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The Journal of Real Estate Finance and Economics

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02-08-2019

Achieving Effective Mortgage Modifications: The Importance of Household Characteristics

Authors: Thomas P. Boehm, Alan M. Schlottmann

Published in: The Journal of Real Estate Finance and Economics | Issue 2/2020

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Abstract

This analysis uses the Panel Study of Income Dynamics (PSID) from 2009 to 2013 to model the likelihood that a mortgage modification can be obtained by homeowners with mortgages that are in foreclosure, who have defaulted on one or more payments, or who have not yet defaulted on a payment but who are concerned they may do so in the immediate future. In addition, for those households who obtain a mortgage modification, the likelihood that the modification eliminates their mortgage problem is also estimated. Consistent with the existing literature, we find little direct evidence (significance of categorical variables identifying the borrowers race/ethnicity, sex/marital status, etc.) that being in a protected or otherwise potentially at risk class influences the likelihood that these households will get a mortgage modification, or that it will effectively deal with their payment problem. However, the analysis does find substantial indirect evidence that many of these households are at disadvantage because of differences in their socioeconomic characteristics. Some interesting finding are that independent variables which capture educational attainment, access to the internet, financial experience, and early identification of potential mortgage payment problems all have significant impacts on the likelihood that households will receive effective mortgage modifications.

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For a detailed presentation of HAMP and a thorough examination of its effectiveness, see Agarwal et al. (2017).

Collins et al. (2015), p. 168

An REO or “real estate owned” is real estate that is owned by a financial institution, government agency, or government insurer after an unsuccessful foreclosure sale. Typically, this happens because the opening bid at a foreclosure auction is set at the amount of the loan, and the value of the house is less than the amount owed.

Voicu et al. (2012) does find some evidence that minority mortgagors are less likely to cure their default by obtaining a modification.

See, for example, Stiglitz (1973) p. 289, footnote 9.

Households do not appear in both sub-samples, to avoid the need for adjustments to the empirical analysis as suggested by the econometrics literature on pooled, time-series cross-sectional analysis.

The PSID asked households if they are somewhat likely to miss a payment, or very likely to miss a payment. Both are included in problem type 1.

Bucks and Pence (2008), in their article titled “Do borrowers know their mortgage terms?” provide evidence that borrowers often either don’t know or misreport their mortgage terms. Across both time periods, there were 862 households that answer yes regarding working with the bank to restructure or modify their loan. Of those, 817 had changes in payments from the beginning to the end of the observation problem in which both sets of mortgage terms appeared to be meaningful and consistent with one another and, therefore, consistent with an actual modification having taken place. Consequently, 45 of the “yes” answers were deemed not to have actually received mortgage modifications.

Experimentation with randomly allocating these 13 observations to one period or the other did not change the results in any substantive way.

Access to this sensitive data must be provided through a formal agreement between the University of Michigan and the institution at which the author is employed. For details of the process and forms involved in obtaining the data, contact the Survey Research Center at the University of Michigan. The geo-coded data allows the researcher to identify the state county MSA (or PMSA) and the Census tract in which each household resides in every year for the entire time the PSID has been collected (1968 to the present).

The Neighborhood Stabilization Program was established originally under the Economic Recovery Act of 2008, and the data collected as part of “Program 3” was funded by the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010.

The PSID data contains information on the month in which foreclosure proceedings began and the NSP3 data contains information on the actual number of foreclosures that were initiated in a given Census tract in a given period. For the period of overlap (January to June 2010), those few foreclosures that overlapped between the PSID and NSP3 data sets could be subtracted from the NSP3 totals to avoid endogeneity issues in the use of foreclosure rates (calculated from the NSP3 data) as independent variables in the estimation of the models.

As evidence of this geographic variation, in an article appearing on CNNMoney.com (2012) titled “Foreclosure: America’s Hardest Hit Neighborhoods”, author Les Christie notes that, based on data generated by Realty Trac Inc., in 2011, 82% of the zip-codes hardest hit by foreclosures were in western states. In particular, 82 of the 100 worst zip codes are in California and 28 are in Nevada. Furthermore, all 5 of the hardest hit zip codes are in Las Vegas with 2,469 foreclosures in one North Las Vegas zip code (89031).

For example, in some neighborhoods, house price depreciation may be particularly extreme. This could make most loan modifications untenable from a financial institution’s perspective; increasing the likelihood of default and, ultimately, foreclosure in those neighborhoods. If less educated individuals are more likely to own homes in neighborhoods with worse house price depreciation, a variable measuring educational attainment could pick up both the neighborhood effect as well as any direct impact education might have on this outcome. This concern can be mitigated if a control variable such as the neighborhood foreclosure rate can be included in the estimation. Certainly, there could be other neighborhood factors that would influence the likelihood of receiving an effective modification, but their net impact would be reflected in neighborhood foreclosure rates.

The households who fall in the category “likely to miss payments” consider themselves to be either “somewhat” or “very” likely to miss a payment in the near future. All households who attempted to get a modification, but had not yet missed a payment, are in this category.

Note that a difference of opinion has developed in the literature regarding the implications of a household receiving a modification prior to the existence of an actual mortgage problem. Proponents argue that early intervention to provide debt relief will prevent excessive foreclosures that may otherwise lead to deadweight losses for borrowers and lenders, especially if debt contracts are incomplete (Bolton and Rosenthal 2002), and generate negative externalities for society (Campbell et al. 2011; Guiso et al. 2013; Melzer 2017). In contrast, critics argue that such policies could potentially generate moral hazard problems that are likely to raise the cost of credit in the long run.

In both cases, part of the marginal effect calculated is due to a significant interaction effect between these two variables.

One financial variable that was not available, but that might be expected to affect this outcome, is the household’s credit score. However, these households’ credit scores were sufficient to allow them to initially obtain a mortgage. In addition, variables capturing the household’s home equity position, whether they have a second mortgage, whether or not they are HAMP eligible, their front-end ratio, their net-wealth, and whether or not they had a checking and/or savings account are all included in the estimation. Thus, the model specification controls for the primary financial factors (related to the mortgagor’s credit score) which are most likely to have a significant impact on the probability that a household would apply for and receive a mortgage modification.

This is an example of a variable whose sign and significance represent the net outcome of households’ interest in obtaining a modification and the possible reluctance of the financial institutions to provide one. Households facing unemployment for the household head and/or the spouse would be more likely to need, and attempt to obtain, a mortgage modification from their lender. Alternatively, one would expect lenders to be less likely to provide modifications for households experiencing such problems. Our results suggest that the ability of borrowers to obtain modifications is the dominant force in this sample.

See Gerardi et al. (2007), and Capozza and Thomson (2005)

See for example Collins et al. (2015), p. 170.

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Agarwal, S., Amromin, G., Ben-David, I., Chomsisengphet, S., Piskorski, T., & Seru, A. (2017). Policy intervention in debt renegotiation: Evidence from the home affordable modification program. Journal of Political Economy, June 2017, 125(3), 654–712.CrossRef

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Title: Achieving Effective Mortgage Modifications: The Importance of Household Characteristics
Authors: Thomas P. Boehm
Alan M. Schlottmann
Publication date: 02-08-2019
Publisher: Springer US
Published in: The Journal of Real Estate Finance and Economics / Issue 2/2020
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-019-09719-7

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