Abstract
House price volatility; lender and borrower perception of price trends, loan and property features; and the borrower’s put option are integrated in a model of residential mortgage default. These dimensions of the default problem have, to our knowledge, not previously been considered altogether within the same investigation framework. We rely on a sample of individual mortgage loans for 20 counties in Florida, over the period 2001 through 2008, third quarter, with housing price performance obtained from repeat sales analysis of individual transactions. The results from the analysis strongly confirm the significance of the borrower’s put as an operative factor in default. At the same time, the results provide convincing evidence that the experience in Florida is in part driven by lenders and purchasers exhibiting euphoric behavior such that in markets with higher price appreciation there is a willingness to accept recent prior performance as an indicator of future risk. This connection illustrates a familiar moral hazard in the housing market due to the limited information about future prices.
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Notes
For example, see Demyanyk and Van Hemert 2008.
According to Robert Shiller (2005) the term “irrational exuberance” derives from some words that Alan Greenspan, the then Chairman of the Federal Reserve Board in Washington, used in a black-tie dinner speech entitled “ The Challenge of Central Banking in a Democratic Society” before the American Enterprise Institute at the Washington Hilton Hotel December 5, 1996.
See Baker and Wurgler 2007, page 129.
See, for example, Stan Liebowitz, “New Evidence on the Foreclosure Crisis,” Wall Street Journal, page A13, July 3–5, 2009.
The main research to date on mortgage default risk has been cross-sectional in nature. This includes linear discriminant analysis, cross sectional regression models and hazard models.
Foote, Gerardi and Willen (2008) conclude as long as the value of housing services derived from a house exceeds the amount of the monthly cost the borrower will seek to avoid default, even with "negative equity" so long as wealth and income permit.
See the bibliography in Shiller, Irrational Exuberance, 2 nd Edition, (2005) for extensive references to the work of both Case and Shiller.
Short selling in this context refers to the financial reference and not the current use of the term in residential real estate, selling at a price below the mortgage balance.
The economic characteristics of housing accord well in several respects with the types of securities that finance researchers regard as likely to be affected by “sentiment,” in pricing; they are economically small, heterogeneous, and thinly traded.
Some dependent variables among the seven estimating equations are also used as explanatory variables in other of the equations. Thus it can be argued that there is simultaneity among the equations, implying the need to use instrumental variables. The coefficients of potentially simultaneous variables are, however, being estimated simply as controls. Since the focus here is on the time trend (i.e. the quarterly indicators from each equation) and we are not attempting to interpret the other coefficient estimates, we believe it is appropriate to disregard any potential biases from endogeneity.
Due to truncated data, FHFA (formerly OFHEO) MSA price indices were used for these counties: Collier, Manatee, Pasco and Palm Beach.
The data are made available via a research affiliation between one of the authors and the Federal Reserve Bank of Richmond providing an access agreement between that author and LPS Analytics, Inc.
We expect valid euphoria indicators to be related to house price changes. However, our regressions of the euphoria variables are not on changes but on house price levels. This is because the house price indices for each county begin at a value of one for the initial quarter (1999 Q1). Thus, the level of the index is, in effect, a complete summary of lagged appreciation rates over the study period. This provides more information about past house price movements than we would obtain using quarterly appreciation rates.
We made extensive efforts to identify all second mortgages in the larger data set to match them as much as possible with first mortgages. Our estimated loan-to-value ratios include these second mortgages wherever possible.
To be consistent with the estimation of our euphoria equations (Eqs. 1A–7A), we restrict the sample to observations having original loan-to-value ratios between 50 and 125%. However in tests using the full available sample, which is about 20% larger, we found very little change in results. Our primary coefficients of interest—euphoria proxies and put coefficients—never differed by more than 7%, with an average difference of less than 2%.
Because a large portion of the sample of loans lack a debt-to-income ratio we also employ a statistical device sometimes referred to as modified zero order regression. (See Maddala 1977, p. 202) This variable allows use of the incomplete variable without loss of sample size.
We deleted from the estimation any case with a loan-to-value ratio exceeding 4 due to the likelihood of recording error. This eliminated 117 observations out of 963,163 available.
To estimate the distribution shift we use the derivative of LTV with respect to house value, which is –LTV/V.
See, for example, “Monetary Policy and the Housing Bubble,” a speech of Chairman Ben Bernanke at the Annual Meeting of the American Economics Association, Atlanta, January 3, 2010.
