Report: Subprime Crisis Fueled by Lack of Borrower Contact

There's plenty of blame to go around for America's subprime mortgage crisis, but according to a researcher at the ""Ross School of Business"":http://www.bus.umich.edu/ in Ann Arbor, Michigan, the types of data banks used to evaluate risk may be the culprit for its proliferation.
Uday Rajan, associate professor of finance at the Ross School, says statistical models that predict loan defaults failed to warn lenders about risky borrowers because they relied too much on hard information, such as credit scores and loan-to-value ratios, and not enough on soft information from personal contact with borrowers, such as a person's job security, upcoming expenses, or other observable behaviors that may help predict the likelihood of default.
Rajan said, ""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. When incentives change, the link between the data and predicted outcomes changes in a fundamental manner.""
""Rajan and colleagues"":http://papers.ssrn.com/sol3/papers.cfmxabstract_id=1296982 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 under-predicted for those borrowers with little documentation, low FICO scores, and high loan-to-value ratios, who instead
The researchers say lenders' incentives to collect soft, behavior-telling 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, the researchers pointed out.
Conversely, in a world without securitization, default by the borrower directly hurts the lender, they say, and that increases the incentive to analyze soft information.
Rajan explained, ""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. In particular, the lender screens out borrowers who are poor credit risks based on their soft information.""
Rajan continued, ""But the incentive to acquire soft information about borrowers is lost under securitization, since only hard data can be transmitted credibly to the (third-party) 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 his 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.
Rajan added, ""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. 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.""
To access the full report, ""click here"":http://papers.ssrn.com/sol3/papers.cfmxabstract_id=1296982.