Credit Standards and Segregation* Amine Ouazad† Romain Rancière‡ January 2014 Abstract This paper explores the effects of changes in lending standards on racial segregation within metropolitan areas. Such changes affect neighborhood choices as well as aggregate prices and quantities in the housing market. Using the credit boom of 2000-2006 as a large-scale experiment, we put forward an IV strategy that predicts the relaxation of credit standards as the result of a credit supply shock predominantly affecting liquidity-constrained banks. The relaxed lending standards led to significant outflows of Whites from black and from racially mixed neighborhoods: without such credit supply shock, black households would have had between 2.3 and 5.1 percentage points more white neighbors in 2010. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! *!We thank Patrick Bayer, Leah Platt Boustan, Jess Benhabib, Christopher Crow, Giovanni Dell’Ariccia, Susan Dynarski, Denis Gromb, Maria Guadalupe, Brian Jacob, Robert Hunt, Jean Imbs, Matthew Kahn, Luc Laeven, Ross Levine, Alan Manning, Loretta Mester, Atif R. Mian, Guy Michaels, Max Nathan, Joel Peress, Gordon Philipps, Thomas Piketty, Steve Pischke, Rodney Ramcharan, Stephen Ross, Emmanuel Saez, Jose Scheinkman, John Van Reenen, Dubravka Ritter, Aaron Tornell, and Ekaterina Zhuravskaya for discussions and helpful suggestions on preliminary versions of this paper as well as Elena Loutskina and Luc Laeven for sharing some data. For insightful comments we thank the audiences at University of Amsterdam, Duke, Georgetown, LSE, UCLA, Michigan, PSE, INSEAD, RAND, Syracuse, the Federal Reserve Bank of Philadelphia, the CEPR Public Policy Symposium, Society of Labor Economists conference, the Urban Economics Conference, the Econometric Society, and the Rimini conference meeting. The usual disclaimers apply. We thank INSEAD, the International Monetary Fund, and the Paris School of Economics for financial and computing assistance. † INSEAD. ‡ International Monetary Fund and Paris School of Economics. 1 ! 1. INTRODUCTION The availability and affordability of mortgage credit is a key determinant of housing choices. Large aggregate changes in mortgage lending standards, as was experienced during the last mortgage credit boom, could thus have large effects on the sorting of households by income, race, or education across neighborhoods. This paper focuses on the role played by credit market conditions on the dynamics of urban segregation, using the last U.S. mortgage credit boom as a large-scale experiment. While there is a vast literature on the determinants of urban racial segregation (e.g., Bayer, McMillan, and Rueben 2004; Cutler, Glaeser, and Vigdor 2008), the role played by mortgage credit standards in shaping aggregate racial segregation has so far received little attention. 1 Some have suggested that the last mortgage credit boom could have contributed to the decline in segregation over the last decade.2 Yet, the effect of a change in lending standards on metropolitan area segregation has, to the best of our knowledge, never been formally tested. Minorities are generally considered to be more credit constrained than other groups (Ross and Yinger, 2002) and thus minorities could be expected to benefit more from relaxed lending standards. With an increased availability of mortgage credit, minority households have access to a larger set of housing options. These include the possibility to relocate into more racially mixed neighborhoods but also into neighborhoods with a comparable racial mix but with more desirable characteristics. This partial equilibrium perspective ignores, however, the role of general equilibrium effects. An increase in the supply of mortgage credit affects how the preferences for neighborhoods and the preferences for rental vs. homeownership of all households translate into actual housing decisions. In general equilibrium, these decisions lead to changes in housing prices, neighborhood demographics, and the supply of housing. Changes in credit market conditions can therefore lead to either a decline or an increase in urban segregation. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 This paper’s focus on metropolitan level changes in segregation due to market forces is key – there is an extant literature on discrimination in mortgage lending and redlining (Ross and Yinger 2002).! 2!“Several of the metropolitan areas with the greatest declines in segregation are also areas associated with significant exposure to the subprime mortgage market. It is also true that several metro areas with significant subprime exposure—such as Miami and Las Vegas—appear to have followed fairly unremarkable segregation trajectories over the past decade.” (Glaeser and Vigdor 2012)! 