Report to the Congress on Credit Scoring and Its Effects on the Availability and Affordability of Credit
Submitted to the Congress pursuant to section 215 of the Fair and Accurate Credit Transactions Act of 2003
August 2007
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Findings on Loan Performance and Credit Availability and Affordability
This section presents an assessment of the relationship of credit scores to loan performance and to the availability and affordability of credit for different populations. The assessment begins with a discussion of the three credit scores considered in the study that serve as the basis for the analysis. The assessment then focuses on (1) the distribution of credit scores across different populations; (2) the extent to which other demographic, credit, and economic characteristics explain differences in credit scores across populations; (3) the stability of the credit scores of individuals over time; (4) the relationship between credit scores and loan performance measured in a variety of ways; (5) the extent to which, given score, performance varies across populations; (6) the extent to which differences in credit availability and affordability across populations can be explained by credit score; and (7) whether differences in performance, credit availability, and pricing may be explained by factors not considered in our analysis.
The Three Credit Scores Used in the Study
The distribution of credit scores for the whole population of scorable individuals is publicly available, but much less is known about the distribution of credit scores for subpopulations.113 The analysis that follows does address subpopulations. It reports the distribution of the three credit scores used in this study--the TransRisk Score, the VantageScore, and the Federal Reserve's estimated base score (FRB base score)--across individuals grouped by their race or ethnicity; national origin, sex and marital status, and age; and by the relative income, degree of urbanization, and racial composition of the census tracts in which they reside. The report of the distribution for each subpopulation consists of summary statistics, cumulative distributions, and a decomposition of the demographic characteristics of the individuals at different credit-score ranges.
Comparing credit scores derived from different credit-scoring models requires "normalizing" the scores to a common scale. However, no natural, universal normalization formula exists. Because the particular normalizations used for the TransRisk Score and VantageScore are unknown, it was decided to renormalize each of the scores used in this study, including the FRB base score, to a common rank-order scale. The normalization was based on the 232,467 individuals in our sample for whom all three credit scores were available as of June 2003. Individuals were ranked by the raw values of each of the three credit scores, with a higher rank representing better performance. Individuals at the 5 percent cumulative distribution level for each credit score were assigned a score of 5; those at the 10 percent level were assigned a score of 10; and so on, up to 100 percent. Linear interpolations were used to assign credit scores within each 5 point interval to ensure the functional form was smooth.
Under this method of normalizing, each individual's rank in the population is defined by his or her credit score: For example, a score of 50 places that individual at the median of the distribution, and a positive change of 5 points in an individual's credit score means that individual moves up 5 percentage points in the distribution of credit scores. Because each score is normalized in exactly the same way, comparisons of the overall distributions across the three scores are not meaningful. However, the normalization facilitates comparisons across different populations for each of the three scores.
The Distribution of Credit Scores
Mean score, median score, standard deviation of score, and the proportion of individuals in the lowest score deciles vary widely across subpopulations and across the three credit scores (tables 14.A--C and figures 2.A--C). Differences in credit scores among racial or ethnic groups and age cohorts are particularly large. For example, according to self-reported (SSA) data on race or ethnicity, the mean TransRisk Score for Asians is 54.8; for non-Hispanic whites, 54.0; for Hispanics, 38.2; and for blacks, 25.6. The proportions of the subpopulations in the lowest two score deciles also differ greatly: The proportions of the subpopulations in the lowest two score deciles is, for Asians, 12.3 percent; non-Hispanic whites, 16.3 percent; Hispanics, 30.1 percent; and blacks, 52.6 percent. Foreign-born individuals appear to have a score distribution similar to the general population, with a smaller representation at the extremes of the distribution.114
When the racial composition of the census block is used as a proxy for the race or ethnicity of the individual, the differences in scores across groups, although still substantial, are smaller than when the individual's race or ethnicity derived from SSA data are used. For example, when the census-block proxy for race is used, the mean difference in the TransRisk Score between blacks and non-Hispanic whites falls from 28.4 points to 15.1 points.
The distribution of credit scores for unmarried and married individuals also differs. For all three score measures, the mean score for married individuals is about 12 points higher than for a single individual of the same sex. Scores vary little by sex.
Credit scores differ substantially by age and increase monotonically from young to old. The mean TransRisk Score for individuals younger than age 30 was 34.3; for those aged 62 and older, it was 68.1. The range is wider for the VantageScore; the mean VantageScore for individuals younger than age 30 was 31.1 and for those aged 62 and older, 67.7.115 The proportion of individuals younger than age 30 in the lowest two TransRisk Score deciles was 31.7 percent; the proportion for those 62 and older was 7.2 percent.
Mean credit scores for individuals grouped by the income or minority proportions of their census tract also differ notably. Individuals in high-income census tracts have a mean TransRisk Score of 57.9; in low-income census tracts, the mean is 32.5. The mean TransRisk Score for residents of census tracts with less than 10 percent minority population was 55.7; for individuals in census tracts with 80 percent or more minority population, it was 34.6. Individuals living in urban and rural areas have very similar credit-score distributions.
