The Role of Homebuyers Price Expectations in Mortgage Leverage Choice

Transcription

1 The Role of Homebuyers Price Expectations in Mortgage Leverage Choice Josh Morris-Levenson 1 During the last decade, a boom and bust in the US housing market contributed to a financial crisis and severe economic downturn. This paper provides a coherent explanation of this event by connecting two explanations put forth by economists. Atif Mian and Amir Sufi argue in their 2009 paper that mortgage securitization allowed the extension of mortgage credit to subprime borrowers, setting up a wave of defaults and subsequent credit crunch. In a 2007 NBER paper, Robert Shiller argues that homebuyers speculative psychology drove the housing boom, and that the expansion of subprime loans was a consequence of borrowers irrationally high home price expectations. This paper bridges the gap between these two theories by drawing an empirical connection between homebuyers price expectations and mortgage choices. Using American Housing Survey data from , I find that homebuyers who expected higher growth in the value of their homes chose mortgages with a higher loan to value ratio. This finding integrates the credit supply- and psychology-based theories of the housing boom. 1 Josh Morris-Levenson is a member of the class of 2014 at the University of Chicago.

2 Introduction and Motivation A boom and bust in the housing market throughout the last decade resulted in a financial crisis and severe economic downturn. The United States housing market experienced steady and unprecedented price growth: real home prices grew 86% from 1996 to 2006 (Shiller). Accompanying the price boom was a rapid expansion of mortgage credit in the early 2000s. Lenders eased screening practices in order to originate more mortgages that could be sold into securitized pools (Keys, et al. 310). Mortgage lending became increasingly unrestrained, fueling the demand for housing, which spurred home price growth even more and, in turn, fed back into the credit market, and so on (Diamond and Rajan; Shiller). This feedback loop between housing prices and mortgage lending drove the expansion of mortgage credit in general, particularly to subprime borrowers. Mian and Sufi (1483) argue that because of the proliferation of mortgage-backed securities, a slew of defaults turned a housing market crash into a recession. This supply side-driven narrative of the housing and mortgage markets in the early 2000s explains how the housing market crash sparked a widespread economic crisis, but it does not explain the emergence of the price bubble that started this feedback loop in the credit market. Shiller argues that elevated price expectations fueled the speculative bubble in the housing market in the early 2000s (7). He documents the emergence of a general perception that housing was a good investment because prices would rise indefinitely (17). Shiller concludes that financial innovation and institutional changes in mortgage lending were byproducts of high price expectations and speculative behavior, not an exogenous accompaniment to it (35). The housing bubble had psychological, rather than economic, roots. Empirical evidence supports both the homebuyer expectations-based theory of the housing boom (Case and Shiller 322; Benitez-Silva, et al.) and the subprime credit-based theory of the subsequent financial collapse (Mian and Sufi). In this paper, I use data from the American Housing Survey (AHS) to analyze the role of homebuyers price expectations in leverage decisions, drawing an empirical connection between these two arguments. Since there are no prior empirical studies of this topic, I examine borrowers in general, not just subprime borrowers, and I use the loan-to-value ratio 2 (LTV) at the time of purchase as a general measure of mortgage risk. 3 I find that, holding borrower characteristics constant, price expectations are positively and significantly correlated with LTV, and uncorrelated with other borrower characteristics. My results suggest that irrational demand-side price expectations play a role not only in demand for owneroccupied housing, but also in the mortgage decisions of homebuyers. Section 1 lays out a theoretical framework that explains the role of homebuyers price expectations in the mortgage credit market. Section 2 discusses the technique used to distinguish the effects of demand- and 2 The mortgage principle divided by the appraised value of the home, both in dollars. 3 Hendershott, et al argue that LTV is an important part of mortgage choice because it determines the difference between the cost of government-sponsored and private mortgage insurance, i.e., private insurance is more costly for higher-ltv mortgages (203). Qi and Yang find that LTV can be used to segment mortgages by risk (789). Spring