2 ! The objective of this paper is twofold. First, we design an empirical strategy to identify the causal effect of the relaxation of mortgage lending standards on racial segregation across neighborhoods at the metropolitan area level during the recent credit boom. Second, we use additional micro-level data to show how credit supply affects population flows by race, and how these flows are facilitated or hindered by general equilibrium changes in the relative price of housing in neighborhoods with different racial compositions within a metropolitan area. We examine the impact of credit standards on segregation by combining information from the universe of mortgage loan applications3, made publicly available through the Home Mortgage Disclosure Act, with Census-based information on racial demographics. The mortgage credit boom of 2000 to 2006 saw large changes in mortgage applications’ approval rates by lenders and in mortgage borrowers’ loan-to-income (LTI) ratios. While banks approved 70% of mortgage applications in 2000, this rate jumped to 84% in 2006, a 14 percentage point jump that is comparable to the increase for Hispanics (13 percentage points) and Blacks (13.1 percentage points). In 2000, the average new homeowner borrowed 1.9 times his income, whereas by 2004 this ratio had risen to 2.4 times his annual income. Using demographic information at the census tract level, we construct a set of standard measures of metropolitan-area level racial segregation across tracts, in 2000 and 2010. OLS estimation results show that in metropolitan areas that experienced larger increases in LTI ratios and mortgage loan approval rates during the credit boom of 2000 to 2006, black households had fewer white neighbors – a decline in black exposure to Whites – and black segregation declined slower than in other metropolitan areas. Although this positive correlation between lending standards relaxation and black segregation is intriguing, there are significant challenges when identifying the causal impact of relaxing mortgage lending standards on segregation. Observed approval rates and loan-to-income ratios – two measures of lending standards – reflect both supply and demand factors. This paper’s identification strategy relies on instruments that identify the relaxation of lending standards due to an increase in credit supply separately from an increase in credit demand. The instrumental variables are measures of banks’ liquidity conditions at the metropolitan level in the !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 3 We use the sample of mortgage applications for single-family owner-occupying purchases, excluding loans by the Federal Housing Administration. 3 ! early 1990s, that is, prior to a number of key transformations that affected the mortgage industry and favored the rise of securitization. The underlying hypothesis is that increased securitization activity allowed banks with initially low levels of liquidity to catch up by increasing their approval rate and median loan-to-income ratios relative to banks with initially high levels of liquidity. First stage estimation results show that indeed metropolitan areas with a low level of bank liquidity in 1990-1994 exhibited both a greater relaxation of lending standards, and a higher growth of mortgage securitization volumes, between 2000 and 2006, than metropolitan areas with an initially high level of liquidity. Bank liquidity in the early 1990s is a strong predictor of future changes in approval rates and in originations’ loan-to-income ratio during the boom, yet it is uncorrelated with observable factors (most importantly, Hispanic inflows, and income changes on average and by racial group) affecting mortgage credit demand. In addition, we find that our instruments do not display any significant correlation with a number of other potential confounders affecting racial segregation such as local amenities, the level of crime, the college premium, and income inequality. Instrumental variable (IV) regression results suggest that the decline in lending standards during the boom had a robust and significant effect on segregation. The effect is economically important: in our estimations, the magnitude of the boom’s observed increase in loan-to-income ratios (resp. approval rates) lowered the fraction of white neighbors in the tract of an average black resident by 5.1 (resp. 2.3) percentage points, while having no significant and robust impact on the fraction of Hispanic neighbors in the tract of an average black household. Given the decline of segregation during the last decade4, our results suggest that the increased supply of credit slowed down the racial integration of cities. The paper’s findings survive a series of robustness tests. In particular, census-based measures of segregation are decennial; hence post-boom (2007–2010) credit conditions could substantially alter our conclusions. However, controlling for metropolitan area 2007-2010 foreclosure rates leaves our main estimates statistically and economically unchanged. Additionally, two data sets provide racial segregation measures for 2006 and 2008: (i) the 2006- 2010 American Community Survey (ACS) provides tract-level data for households interviewed between 2006 and 2010, in which the median household responded in 2008, two years before the !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 4!See! Glaeser and Vigdor (2012)!.! 4 ! 