Cumulative Distributions
The summary statistics described above do not fully convey the credit-score differences across populations. A fuller picture is obtained with cumulative distributions (figures 3.A--C). Here, a cumulative distribution aggregates the number of individuals at each score point, starting with the lowest score; by the time the highest score point--100--is reached, 100 percent of the individuals have been counted. If, for example, 50 percent of a group has been counted up through a score of 20, then 50 percent of that group has a score of 20 or less. More generally, if a group's cumulative distribution of scores is uniformly above another, then at each credit-score level, the population with the higher distribution has a larger percentage of its individuals with credit scores below that level than does the other population.
Cumulative distributions show that the credit-score patterns suggested by the means and medians hold for the various subpopulations. For example, across all three credit-score measures, the cumulative distributions of scores for blacks and Hispanics are consistently higher than those for non-Hispanic whites and Asians. Cumulative distributions by age are also consistently ordered, with the cumulative distribution of younger individuals higher than that of individuals aged 62 or older. Cumulative distributions for census-tract groupings by racial or ethnic composition or relative income are also consistent with the patterns implied by the summary statistics for these groups.
Demographic Composition of Score Deciles
Another way of describing differences in credit-score distributions across groups is to look at the demographic composition of the populations in each credit-score decile (figures 4.A--C). With the exception of sex, the composition of the population varies greatly across deciles. Taking the TransRisk Score as an example, 27.2 percent of the individuals in the lowest decile are black, whereas in the highest decile, 3.0 percent are black. Similarly, 23.7 percent of those in the lowest decile are younger than 30 years of age versus 0.3 percent of those in the highest decile.
Notable differences in the composition of the population are also evident when individuals are sorted by the relative income. For example, 7.9 percent of the individuals in the lowest TransRisk Score decile reside in low-income areas, compared with 1.5 percent in the highest score decile.
Multivariate Analysis of Differences in Credit Scores
Demographic factors may be correlated. For example, some of the differences in credit scores by race or ethnicity could arise from differences in the distribution by age or marital status of the different racial or ethnic groups. This section presents the results of a multivariate analysis conducted to isolate the effects of each demographic or census-tract characteristic by controlling for the other characteristics.
The first step in identifying the independent effect of race or ethnicity on credit-score differences across populations was to fit a regression model to predict credit scores of non-Hispanic whites according to their age (using linear splines for each of the five age cohorts), sex, and marital status. The age splines were fully interacted with sex and marital status (that is, for each sex and marital status, a separate linear spline was created). Predicted values from this equation were then used to predict the scores for blacks, Hispanics, and Asians. Differences between a group's actual credit scores and its predicted scores can be interpreted as unexplained racial or ethnic effects.116
Credit records generally do not include information about individuals' economic or financial circumstances, such as their income, wealth, and work-related experience, nor do the other databases against which the credit-score sample was matched. Thus, this information is not available for this study. As discussed in a later section, populations differ widely along many economic and financial dimensions, and variations in credit scores may reflect such differences. Ideally, one would like to account for the effects of these other circumstances in explaining differences in credit scores across populations. The credit-record data do, however, include information on the location of residence. This information was used to construct a number of additional control variables, and the multivariate analysis was broadened to include these additional measures.
A proxy measure of income was developed from census information. The 2000 decennial census provides the distribution of income for each racial or ethnic group segmented in seven age categories for each census tract. These distributions allow a calculation of an estimated average income for each racial or ethnic group by age within each census tract. This variable was used as an estimate of the income for each individual in the sample. (Individuals missing race or ethnicity were assigned the mean for their age group in their census tract of residence.)
The empirical estimation was expanded to include the following location-based controls: the estimated income variable, the relative income of the census tract of residence, and the mean TransRisk Score of the individual's census tract of residence.117 Because the TransRisk Score was used as the dependent variable in the regression and to derive the mean score for each census tract, the equation using the mean census-tract credit score can be interpreted as a "fixed effects" model, that is, a model structured to fully account for all types of socioeconomic differences among census tracts.
The sample used for the multivariate estimation was reduced 11 percent by excluding individuals with unknown age or census tract. As shown in table 15, panel A, the gross difference between non-Hispanic whites and blacks for the TransRisk Score in the multivariate estimation sample was 28.3 credit-score points (54.0 minus 25.6 with rounding). The difference between non-Hispanic whites and blacks declines to 22.8 points when marital status and age are accounted for; the difference falls to 18.7 points when census-tract income and the estimated income of the individual are taken into account. Accounting for the mean census-tract credit score causes the difference to fall further, to 13.4 points. The gross difference in mean TransRisk Scores between Hispanics and non-Hispanic whites (15.7 points, again with rounding) falls relatively more than for blacks and non-Hispanic whites; after accounting for all factors, only a 3.9 point differential remains unexplained.
When the census-block proxy is used to identify the race or ethnicity of individuals, a similar reduction is observed in the differences across racial or ethnic groups once other factors are taken into account (table 15, panel B). These results differ from those using individual race or ethnicity; however, differences in that gross score and the differences that remain after all available factors are taken into account are smaller. For example, the analysis using the census-block proxy for race or ethnicity finds an unexplained difference of 2.5 points between non-Hispanic whites and blacks. In contrast, an unexplained difference of 13.4 points remains between these two groups when the individual's race or ethnicity is used in the analysis.
