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Unemployment, Negative Equity, and Strategic Default∗

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Unemployment, Negative Equity, and Strategic Default∗ Can't Pay or Won't Pay? Unemployment, Negative Equity, and Strategic Default∗ Kristopher Gerardi,y Kyle F. Herkenhoff,z Lee E. Ohanian,x and Paul S. Willen{ June 12, 2015 Abstract Prior research has found that job loss, as proxied for by regional unemployment rates, is a weak predictor of mortgage default. In contrast, using micro data from the PSID, this paper finds that job loss and adverse financial shocks are important deter- minants of mortgage default. Households with an unemployed head are approximately three times more likely to default compared to households with an employed head. Sim- ilarly, households that experience divorce, report large outstanding medical expenses, or have had any other severe income loss are much more likely to exercise their default option. While household-level employment and financial shocks are important drivers of mortgage default, our analysis shows that the vast majority of financially distressed households do not default. More than 80% of unemployed households with less than one month of mortgage payments in savings are current on their payments. We argue that this has important implications for theoretical models of mortgage default as well as loss mitigation policies. Finally, this paper provides some of the first direct evidence on the extent of strategic default. Wealth data suggest a limited scope for strategic ∗We are grateful for comments by Gene Amromin, Jan Brueckner, Satyajit Chatterjee, Morris Davis, Andra Ghent, John Krainer, Edward Kung, Stuart Gabriel, Erwan Quintin, Joe Tracy, and Rob Valetta as well as comments from seminar participants at the 2014 FRBSF-Ziman Center Housing Conference, 2014 HULM Conference at FRB Chicago, and 2015 AREUEA. Jaclene Begley and Lara Lowenstein provided excellent research assistance. yGerardi: Federal Reserve Bank of Atlanta, Email: [email protected] zHerkenhoff: University of California - Los Angeles, Email: [email protected], Herkenhoff thanks the Ziman Center for funding. xOhanian: University of California - Los Angeles and Center for the Advance Study in Economic Effi- ciency, Arizona State University, Email: [email protected] {Willen: Federal Reserve Bank of Boston, Email: [email protected] 1 1 default, with only 3 of underwater defaulters having enough liquid assets to cover 1 month's mortgage payment. 2 1. Introduction The impact of household-level financial shocks on mortgage default decisions has been de- bated for forty years.1 Anecdotal and limited survey evidence suggest that life events such as job loss, illness and divorce play an important role, but no systematic evidence has been found due to the lack of information on household-level employment and income shocks in the loan-level datasets used by researchers to study mortgage performance. Many researchers have attempted to use aggregated unemployment and divorce rates as proxies for household- level shocks, but have found, at best, only weak correlations with default behavior.2 In this paper, we use new mortgage default data from the Panel Study of Income Dynamics (PSID) to fill this gap in our understanding of the household mortgage default decision. The evidence we find largely confirms the anecdotes and survey findings.3 The first major finding of the paper is that household-level financial shocks play a major role in the default decision. In the sample as a whole, the default rate for households in which the head is unemployed is three times higher than for households in which the head is employed. Similarly, households that experience divorce, report large outstanding medical expenses, or have had any other severe income loss are much more likely to exercise their default option. Approximately 43% of households in default have suffered what we identify to be significant cash flow shocks (job loss, divorce, and/or severe income loss) in the year prior to default as opposed to only 16 percent in the population as a whole. Regression analysis confirms these basic patterns in the data. Controlling for the loan-to-value (LTV) ratio and a host of demographic, regional, and mortgage controls, we still find that unemployment 1Early empirical work on mortgage default includes Campbell and Dietrich [1983], Foster and Order [1985], Vandell [1995], Deng et al. [1996], Deng et al. [2000], B¨oheimand Taylor [2000], among others. Recent work on empirical mortgage default includes Foote et al. [2008], Haughwout et al. [2008], Mayer et al. [2009], Gathergood [2009], Goodman et al. [2010], Elul et al. [2010], Bhutta et al. [2011], and Mocetti and Viviano [2013] among others. 2See Gyourko and Tracy [2014] for a detailed discussion of how using aggregate unemployment rates as proxies for individual shocks could mask the true relationship between unemployment and mortgage default. 