Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring

Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring 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. The Journal of Real Estate Finance and Economics Springer Journals

Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring

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Springer US
Copyright © 2007 by Springer Science+Business Media, LLC
Economics; Regional/Spatial Science; Financial Services
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