Identifying the independent effects of sex on credit scores involved an analysis similar to that conducted for race or ethnicity. A regression model was fit to predict the credit scores of males by age, race or ethnicity, and marital status. Additional models were estimated adding the same demographic or location characteristics used in the race or ethnicity analysis. Controlling for these additional factors does little to explain the gross difference of 1.6 points in the mean TransRisk Score between females and males (table 15, panel C).
The analysis to account for differences by age was conducted in a somewhat different manner from that for race or ethnicity because there was no natural comparison or base group. Using the same approach for estimating an age-neutral model, to be described in a later section, age was included as a regressor in the estimation to estimate coefficients for the other variables in as age-neutral a way as possible. Scores for each group were then predicted under the assumption that the age of each individual was the average age for the population. Residuals for each age group were expressed as differences from the mean residuals of those aged 62 or older.
The regressions suggest that only a minor portion of the differences across age cohorts can be explained by the other factors (table 15, panel D). For example, the gross difference of 33.9 points in the mean TransRisk Score between those younger than age 30 and those aged at least 62 is reduced only to 29.4 points when these factors are taken into account.
The Stability of Credit-Score Differences over Time
The data obtained for this study provide an opportunity to assess changes in credit scores over time for each population group. The data contain credit scores at the beginning of the performance period (June 2003) and at the end, 18 months later (December 2004); the scores for both periods are normalized in the same way using the rank-order distribution of the June 2003 population.
A population group disproportionately subject to adverse economic shocks (such as a job loss) or other so-called trigger events (such as illness or divorce) are expected to exhibit greater reductions in credit scores than other groups.118 Moreover, if the reductions in scores are caused primarily by temporary trigger events, then scores of individuals in the lower credit-score ranges would tend to rise over time. That increase in scores would, however, be only gradual, as adverse information is removed from credit records only after a number of years.
Changes in the TransRisk Score for individuals in each population group are shown in table 16. The mean score for virtually every group is little changed over the 18-month period. The mean score for the entire population increases only 0.1 percent. However, 17 percent of individuals experienced a credit-score increase of 10 points or more, and 17 percent experienced a decrease of 10 points or more. Significant changes in scores are relatively rare and not symmetric; 2.3 percent of individuals experienced a decline of 30 points or more, but only 1.6 percent of individuals experienced an increase of 30 points or more.
Some evidence suggests that, over time, scores tend to migrate toward the middle of the distribution. For example, the scores of 71 percent of the individuals in the lowest score decile in June 2003 rose over the performance period, whereas the scores of only 23 percent of individuals in the top decile rose. The pattern of migration of scores toward the middle varies by subpopulation. For example, only in the lowest decile did the majority of blacks experience an increase in score; the majority of non-Hispanic whites experienced an increase in all but the top three deciles. And borrowers younger than age 30 showed less of a tendency to experience increases in scores than individuals in other age groups: For each score decile, the percentage of younger individuals experiencing an increase was lower than for any of the other age groups.
Taken together as explanations for racial and age differences in scores, these data provide at most only a partial explanation for score differences across populations, or they suggest that, for certain populations, trigger events either are persistent or happen more often than they do to other populations.
Credit Scores and Performance
The Fact Act asks for an analysis of the statistical relationship, using a multivariate analysis, between credit scores and the "quantifiable risk and actual losses experienced by businesses" for different populations. The credit-record data do not include direct information on losses. However, a common metric used by the industry as a proxy for losses is a measure of loan default. There are various ways to define default. Typically, they would include accounts that became 90 or more days delinquent or were in foreclosure or collection, or were otherwise in serious distress or loss. This is the approach used here. We define five measures of credit-account performance for the 18- month performance period contained in our data. These five performance measures are compared with credit scores at the beginning of the performance period.
Four of the credit-account measures (numbered 1--4 below), are commonly used in the industry. The fifth measure is one developed specifically for this study.
- any-account
- new-account
- existing-account
- random-account
- modified new-account
We used the any-account measure to estimate the FRB base score. The any-account measure is based on the performance of new or existing accounts and measures whether individuals have been late 90 days or more on one or more of their accounts or had a public record item or a new collection agency account during the performance period.
New-account performance is defined in the same way as that for the any-account measure, but the accounts it covers are limited to those opened between July 2003 and December 2003. Unlike the any-account measure, the new-account measure does not consider public records or collection agency accounts.
Existing-account performance is limited to credit accounts that were opened before July 2003 and remained open during at least a portion of the performance period. The existing-account measure does not consider public records and classifies the performance of individuals with a collection account and no other bads as indeterminate rather than bad.
Random-account performance defines performance on each credit account in the same manner as the any-account measure, but instead of defining an individual's performance as good or bad, performance is defined as the percentage of the individual's accounts that have bad performance. Public records and collection accounts are not used in this calculation. This measure of performance is similar to the one used in developing the VantageScore.
The precise time when an account became bad often cannot be determined. Consequently, rules are developed to implement somewhat arbitrary decisions about how to determine whether an account was bad before the beginning of the performance period or whether it went bad subsequently. Errors in those decisions can create a spurious correlation between the performance measure and the score at the beginning of the performance period. Consequently, modelers generally validate performance using only unambiguously out-of-sample performance measures, such as accounts that are known to have been opened after the beginning of the performance period.
