The Rodney L . White Center for Financial Research What “ Triggers ” Mortgage Default ?

This paper assesses the relative importance of two key drivers of mortgage default: negative equity and illiquidity. To do so, we combine loan-level mortgage data with detailed credit bureau information about the borrower's broader balance sheet. This gives us a direct way to measure illiquid borrowers: those with high credit card utilization rates. We find that both negative equity and illiquidity are significantly associated with mortgage default, with comparably sized marginal effects. Moreover, these two factors interact with each other: The effect of illiquidity on default generally increases with high combined loan-to-value ratios (CLTV), though is significant even for low CLTV. County-level unemployment shocks are also associated with higher default risk (though less so than high utilization) and strongly interact with CLTV. In addition, having a second mortgage implies significantly higher default risk, particularly for borrowers who have a first-mortgage LTV approaching 100 percent. * Ronel Elul (corresponding author): Federal Reserve Bank of Philadelphia, Philadelphia, PA 19106 (email: ronel.elul@phil.frb.org); Nicholas S. Souleles: University of Pennsylvania, Philadelphia, PA 19104 (email: souleles@wharton.upenn.edu); Souphala Chomsisengphet: Office of the Comptroller of the Currency, Washington, DC 20219 (email: souphala.chomsisengphet@occ.treas.gov); Dennis Glennon: Office of the Comptroller of the Currency, Washington, DC 20219 (email: dennis.glennon@ occ.treas.gov); Robert M. Hunt: Federal Reserve Bank of Philadelphia, Philadelphia, PA 19106 (email: bob.hunt@phil.frb.org). Session title: “Mortgage Market and the Financial Crisis” (chair: Nancy Wallace, discussant: Benjamin Keys). The views expressed in this paper are those of the authors and not necessarily those of the Federal Reserve Bank of Philadelphia, the Federal Reserve System, or the Office of the Comptroller of the Currency. This paper is available free of charge at www.philadelphiafed. org/research-and-data/publications/working-papers/. The authors thank Bob O’Loughlin for outstanding research assistance, and the discussant, Benjamin Keys, for helpful comments. 2 The “option model” of mortgage default is traditionally interpreted as implying that borrowers should default if and only if they have negative equity in their home. However, numerous studies have found that many borrowers with negative equity do not default; and, conversely, default is often associated with “shocks,” such as unemployment. 2 One standard way of reconciling the model and the data is to introduce transaction costs of defaulting, such as moving costs, reputation costs (e.g., lost access to credit), and stigma. But such costs can be difficult to identify. Moreover, properly understood, the option model does not imply that negative equity alone is sufficient for default. By defaulting today, one gives up the option to default in the future; as a result, even with negative equity, one might prefer to wait and see if house prices recover (James Kau et al., 1994). This paper focuses on another — not mutually exclusive — explanation. The cost of continuing to pay one’s mortgage also depends on one’s idiosyncratic discount factor and thus on one’s liquidity position. For someone who is very illiquid, it can be costly to wait for house prices to recover. Indeed, in the extreme, he might literally not be able to find the cash to make the next mortgage payment. See Peter Elmer and Steven Seelig (1999), Kristopher Gerardi et al. (2007), and Patrick Bajari et al. (2008). This paper assesses the relative importance of these two factors for mortgage default: negative equity and illiquidity. To do so, we combine loan-level mortgage data with detailed credit bureau information about the borrower’s broader balance sheet. This gives us a direct way to identify illiquidity, using credit-card utilization rates. Sumit Agarwal et al. (2007) and David 1 For example, Chester Foster and Robert Van Order (1984), and Neil Bhutta et al. (2010). 2 Some papers have noted that it might be the “double-trigger” combination of negative equity and shocks that leads to default. See, for example, the discussion in Kerry Vandell (1995). But few papers have actually allowed for an interaction between these variables in the estimation, as we do below; one exception is Christopher Foote et al. (2009). 3 Ethan Cohen-Cole and Jonathan Morse (2009) examine the choice between mortgage versus credit-card default. 3 B. Gross and Nicholas S. Souleles (2002a) have shown that households who have “maxed-out” their credit cards display high propensities to spend in response to increases in income, consistent with their being liquidity constrained. Also, while illiquidity is conceptually distinct from shocks, high credit-card utilization may reflect prior shocks (e.g., James X. Sullivan, 2008), which otherwise might be hard to observe directly. Another benefit of using credit bureau data is that it allows us to measure total housing debt and thus the borrower’s combined loan-to-value ratio (CLTV). By contrast, the mortgage data sets typically used in the literature (e.g., from Loan Performance and Lender Processing Services [LPS]) have spotty information on second liens, at best, and so mis-measure the contribution of negative equity to default. This effect can be economically significant. For example, for the 26 percent of borrowers in our sample with a second mortgage, using only the first-mortgage loan-to-value ratio (LTV) underestimates their total CLTV by 15 percentage points. Disentangling these two determinants of mortgage default (negative equity and illiquidity) is also important for the policy debate over loan modifications. If negative equity dominates, one might tend to focus more on reducing principal, ceteris paribus. By contrast, if illiquidity is also important, temporary reductions in payments may also be useful. I. Data Our mortgage data are from the LPS dataset. We focus on first mortgages originated in 2005 and 2006, since these cohorts are the most likely to have negative equity during our sample period. The LPS data cover about 70 percent of all mortgage originations in these years. For 4 Formerly known as McDash, this dataset has been used extensively to study mortgage default. See, for example, Ronel Elul (2009) and the references therein. 4 brevity, we limit our sample to fixed-rate mortgages (FRMs). We further restrict attention to owner-occupied houses and exclude multifamily properties. We consider the three most common maturities: 15, 30, and 40 years. This sample represents about three-quarters of all FRMs in the LPS data. We follow our borrowers through April 2009. Our credit bureau data are from Equifax, one of the three major credit reporting agencies in the United States. The dataset contains a random subsample of credit users. The data include comprehensive summaries of key characteristics of the different types of debt held by individual borrowers (e.g., total credit-card balances and limits). In addition, the dataset includes loan-level information on these borrowers’ mortgage trades. We linked this dataset to the LPS dataset through the characteristics of the first mortgages, in particular, open date, initial balance and ZIP code. (To be conservative, we used only unique matches.) We matched about one-third of the potential overlap between the two datasets. Our final sample consists of approximately 364,000 FRMs. We also added MSA-level house price indexes from the Federal Housing Finance Agency and county-level unemployment rates from the Bureau of Labor Statistics. Since the house-price index and bureau data are available quarterly, we follow the mortgages quarterly.