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The Journal of Real Estate Finance and Economics

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27-06-2019

Liquidity Constraints, Home Equity and Residential Mortgage Losses

Authors: Hung Xuan Do, Daniel Rösch, Harald Scheule

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

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Abstract

This paper analyses how mortgage borrower liquidity constraints and home equity drive the realized loss rates given default using loan-level data. We define defaulted loans with zero loss as cures and those with non-zero loss as non-cures. We find economically that borrower liquidity constraints and positive equity explain cure, while negative equity explains non-zero loss. The findings provide an important economic-rationale for a separation of the cure and loss processes in mortgage loss models and their applications such as loan pricing and bank capital regulation. The results have great relevance for the multi-trillion dollar mortgage industry for a more efficient capital allocation, better mortgage pricing and more forward-looking loan loss provisioning.

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In this paper, we use the two terms non-zero LGD loan (zero LGD loan) and non-cured loan (cured loan) interchangeably.

In the study of Agarwal et al. (2012), adverse selection bias is broadly defined as the subjective decision of lenders to retain some specific types of loans (e.g., loans with high quality) in their portfolio while selling others to investors (e.g., loans with lower quality).

The procedures to calculate investor losses are detailed in the Residential Mortgage-Backed Security (RMBS) prospectus and the pooling and servicing agreement. Cordell et al. (2008) show that servicers have a liability to act in the best interest (i.e., maximising value) of investors.

Clauretie and Daneshvary (2009) show that mortgage foreclosure may result in various outcomes: third party sales, deed in lieu (transfer of title to lender), real-estate owned (lender owns the property after it fails to sell) or short sale (sale by homeowner with consent of lender). The data used in our paper is reported by loan servicers and we do not observe the channel that led to the loan loss.

We follow Bekaert et al. (2014) to define the start of the subprime mortgage crisis as 7 August 2007, due to the initial fall of equity markets and the first intervention of central banks to provide liquidity to financial markets. The end of the GFC is defined as 29 May 2009 with reference to the National Bureau of Economic and Research.

Realised losses are generally lagging the real economy. The study shows that LGDs imply beta coefficients for North America of approximately 50%. Applying a market risk premium of 6% translates into a risk premium of 3%.

We set LC_it to zero if the current balance and scheduled balance are both zero, indicating that the borrower has repaid the loan in full and does not experience a liquidity constraint. We consider LC_it as a missing value (and exclude from analyses) if the scheduled balance equals zero but the current balance is positive. This has no further implications on the results as this filter rule only applies for six defaulted loans.

In either of these situations, the borrower does not experience a liquidity constraint. Curtailments refer to the cases where borrowers make extra principal loan repayments for their mortgages, and therefore, current balances are less than scheduled balances (see for example, Amromin et al. 2007; Adelman et al. 2010).

We also perform a robustness check below using Zillow HPI as well as the 3-digit and 5-digit Zip Code FHFA HPI at the zip code level and obtain consistent results. This is consistent with Glennon et al. (2018) who find that the FHFA HPI index is comparable to other commercial HPI measures such as the Case-Shiller MSA index, and the Black Knight index (at the zip code , MSA , and state level).

We exclude loans with missing zip codes.

We do not include the loan age in our set of control variables because of the collinearity between loan age and the DefaultToEOO (a variable that we use to control the resolution bias), which results from standard maturities (in particular 30 years) applied to most of the analysed mortgage contracts. For state foreclosure laws, we do not include judicial process (JP) in the cure equation but the non-zero LGD equation. This helps to differentiate sets of covariates used in the two equations of our two-step selection approach (see Section 4) to avoid an identification problem for the selection model.

To conserve space, we do not provide estimated results of these preliminary models. Details are available upon request.

Our model differs as we condition on foreclosure.

We have also tested non-linear transformation models with clustered standard errors (such as logit and probit) for non-zero LGD and find consistent results. Details are available on request.

To address the concern that most of the defaults occur in the crisis subsample, we also perform a robustness check with the after-crisis subsample which covers from 2009:Q3 to 2015:Q1. Our main results remain consistent. Details are available upon request.

This is consistent with our observation that approximately 61% of defaulted loans that did not experience positive borrower liquidity constraints did not have negative equity.

At origination, the loan size dynamics are strongly consistent with the house value since the majority of mortgage loans are originated with a loan-to-value ratio of 80%.

See www.fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/wp1601_FAQs_ZIP5_HPIs.pdf.

In the full-information framework, we impose no restriction on the correlation between cure and non-zero LGDs. Meanwhile, in the limited information framework we restrict the correlation to be zero. Besides, we find that the OLS model specification with no spline terms provides the best out-of-time predictive performance in terms of Root Mean Square Errors. Therefore, we employ this specification for a forecasting comparison.

We do not assess the years from 2013 to 2015 to avoid a loss information bias as most of the non-zero losses are observed within three years of the default events.

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Title: Liquidity Constraints, Home Equity and Residential Mortgage Losses
Authors: Hung Xuan Do
Daniel Rösch
Harald Scheule
Publication date: 27-06-2019
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/s11146-019-09709-9

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