3 supply-side price expectations on mortgage choices. Section 3 details the data sources and regression model. Section 4 analyzes regression results. Section 5 presents the conclusion. 1 Theoretical Framework This section describes the role of homebuyers price expectations in determining leverage and helps motivate the empirical techniques used to isolate this effect. I consider each mortgage contract to be an equilibrium outcome under a framework similar to that described by Keys, et al (317). A prospective homebuyer identifies a house that he or she wishes to own and goes to a bank to obtain a mortgage. 4 The lender offers a menu of contracts based on the borrower s credit history, current income and wealth, and prospective income, which depends on age, education level, and marital status (Ling and McGill 395); as well as the monthly non-mortgage cost of living in the home, which will affect the borrower s ability to meet the terms of the mortgage contract. The lender also takes into account macro-level considerations that affect the terms the lender can offer, including the lender s ability to sell the mortgage into a securitized pool, the lender s price expectations (rationally based on participation in and observation of the local housing market), and interest rates (Mian and Sufi 1464). The borrower selects a contract based on his or her current wealth and ability to meet future financial commitments, again based on current income, age, education level, marital status, and monthly housing costs. The borrower takes into account only one macro-level consideration, however: expected house price growth. Higher expected appreciation induces the borrower to purchase a more expensive home and take out a larger mortgage with a higher LTV in anticipation of higher return on the investment in housing. 5 2 Separating Supply and Demand A challenge facing this study is to distinguish the effect of borrowers and lenders price expectations on mortgage outcomes. Mian and Sufi argue that securitization drove the growth of the subprime mortgage market (1484). But they take steps to rule out the effect of lenders price expectations on loan quality: elevated price expectations would decrease lenders expected loss given default and so induce them to make riskier loans. Mian and Sufi use Saiz s geography-based measure of housing supply elasticity 6 to isolate the influence of lenders price expectations. 4 Whether these choices are consecutive or concurrent is irrelevant. 5 High price expectations will also draw prospective borrowers into the market for owner-occupied housing. 6 Based on the amount of developable land in every United States Metropolitan Statistical Area (MSA), the standard unit of analysis in statistical studies of cities. Spring

4 D' Figure 1: Effect of a Demand Shock in High (HE) and Low (LE) Supply Elasticity Markets LE HE Price D P' LE P' HE P Quantity Theoretically, increased demand for housing in areas with more flat, undeveloped land should have a smaller effect on prices relative to areas with less space for new construction. Figure 1 presents a simple supply-and-demand diagram illustrating this concept. The steeper upward-sloping line, labeled LE, is the supply curve in a low-elasticity market. The other upward-sloping line, labeled HE, is the supply curve in a high-elasticity market. Both markets experience an identical outward shift in demand from D to D. Both markets have the same price level, P, but because the housing stock cannot expand as much in the LE compared to the HE market, prices in the LE market rise to P LE > P HE. Mian and Sufi confirm this theory empirically by comparing price growth in the top and bottom elasticity deciles. Figure 2 (analogous to the lower-left hand panel of Mian and Sufi s Figure VI [1485] (see Appendix)) shows that the annualized change in the Zillow price index 7 accelerated in the bottom decile of elastic areas and remained flat in the top decile. 7 Zillow.com is an online real estate firm that collects and publishes home price data. Spring

5 Figure 2: Price Growth by Elasticity Decile Annualized Price Change (Percent) Year Top Decile Bottom Decile Mian and Sufi assume that lenders are informed and rational, so their expectations of price growth remained constant throughout the housing boom in high-elasticity areas. I accept their assumption, but while Mian and Sufi use it to isolate the effect of securitization on subprime lending, I use it to isolate the effect of homebuyers price expectations on leverage decisions. 3 Data and Methodology i. Data I use three data sources for this study. The American Housing Survey (AHS) is a longitudinal survey of housing units conducted biannually in odd years. I use data from the 2001 to 2009 waves of the national sample, which follows 55,000 housing units, including various types of owner- and renter-occupied properties, as well as condos and mobile homes. The AHS records variables at the household and individual level describing the attributes and cost of living in the unit, the economic and demographic characteristics of the occupant, and mortgage characteristics when applicable. For this study, I use household-level variables. I also use the AHS survey weights for regression analysis and to compute averages, as shown in Figure 3. These weights adjust for the probability of selection in the AHS sample and also correct for nonresponse. I use the AHS to construct initial LTV and expected appreciation (EA). Homeowners EA is the ratio of the variable VALUE to the purchase price of the home, where VALUE is the householder s reported estimate of the current market value of their home. By restricting the sample to homeowners who purchased Spring

