained by growth in mortgage supply. However, in my dataset this increase is about 236%. Therefore, the realized increase in mortgage supply can only explain 54.5% of the jump in house price growth rates. Comparing to Glaeser et al. (2010), who argue that mortgage supply only explains 20% of house price growth, my results show a bigger share, although still suggesting that not all the boom can be explained by growth of mortgage supply. V. Differential House Price Growth Rate for CRA-eligible zip codes Did CRA-eligible zipcodes experience higher house price growth relative to comparable, but CRA-non-eligible, zipcodes? The results that we have seen so far in previous sections suggest so. First, CRA-eligible zipcodes received more mortgages, and second, higher moretgage supply results in higher house price growth. To estimate the effect of CRA regulation on the affected zipcodes, I estimate the differential house price growth rate for CRA-eligible zip codes versus other zip codes. As we can see from Table X, the most conservative estimate suggests that CRA-eligible zip codes experienced percentage points higher yearly growth rate of house prices. This, in the course of nine years from 1998 until 2006, translates to 1.78% higher growth in house prices for CRA-eligible zip codes relative to other zip codes, controlling for a host of covariates. 15

16 VI. Conclusion In this paper I used the amendments to the community reinvestment act in 1995 as a quasi-experiment to instrument for the supply of credit and study the elasticity of house price growth to mortgage supply. I found that CRA led to increased mortgage supply by regulated lenders in CRA-eligible census tracts versus comparable non-eligible census tracts. I then used the CRA regulations as an instrument for mortgage supply, and estimated the elasticity of house price growth rates to mortgage supply and found that the credit expansion of early 2000s can in fact explain slightly more than 50% of the increased house price growth rate in the same period. My results add to the ongoing discussion on the effects of credit supply on real economy. Although I only consider house prices, there is little doubt that increased house prices have strong consequences for real economic activity by, for example, changing investors behavior toward real estate, households savings motives, and also labor market re-allocations towards real estate-related activities. 16

17 VII. Robustness Checks A. Income segregation within zip codes One may argue that the mere geographical distribution of income between neighborhoods within a zip code can affect house prices. In this sense, my results may be only due to the alternative explanation that zipcodes with more segregated income distribution had higher house price growth rates. To control for this effect I construct a variable, called income segregation, which measures how much a zip code is divided into high versus low income neighborhoods. This is done by calculating the standard deviation of median family income of all census tracts that form a zipcode. In short, if this variable is zero for a zipcode, it means that all the neighborhoods of that zip code are similar in income. If this variable is high it means that the zip code consists of neighborhoods with considerably different levels of income. I run all my IV regressions once again by adding this new variable and as we can see in Table XI (only the second-stage results) the results stay unchanged. 17

18 REFERENCES Abadie, Alberto, and Guido W. Imbens, 2006, Large Sample Properties of Matching Estimators for Average Treatment Effects, Econometrica 74, Adelino, Manuel, Antoinette Schoar, and Felipe Severino, 2012, Credit Supply and House Prices: Evidence from Mortgage Market Segmentation, NBER Working Papers 17832, National Bureau of Economic Research, Inc. Agarwal, Sumit, Efraim Benmelech, Nittai Bergman, and Amit Seru, 2012, Did the Community Reinvestment Act (CRA) Lead to Risky Lending?, Working Paper 18609, National Bureau of Economic Research. Angrist, Joshua D., and Jorn-Steffen Pischke, 2008, Mostly Harmless Econometrics: An Empiricist s Companion, first edition (Princeton University Press). Bernanke, Ben, 2005, The Global Saving Glut and the U.S. Current Account Deficit, Sandridge Lecture, Virginia Association of Economics, Richmond, VA. Caballero, Ricardo J., Emmanuel Farhi, and Pierre-Olivier Gourinchas, 2008, An Equilibrium Model of Global Imbalances and Low Interest Rates, American Economic Review 98, Favilukis, Jack, Sydney Ludvigson, and Stijn Van Nieuwerburgh, 2013, The Macroeconomic Effects of Housing Wealth, Housing Finance, and Limited Risk-Sharing in General Equilibrium, Working paper, SSRN. Glaeser, Edward L., Joshua D. Gottlieb, and Joseph Gyourko, 2010, Can Cheap Credit Explain the Housing Boom?, Working Paper 16230, National Bureau of Economic Research. Himmelberg, Charles, Christopher Mayer, and Todd Sinai, 2005, Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions, Working Paper 11643, National Bureau of Economic Research. 18