2010 Census; results based on these data are very similar to those based on Censuses. (ii) The Department of Education’s annual school demographics allow us to compute metropolitan area measures of racial segregation across schools in 2000 and 2006. The effect of credit standards relaxation on school segregation is strongly similar to its effect on urban segregation. Our metropolitan area findings could result from black mobility into black neighborhoods or from white mobility out of black neighborhoods. We show using census tract level data that the decline in lending standards has contributed to foster significant white mobility out of both racially mixed and mostly black census tracts, i.e. tracts with between 10% and 60% black population, and tracts with at least 60% black population, and into mostly white census tracts (tracts with less than 10% of black population). This mobility pattern with outflows starting at about 10–15% of black population are consistent with a tipping point model (Card, Mas, and Rothstein 2008). In addition, we find that lending standards relaxation led to significant black residents mobility into racially mixed tracts, which were experiencing whites outflows, but did not lead to black mobility into tracts that were mostly white in 2000. General equilibrium effects may explain such lack of black mobility towards mostly white tracts in metropolitan areas that experienced a decline in lending standards. Indeed, the simple model of neighborhood choice with endogenous prices and borrowing constraints, presented in the appendix, suggests that lending standards relaxation leads to an increase in prices in desirable neighborhoods, potentially pricing out minorities from such neighborhoods. In our model, an increase in credit supply leads to an increase in segregation whenever the partial equilibrium effect (the effect at given prices) is offset by the general equilibrium effect of prices on segregation. Empirically, we find that, during the boom, house prices increased significantly more in mostly white tracts than in either racially mixed or mostly black tracts, further hindering black households’ mobility into such tracts, but only in metropolitan areas that experienced a significant decline in lending standards. This price result within metropolitan areas, across tracts, holds even after controlling for migrations, foreclosures, and metropolitan area house price changes. Indeed, such micro-level evidence on household mobility and price increases suggests that metropolitan areas with less elastic housing supply might have experienced a stronger effect of lending standards on racial segregation. At the metropolitan area level, using the housing supply elasticity measures of Saiz (2008), we indeed find that the positive impact of the relaxation of 5 ! lending standards on segregation is much stronger in metropolitan areas with low housing supply elasticity. The rest of the paper proceeds as follows. Section 2 discusses related literature. Section 3 presents the main data sources, the evolution of segregation in the past decade, and the change in credit conditions during the boom; it also describes the observed strong correlations between changes in segregation and changes in credit conditions. Section 4 presents the instrumental variables strategy and the paper’s main results, and discusses its robustness. Section 5 identifies economic mechanisms that are consistent with a simple model of neighborhood choice with credit constraints. Section 6 concludes. 2. RELATED LITERATURE In 2000, a majority of urban Blacks lived in highly segregated neighborhoods (Massey 2004), and ample evidence suggests that racial segregation has negative impacts on black welfare (Cutler and Glaeser 1997) in a number of dimensions: education (Card and Rothstein 2007), black well-being (Massey, Condran, and Denton 1987), and labor market opportunities of black youth (Raphael 1998). Analysis of racial segregation in the first half of the 20th century stressed the importance of white households’ collective action, including land use regulations and racial covenants, in shaping the distribution of races across neighborhoods (Cutler, Glaeser, and Vigdor 1999; Brooks 2011). Literature on racial segregation in the second half of the 20th century, both theoretical and empirical, model segregation across neighborhoods as the result of an equilibrium in which large price differences across white and minority neighborhoods reflect differences in the quality of local amenities (Epple and Sieg 1999), differences in households’ income and education (Benabou 1996), and white households’ preference for same-race neighbors (Krysan and Farley 2002). The mobility of white households away from black neighborhoods, also called white flight, described in Schelling’s (1971) seminal work, was facilitated by declining transportation costs (Baum-Snow 2007), triggered by the presence of minorities, and by inequalities in the quality of public services and education across neighborhoods (Boustan 2010). This paper’s main goal is to contribute to this literature by understanding whether large changes in mortgage credit markets hinders or facilitates the mobility of white and minority households across neighborhoods. The nature and extent of racial discrimination in mortgage loan approvals has been 6 ! estimated using both observational data with a large range of covariates (Munnell et al. 1996) and audit pair studies (Ross and Yinger 2002). Literature suggests that banks may have conditioned their mortgage approval decisions on the neighborhood’s racial composition, a phenomenon called ‘redlining’ (Ross and Tootell 