Predicting the Likelihood of Lodging CMBS Loan Default

Volatility, Mortgage Default, and CMBS Subordination

This paper calculates loan-by-loan estimates of commercial real estate implied volatility using all commercial mortgages in 206 public CMBS deals from 1996 through 2005 — a total of over 14,000 loans. The implied volatilities average about 20–24% per annum, with some differences across property types. Using these implied volatilities, we compute the distribution of default rates for representat...

Predicting Probability of Loan Default Stanford University , CS 229 Project report

Stanford University, CS229 Project report Jitendra Nath Pandey, Maheshwaran Srinivasan 12/15/2011 Abstract: Extending credit to individuals is necessary for markets and societies to function smoothly. Estimating the probability that an individual would default on his/her loan, is useful for banks to decide whether to sanction a loan to the individual and is also useful for borrowers to make bet...

When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan over and beyond the financial and demographic variables commonly used in models pr...

Predicting Sovereign Default

With the financial crises ongoing in Greece and Venezuela, sovereign debt crises have become more and more prominent in the public eye. Thus, it has become important to be able to predict when nations will enter such debt crises. We collected publicly available data in order to train models to predict, given a nation’s economic status in one year, whether they would be in a debt crisis the next...