Where a “piggy-back” second mortgage (simultaneous second) was identifiable, its value was added to the first mortgage.
We assume that loans with an original LTV above 125% are likely to have erroneous data. We expect loans with an original LTV below 50% to reflect different clienteles and different behavior from those above that level.
References
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This paper has benefited from helpful conversations with Brent Ambrose, Allen Goodman, and Edward Prescott. Also, we thank Shane Sherlund, Tomasz Piskorski and participants in the Conference on Household Portfolio-Choice and Financial Decision-Making at the Rodney L. White Center for Financial Research, Wharton. Mark Watson of the Federal Reserve Bank of Kansas City provided invaluable assistance with the data set. Finally, we want to thank discussants at several meetings of the American Real Estate and Urban Economics Association for valuable suggestions. We are indebted to the Federal Reserve Bank of Richmond and LPS Applied Analytics for providing access to the data via a research affiliation between Brent C Smith and the Federal Reserve Bank of Richmond. All views and errors, however, are the responsibility of the authors and do not reflect those of the Federal Reserve Bank of Richmond, the Federal Reserve System or LPS Applied Analytics.
Appendix—Euphoria Effects Proxy Equations
Appendix—Euphoria Effects Proxy Equations
The effects proxies will be the time trend indicators with respect to the following critical lending practice variables, after controlling for risk compensating factors in the lending process. We focus first on the behavior of the trend in the original loan-to-value ratio. To ascertain any trend in the loan-to-value ratio of new loans after controlling for other relevant factors the following equation is estimated:
Mortgage type is distinguished by fixed or adjustable interest rate and interest only and negative amortization. In this particular equation we restrict our sample to fixed rate loans. Tenure is limited to owner-occupied residences, and mortgages to those with first liens.Footnote 21 We also restrict loan-to-value ratio to no more than 125%, and no less than 50%.Footnote 22 As a control to account for cross sectional variation in underwriting information, we use the original, debt-to-income ratio. Additional cross sectional controls include the FICO score (standardized), and the spread between the prevailing interest rate and the rate on the loan. Finally, quarterly indicators representing the date the loan was originated are included and serve as indicators for variation over time.
The second effects indicator for euphoria, trend in original debt-to-income ratio, is estimated with the following equation:
The estimating sample is, again, restricted to fixed rate loans on owner occupied residences. Other controls are similar to the previous equation, except for the substitution of LTV with DTI.
The third effects indicator for euphoria is the trend in the incidence of adverse features, and is examined with the following equation:
where adverse features include any feature that can increase the balance and payments in subsequent years, including adjustable rate, interest-only loans, and negative amortization loans. The dependent variable is dichotomous, indicating the presence of such a feature. Controls remain consistent with prior euphoria equations, but with adjustable rate loans retained in the sample for this and subsequent equations.
The fourth effects indicator for euphoria focuses on the trend in probable Alt-A or subprime loans, referred to as high risk loans, as indicated by being low/no-doc or having a prepayment penalty. Thus we formulate the euphoria indicators as follows:
The right-hand variables and limits are consistent with the previous equations. Again, the dependent variable is dichotomous.
The fifth effects indicator for euphoria is the trend in those loans secured by relatively risky property types, including condominium, renter-occupied and 2–4 unit residences. This is examined via the following equation:
Controls are as before, except that for this equation non-owner occupied cases are included. The dependent variable is a dichotomous indicator.
The sixth indicator equation is the trend in presence of a prepayment penalty, alone. We isolate this factor because of its apparent uniquely strong relationship to defaulted loans (Quercia et al. 2005).
Controls and limits are as before, and the dependent variable is binary.
The last indicator dependent variable, low documentation, refers to the trend in loans classified as low document loans over the observation period as follows:
Controls and limits are as before, and the dependent variable is, again, a binary indicator.
The first two equations involve continuous dependent variables and are estimated using OLS regression. The remaining five equations have binary dependent variables and are estimated via logit regression.
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Archer, W.R., Smith, B.C. Residential Mortgage Default: The Roles of House Price Volatility, Euphoria and the Borrower’s Put Option. J Real Estate Finan Econ 46, 355–378 (2013). https://doi.org/10.1007/s11146-011-9335-y
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DOI: https://doi.org/10.1007/s11146-011-9335-y