Credit report accuracy and access to credit

Federal Reserve Bulletin, Summer, 2004 by Robert B. Avery, Paul S. Calem, Glenn B. Canner

Credit History Scores in the Sample

The credit-reporting agency provided credit history scores for about 250,000, or 83 percent, of the individuals in the sample. The agency used its proprietary credit-risk-scoring model as of the date the sample was drawn to generate the scores (one for each individual), which it constructed from selected factors of the type described previously. The proprietary credit-risk score is like other commonly used consumer credit history scores in that larger values indicate greater creditworthiness. The agency did not assign scores to anyone who did not have a credit account. A small proportion of individuals without scores did have credit accounts, but most of these individuals were not legally responsible for any debt owed.

To facilitate this discussion, we have adjusted the proprietary credit-risk scores assigned to individuals in the Federal Reserve sample to match the distribution of the more familiar FICO credit history scores, for which information is publicly available. (20) Among the individuals in our sample who had scores, about 60 percent had adjusted scores of 701 or above (chart 1). Individuals with FICO scores in this range are relatively good credit risks. According to Fair Isaac Corporation, less than 5 percent of such consumers are likely to become seriously delinquent on any debt payment over the next two years. (21) In contrast, about 13 percent of individuals in our sample had adjusted scores at or below 600. According to Fair Isaac, more than half of these consumers are likely to become seriously delinquent on a loan over the next two years.

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Because credit history scores can be used to measure credit risk, creditors use them, along with other measures of creditworthiness, such as collateral, income, and employment information, to determine whether to extend credit and, if so, on what terms. Credit history scores are closely aligned with the interest rates offered on loans--that is, higher scores are associated with lower interest rates. For example, as of August 30, 2004, the national average interest rate for a thirty-year fixed-rate conventional mortgage for an individual with a FICO score of 720 or more was 5.75 percent, whereas the average interest rate for someone with a score below 560 was 9.29 percent. (22)

Assessing the Effects of Data Limitations

The analysis to assess the potential effects of data limitations on an individual's access to credit involves two steps: identifying data problems in an individual's credit record and simulating the effects of "correcting" each problem on the availability or price of credit as represented by the change in the individual's credit history score. To conduct this exercise, one must know (1) the factors used to construct the score, (2) the points assigned to these factors in deriving an individual's score, and (3) the process used to create the underlying factors from the original credit records.

The Federal Reserve's sample includes all the information that would be necessary to construct any credit history score and its underlying factors from the original credit records. However, the details of the credit-reporting agency's credit-scoring model, including the factors and point scales used in the model, are proprietary and were not made available to the Federal Reserve. Nevertheless, we were able to approximate the model by using three types of information: (1) the proprietary credit-risk score assigned to each individual in our sample; (2) a large set of credit factors for each individual--a subset of which was known to comprise the factors used in the proprietary credit-scoring model; and (3) detailed account-level information in each individual's credit record. We used the first two items to construct an approximation of the proprietary credit-scoring model, employing regression techniques to estimate the points to assign to each factor. We used the second and third items to "reverse-engineer" the credit factors included in our version of the credit-scoring model. This information enabled us to recalculate how the factors--and ultimately the credit history scores--would change if alterations were made to the underlying credit records so that we could simulate the effects of correcting a data problem or omission.