Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Real Estate Finance and Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. 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.

  2. 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.

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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

  12. 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.

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

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

  15. 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.

  16. 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.

References

  • Ambrose, B. W., & Sanders, A. B. (2003). Commercial mortgage-backed securities: prepayment and default. Journal of Real Estate Finance and Economics, 26(2–3), 179–196.

    Article  Google Scholar 

  • Ambrose, B. W., Capone, C. A., & Deng, Y. (2001). Optimal put exercise: an empirical examination of conditions for mortgage foreclosure. Journal of Real Estate Finance and Economics, 23(2), 213–234.

    Article  Google Scholar 

  • Ambrose, B. W., Sanders, A. B., & Yavas, A. (2010). Special servicers and adverse selection in informed intermediation: Theory and evidence. Pennsylvania State University, Institute for Real Estate Studies Working Paper.

  • Amemiya, T. (1985). Advanced econometrics. Boston: Harvard University Press.

    Google Scholar 

  • An, X., Deng, Y., & Gabriel, S. (2011). Asymmetric information, adverse selection, and the pricing of CMBS. Journal of Financial Economics, 100(2), 304–325.

    Article  Google Scholar 

  • Archer, W. R., Elmer, P. J., Harrison, D. M., & Ling, D. C. (2002). Determinants of multifamily mortgage default. Real Estate Economics, 30(3), 445–473.

    Article  Google Scholar 

  • Brown, D., Ciochetti, B., & Riddiough, T. (2006). Theory and evidence on the resolution of financial distress. Review of Financial Studies, 19, 1357–1397.

    Article  Google Scholar 

  • Ciochetti, B. A., & Vandell, K. D. (1999). The performance of commercial mortgages. Real Estate Economics, 27(1), 27–62.

    Article  Google Scholar 

  • Ciochetti, B. A., Deng, Y., Gao, B., & Yao, R. (2002). The termination of mortgage contracts through prepayment and default in the commercial mortgage markets: a proportional hazard approach with competing risks. Real Estate Economics, 30(4), 595–633.

    Article  Google Scholar 

  • Ciochetti, B. A., Deng, Y., Lee, G., Shilling, J., & Yao, R. (2003). A proportional hazards model of commercial mortgage default with originator bias. Journal of Real Estate Finance and Economics, 27(1), 5–23.

    Article  Google Scholar 

  • Clapp, J. M., Deng, Y., & An, X. (2006). Unobserved heterogeneity in models of competing mortgage termination risks. Real Estate Economics, 34(2), 243–273.

    Article  Google Scholar 

  • Deng, Y., & Gabriel, S. (2006). Risk-based pricing and the enhancement of mortgage credit availability among underserved and higher credit-risk populations. Journal of Money, Credit and Banking, 38(6), 1431–1460.

    Article  Google Scholar 

  • Deng, Y., Quigley, J. M., & Van Order, R. (1996). Mortgage default and low downpayment loans: the cost of public subsidy. Regional Science and Urban Economics, 26(3–4), 263–285.

    Article  Google Scholar 

  • Deng, Y., Quigley, J. M., & Van Order, R. (2000). Mortgage terminations, heterogeneity and the exercise of mortgage options. Econometrica, 68(2), 275–307.

    Article  Google Scholar 

  • Green, J., & Shoven, J. B. (1986). The effect of interest rates on mortgage prepayment. Journal of Money, Credit and Banking, 18, 41–50.

    Article  Google Scholar 

  • Harding, J. P., & Sirmans, C. F. (2002). Renegotiation of trouble debt: the choice between discounted payoff and maturity extension. Real Estate Economics, 30(3), 475–503.

    Article  Google Scholar 

  • Heckman, J. (1976). Sample selectivity problems as a specification error. Econometrica, 47(1), 153–162.

    Article  Google Scholar 

  • Hendershott, P., & Van Order, R. (1987). Pricing mortgages: an interpretation of the models and results. Journal of Financial Services Research, 1, 77–111.

    Article  Google Scholar 

  • Kau, J. B., Keenan, D. C., Muller, W. J., III, & Epperson, J. F. (1987). The valuation and securitization of commercial and multifamily mortgages. Journal of Banking and Finance, 11, 525–546.

    Article  Google Scholar 

  • Kau, J. B., Keenan, D. C., Muller, W. J., III, & Epperson, J. F. (1990). Pricing commercial mortgages and their mortgage-backed securities. Journal of Real Estate Finance and Economics, 3(4), 333–356.

    Article  Google Scholar 

  • Lekkas, V., Quigley, J. M., & Van Order, R. (1993). Loan loss severity and optimal mortgage default. AREUEA Journal, 21(4), 353–371.

    Google Scholar 

  • Quigley, J. M., & Van Order, R. (1990). Efficiency in the mortgage market: the borrower’s perspective. AREUEA Journal, 18(3), 237–252.

    Google Scholar 

  • Quigley, J. M., & Van Order, R. (1995). Explicit tests of contingent claims models of mortgage default. Journal of Real Estate finance and Economics, 11(2), 99–117.

    Article  Google Scholar 

  • Riddiough, T. J. (2000). Forces changing real estate for at least a little while: market structure and growth prospects of the conduit-CMBS market. Real Estate Finance 2000(Spring): 52–61.

  • Riddiough, T. J., & Wyatt, S. B. (1994a). Strategic default, workout, and commercial mortgage valuation. Journal of Real Estate Finance and Economics, 9, 5–22.

    Article  Google Scholar 

  • Riddiough, T. J., & Wyatt, S. B. (1994b). Wimp or tough guy: sequential default risk and signaling with mortgages. Journal of Real Estate Finance and Economics, 9, 299–321.

    Article  Google Scholar 

  • Schwartz, E. S., & Torous, W. N. (1989). Prepayment and the valuation of mortgage-backed securities. Journal of Finance, 44(2), 375–392.

    Article  Google Scholar 

  • Springer, T. M., & Waller, N. G. (1993). Lender forbearance: evidence from mortgage delinquency patterns. AREUEA Journal, 21(1), 27–46.

    Google Scholar 

  • Titman, S., & Torous, W. (1989). Valuing commercial mortgages: an empirical investigation of the contingent-claims approach to pricing risky debt. Journal of Finance, 44(2), 345–373.

    Google Scholar 

  • Vandell, K. D. (1984). On the assessment of default risk in commercial mortgage lending. AREUEA Journal, 12(3), 270–296.

    Google Scholar 

  • Vandell, K. D. (1992). Predicting commercial mortgage foreclosure experience. AREUEA Journal, 20(1), 55–88.

    Google Scholar 

  • Vandell, K., Barnes, W., Hartzell, D., Kraft, D., & Wendt, W. (1993). Commercial mortgage defaults: proportional hazards estimations using individual loan histories. AREUEA Journal, 21(4), 451–480.

    Google Scholar 

  • Wheaton, W., & Torto, R. (1988). Vacancy rates and the future of office rents. AREUEA Journal, 16(4), 430–436.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moody’s Analytics, 405 Howard Street, Suite 300, San Francisco, CA, 94105, USA

    Jun Chen

  2. Institute of Real Estate Studies, National University of Singapore, 21 Heng Mui Keng Terrace, #04-02, Singapore, 119613

    Yongheng Deng

Authors
  1. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongheng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongheng Deng.

Additional information

We thank Kwong Wing Chau, Maurice Tse and participants at the 2010 Asian Pacific Real Estate Symposium for helpful comments.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, J., Deng, Y. Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans. J Real Estate Finan Econ 46, 609–632 (2013). https://doi.org/10.1007/s11146-012-9374-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11146-012-9374-z

Keywords

Search

Navigation