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Journal of Financial Services Research

Erschienen in:

14.07.2016

Foreclosure Delay and Consumer Credit Performance

verfasst von: Paul S. Calem, Julapa Jagtiani, William W. Lang

Erschienen in: Journal of Financial Services Research | Ausgabe 3/2017

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Abstract

The deep housing market recession from 2008 through 2010 was characterized by a steep rise in number of foreclosures. The average length of time from onset of delinquency through the end of the foreclosure process also expanded dramatically. Although most individuals undergoing foreclosure were experiencing serious financial stress, the extended foreclosure timelines enabled them to live in their homes without making mortgage payments until the end of the foreclosure process, thus providing temporary income and liquidity benefits from lower housing costs. This paper investigates the impact of extended foreclosure timelines on borrower performance with credit card debt. Our results indicate that a longer period of nonpayment of mortgage expenses results in higher cure rates on delinquent credit cards and reduced credit card balances. Thus, foreclosure process delays may have mitigated the impact of the economic downturn on credit card default—suggesting that improvement in credit card performance during the post-crisis period would likely be slowed by the removal of the temporary liquidity benefits as foreclosures reach completion.

Vorheriger Artikel Evidence of Forward-Looking Loan Loss Provisioning with Credit Market Information

Nächster Artikel Household Access to Mortgages in the UK

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The term “income benefit” here refers to the standard “income effect” from a fall in commodity price. In effect, a household undergoing foreclosure and no longer making mortgage payments is facing a temporary decline in housing costs.

The classification of judicial states in Cordell et al. (2016) is adopted from Cutts and Merrill (2008), and lists CT, DE, FL, IA, IL, IN, KS, KY, LA, ME, ND, NE, NJ, NM, NY, OH, OK, PA, SC, SD, VT, and WI as having a judicial foreclosure process. Gerardi, Lambie-Hanson, and Willen (2013) point to minor differences in the classification of judicial foreclosure states across research studies, reflecting ambiguities in foreclosure rules in a few states.

Figure 1 is based on foreclosure data compiled by RealtyTrac.com. Our empirical analysis relies primarily on loan-level mortgage data from the Loan Processing Services (LPS) database, which is insufficiently populated pre-2004. Thus, the RealtyTrac.com data may provide more reliable time series back to 2000, although overall trends are similar to those observed in the LPS data.

The total number of foreclosure filings includes re-filings following withdrawal or rejection of an initial filing, which may occur because of legal technicalities or obstacles, or following a loan modification. In some states, primarily those with longer, judicial foreclosure processes, total number of foreclosure filings continued to rise through 2012, likely due to such re-filings. For example, foreclosure filing rates rose about 50 % from 2011 to 2012 in New Jersey, Florida, Connecticut, and Indiana and by about 33 % in Illinois and New York.

Although relief from housing payments adds to household liquidity, not making mortgage, property tax, and maintenance payments has costs. The borrower may need to put up a legal defense to maintain occupancy, in which case there may be legal expenses. Cordell et al. (2016) describe the added losses to lenders given foreclosure delays. Also, reduced maintenance may lead to unanticipated, unavoidable repair costs. Such costs may limit the impact of longer foreclosure timelines on the borrower’s performance with credit card or other consumer debt.

Our findings are consistent with Ambrose et al. (1997), who find that credit scores of those consumers who foreclosed in the 1990s tend to recover after the foreclosure.

In retail credit markets more generally, supply and demand for credit are sensitive to which the legal environment is protective of creditors and punitive toward delinquent debtors. Araujo and Funchal (2015), for example, find that there is an intermediate level of debtor punishment that low levels of creditor protection inhibits the supply of credit, while high levels of debtor punishment inhibits demand. They conclude that “the optimal punishment level is one that allows a fresh start for debtors and a significant recovery for lenders in case of bankruptcy.”

Limbo loans are defined as mortgage loans that have been delinquent for extended periods of time but have not progressed to any form of resolution, such as property sale, refinancing, modification, or foreclosure.

There are also potential substantial costs associated with foreclosure delay. For example, Gerardi, Rosenblatt, Willen, and Yao (2015) find that lengthening foreclosure timelines have exacerbated the negative impact of mortgage distress and adversely impacted neighborhood home prices.

They used loan-level data from Blackbox Logic’s BBx database, which covers 90 % of non-Agency residential securitized deals, including prime, Alt-A, and subprime.