To address the concern that a seemingly new account in the present database may have actually existed and gone bad before the opening of the performance period, an additional measure of new-account performance, called the "modified new-account" measure, was constructed from the credit records. Under the modification, new accounts were eliminated if they appeared to have a high propensity to be reported only when performance is bad.
The accounts excluded to create the modified new-account measure consisted of student loans and utility, medical, and factoring accounts. Whenever any such account appears in the June 2003 data as new, it likely instead was already in existence but was not reported as opened until the later time. All these accounts were excluded regardless of their performance; doing so eliminated only about 10 percent of the new accounts but removed more than 50 percent of all bads. To better emulate industry out-of-sample performance measures, the modified new-account measure was computed at the account level rather than--as in the new-account measure--at the person level. Bad performance in the modified new-account measure is defined as it is in the other four performance measures (major derogatory or 90 or more days delinquent during the performance period).
The percentage of accounts that become bad varies greatly across the five performance measures and population groups (table 17). Twenty-eight percent of individuals exhibited bad performance using the any-account measure, compared with only 3.4 percent of modified new accounts. Performance across groups varied greatly, a topic examined in the next section.
Overall Performance
Regardless of the specific performance measure considered, each of the three credit scores used in this study predicts future loan performance: Figure 5 displays the actual average performance at each credit-score level for the three scores and for the five measures of performance. As shown, the percentage of bads consistently decreases as credit scores increase for all three scores and for all five measures of performance. The performance of those in the bottom 30 percent of the distribution differs substantially from those above that level. For example, for the TransRisk Score, 78.4 percent of the individuals with credit scores in the bottom three score deciles had at least one account go bad over the performance period, while only 1.8 percent of individuals in the top 30 percent of the score distribution had an account go bad.
Another way of illustrating the predictiveness of the scores is to plot the cumulative distribution of goods and bads by score (as shown earlier in figure 1). For each score and for each performance measure, the cumulative distribution of the bads is considerably to the left of that of the goods, a confirmation that the scores have considerable predictive power.
The poor performance of individuals in the lowest portion of the credit-score distribution warrants closer attention. The potential losses from extending credit to individuals in this credit-score region appear to be substantial. For example, the random-account performance measure indicates that 52.7 percent of new or existing accounts extended to individuals in the bottom 20 percent of the score distribution would be expected to go bad over an 18-month period. Not all of this poor performance necessarily reflects lender decisions on newly extended credit because it also potentially reflects deteriorating performance on existing accounts, which are those opened before the beginning of the performance period. However, credit-record data indicate that 17.9 percent of the individuals in the bottom two score deciles of our sample were extended credit in the last six months of 2003 (modified new account) and that about 16.1 percent of these accounts defaulted. Under the presumption that lenders screen for credit risk, the high incidence of bad performance in the two lowest deciles likely would have been even higher had more individuals in these low score deciles been extended credit.
Performance by Population Group
Credit scores appear to differentiate risk well within all population groups (figures 6.A--E; data given are only for the TransRisk Score, as the data for the other two scores are similar). The general shapes of the performance curves are similar across groups, as is the separation of the goods and bads (figures 7.A--E; again, data only for the TransRisk Score are shown). Within populations, the performance curves are not identical. Of particular interest for this study are performance curves for populations that are uniformly above or below that for others. A performance curve that is uniformly above (below) means that that group consistently underperforms (overperforms), which in turn means that the group performs worse (better) on their loans, on average, than would be predicted by the performance of individuals in the overall population with similar credit scores.
Another way of comparing performance across groups is to compute performance residuals. First, the mean performance for all individuals is computed at each score level (rounded to half a point). Residuals for each population group at each score level are derived as the difference between the mean performance of the population group at that score level and the mean performance of the full population at that score level. The group residual is calculated by averaging residuals over all score levels (results shown in tables 18.A--C). Consistently, across all three credit scores and all five performance measures, blacks, single individuals, individuals residing in lower-income or predominantly minority census tracts show consistently higher incidences of bad performance than would be predicted by the credit scores. Similarly, Asians, married individuals, foreign-born (particularly, recent immigrants), and those residing in higher-income census tracts consistently perform better than predicted by their credit scores.119
Results for age are mixed: For the TransRisk Score and FRB base score, individuals younger than age 30 consistently show higher incidences of bad performance than would be predicted by their credit scores. However, for the VantageScore, for some measures of performance, younger individuals perform better than would be predicted by this score. Differences in the results across scores are driven by the fact that the mean credit score for individuals younger than 30 is lower for the VantageScore than for the other two scores. As noted earlier, the primary reason for the relatively lower VantageScores for younger individuals is the choice of the random-account performance measure in estimating the model. The choice of this performance measure in estimation tends to lower scores for individuals with a small number of credit records (who are disproportionately younger) relative to those with many records.120 Indeed, when the VantageScore performance residuals are calculated using the random-account performance measure, younger individuals perform about as predicted.
All the performance residual calculations are relative measures in that the mean performance residual for the whole population is normalized to zero for each credit-score measure and for each measure of performance. Thus, a positive average performance residual means that, on average, and controlling for credit score, the performance of the group was worse over the performance period used here than the average for the whole population.