19 Keys, Benjamin J., Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, 2009, Financial regulation and securitization: Evidence from subprime loans, Journal of Monetary Economics 56, Keys, Benjamin J., Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, 2010, Did Securitization Lead to Lax Screening? Evidence from Subprime Loans, The Quarterly Journal of Economics 125, Khandani, Amir K., Andrew W. Lo, and Robert C. Merton, 2009, Systemic Risk and the Refinancing Ratchet Effect, Working Paper 15362, National Bureau of Economic Research. Maddaloni, Angela, and Jos-Luis Peydró, 2011, Bank Risk-taking, Securitization, Supervision, and Low Interest Rates: Evidence from the Euro-area and the U.S. Lending Standards, Review of Financial Studies 24, Maggio, Marco Di, and Amir Kermani, 2014, Credit-induced Boom and Bust, Research Paper No , Columbia Business School. Mayer, Christopher, and Todd Sinai, 2009, U.S. House Price Dynamics and Behavioral Finance, In Policy Making Insights from Behavioral Economics, edited by Christopher L. Foote, Lorenz Goette, and Stephan Meier, chapter 5, Federal Reserve Bank of Boston. Mayer, Christopher J., and Karen Pence, 2008, Subprime Mortgages: What, Where, and to Whom?, Working Paper 14083, National Bureau of Economic Research. Mian, Atif, and Amir Sufi, 2009, The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis, The Quarterly Journal of Economics 124, Mian, Atif R., and Amir Sufi, 2015, Fraudulent Income Overstatement on Mortgage Applications during the Credit Expansion of 2002 to 2005, Working Paper 20947, National Bureau of Economic Research. 19

20 Overby, Brooke A., 1995, Community Reinvestment Act Reconsidered, University of Pennsylvania Law Review 143, Saiz, Albert, 2010, The Geographic Determinants of Housing Supply, The Quarterly Journal of Economics 125, Shiller, Robert, 2005, Irrational Exuberance, second edition (Princeton, NJ: Princeton University Press). Shiller, Robert, 2006, Long-Term Perspectives on the Current Boom in Home Prices, The Economists Voice 3, Stock, James H., and Motohiro Yogo, 2005, Testing for Weak Instruments in Linear IV Regression, chapter 5 (Cambridge University Press). 20

21 VIII. Figures Figure 1. Relative Growth in Total Amount of Mortgages The figure depicts the relative growth of mortgages for CRA-eligible census tracts versus non-eligible census tracts, separately for mortgages generated by two types of financial institutes, i.e., those that have to comply with CRA and the others. The confidence bound is two standard error wide. The sample is generated as explained in Section III.F. 21

22 IX. Tables Table I. CRA Test Components and Overall Rating Scale CRA Test Components Lending Investment Service Overall CRA Rating Outstanding High Satisfactory Low Satisfactory Needs to Improve Substantial Noncompliance Table II. Summary Statistics of the Census Tract Level Sample N Mean Std. Dev. Min Max Log(Mortgages) by CRA-examined Institutes Log(Mortgages) by Other Institutes Tract s MFI MSA s MFI The table presents summary statistics of the census-tract-level sample. The sample is generated as explained in Section III.F. Table III. Summary Statistics by CRA-eligibility of Census Tracts NonCRA CRA Log(Mortgages) by CRA-examined Institutes (1.394) (1.402) Log(Mortgages) by Other Institutes (1.817) (1.877) Tract s MFI (8.653) (8.110) MSA s MFI (10.375) (10.401) Observations This table presents the summary statistics for the two groups of CRA-eligible census tracts and non-eligible census tracts. The reported statistics are sample averages. Standard deviations are presented in parenthesis. The sample is generated as explained in Section III.F. 22

23 Table IV. Unconditional Difference in Means Non-CRA Tracts CRA Tracts Log(Mortgages) by Before After Before After Dif-in-Dif (1) (2) (3) (4) [(4)-(3)] - [(2)-(1)] CRA-regulated ins ** Non-regulated ins DDD 0.025*** * p < 0.1, ** p < 0.05, *** p < Standard errors in parentheses. This table presents the results of unconditional difference-in-differences, separately for mortgages generated by the two groups of banks, i.e., the ones which are regulated under CRA and the ones that are not, and also the triple difference estimate. The sample is generated as explained in Section III.F. Table V. Mortgage Growth Results Log(Mortgage) for Regulated Institutes Log(Mortgage) for Non-Regulated Institutes CRA 0.161*** 0.160*** 0.051* 0.137*** 0.136*** 0.117*** (0.031) (0.031) (0.029) (0.048) (0.049) (0.042) After 1.097*** 1.379*** (0.036) (0.054) CRA After 0.041** 0.040** 0.038* (0.020) (0.020) (0.020) (0.026) (0.026) (0.025) Tract s MFI 0.063*** 0.075*** (0.005) (0.006) Observations R * p < 0.1, ** p < 0.05, *** p < Standard errors in parentheses. This table presents the results of difference-in-differences regressions, separately for mortgages generated by the two groups of banks, i.e., the ones which are regulated under CRA and the ones that are not, and also the triple difference estimate. The sample is generated as explained in Section III.F. Table VI. Matching Estimation Results CRA-regulated ins. Non-regulated ins. ATET Bootstraped ATET DDD 0.101*** 0.101*** (0.034) (0.027) 0.058*** 0.050* 0.066* (0.006) (0.035) (0.037) * p < 0.1, ** p < 0.05, *** p < Standard errors in parentheses. This table presents the results of the matching estimations separately for mortgages generated by the two groups of banks, i.e., the ones which are regulated under CRA and the ones that are not. Standard errors in the first column are based on Abadie and Imbens (2006). In the second column I report bootstraped standard errors. The sample is generated as explained in Section III.F. 23