Credit Cards Online Information

A NEW SOURCE OF DATA PROVIDES A FIRMER FOUNDATION FOR CREDIT ANALYSIS AND DECISIONS.

This article demonstrates the value of aggregated credit bureau data for benchmarking portfolio performance and modeling trends in household borrowing and payment behavior. The analysis utilizes a unique database built from a series of large random samples of U.S. consumer credit histories drawn quarterly since 1992. The data provide a more accurate picture of borrowing behavior at the regional, state, and local level than the aggregate statistics available from the federal government and industry associations. Their predictive power is apparent in models built to explain county-level patterns in personal bankruptcies and three types of consumer loan delinquencies from 1993-1998.

A new and promising tool has recently become available for tracking and forecasting consumer borrowing and payment behavior. For many years, credit grantors and insurance firms have used consumer credit reports to evaluate the repayment risk of individual applicants for loans and insurance. The rich detail in individual credit reports has supported the development of sophisticated statistical models that estimate an individual's repayment risk with remarkable accuracy. However, until very recently, this information was available only at the individual level. Timely, reliable data on consumer borrowing and payment activity, aggregated to the local, state, or regional level, have been largely unavailable except through proprietary marketing research surveys or infrequent government-sponsored household interviews.

Actual observations (as opposed to self-reported survey responses) on borrowing and payment behavior could have enormous value in calculating household debt burden, forecasting consumer spending behavior, estimating demand for consumer durables at the local or regional level, and benchmarking portfolio performance. The three major U.S. credit bureaus have recognized the value of aggregating the individual-level information in their archived files and are beginning to market such data products.

This article provides examples of how aggregated credit bureau data can be used for benchmarking and modeling to identify the factors that influence bankruptcy and delinquency trends at the county level. The following sections utilize a unique database assembled by Trans Union LLC. Dubbed TrenData [1], this new tool is based on a series of large random samples of U.S. consumer credit histories drawn quarterly since 1992. Each quarterly sample contains approximately 30 million depersonalized credit reports. From this underlying database, variables have been built to describe consumer borrowing and payment behavior aggregated to the county, state, and national level. The Credit Research Center (CRC) at Georgetown University's MoDonough School of Business is collaborating with Trans Union to explore the predictive value of TrenData variables. [2]

Advantages of Aggregated Credit Bureau Files

The rich detail of individual credit file data supports the creation of a host of aggregate variables. TrenData provides more than two hundred variables for analysis, all aggregated to the county level on a quarterly basis. The following brief list conveys the scope of what is available:

* Average mortgage, installment, and revolving debt, per borrower

* The percent of bank card holders thirty, sixty, or ninety days past due

* Percent of revolving credit lines utilized, per borrower

* Dollar amount of new automobile credit extended in the previous three-month period

* Average monthly minimum debt payment (consumer + mortgage), per borrower

* Number of new installment or revolving loan accounts opened in the previous three months.

Do aggregate credit bureau data provide additional insights into the current environment? Two examples illustrate the contribution of this new tool. First, consider the most widely-used measure of the rate of growth of consumer installment credit. The Federal Reserve Board (FRB) has reported that the total dollar amount of consumer (nonmortgage) credit grew 6.2 percent dining the twelve months ending with the first quarter of 1999, up from a 4.1 percent growth pace during the twelve-month period ending in first quarter, 1998. Because TrenData is constructed from individual borrower files, it can provide additional insight regarding the composition of the aggregate growth. Interestingly, TrenData indicates that the average amount of nonmortgage debt per borrower actually fell slightly (3.2 percent) from the first quarter, 1998, to the first quarter, 1999. However, during the same period account openings accelerated. The average number of new accounts opened each quarter from March 1998 through March 1999 was 25.2 per hundred borrowers, as compared to an average quarterly rate of 23.0 per hundred borrowers during the twelve months from March 1997 to March 1998. The small decline in debt per borrower during a period of rapid account openings and rising aggregate consumer installment debt suggests that borrowers with little or no previous debt accounted for much of the aggregate growth. This insight can not be derived from the FRB aggregate statistics alone.

# posted by Medical Information : 2:34 AM