6 their home in the same year that they were surveyed, 8 I can take VALUE/PURCHASE to be the expected house price appreciation over the current year. In order to expand the sample size, I also included homeowners who purchased in the year prior to the survey year. 9 For those who purchased in a non-survey year, I interpret VALUE/PURCHASE as the cumulative EA from the time of purchase to the end of the survey year. I annualize this value by taking its square root and convert EA and LTV to percentages. Figure 3 plots weighted-average EA together with the weighted-average change in the Zillow index in the top elasticity quartile. 10 In accordance with the literature on price expectations, EA does not track the Zillow index. As evidence of the validity of the EA variable, I point out three other features of Figure 3 that match the findings of previous studies. First, though EA does not track the Zillow index, it does not differ from it substantially: from 2000 to 2005, the two measures averaged 5.31% and 5.13%, respectively. In accordance with this observation, Bucks and Pence report that homeowners estimated their home values fairly accurately in 2001 (14). Second, expectations take a dive between 2001 and 2003 before spiking up, reflecting the finding that home value estimates are procyclical: they exhibit a positive correlation with the state of the national economy (Benitez-Silva, et al. 26). Finally, even after actual price growth dips in 2006, EA continues to fluctuate around the pre-crash level of 5%. Haurin and Croce also find evidence of persistent over-optimism after 2006 in the Reuters/Michigan Survey of Consumers. These three findings support the validity of EA as a measure of homeowners price expectations. 8 Some households reported moving into their homes prior to purchase. I assumed that this was due to miscoding and excluded these records. 9 Because the AHS records income at the time of the survey rather than the time of purchase, I had to limit my selection of even-year purchases to those who report having the same income level in the year prior to the survey. This restriction allows me to use their income as reported in the survey as a regressor. 10 I use the top quartile rather than the top decile (Mian and Sufi s sample) because the top 10% is too restrictive for regression purposes. The top quartile of MSAs had similar price growth to the top decile (Figure 4), so expanding the sample does not hinder my analysis. In Figure 3, I plot the weighted average annual change in the Zillow index for observations in the top quartile of MSAs instead of the average change in the top quartile of MSAs. This means that the change in the Zillow index represents the average expectations of lenders in these areas, so it is directly comparable to the weighted average EA (borrowers expectations). Spring

7 Figure 3: Price Growth and Expected Appreciation (Top Elasticity Quartile) Annualized Price Change (Percent) Year Zillow Index EA I had to exclude observations of some variables to account for topcoding and miscoding in the AHS. The AHS topcodes (and bottomcodes) many dollar-denominated variables (mortgage amount, purchase price, house value, etc.) to protect the privacy of survey respondents. I exclude topcoded observations in order to ensure the validity of the two variables I constructed. Examination of the data and the constructed variables revealed potential miscoding. For instance, many records reported purchase prices of $1, resulting in astronomical values of LTV and EA. After dropping topcoded observations, I trimmed the top and bottom percentiles of topcoded variables to exclude outliers that I suspected were miscoded. I then used these trimmed and topcode-corrected values to recalculate LTV and EA. In addition to the AHS, I use Zillow s county-level price index 11 and Saiz s measure of housing supply elasticity (the same measure used by Mian and Sufi). Saiz uses satellite-generated data on elevation and bodies of water to compute the amount of developable land in 223 county-consistent metropolitan areas (3). 12,13 ii. Methodology To estimate the effect of EA on mortgage decisions, I regress LTV on EA and a number of controls representing the borrower s current and prospective income. I restrict the sample to single-unit (attached and 11 Available from their research website: 12 Based on 2000 definitions of Metropolitan Statistical Areas and New England County Metropolitan Areas (MSA/NECMAs). 13 I thank Ben Keys for providing these data and for aggregating the Zillow index to the MSA/NECMA level. Spring

8 unattached), owner-occupied homes purchased within a year of the survey year. Although negative equity mortgages were not uncommon during the housing boom, I exclude observations with an LTV greater than one because it is difficult to distinguish legitimate mortgages from miscoded data. In order to control for the effect of lenders price expectations on LTV, I employ Mian and Sufi s technique of analyzing only high-supply elasticity MSAs (see Section 2). However, the top supply elasticity decile of MSAs does not provide a large enough sample from the AHS, so instead I use the top quartile, which experienced similar price growth (Figure 4). Applying the restrictions from the previous paragraph, limiting the sample to the top elasticity quartile, and excluding MSAs without Zillow price data left me with a sample of 312 observations for regression analysis. Figure 4: Price Growth in High and Low Elasticity Areas By Decile By Quartile Annualized Price Change (Percent) Year Year Top Bottom I use the following model for regression analysis: LTV! = α + x! β + δea! + ρz! + γ t + u! where α is the intercept; x! is a vector of borrower characteristics including age (Age), indicators for college education (College) and marital status (Married), log family income (Income), log non-mortgage monthly housing costs (MHC), and an indicator for first-time homebuyers (First); EA is the price expectations variable, Z! (Zillow) is the change in the Zillow index between the year of purchase and the year Spring

