Subprime Mortgage Crisis_ Failure to Predict Failure

Researchers at three business schools say that statistical models that were used to predict loan defaults didn’t work because these models relied too heavily on hard information, such as credit scores and loan-to-value ratios. Soft information, such as job security, upcoming expenses, and behavior, would have been more useful. The incentive to collect more information, though, was de-emphasized because of the high-securitization period that we were living in. Indeed, lenders, because they were packaging these loans and selling them to third parties, had no reason to scrutinize borrowers. From the press release:Rajan and colleagues Amit Seru of the University of Chicago and Vikrant Vig of the London Business School examined data on securitized subprime loans issued from 1997 to 2006. They found that in a high-securitization period — a lending environment where greater numbers of loans are sold to third parties — interest rates on new loans relied increasingly on hard information about borrowers (e.g., FICO scores and loan-to-value ratios) rather than more personalized soft information.However, statistical models designed for low-securitization periods — where the original lender holds the loan — rely on more personal information. Those models break down when applied in a high-securitization period, the research shows. The result is that defaults are underpredicted for borrowers for whom soft information is more valuable, such as those with little documentation, low FICO scores and high loan-to-value ratios.The researchers say lenders’ incentives to collect soft information changed because of the tremendous growth in securitization in the subprime sector after 2000. When a lender sells the loan to a third party, the original lender no longer bears the risk of default on the loan.