The impact of post-default experience on the decision to default has been examined along several other dimensions. Ghent and Kudlyak (2011) find that mortgage borrowers are less likely to default in recourse states (where mortgage lenders have the right to pursue a borrower’s other assets if the property collateral is not sufficient to cover the mortgage amount), controlling for degree of negative equity. In addition, mortgage lenders were more likely to pursue alternatives to foreclosure in the recourse states. Mayer et al. (2014) and Jagtiani and Lang (2011) find that access to loan modification programs impact the costs and benefits associated with mortgage delinquency and thereby influence default behavior.

There may be more mild instances of delinquency where consumers maintain interest payments but fall behind on scheduled principal payments. These situations are more likely to result in rescheduling of the mortgage payments than a foreclosure situation.

Open accounts have a date of last activity in the prior six months and positive balances.

We use the zip code from the FRBNY Consumer Credit Panel/Equifax corresponding with the quarter in which the mortgage first appears in the Equifax database. The first available quarter is generally within one or two quarters following the open date.

In the FRBNY Consumer Credit Panel/Equifax, 14.3 % of individuals who are delinquent on a mortgage have two or more mortgages and thus are excluded by this criterion. We also note that out of the overall percentage of the population of individuals with a mortgage (delinquent or not), 12.1 % have two or more mortgages.

With this refinement, the match rate rises to 3.8 %.

The LPS data are less comprehensive pre-2004, while foreclosure start dates in 2013 or later were omitted because of insufficient length of the observed performance period after the initiation of foreclosure.

Restricting the foreclosure start date to 2004Q1 through 2012Q4 leaves 360,804 mortgages. Of these 328,883 complete the foreclosure process in 2014 Q2 or earlier, with 292,577 having a foreclosure end type other than servicing transfer.

In this paper, we only report results using the merged data. However, we have also conducted our analysis without merging loan-level data by merging in to the FRBNY Consumer Credit Panel/Equifax with the average foreclosure timelines from LPS geography and loan size range, and we obtained qualitatively similar results.

The card cure and card balance change results reported in this paper are robust to employing an alternative, narrower definition that equates foreclosure timeline with the period between foreclosure start and end.

An endogenous relationship may arise, for example, if borrowers choosing to make partial payments on the mortgage while allowing their cards to remain delinquent might receive more favorable treatment from servicers, as reflected in delaying the foreclosure process. Moreover, individual foreclosure and card outcomes might be jointly influenced by borrower behavioral characteristics. For example, households with time-inconsistent preferences might choose a prolonged foreclosure process for its near-term liquidity benefits, and such “hyperbolic discounting” could also influence their card repayment behavior. For discussion of hyperbolic discounting among credit card borrowers more generally, see Incekara-Hafalir (2015).

We use STATA’s IVPROBIT estimator.

The results are robust to including control variables for borrower age.

The LPS loss mitigation flag is used to determine modification status.

Various other explanatory variables were tested, including additional indicators for the type of mortgage (for example; adjustable rate, interest-only); total number of open, nonmortgage credit accounts; ratio of mortgage payment to credit card balance; and local area house price appreciation. The results presented here and in the next two sections were not materially affected by including these variables, which were not statistically significant.

We use the foreclosure start date for assigning observations to periods.

For discussion of the limitations placed on consumers by the BAPCPA, see DeFalaise (2006), Gargotta (2006), and Lesperance (2006).

The BAPCPA restrictions may have had an impact on the timelines of foreclosures that commenced some time prior to October 2005 by restricting a borrower’s ability to refile for bankruptcy later on. Therefore, for the first-stage, foreclosure timeline regression, we extend the BAPCPA period (set BAPCPA = 1) to include all of 2005.

Examples include invalid geography being reported in the data; missing risk score; and improperly reported mortgage origination date.

The remaining (pre-BCPCPA) portion of the pre-crisis period contains only 4 % of the sample. With the BCPCPA period is extended to include all of 2005 (for the first-stage regression), the remaining portion of the pre-crisis period contains only 2 % of the sample.

We are grateful to an anonymous referee for alerting us to this possibility and suggesting a distinction between judicial and non-judicial states.

Standard errors for these regressions and those presented later in the paper are clustered at the county level.

The results are consistent with the hypothesized linear relationship for the pooled sample approximates an overall cubic relationship such that the steepest slope is in the intermediate timeline range where judicial and non-judicial states overlap.

The dummy variables indicating the period of foreclosure filing are not included in the final model because they are correlated with the foreclosure timelines.