For some of the population groups, the calculated underperformance or overperformance is not small, particularly for the new-account performance measure. The mean account performance data, shown earlier in table 17, together with the residuals shown in tables 18.A--C indicate how much of the performance can be predicted by score and how much is unexplained. For example, for the any-account performance measure, the mean bad rate for blacks is 65.9 percent; for the new-account measure, it is 21.7 percent. The TransRisk Score residual for these two performance measures for blacks are 5.6 percent and 3.4 percent respectively. We subtract the residual from the mean bad rate to find that the predicted performance for blacks based on the TransRisk score for the any-account measure would be 60.3 percent bad and for new accounts 18.3 percent bad (derived from tables 17 and 18.A). Thus, the residual, or the component of average black performance that is unexplained, is not small: For example, the actual new-account percent bad is about one-sixth higher than would be predicted from the TransRisk Scores for blacks. At the other end of the spectrum, for recent immigrants the actual any-account percent bad is 5 percent lower than would be predicted, but for modified new accounts it is more than 25 percent lower.
One possible concern is that the performance measures may include performance on accounts that are not consistently reported. Three such items are student loans, noncredit-related collection agency accounts or public records such as those for medical or utility bills, and authorized user accounts (that is, accounts for which the individual is not responsible for repayment). The preceding analysis was repeated with any-account performance residuals adjusted to remove (1) student accounts, (2) noncredit collections and public records, and (3) authorized user accounts.
Not surprisingly, individuals younger than age 30 were the most affected by the removal of student loans or authorized user accounts; however, the effects were quite modest. The any-account TransRisk performance residual for the younger group fell from 1.5 to 1.3 when these account types were removed from the measurement of performance (results not shown in tables). Performance residuals for other populations were little changed when student loans or authorized user accounts were removed from the measurement of performance.
Removing collection and public record items had the largest effect on blacks, but the effect was very modest. Performance residuals for blacks fell about 0.1 point (or about 2 percent) for each score.
An Implication of Underperformance
Underperformance relative to the performance implied by the credit score has an implication for the groups involved, as it relates to the expected changes in credit-score levels over time. The score levels of groups that consistently underperform would be expected to deteriorate over time because payment performance is a significant factor in credit-scoring models. The deterioration would be particularly pronounced to the extent that new accounts without a performance history are in the credit records. Alternatively, groups that consistently overperform would be expected to experience an increase in credit scores over time as a result of their good performance. The fact that groups with the largest performance residuals--blacks, single individuals, those younger than age 30 (for the TransRisk Score and the FRB base score), and residents of lower-income and predominantly minority census tracts--have score levels that are consistently lower than average might be due to underperformance in the past. Similarly, the fact that groups that consistently overperform--married individuals, foreign-born individuals, and individuals residing in higher-income census tracts--have higher-than-average credit-score levels suggests that, over time, overperformance leads to higher scores for these groups.
Multivariate Analysis of Performance Residuals
In the preceding discussion, the performance residuals presented were univariate statistics. As was the case with the differences in credit-score levels across groups, the performance residuals for one population may reflect, at least partly, differences coming from other factors. To address that possibility, a multivariate analysis was conducted in a manner similar to that performed for score levels.
To identify the independent effect of race or ethnicity on differences in performance residuals, a regression model was fit to predict performance residuals using only non-Hispanic white individuals based upon their age (separated into five linear splines), sex, and marital status. The age splines were fully interacted with sex and marital status. For comparability with the score-level analysis and with the mean credit scores by census tract, the performance residual used for this analysis was based on the TransRisk Score. An additional advantage of using the TransRisk Score is that the performance residual is truly out-of-sample. The TransRisk Score was developed and available before June 2003, whereas both the VantageScore and the FRB base score were estimated using approximately the same performance period as that used here.
Predicted values from this equation were used to predict performance residuals for blacks, Hispanics, and Asians. Differences between individuals' actual performance residuals and their predicted performance residuals can be interpreted as unexplained racial or ethnic effects. The empirical estimation was then expanded to control for the census-tract estimate of the individual's income, the relative income of the individual's census tract, and the mean credit score of the individual's census tract. All regressions were conducted separately for individuals in the lowest TransRisk Score quintile, in the second-lowest quintile, and in the top three quintiles combined. The TransRisk Score and the TransRisk Score squared were also included in each regression. As with the analyses of score differences, the regressions were also run using only males, controls for age, and weights for the percentage of non-Hispanic whites in the census block.
The analysis was conducted with each of the five performance measures (tables 19.A--E). Unlike the case of the multivariate analysis of credit-score distributions, controlling for other personal demographic and census-tract factors appears to have only a modest effect on performance residuals across populations. For example, the performance residual for the any-account performance measure for blacks has a 5.6 percent bad rate, which is only reduced to 4.7 percent when other factors are taken into account. Thus, the performance residuals appear to largely reflect the group characteristic itself (or, as discussed below, other factors related to the group characteristic that were not included in the model) and not the confounding effect of other personal demographic factors.
Loan Terms and Performance
The preceding sections focus on explaining group differences in performance residuals that may be due to demographic characteristics. Another possible explanation for performance differences may be that different populations use different types of credit, borrow from different types of lenders, and receive different loan terms even when they have similar credit scores. The account details in the credit records allow for a limited assessment of these explanations.