2004). Such lender behavior limits minorities’ opportunities to relocate in more racially diverse neighborhoods. The estimated magnitude of racial discrimination in mortgage approvals can be large, but their interpretation as reflecting banks’ behavior rather than minority applicants’ unobservables remains controversial. This paper stresses the possibility of large impacts of mortgage lending standards on racial segregation even absent significant racial discrimination in mortgage lending. In particular, recent analysis of the credit boom has suggested that the credit boom saw a large increase in house prices (Himmelberg, Mayer, and Sinai 2005; Campbell, Davis, Gallin, and Martin 2009) as well as a large increase in the dispersion of prices across neighborhoods (Gyourko, Mayer, and Sinai 2006). Evidence also suggests that the later period of the housing boom (2000-2006) saw substantial population flows across neighborhoods (Guerrieri, Hartley, and Hurst 2012). Because of such large changes in the relative prices and the demographic composition of neighborhoods across time, a relaxation of lending standards, which increases applicants’ neighborhood choice set at given prices, may actually reduce applicants’ choice set, when accounting for neighborhood price changes and demographic flows. The simple model presented in the appendix formalizes this last point. Identifying the impact of changes in lending standards, as opposed to changes in the demand for credit, is subtle, in large part because of the simultaneity problem: median loan-to- income ratios and approval rates reflect changes in both the demand and the supply of credit. This paper is focused on identifying the impact of the latter on metro-level racial segregation. The identification strategy we adopt here predicts changes in mortgage lending standards during the boom at the metropolitan area level using banks' balance sheet structure in the early 1990s. Focusing on the supply side of the credit market is well in line with recent literature showing that credit supply – e.g., through more lenient lending standards – is responsible for a large share of the rise in leverage and approval rates during the credit boom. Mian and Sufi (2009) use ZIP code level data to demonstrate that a supply-based channel is the most likely explanation for the mortgage market’s expansion during the boom. Dell’Arriccia, Igan, and Laeven (2009) document using Home Mortgage Disclosure Act application data that growing origination 7 ! volumes were correlated with relaxed lending standards. Favara and Imbs’ (2010) paper is another piece of evidence that confirms the role of credit supply, as they show how the timing and extent of interstate banking and branching deregulation increased loan volume and LTI ratios, and led to falling denial rates.5 Keys, Mukherjee, Seru, and Vig (2010) demonstrate how securitization led to both increasing mortgage credit supply and declining lending standards. This securitization boom led to the development of the originate-to-distribute model, which, as Purnanandam (2011) shows, strongly benefited capital-constrained banks. However, we do not exclude that changes in housing or credit demand partly drove the rise in credit supply and the relaxation of credit standards. In particular, Ferreira and Gyourko (2011) use disaggregated census tract level data and argue that local income shocks preceded increases in property prices. Hence, an important identification challenge in the literature is to identify the impact of lenders’ underwriting policies separately from the impact of income changes, when estimating the impact of credit supply on racial segregation. Our new identification strategy provides instruments that predict loan-to-income ratio and approval rate changes at the metropolitan area level. We will show how these instruments can be used both in metro-level IV regressions, to identify the causal impact of a relaxation of lending standards on segregation (Section 4), and at the census-tract level to analyze how demographic changes between neighborhoods within metro areas vary with the predicted changes in lending standards (Section 5). 3. DATA SET AND DESCRIPTIVE EVIDENCE 3.1 Data Sources We use mortgage data for the years 1995–2007 that was compiled in accordance with the Home Mortgage Disclosure Act (HMDA), which mandates reporting by most depository and non- depository lending institutions.6 HMDA disclosure requirements thus apply to more than 90% of all mortgage applications and originations (Dell'Arriccia, Igan, and Laeven 2009), and for each mortgage lender report the loan amount, the applicant’s income, the applicant’s race and gender, !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 5 Ng (2012) and Adelino, Schoar, and Severino (2012) provide additional empirical support for a supply- driven credit boom. 6 Specifically, HUD regulates for-profit lenders that have combined assets exceeding $10 million and/or originated 100 or more home purchase loans (including refinancing loans) in the preceding calendar year. 8 ! and the census tract of the house. We focus on credit standards for single-family, owner- occupied mortgages. The Census Bureau’s Summary File I provides census tract–level demographics for the 2000 and 2010 censuses. We construct measures of racial demographics and racial segregation across census tracts for each metropolitan area, following measures described in Cutler, Glaeser, and Vigdor (1999) and Massey, White, and Phua (1996). We equate “metropolitan areas” with the widely used Core Based Statistical Areas (CBSAs) in 2003 borders.7 CBSAs encompass both metropolitan statistical areas (MSAs) and “micropolitan” statistical areas (µSAs). 