There are 231,753 individuals with a least one credit card, of whom 210,342 have card balances totaling at least $100 and 76 have balances exceeding $250,000.

We use STATA’s IVREGRESS estimator.

Consistent results are obtained when the continuous dependent variable defined by (1) and (2) for the stage two equation is replaced by a binary variable Y indicating balance decrease after six quarters (Y = 1) or not (Y = 0); a longer instrumented timeline is associated with a greater likelihood of balance decline.

Similar results are obtained employing a one-stage ordinary least squares regression with a segment-average foreclosure timeline as the instrument for individual timeline, with or without county-fixed effects added to the regression equation.

We calculate marginal effects at representative values by applying our estimated models to all individuals replacing the instrumented timeline with 6, 12, 18, 24, 30, and 36 months, and then averaging the estimated cure rates and balance changes across individuals. We then subtract the mean of estimated balance change or cure rate at 6 months from the mean for a longer timeline to show the change relative to the six month baseline.

We drop Dummy Card 60+ DPD 12 Months Prior, which is not relevant in the context of this population that consists entirely of individuals emerging from a period of financial stress.

Allen L, Peristiani S, Tang Y (2014) Bank delays in the resolution of delinquent mortgages: the problem of limbo loans. J Real Estate Res, forthcoming

Ambrose BW, Buttimer RJ Jr, Capone CA (1997) Pricing mortgage default and foreclosure delay. J Money Credit Bank 29(3):314–325CrossRef

Araujo A, Funchal B (2015) How much should debtors be punished in case of default? J Financ Serv Res 47(2):229–245CrossRef

Cordell L, Geng L, Goodman L, Yang L (2016) The cost of foreclosure delay. Real Estate Econ (forthcoming)

Cutts AC, Merrill WA (2008) Interventions in mortgage default: policies and practices to prevent home loss and lower costs. Joint Center for Housing Studies - Harvard University Working Paper UCC08-15

DeFalaise MA (2006) Means testing and preventing abuse by debtors. U S Attorneys’ Bulletin 54(4):2–8

Gargotta CA (2006) Selected new consumer provisions to the bankruptcy abuse prevention and consumer protection act. U S Attorneys’ Bull 54(4):8–11

Gerardi K, Lambie-Hanson L, Willen P (2013) Do borrower rights improve borrower outcomes? Evidence from the foreclosure process. J Urban Econ 73(1):1–17CrossRef

Gerardi K, Rosenblatt E, Willen P, Yao VW (2015) Foreclosure externalities: some new evidence. J Urban Econ 42(3):42–56CrossRef

Ghent A, Kudlyak M (2011) Recourse and residential mortgage default: evidence from U.S. States. Rev Financ Stud 24(9):3139–3186CrossRef

Incekara-Hafalir E (2015) Credit card competition and naive hyperbolic consumers. J Financ Serv Res 47(2):153–175CrossRef

Jagtiani J, Lang WW (2011) Strategic defaults on first and second lien mortgages during the financial crisis. J Fixed Income 20(4):7–23CrossRef

Jones LD (1993) Deficiency judgments and the exercise of the default option in home mortgage loans. J Law Econ 36(1):115–138CrossRef

Lee D, Mayer C, Tracy J (2013) A new look at second liens. In: Glaeser EL, Sinai T (eds) Housing and the financial crisis. University of Chicago Press, Chicago, Chapter 5

Lesperance JR (2006) Bankruptcy abuse prevention and consumer protection act and the automatic stay. U S Attorneys’ Bull 54(4):12–16

Mayer C, Morrison E, Piskorski T, Gupta A (2014) Mortgage modification and strategic behavior: evidence from a legal settlement with countrywide. Am Econ Rev 104(9):2830–2857CrossRef

Pence K (2006) Foreclosing on opportunity: state laws and mortgage credit. Rev Econ Stat 88(1):177–182

Zhu S, Pace RK (2015) The influence of foreclosure delays on borrowers’ default behavior. J Money Credit Bank 47(6):1205–1222CrossRef

Titel: Foreclosure Delay and Consumer Credit Performance
verfasst von: Paul S. Calem
Julapa Jagtiani
William W. Lang
Publikationsdatum: 14.07.2016
Verlag: Springer US
Erschienen in: Journal of Financial Services Research / Ausgabe 3/2017
Print ISSN: 0920-8550
Elektronische ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-016-0257-y

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