ANN ARBOR—There’s plenty of blame to go around for America’s subprime mortgage crisis among borrowers and lenders, but there may be another culprit.

A researcher at the University of Michigan says that statistical models that predict loan defaults failed to warn lenders about risky borrowers because these models relied too much on credit scores and loan-to-value ratios and not enough on soft information gleaned through personal contact with borrowers (e.g., How secure is their job? Any upcoming expenses? Any observable behaviors that may help predict the likelihood of default?).

“A fundamental cause for this failure was that the models ignored changes in the incentives of lenders to collect soft information about borrowers and residential properties,” said Uday Rajan, associate professor of finance at U-M’s Ross School of Business. “When incentives change, the link between the data and predicted outcomes changes in a fundamental manner.”

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 as the degree of securitization increased, interest rates on new loans relied increasingly on hard information about borrowers (e.g., FICO scores and loan-to-value ratios).

Moreover, their results show that a statistical default model fitted in a low-securitization period breaks down in the high-securitization period in a systematic manner, thereby underpredicting defaults for borrowers for whom soft information is more valuable—those borrowers with little documentation, low FICO scores and high loan-to-value ratios.

The researchers say that incentives of lenders to collect soft information changed because of the tremendous growth in securitization in the subprime sector after 2000. When a lender securitizes a loan, it sells the loan to a third party, and no longer bears the risk of default on the loan. In a world without securitization, default by the borrower directly hurts the lender, they say.

“In addition to collecting hard data about a borrower, such as a credit score, a lender also has an incentive to verify undocumented information, or soft information, about the borrower,” Rajan said. “In particular, the lender screens out borrowers who are poor credit risks based on their soft information.”

“But the incentive to acquire soft information about borrowers is lost under securitization, since only hard data can be transmitted credibly to the investor. As a consequence, borrowers who are poor credit risks on the dimension of soft information, but apparently creditworthy based on their hard information, also receive loans. Thus, when one examines loans that have been approved, the same hard data have very different implications for borrower creditworthiness with and without securitization. That is, the hard information can mean something very different across these two worlds.”

Rajan and colleagues say their results partly explain why statistical default models severely underestimated defaults during the subprime mortgage crisis. In other words, the models failed to account for the change in the relationship between observable borrower characteristics and default likelihood caused by a fundamental change in lender behavior.

One broad implication of their findings is that regulations that rely on such models to assess default risk may be undermined by the actions of market participants. For example, current guidelines identify default risk as a key factor in setting capital requirements for banks and allow for the use of models by external institutions such as rating agencies in determining default risk.

“Even sophisticated agents such as regulators setting capital requirements or rating agencies will take some time to learn the exact magnitudes of relevant variables following a regime change,” Rajan said. “The assessment of default risk must be extra conservative during this period, and the true challenge for market participants is to recognize such shifts in real time.”

For a copy of the study: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1296982

More on Rajan: www.bus.umich.edu/FacultyBios/FacultyBio.asp?id=000670813

Seru: http://faculty.chicagobooth.edu/amit.seru/papers.htm

Vig: http://faculty.london.edu/vvig/

Copy of studyRajanSeruVig