The evaluation could technically be done for both existing credit accounts and for new accounts. The drawback to using existing accounts is that such accounts were opened at various times preceding the draw of sample credit records and thus may not reflect an individual's current credit circumstances. However, by focusing on accounts opened during the first six months of the performance period--July to December 2003--the credit records of June 2003 more credibly reflect the credit circumstances of the individuals when these loans were underwritten. Therefore, the analysis focuses on all accounts opened during that six-month period and contained in the December 2004 credit records. The analysis uses the modified new-account performance measure because of all the measures, the coverage of that one is the most likely to be truly new loans.
Data in the credit records allow for the classification of new loans along several dimensions: the type of lender--bank or thrift institution, finance company, credit union, and other (for example, retail stores); the type of loan--mortgage, auto, other installment, credit card, and other open-ended loans; largest amount owed; the month the loan was taken out; and, for mortgage loans and installment loans, the loan terms (loan maturity and monthly payment) and a derived estimate of the current interest rate.121
The analysis begins with simple univariate relationships describing differences in the types and terms of new loans for different population groups after controlling for credit scores. Tables 20.A--C present information on the distribution of loan type, interest rate, and subsequent performance for different groups of individuals in three segments of the TransRisk Score distribution: the lowest quintile; the second-lowest quintile; and the top three quintiles combined. On the basis of credit score alone, individuals in the lowest quintile would likely be in the subprime portion of the loan market. Those in the top three quintiles correspond roughly to individuals in the prime portion of the loan market, and those in the second-lowest quintile fall between these two groups.
The data indicate differences in the types of loans taken out by different population groups. For example, in all three score groups, the share of installment loans with finance companies is significantly larger among black and Hispanic borrowers than non-Hispanic white borrowers. Blacks and Hispanics are less likely to take out mortgages or other loans at banks than are non-Hispanic white borrowers. Not surprisingly, individuals younger than age 30 are less likely to take out mortgages but are more likely, at least in the upper four score quintiles, to have credit card accounts. Males are more likely to have mortgages than auto loans, but females are more likely than males to have "other" loans, primarily retail or store loans. Estimated interest rates also differ across populations after controlling for loan type and score quintile. Credit accounts of black borrowers have higher interest rates than those of non-Hispanic whites for each loan category in which rates can be determined, although differences were small for some loan products. This pattern is found across all the credit-score quintiles, including the top three score quintiles, where credit-risk differences, at least as measured by credit history, are smaller. Interest rate patterns for Asians differ, as interest rates paid by Asians are typically lower or about the same, on average, as those paid by non-Hispanic whites across all credit-score quintiles and all product categories for which rates could be estimated.
Very few consistent patterns emerge for interest rate by national origin or sex. Interest rates vary by age, although they exhibit different patterns across different products and credit-score quintiles.
The data also track the performance difference for each loan category by credit-score group. In almost every category, blacks show a higher incidence of default than non-Hispanic white borrowers, although differences are, in some cases, small. However, two product areas, auto loans from finance companies and credit card loans, show consistently higher and larger default rates for blacks than for non-Hispanic white borrowers for all credit-score quintiles.
For each credit-score quintile, younger individuals show higher default rates for bank-issued credit cards than older borrowers. Patterns for other products are inconsistent. For example, in the lowest quintile, the largest performance differences between young and old are for credit cards from finance companies, whereas for the second quintile, the largest performance gap is for auto loans from finance companies.
To better identify the possible effects of loan terms and interest rates on performance differences by race or ethnicity, a multivariate analysis similar to that presented in the previous section was conducted. A regression model was estimated using modified new accounts among non-Hispanic white individuals to predict performance residuals by type of loan and lender, the month the loan was taken out, the loan amount, and, when calculable, the interest rate. The empirical estimation was then expanded to taken into account age, marital status, sex, census-tract characteristics, and the census-tract-based estimate of the individual's income.
As before, all regressions were conducted separately for individuals in three TransRisk Score groupings: the lowest quintile, the second-lowest quintile, and the top three quintiles combined; the TransRisk Score and the TransRisk Score squared were also included in each regression. Also as before, the regressions were estimated using only males, with age controls, and weighted by the percentage of non-Hispanic white individuals in the census block.
Loan terms and interest rates explain virtually none of the differences in performance residuals by race, sex, or age (table 21). The results hold when loan terms and interest rates are considered without other controls or along with other demographic and location factors. Thus, differences in the kinds of loans used by different populations and the interest rates paid do not appear to be the source of differences in performance once credit score is taken into account.
Credit Scores and Credit Availability and Affordability
The credit-record data assembled for this study can be used to investigate the effects of credit scores on the availability and affordability of credit. However, there are a number of issues that need to be addressed in such an investigation. The first issue in using credit-record data for this purpose is that we observe an individual's credit score at a particular point in time. Unfortunately, the timing of new credit does not necessarily correspond to the same point in time at which the scores are calculated. As discussed in the previous section, some of the timing issues can be mitigated by focusing on new credit issued within a short period of time after the credit score was calculated.