9 prior to purchase; 14 β, δ, and ρ are the regression coefficients of x!, EA, and Z, respectively; is a purchase year fixed effect, which I include to control for changes in lending standards over the sample period, and u! is an error term assumed to be normally distributed. I chose to use purchase year fixed effects rather than explicitly controlling for mortgage characteristics for two reasons. First, other mortgage traits, such as periodic payment and term, may be mechanically related to LTV. Second, many of the variables in the AHS describing more exotic mortgages that proliferated during the credit boom, such as adjustable rates and balloon payments, have only one level: they indicate which mortgages have the characteristic without identifying those that do not. Lam and Kaul (103) caution AHS users not to rely on these variables. 15 I compute both unadjusted and MSA-clustered standard errors to account for the possibility that homebuyers in the same MSA display similar borrowing behavior. Theoretically, it is difficult to say whether observations are likely to be clustered by MSA. On the one hand, borrowers living in close geographic proximity face similar credit conditions. On the other hand, Mian and Sufi note significant within-msa variation in mortgage market activity, which is why they use zip code-level data instead (1459). The effect of clustering in my sample is ambiguous, so I include both sets of results in this paper. 4 Results Regression results with unadjusted standard errors are presented in Table 1. All regressions use the AHS survey weights. Column (1) is the baseline specification. Column (2) includes the EA variable. Holding all else constant, a 1% increase in house price expectations is associated with a 0.175% increase in LTV. Comparing the coefficient estimates in the first two regressions, EA appears to be uncorrelated with the other determinants of leverage. In line with the theoretical framework laid out earlier, homebuyers with higher price expectations appear to choose mortgages with higher leverage (conditional on the choices they are offered). Table 1: Unadjusted Standard Errors (1) (2) (3) Intercept *** ** * (-21.18) ( ) (-32.72) Age (-0.083) (-0.083) (-0.083) College (-1.775) (-1.757) (-1.771) 14 I use the Zillow index as a proxy for lenders knowledge of the housing market. I include Zillow in one regression specification to test its correlation with EA and the other regressors. 15 Lam and Kaul also urge data analysts to take care in constructing LTV measures (104), which is why I inspected every observation to ensure that the underlying components were reasonable. Spring

10 Married (-1.855) (-1.861) (-1.862) Income (-1.794) (-1.811) (-1.81) MHC (-0.935) (-0.948) (-0.947) First 7.788*** 7.679*** 7.689*** (-2.22) (-2.214) (-2.217) EA (-0.092) (-0.092) Zillow ( ) R-squared F N p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Column (3) adds the previous year s change in the local Zillow index. Comparing (3) and (2), the Zillow index is uncorrelated with any of the other regressors (except for the intercept, which I discuss below), most importantly EA. 16 These results support the theory that borrowers have minds and beliefs of their own and that these beliefs play a role in mortgage market outcomes, though they are not necessarily based on actual market trends. I do not put interpretive stock in the results of column (3) beyond the finding that EA and Zillow are uncorrelated because the coefficient on the Zillow index is negative. This does not fit the theory that lenders will offer greater leverage if they expect house prices to rise. This counterintuitive result probably indicates collinearity between the Zillow index and the regression intercept. The regression sample is restricted to high housing supply elasticity areas where price growth was roughly constant from It makes sense, then, that the change in the Zillow index is largely collinear with the intercept, and that including it in the regression increases the standard error of the intercept. Table 2: Clustered Standard Errors (1) (2) (3) Intercept *** * ** ( ) ( ) ( ) Age (-0.097) (-0.098) (-0.097) College (-1.774) (-1.759) (-1.786) 16 COV(EA, ZILLOW) = Spring

