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The Journal of Real Estate Finance and Economics

Erschienen in:

01.05.2013

Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans

verfasst von: Jun Chen, Yongheng Deng

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 4/2013

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Abstract

This study recognizes that commercial mortgage default is not a one-step process and examines a previously under explored aspect in the whole default process, that is the stage between the initial delinquency and default. We distinguish the servicers’ behavior from the borrowers’ behavior. A multinomial logit model is applied to analyze the servicers’ choice of workout options and a proportional hazard model is applied to analyze the borrower’s default decision-making process under time-varying conditions. We find that cash flow condition is the most significant factor in the servicers’ decision making process. We also find that borrowers make default decisions based upon both the equity position in the mortgage and the cash flow condition in the space market. Key real estate space market variables, such as market-level vacancy rates, also provide useful information in explaining commercial mortgage defaults. We find that special service seems to be successful in reducing the probability that a troubled loan will default. Finally, sensitivity analysis shows nontrivial economic significance of the impact of explanatory variables, real estate market variables in particular have the most significant impact on the pricing of special-serviced loans.

Vorheriger Artikel Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

Nächster Artikel On The Operating Performance of REITs Following Seasoned Equity Offerings: Anomaly Revisited

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Conceivably it would be even better to model the connections between the two processes under a more refined structural framework that potentially draws upon interactive decision theory. While such a framework is likely a fruitful direction for future research, our current empirical work is limited by the observed data that cannot reliably shed light on how exactly such interactive decisions were being made.

See Hendershott and Van Order (1987), Ambrose et al. (2001) for a discussion of put option theory and mortgage default, and Deng and Gabriel (2006) for an empirical application in residential mortgage studies.

An, Deng and Gabriel (2011) use the similar data to study the asymmetric information and adverse selection in the commercial mortgage backed security market.

We are grateful for Property & Portfolio Research, Inc. (PPR), a Boston-based independent commercial real estate research consulting firm, for providing these real estate market data.

While the empirical model could be potentially mis-specified without borrower characteristics, the problem is most likely muted in the CMBS universe. The institutional structure of the CMBS market does not provide incentive for developing direct and long-term relationship between the investors and borrowers. By definition, special servicers are impersonal to the situation as they come into play only when the loans have had troubles and they are out of the relationship immediately after the troubled loans are resolved; furthermore, special servicers are required by contractual obligations to act in an equitable and fair fashion. There is no incentive the special servicers to act more ruthlessly against certain types of borrowers, e.g. those with a good will (as they have a higher “cost” of default), as such action could be perceived as biased, unfair practice that could create unnecessary troubles to the special servicers.

Instead of loan term modification that is long term, servicers could offer forbearance, which is short term, to help borrowers overcome temporary cash flow problems for low LTV borrowers.

The growth rates are calculated as year-to-year for NOI and same for all other variables. This is because commercial mortgage borrowers are typically required to submit accurate annual financial statements regarding the performance of collateral properties. A similar situation happens on the equity side. While many equity owners of the commercial properties track leasing activity and income/expense performance on a quarterly basis, the most reliable measure remains at the annual frequency.

Note that in theory, the servicers should be more concerned about future rather than current or historical NOI changes. In this sense, we use the observed NOI change to serve as proxy for expected future changes (thanks to K.W. Chau for pointing this out). Note also that we implicitly assume that servicers do not intend to profit by foreclosing a property in a “hot” market in order to sell the property at a higher price than the principal loan amount. We believe this assumption is reasonable given that most servicers have no intention to take advantage of the temporary hardship of borrowers so that foreclosure is always the last and least preferred strategy of lenders facing defaults.

While there exists overlap in terms of the informational content from observed vacancy rates and rental growth rates as both adjust to external shocks simultaneously, they also represent different dimensions of information that borrowers use to assess future market conditions: observed rental growth rates help inform the near-term future rents through potential momentum effect in the illiquid and imperfect commercial space market; and the observed vacancy rates help inform the median to longer-term future rents because vacancy rates tend to be sticky (it takes up to 3–4 years to complete a commercial construction project hence supply is always lagging and slow to adjust). Its persistence and stickiness makes vacancy a useful indicator for future rental growth rates. See, for example, Wheaton and Torto (1988) and Ambrose et al. (2010). It should be noted that vacancy rates are traditionally used in the commercial real estate studies in the sectors of apartment, office, retail and warehouse, while occupancy rates are preferred measure for hotel properties.

In practice, special servicers are obligated by servicing agreements to make workout decisions that are most beneficial to the entire trust, i.e. the decisions should be made based on the best interest of all bond holders of a deal. This obligation can be analytically expressed as to maximize the NPV to the entire trust, which in principle is also equivalent to our utility function here.

See Clapp et a. (2006) for an example of application of multinomial logit function to model mortgage default.

Defining defaults as 90-days-late or more is consistent with many other studies, e.g. Archer et al. (2002). Loans that are delinquent for 90 days or more are also called “serious delinquency” in the mortgage industry. Foreclosure becomes a viable option only at this stage.

We should not conclude from our sample data that the majority of special serviced loans would not default because our data sample is censored.

See Clapp et al. (2006) for more discussions on application of Cox model study mortgage prepayment and default.

It is quite common that property owners only appraise the property value once in a long while. Even in the institutional real estate industry, property owners don’t re-appraise very often.

There are different ways to calculate the price of mortgages. We opt to use a simple approach to illustrate the main points without complicating the analytics of this study. This approach calculates loan price (Price) through a simple function: \( {\text{Price}} = {\text{Ps}} * {1}00 + \left( {{1} - {\text{Ps}}} \right) * \left( {{1} - {\text{LS}}} \right) * {1}00 \), where Ps is the probability of survival and LS is the loss severity conditional on default. The formula suggests that the upper limit of loan price is 100 when the probability of survival is 100 % and that the lower limit of loan price is 100*(1-LS) when the probability of survival is 0 %. In the sensitivity analysis that follows, loss severities are chosen to reflect realistic historical experience.

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Titel: Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans
verfasst von: Jun Chen
Yongheng Deng
Publikationsdatum: 01.05.2013
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 4/2013
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-012-9374-z

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