Imagine trying to decide whether to buy the stock of a company based solely on its assets or revenue, without any information on its liabilities or its ability to repay debt.

As illogical as it sounds, that is essentially how the mortgage securities market has functioned.

Despite the availability of forward-looking, predictive information about homeowners' ability to repay their mortgages, market participants have not used this valuable data to value and price the securities. Instead, they have relied more on property values, average credit scores at origination, mortgage payment history, and public ratings.

Updated consumer credit risk data relating to nonmortgage obligations have a remarkable predictive capability.

A recent Experian analysis of nondelinquent mortgages showed that the chances that a loan will default within 12 months were four times higher if the borrower had a significant recent change in other credit obligations. We can take this predictive individual risk data and aggregate it to portfolio-level analytics for the purpose of valuing and pricing mortgage-related securities. This allows for much more transparency and a more accurate view of credit risk while preserving the confidentiality of the underlying consumers.

As the Treasury Department considers investing hundreds of billions of taxpayer dollars in troubled mortgage assets under the recently approved economic rescue plan, it has the opportunity to use this updated and predictive consumer credit information to restore transparency and liquidity to the market.

The data, analytics, and technology exist to support both the Treasury's short-term needs and the long-term needs of the structured finance industry. In fact, this type of constant credit analysis is done regularly by U.S. retail banks.

Why is this updated credit risk data so valuable? Because consumer credit behavior is in constant flux. The rate of change in relevant credit information is staggering. Experian updates a database of 220 million consumers and more than a billion tradelines on a daily basis. (A tradeline can be a loan, a line of credit, a credit card, or another type of consumer credit.) Within a three-day period, over 1.5 million of these tradelines change from "current" status to "delinquent" or "derogatory." As the data is continually refreshed, credit scores and predictive attributes can be updated.

The Treasury, structured finance investors, rating agencies, and other interested parties should have insight into these changes, so they can assess their impact on the value of mortgage securities.

The failure to use this updated, forward-looking data was a key factor in the collapse of the mortgage securities market. When property values fell, there was insufficient information about borrowers' ability to meet their obligations.

It is time for the securitization market to use data and tools that provide greater insight into consumer credit risk and its impact on the valuation of mortgage securities.

There would be many benefits to using a system that provides critically needed and frequently refreshed risk data. First, it would add transparency to the market and promote more accurate pricing. Investors and other market participants would be able to monitor changes in credit quality on a timely basis and make more orderly, informed decisions. This would allow the securitization market to return to normal more quickly.

With more up-to-date and predictive data at their disposal, mortgage servicers would gain valuable lead time in setting up loss-mitigation programs and identifying the most appropriate mortgages to target for modifications.

Finally, and of great importance to the American public, if this information were used by the Treasury Department, taxpayers would be assured that their dollars are being put to good use, and that they will not pay inflated values for mortgage securities.

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