11 Married (-1.35) (-1.376) (-1.381) Income (-1.556) (-1.503) (-1.51) MHC (-1.04) (-1.061) (-1.069) First 7.788** 7.679** 7.689** (-2.264) (-2.262) (-2.267) EA (-0.097) (-0.098) Zillow ( ) r F N p<0.10, * p<0.05, ** p<0.01, *** p<0.001 The results in Table 1 are dubious because of the low F statistics. This may be attributable to model misspecification, the limited sample size, or clustering in the data. Dealing with the latter issue, Table 2 presents regression results with MSA-clustered standard errors. All three models are now jointly significant, but the p-value of the EA coefficient rises from to I believe that this, combined with the increase in the R-squared value from column (1) to column (2), still provides some evidence that price expectations play a role in mortgage choice. As I mentioned at the end of the previous section, there are arguments both for and against the necessity of MSA clusters in household data. The results in Table 2 suggest that clustering corrected for some within-msa correlation because each model is now jointly significant, even though some coefficients have higher standard errors. But even if the sample is actually clustered, there are two reasons that clustering may be problematic. First, the most elastic quartile of MSAs includes only 29 cities, which is on the low end of acceptable numbers of clusters. The second and more serious concern is that the clusters do not have similar numbers of observations. Some MSAs contain only a single observation, while others have more than thirty. In light of this ambiguity, I include both unadjusted and clustered standard errors. 5 Conclusion This paper provides empirical evidence that mortgage borrowers price expectations play a role in their leverage decisions. Shiller describes the speculative psychology of homebuyers that initiated the house price bubble; Mian and Sufi analyze the subprime credit boom that made the eventual bust so damaging to the broader economy. This paper bridges the gap between these studies. I use the American Housing Survey Spring

12 to construct a measure of borrowers house price expectations at the time of home purchase. Assuming that lenders are rational, I isolate the effect of homebuyers price expectations by restricting my sample to areas with high housing supply elasticity and therefore flat price growth. I find that borrowers price expectations are positively correlated with the loan-to-value ratio. This study has two limitations that suggest possibilities for future research. First is the small sample size. One remedy is to simply include more waves of the AHS, though this raises some difficulties and occasional inconsistencies with variables. Another option is to use a different survey, such as the Survey of Consumer Finances (SCF), a triennial survey of household income and balance sheets conducted by the Federal Reserve. 17 The second issue is that my analysis does not account for the effect of price expectations on homeownership. High price expectations may have drawn more households to own rather than rent their homes during the boom. Presumably, many of these new homebuyers were excluded from ownership by budget constraints prior to the housing boom. These people would have to take on more leverage than other market participants just to make homeownership possible. In order to control for this effect, one would need a measure of renters as well as owners price expectations. The best way to build such a measure is probably through a survey similar to Case and Shiller s questionnaire, which asked respondents whether and by how much they expected property values to increase or decrease in their city over the next twelve months and ten years. 17 For more information, visit Spring

13 Bibliography Benitez-Silva, Hugo, et al. How Well do Individuals Predict the Selling Prices of their Homes? Levy Economics Institute Working Paper No. 571, March 7, Web. May Bucks, Brian, and Karen Pence. Do Homeowners Know Their House Values and Mortgage Terms? Federal Reserve Board Working Paper No , January Web. May Case, Karl E., and Robert J. Shiller. Is There a Bubble in the Housing Market? Brookings Papers on Economic Activity, (2003): Diamond, Douglas W., and Raghuram Rajan. The Credit Crisis: Conjectures About Causes and Remedies. NBER Working Paper No , February Web. May Haurin, Donald, and Roberto Croce. Commentary on House Price Trends. University of California Irvine: Proceedings of the Housing Policy Symposium, February 19-20, Web. May Hendershott, Patric, Lafayette, William, and Donald Haurin. Debt Usage and Mortgage Choice: The FHA- Conventional Decision. Journal of Urban Economics 41.2 (1995): Keys, Benjamin, et al. Did Securitization Lead to Lax Screening? Evidence from Subprime Loans. Quarterly Journal of Economics (2010): Lam, Ken, and Bulbul Kaul. Analysis of Housing Finance Issues Using the American Housing Survey (AHS). United States: Department of Housing and Urban Development, Office of Policy Development and Research, April Web. May Ling, David, and Gary McGill. Evidence on the Demand for Mortgage Debt by Owner-Occupants. Journal of Urban Economics 44.3 (1998): Mian, Atif, and Amir Sufi. The Consequences of Mortgage Credit Expansion: Evidence from the US Mortgage Default Crisis. Quarterly Journal of Economics (2009): Qi, Min, and Xiaolong Yang. Loss given default of high loan-to-value mortgages. Journal of Banking & Finance 33.5 (2009): Saiz, Albert. On Local Housing Supply Elasticity. Institute for the Study of Labor Working Paper, July 31, Web. May Shiller, Robert. Understanding Recent Trends in House Prices and Home Ownership. NBER Working Paper 13553, October Web. May Spring

14 Appendix Mian and Sufi Figure VI, lower-left panel Spring