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Predicting Commercial Mortgage Foreclosure Experience


  • Kerry D. Vandell


This study has two objectives: (1) it directly evaluates the relationship between commercial mortgage default incidence and characteristics of the mortgage, borrower, property, market, and general economic conditions, and (2) it uses this relationship to predict the exposure of life insurers to future mortgage defaults and to examine the relative importance of various causes of current and past credit quality problems. A theoretical model of the default decision predicts that the decision would be expected to be driven primarily by the borrower's current equity stake in the property, or the ratio of the market value of the loan to property value (M t /V t ), but that the presence and magnitude of transaction costs associated with default would be expected to result in underexercise of the default option. Empirical estimation making use of American Council of Life Insurance (ACLI) and National Council of Real Estate Investment Fiduciaries (NCREIF) data confirms both expectations. A high proportion of the longitudinal variation in foreclosure incidence is explained by variations in M t /V t , but even at high ratios M t /V t in excess of 1.1. only 5% to 8% of mortgagors default, although this magnitude of underexercise is probably overstated because of problems in measuring M-super-t and for other reasons. Simulations using the model provide a pessimistic outlook for future defaults. Default rates are predicted to double in the five-year period 1988-93. Other simulations examine the relative importance of interest rate fluctuations, property value declines, and geographic or temporal correlations in lending during the 1976-88 period on current default experience. Copyright American Real Estate and Urban Economics Association.

Suggested Citation

  • Kerry D. Vandell, 1992. "Predicting Commercial Mortgage Foreclosure Experience," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(1), pages 55-88.
  • Handle: RePEc:bla:reesec:v:20:y:1992:i:1:p:55-88

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    Cited by:

    1. Grovenstein, Robert A. & Harding, John P. & Sirmans, C.F. & Thebpanya, Sansanee & Turnbull, Geoffrey K., 2005. "Commercial mortgage underwriting: How well do lenders manage the risks?," Journal of Housing Economics, Elsevier, vol. 14(4), pages 355-383, December.
    2. Jun Chen & Yongheng Deng, 2013. "Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans," The Journal of Real Estate Finance and Economics, Springer, vol. 46(4), pages 609-632, May.
    3. Youngha Cho & Soosung Hwang & Steve Satchell, 2012. "The Optimal Mortgage Loan Portfolio in UK Regional Residential Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 45(3), pages 645-677, October.
    4. Bradford Case, 2003. "Loss characteristics of commercial real estate loan portfolios," Basel II White Paper 1, Board of Governors of the Federal Reserve System (U.S.).
    5. James Kau & Donald Keenan & Yildiray Yildirim, 2009. "Estimating Default Probabilities Implicit in Commercial Mortgage Backed Securities (CMBS)," The Journal of Real Estate Finance and Economics, Springer, vol. 39(2), pages 107-117, August.
    6. Tracey Seslen & William C. Wheaton, 2010. "Contemporaneous Loan Stress and Termination Risk in the CMBS Pool: How "Ruthless" is Default?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 38(2), pages 225-255.
    7. Luis C. Mejia, 1999. "Availability of Credit and Loan Default: A Look at the Commercial Mortgage Supply Cycle," Journal of Real Estate Research, American Real Estate Society, vol. 18(1), pages 175-196.
    8. Brian Ciochetti & James Shilling, 2007. "Loss Recoveries, Realized Excess Returns, and Credit Rationing in the Commercial Mortgage Market," The Journal of Real Estate Finance and Economics, Springer, vol. 34(4), pages 425-445, May.
    9. Timothy Riddiough & Paul Childs & Steven Ott, 2001. "Noise, Real Estate Markets, and Options on Real Assets: Applications," Wisconsin-Madison CULER working papers 01-06, University of Wisconsin Center for Urban Land Economic Research.
    10. Yildiray Yildirim, 2008. "Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring," The Journal of Real Estate Finance and Economics, Springer, vol. 37(2), pages 93-111, August.
    11. Leon G. Shilton & John Teall, 1994. "Option-Based Prediction of Commercial Mortgage Defaults," Journal of Real Estate Research, American Real Estate Society, vol. 9(2), pages 219-236.
    12. Carsten Lausberg, 2001. "Evidence of its Importance and Instruments to Handle it. The Real Estate Market Risk of Banks," ERES eres2001_205, European Real Estate Society (ERES).
    13. Hoon Cho & Brian Ciochetti & James Shilling, 2013. "Are Commercial Mortgage Defaults Affected by Tax Considerations?," The Journal of Real Estate Finance and Economics, Springer, vol. 46(1), pages 1-23, January.
    14. Tracey Seslen & William C. Wheaton, 2005. "Contemporaneous Loan Stress and Termination Risk in the CMBS pool: how "Ruthless" is default?," Working Paper 8582, USC Lusk Center for Real Estate.
    15. Deng, Yongheng & Quigley, John M. & Sanders, Anthony B., 2006. "Commercial Mortgage-backed Securities (CMBS) Terminations, Regional and Property-Type Risk," Working Paper Series 2006-24, Ohio State University, Charles A. Dice Center for Research in Financial Economics.

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