The second issue is that we observe in credit bureau records only actual extensions of new credit. The incidence of new credit is effected by both demand and supply factors. Thus, some individuals do not receive new credit because they do not want or need it, others because they believe they will be turned down and are discouraged from applying, and others because they have applied but are denied. Ideally, one would like to isolate the latter two effects, which are direct reflections of the availability of credit. The credit-record data do not indicate direct denials; however, one method employed by the industry to proxy for denials is derived from a review of credit-inquiry patterns. Specifically, credit inquiries observed during a period when an individual does not receive credit are taken as indicators of loan denials.122
A third issue is that, as noted in the previous section, the credit-record data do not provide direct information on the pricing of credit. For open-ended credit, there is no loan term information provided at all in the credit records. For closed-ended credit, the credit records provide information on the loan terms at the time the credit report was drawn, which, as shown earlier, can be used to estimate interest rates. However, for variable-rate loans or for loans for which substantial upfront points or fees were charged, interest rates calculated in this way may not reflect the full pricing of credit.
Subject to these caveats, the approach taken to address affordability and availability parallels that used previously to address issues in loan performance. Specifically, we examine the relationship between our sample's TransRisk Scores, measured in June 2003, and three measures of availability and affordability of credit, as measured over the July 2003 to December 2003 period. The three measures are issuance of any new credit (evidence of availability), credit inquiries without the issuance of new credit (evidence of denial), and interest rates on new closed-end credit (evidence of affordability). These comparisons are made for different population groups and, when possible, for different loan types.
The credit-record data reveal relatively few differences across racial or ethnic groups in the incidence of new credit after controlling for credit-score quintile (shown earlier in tables 20.A--C). Black borrowers were somewhat less likely than others to take out new mortgages and automobile loans from banks and, in general, less likely to open credit card accounts, but they were more likely to take out new installment loans at finance companies. Differences were most pronounced in the lowest two credit-score quintiles. Not surprisingly, the incidence of new credit varied by age group. The general pattern shows younger and older individuals less likely to obtain new loans than middle-age individuals, a pattern consistent with the life-cycle theory of credit use.
For each credit-score quintile, black and Hispanic borrowers have a higher incidence of the denial proxy than non-Hispanic whites. Recent immigrants, younger individuals, single individuals, and individuals that live in low-income areas or areas with a high minority population also show a higher incidence of the denial proxy than do other groups.
Estimated interest rates also differ across populations after controlling for loan type and credit-score quintile. Black borrowers experienced higher interest rates than non-Hispanic whites for each loan category in which interest rates can be determined, although, as noted, some differences were small. Very few consistent patterns appear in the data regarding interest rates by national origin or sex. Interest rates vary by age, but they exhibit different patterns across different products and credit-score quintiles.
The data just presented may mask effects due to variation within credit-score quintiles. To provide a better measure of the continuous relationship between credit scores and the three measures of availability and affordability of credit, figures were constructed showing the continuous relationship between the TransRisk Score and the incidence of new credit, the incidence of the denial proxy, and the estimated interest rates.
For each demographic group, the relationship between credit scores and the incidence of new credit is in the shape of an inverted U (figure 8). The decline in incidence of new credit at higher credit-score levels is almost surely due to demand rather than supply: Individuals with higher scores are less likely to need or desire new credit. In the lower end of the credit-score range, the upward sloping incidence of new credit is much more likely to reflect differences in supply. The patterns for different demographic groups appear to be quite similar.
The incidence of denial, as proxied by the inquiry measure, uniformly declines in credit scores for each demographic group (figure 9). Moreover, both the shapes and levels of the curves appear to be quite similar, but older individuals show a somewhat lower incidence, and younger individuals show a somewhat higher inferred denial rate.
Similarly, estimated interest rates show a monotonically decreasing relationship with credit scores, again with the curves for different population groups exhibiting similar slopes and levels, although auto loan rates for black borrowers and individuals living in low-income census tracts appear to be somewhat higher than for individuals in other groups with similar credit scores (figures 10.A--C). The slopes of the curves do vary across loan products, with interest rates for mortgages showing a flatter pattern than those for automobile or other loans. The relationships for credit scores and other installment loan interest rates appear to be much less consistent than those for mortgage or automobile loans. This difference is likely due to the fact that the collateral for other installment loans is more heterogeneous and that the loan category incorporates a wider range of products.
To address whether population differences between these curves can be narrowed when other factors are controlled for, a multivariate analysis was conducted. The analyses are similar to those conducted for loan performance and include the same demographic characteristics and control factors, specifically, credit score and location.
The dependent variable for the first analysis is the incidence of new credit. Following the approach used for the performance residuals, a regression equation fitted for the non-Hispanic white population was used to predict the incidence of new credit for other racial or ethnic groups. The difference between the actual and predicted incidence of new credit is the unexplained residual. The multivariate analysis was also run for males only, with controls for age, and weighted by the percentage of non-Hispanic white individuals in the census block. The analysis reveals that differences in the incidence of new credit across racial or ethnic groups largely disappear once credit score and other factors are taken into account (table 22.A). Not surprisingly, differences by age are largely unaffected by control factors and remain significant.
A second multivariate analysis was conducted for the inquiry-based proxy for loan denial. Here, the higher incidences shown for black and Hispanic individuals are largely unaffected by controls for other factors (table 22.B). Differences by age, however, are reduced.