8 The banks' balance sheet data used to compute our liquidity measures come from the Federal Reserve’s Reports of Condition and Income, also known as Call Reports. As explained in detail in section 4, these balance sheet data will be merged with HMDA data on mortgage origination by banks in order to produce our MSA-level measures of liquidity (our instrument) in the early 1990s. 3.2 Racial Segregation from 2000 to 2010 From the many available segregation measures (Massey and Denton 1988) we choose the isolation and exposure indices, which have been extensively used in the literature (Cutler, Glaeser and Vigdor 1999). Here isolation is defined as the average fraction of neighbors of the same race in the average census tract of Whites, Blacks, or Hispanics;9 thus, the isolation of Whites is the average fraction of white neighbors for white households. The isolation index is an especially relevant measure when considering the effect of neighbors on outcomes – as in standard models with a linear-in-means peer effects specification (Manski 1993), where average peers’ characteristics are the main contextual input for considering either neighborhood-level (Goux and Maurin 2007) or school-level social interactions (Hoxby 2001). We focus on isolation !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 7 We keep consistent metropolitan area borders throughout the dataset, following the 2003 definitions. 8 For clarity and simplicity we refer to ‘metropolitan areas.’ The Census Bureau defines two kinds of metropolitan areas, metropolitan statistical areas (MSAs) and micropolitan statistical areas (µSAs). A metropolitan statistical area is a contiguous geographic area containing a large population core (of more than 50,000 inhabitants) and adjacent communities that are highly integrated (as measured by commuting time) with that core. The concept of a micropolitan statistical area parallels that of the MSA but with a lower core threshold (i.e., more than 10,000 inhabitants). Our metropolitan areas include both MSAs and µSAs. 9 To keep the discussion manageable, we do not display results for Asians, American Indians and Alaska Natives, or Pacific Islanders. However, our findings for Asians suggest significant effects for this group (results available upon request). 9 ! for clarity, but our results are robust to using instead either dissimilarity10 or normalized isolation (Cutler, Glaeser, and Vigdor 1999).11 The isolation of Whites in metropolitan area k is expressed formally as !ℎ!"#$ !ℎ!"#$ !,! !,! !"#$%&'#( (!ℎ!"#$) = ! !ℎ!"#$ !"!#$%&'"( ! !,! ! where whites is the white population in census tract j of metropolitan area k; whites is the k, j k overall white population in metropolitan area k; and population is the total population in k, j census tract j of metropolitan area k. White isolation decreases as white households are more exposed to neighbors of other races. For instance, the exposure of Whites to Blacks in metropolitan area k may be written as !ℎ!"#$ !"#$%& !,! !,! !"#$%&'( !ℎ!"#$ !"#$%& = ! !ℎ!"#$ !"!#$%&'!" ! !,! ! where blacks is total black population in census tract j of metropolitan area k. In the case of k, j two racial groups, one group’s isolation increases as exposure to other group decreases. From 2000 to 2010, black and white racial segregation across census tracts continued its well-documented decline that began in the 1970s (Glaeser and Vigdor 2012), as shown in Table A1 of the appendix. Black isolation declined in about three quarters of the Metropolitan Statistical Areas (MSAs). In the average metropolitan area, in 2000, the average black resident lived in a census tract for which 50.5% of the population was of the same race (i.e., black isolation was 50.5%); this same fraction declined to 45.4% in 2010. However, black exposure to whites declined over the period in 79 percent of the MSAs, with a median reduction of 2.0 percentage points. Thus, the decline in black isolation is largely explained by the increased exposure of black residents to Hispanics, which occurs in almost all metropolitan areas (98.1%); on average black residents live with 3.7 percentage points more Hispanic neighbors in 2010 than in 2000. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 10 So, for example, the dissimilarity of Blacks is the fraction of Blacks that would need to move in order to yield an even distribution of Blacks across census tracts. The normalized isolation of Blacks is the isolation of Blacks minus the FBIM divided by 1 minus the FBIM, where for notational convenience the acronym stands for “fraction of Blacks in the metro area”. 11 The normalized isolation captures some mechanical demographic changes. We use the non-normalized isolation index and control for demographic changes in the main regression, thereby retaining a more natural interpretation for the coefficients of interest. 10 !