Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring |
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| Authors: | Yildiray Yildirim |
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| Affiliation: | (1) Martin J. Whitman School of Management, Syracuse University, Syracuse, NY 13244, USA |
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| Abstract: | ![]()
This paper provides a comprehensive default estimation of commercial real estate loans with a complete commercial mortgage backed securities (CMBS) loan history database. Standard survival models assume that eventually every observation will experience the event. However, often there is a high proportion of censored observation in the sample. A mixture model is proposed to disentangle the probability of “long-term survivorship” and the timing of default occurrence. Loans within the same geographical area and property type tend to exhibit correlation in default incidence. A multilevel model is proposed to capture this correlation within and between clusters. |
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| Keywords: | Multilevel mixture model Credit risk CMBS |
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