The third set of multivariate analyses focused on the interest rates for new mortgage and auto loans.123 The multivariate regressions were virtually identical to those in the previous section, except that the dependent variable was the loan interest residuals rather than loan performance residuals, and, perforce, the sample for the interest rate analysis was limited to accounts for which interest rates could be calculated. Multivariate results suggest that some, but not all, of the difference in interest rates can be explained by loan type, lender, and amount and the demographic and location controls considered here (tables 22.C and D).124 The gross mortgage interest rate difference between blacks and non-Hispanic whites was 0.39 percentage point after controlling for score; the difference was still 0.39 percentage point after loan terms and lender type were taken into account. (Auto loan rate differences across racial and ethnic groups widen when other factors are taken into account). The difference narrowed to 0.26 percentage point when demographic and location controls were taken into account. Both gross and conditional age differences in interest rates are much smaller and virtually disappear (or reverse sign) when credit score and other factors are considered.125
Accounting for Economic and Financial Factors Not Available in This Study
The multivariate analyses in the previous sections were, perforce, restricted to information contained in the credit records, the SSA file match, and factors based upon an individual's location. Thus, the data assembled for this study can provide only limited insights into the relationship between credit scores and credit performance, availability, and affordability (and essentially no insight into whether the relationship is one of cause and effect). The data do not contain key variables that would need to be taken into account. Missing data include other underwriting factors, such as loan-to-value ratios in the case of mortgages, and the weight given to credit scores relative to these other factors. Missing data also include underlying differences in socioeconomic factors such as employment experience and wealth; only a rough estimate of individual income is available. Moreover, the credit-record data used here cover only a brief period and therefore cannot reflect changes over time in the relationship between credit scores and the availability or affordability of credit.
The multivariate analysis discussed above highlighted unexplained differences in performance, denial rates and loan affordability across age groups as well as across racial and ethnic groups. In this section, we use information from the Federal Reserve Board's 2004 Survey of Consumer Finances (SCF) to explore the possibility that differences in, for example, wealth, employment history, and financial experience might help to explain the remaining differences in credit performance, affordability, and access across groups (tables 23--26).126 Inferences from this analysis are only suggestive because the information cannot be linked to the individuals in the study sample and their credit-related performance or loan terms.
The financial literature on credit evaluation has traditionally pointed to several broad factors (termed "the five C's") that influence the likelihood that borrowers will repay their debts as scheduled: capacity, collateral, capital, conditions, and character.127 Generally, capacity refers to the income flow that is available to service debts; collateral is the value of assets explicitly backing a loan; capital refers to assets that may be available to repay a loan but that do not explicitly back it; conditions refers to trigger events that may disrupt income flows or create unexpected expenses that affect the ability to make loan payments; and character corresponds to the financial experience, skills, or willingness of an individual regarding his or her ability to manage financial obligations. Differences in populations along any of these dimensions could potentially account for the performance differences found in this study and, to the extent they are used by loan underwriters, may affect pricing and loan availability as well.
Younger families differ substantially from older families over a wide variety of financial dimensions. Variation across age groups in income, wealth components, debt-payment burdens, and savings largely reflect the life-cycle pattern of income: Income rises as workers progress through their careers and falls sharply upon retirement. Thus, young families have comparatively low levels of income, wealth, and savings and are more likely to have high debt-payment burdens. Younger families are also more likely to have experienced a recent bout of unemployment. As age and income rise, families accumulate greater financial and nonfinancial assets, including homes, are less likely to suffer job loss, and are increasingly likely to save and reduce their debt burdens. None of these factors were explicitly accounted for in the multivariate performance analysis conducted with the credit-record data and thus could explain at least a portion of the underperformance of younger individuals and overperformance of older individuals.
The SCF data show that income, wealth, and holdings of financial assets are substantially lower for black and Hispanic families than for non-Hispanic white families.128 These racial patterns generally hold even after accounting for age, income, and household type, as shown in the bottom portion of the tables. Overall median net worth and financial assets among black or Hispanic households, for instance, are about 10 percent to 15 percent of the non-Hispanic white median. Black and Hispanic families are less likely than non-Hispanic white families to have any financial assets, so that the disparity in median financial assets for all families (rather than just those with financial assets) is even larger, with the overall medians for black and Hispanic families roughly 5 percent to 7 percent of the non-Hispanic white median. The likelihood of a recent unemployment spell are also higher for blacks and Hispanics. The median payment-to-income ratio for debtors is similar across the four racial and ethnic groups (blacks, Hispanics, non-Hispanic whites, and Asians), but nonwhite families are more likely to have payment-to-income ratios greater than 40 percent.
Finally, some argue that differences in educational attainment and credit-market experience among the four groups may be related to financial literacy. High-school and college graduation rates among Hispanics are below those for blacks, which, in turn, are lower than those for non-Hispanic whites. Each of these factors, none of which were included in the credit-record multivariate analysis, may at least partially explain remaining differences in loan performance and credit access and affordability across racial or ethnic groups.
Taken together, the SCF provides a more comprehensive picture of the varying economic circumstances of different populations than is available from the data in credit records. Differences across groups in these broad measures of economic and social well-being are consistent with the conjecture that disparities in the financial and nonfinancial characteristics of younger, single, nonwhite, and Hispanic families may at least partially explain both the underperformance of these groups for a given score and differences in availability and affordability of credit.