3See for example Hurd and Rohwedder [2010] who added questions to the RAND American Life Panel directly asking about whether or not households fell behind on their mortgage after losing their job (about 8.6% of the 699 households surveyed said they fell behind). 3 and other cash flow shocks have economically and statistically significant effects on the probability of default. We estimate that job loss by the head of household has an impact on default rates that is equivalent to a 45% reduction in home equity. Likewise, job loss by the spouse is equivalent to a 31% reduction in equity. The data clearly show that unemployment and individual household finances have a large influence on a household's decision to default. But the data allow us to query further and to determine how they matter. We start with the standard taxonomy of default popular in the literature in which defaulters are divided into two groups. The first are \can't pay" defaulters, who are unable to afford their mortgage payments because of job loss or some other life event. The second group are \won't pay" defaulters, also known as strategic defaulters in the literature. \Won't pay" households can afford the mortgage payment but treat the mortgaged property as a call option to repurchase the home for the outstanding balance on the mortgage, as in Kau and Keenan [1995]. Using standard option pricing techniques, the \won't pay" households default if the value of the call option falls short of the mortgage payment. The data provide some support for the existence of \can't pay" defaulters. More than 40% of all defaulters are either unemployed, have recently gotten divorced or have exceptionally high medical expenses. Furthermore, approximately two-thirds of households in default have liquid asset holdings that amount to less than one monthly mortgage payment and 84% have holdings of less than two mortgage payments. In contrast, the data suggest the existence of a much smaller group of \won't pay" or strategic defaulters. We show below 1 that the median defaulter can only make 3 of one monthly mortgage payment with their liquid assets. Additionally, only 10% of households in default have four months or more of mortgage payments in the bank and about 5% have more than 5 months. In general we find that households in default have very low levels of wealth. While the evidence on \can't pay" and \won't pay" defaulters is consistent with prior survey findings, the data also present a striking puzzle for the \can't pay"/\won't pay" 4 dichotomy. Suppose we define \can't pay" to be households without a job and with less than one month of mortgage payments in stock, bonds, or liquid assets (net of unsecured debt). The data show that approximately 19 percent of these \can't pay households" are in default as compared to only 4 percent in the population as a whole. What is surprising though is the flip side of that 19 percent: 81 percent of \can't pay" households are current on their loans. In other words, despite no income and no savings, most households in this group continue to pay their mortgages. Furthermore, we show that these striking patterns remain even when conditioning on negative equity. The \double trigger" model of mortgage default that has received a good deal of attention in the literature posits that it is the combination of household-hold level shocks and negative equity that drives the majority of mortgage defaults. While we do find a higher default rate among \can't pay" borrowers with negative equity (33 percent), it is still the case that the majority of these borrowers are current on their mortgage payments (67 percent). On the flip side we find that less than 1 percent of \can pay" households, which we define to be households that are employed and have at least 6 months worth of mortgage payments in stock, bonds, or liquid assets (net of unsecured debt) are in default. Conditioning on negative equity does not have much effect as only 5 percent of \can pay" borrowers with negative equity are in default. Thus, the vast majority of borrowers in positions of negative equity continue paying their mortgages, which suggests that strategic default rarely occurs. The existence of so many \can't pay" but \do pay" or \non-strategic non-defaulters" in the data requires some explanation. The first important insight is that while \can't pay" may seem like a logical concept in principle, it is problematic in the data because income and liquid assets are not the only potential sources of funds for borrowers. Among other things, borrowers can tap unsecured debt markets. The PSID does not provide information on credit limits but the Survey of Consumer Finances (SCF) does and it shows that homeowners in default have available credit equal to 3.6× their monthly mortgage payment, on average.4 In 4The average homeowner in default in the SCF has unused credit (credit limit less outstanding balances) equal to 3.6× their monthly mortgage payment whereas the median household has unused credit equal to 5 other words, borrowers may be taking out unsecured loans to pay their mortgages.
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