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1. The Fact Act, Public Law 108-159, enacted December 4, 2003; section 215 is reproduced in appendix A of this report. Return to text
2. Personal identifying information, such as names and Social Security numbers, was not made available to the Federal Reserve. Return to text
3. TransRisk Account Management Score is a registered trademark of TransUnion LLC, and VantageScore is a service mark of VantageScore Solutions LLC. All other trademarks, service marks, and brands referred to in this report are likewise the property of their respective owners. Return to text
4. Credit inquiries are requests by creditors for an individual's credit report. The lending industry uses the presence of credit inquiries without the issuance of new credit as an indication of loan denial. The data on credit inquiries are likewise used in this study to infer whether an individual likely experienced a credit denial. Return to text
*Sentence as corrected August 23, 2007. Return to text
1. Under the Fair Credit Reporting Act, these organizations are referred to as consumer-reporting agencies. Although these agencies are sometimes elsewhere referred to as credit bureaus, that term includes firms that do not collect information on credit accounts, and such firms are not considered in this report. Return to text
2. Industry participants often refer to credit history scoring models as credit-bureau-based-scoring models. Return to text
3. Under the Home Mortgage Disclosure Act of 1975, as amended in 1989, covered lenders are required to collect and disclose information about the race or ethnicity and sex of individuals applying for mortgages covered by the law. Return to text
4. The Fact Act, Public Law 108-159, was passed by the Congress on December 4, 2003. Return to text
5. Trademarks, service marks, and brands referred to in this report are the property of their respective owners. Return to text
6. Among the prohibited bases under ECOA are race, color, religion, sex, national origin, age, and marital status.Return to text
7. Race, color, religion, sex, and national origin are prohibited bases under the FHA, as under ECOA. Additional prohibited bases under the FHA are handicap and family status but, unlike under ECOA, not age and marital status.Return to text
8. Some courts and agencies have referred to certain forms of particularly blatant discriminatory treatment on a prohibited basis as "overt discrimination."Return to text
9. Refer to Janet Sonntag (1995), "The Debate about Credit Scoring," Mortgage Banking (November), pp. 46-52; Warren L. Dennis (1995), "Fair Lending and Credit Scoring," Mortgage Banking (November), pp. 55-58.Return to text
10. Some credit records include the date of birth or age.Return to text
11. No specific addresses of individuals included in the sample of credit records were included in the data made available to the Federal Reserve.Return to text
12. The credit-record data excluded any personal identifying information.Return to text
13. A "credit characteristic" is a summary measure of an aspect of an individual's credit record, such as the number of credit accounts or the months since the most recent delinquency.Return to text
14. A major-derogatory account, as used in this study, is any account delinquent 90 days or more or that was involved in a repossession or charge-off.Return to text
15. The 19 credit characteristics used in the FRB base model are listed in appendix C.Return to text
16. Information on the race or ethnicity of individuals is generally not available in the data used to develop credit scores. However, if place of residence is known, the racial or ethnic composition of the census tract (or census block) can be used as an approximation of an individual's race or ethnicity. This approach has been used in previous studies that examine the relationship between credit scores and race or ethnicity.
For this report, in addition to the SSA classification of the individual's race or ethnicity, the adult racial or ethnic composition of the individual's census block (available for about 85 percent of the population) or census tract is used as an approximation of the individual's race or ethnicity. The proportion of the block belonging to each racial or ethnic group can be viewed as the probability that a random adult drawn from the block will have that race or ethnicity. The probability is used as a weight in forming the estimates presented in this study.Return to text
17. The term "unexplained" as used here is a statistical concept. The unexplained difference is defined as the difference in average scores in the scorable sample after other factors included in the multivariate regressions are accounted for. Thus, the size of the unexplained component depends on what other factors are included in the model. Adding factors to the model, or subtracting them, will affect the size of the unexplained differences.Return to text
18. The figures in this overview present results only for the TransRisk Score. Figure O-3 is further restricted to the modified new-account performance measure [this footnote as corrected August 23, 2007]. Return to text
19. Calculated as the average age of all credit accounts in an individual's credit record.Return to text
20. The choice of the population group (in this case, non-Hispanic whites) was driven by considerations of sample size alone. In principle, any group could serve as the base population for estimating a model. The non-Hispanic white population was the only population in the sample of sufficient size to provide a basis for model estimation. In general, the selection of the base group may affect conclusions reached regarding differential effect of various credit characteristics.Return to text
1. A discussion of different patterns of borrowing across racial groups and their consequences is in Sheila D. Ards and Samuel L. Myers (2001), "The Color of Money: Bad Credit, Wealth and Race," American Behavioral Science, vol. 45 (October), pp. 223-39. Return to text
2. Section 215 directs the Board of Governors of the Federal Reserve System and the Federal Trade Commission, in consultation with the Office of Fair Housing and Equal Opportunity of the Department of Housing and Urban Development, to conduct the study. Section 215 also directed that similar issues be examined for the use of credit scoring in insurance markets. In preparing the report, the Federal Reserve Board focused on the relationship between credit scoring and credit and the Federal Trade Commission addressed the use of credit scoring in insurance markets. Return to text
3. The full text of section 215 is in appendix A of this report. Return to text
4. Federal Trade Commission (2004), "Public Comment on Methodology and Research Design for Conducting a Study of the Effects of Credit Scores and Credit-Based Insurance Scores on the Availability and Affordability of Financial Products," notice and request for public comment (RIN 3084-AA94), Federal Register, vol. 69 (June 18), pp. 34167-68 (comments received are available at www.ftc.gov/os/comments/creditscoresstudy/index.shtm; and Federal Trade Commission (200f), "Public Comment on Data, Studies, or Other Evidence Related to the Effects of Credit Scores and Credit-Based Insurance Scores on the Availability and Affordability of Financial Products," notice and request for public comment (RIN 3084-AA94), Federal Register, vol. 70 (February 28), pp. 9652-55 (comments received are available at www.ftc.gov/os/comments/FACTA-implementscorestudy/index.htm). Return to text
5. The audit approach would have to be quite broad in its reach to fully represent the credit-scoring models used by the industry. Some models are designed only to evaluate applicants for new accounts, others to predict performance on existing credit accounts; and still others to address both purposes. Also, credit-scoring models use different models or "scorecards" for different segments of the population. For example, a credit-scoring model may have separate scorecards for individuals with "thin" credit files (individuals with few if any records of credit accounts), with "clean" track records (individuals with no record of a serious delinquency), and with track records with a "major derogatory" (individuals with a record of one or more serious delinquencies), to name a few. Each scorecard can be based on different credit-related factors. Finally, credit-scoring models are routinely re-estimated and changed to reflect new technologies and the availability of updated information on the credit experiences of consumers. Thus, the credit-scoring systems and factors that constitute the models are ever changing. Return to text
6. Credit history scoring models are often referred to as credit-bureau based scoring models by industry participants. Return to text
7. Elizabeth Mays (2004), Credit Scoring for Risk Managers: The Handbook for Lenders (Mason, Ohio: South-Western), p. 17. As noted above, a generic credit history score is generated by a model (1) that draws on a representative sample of all individuals in credit-reporting agency records (a feature that makes the model generic) and (2) in which the predictive factors are limited to information contained in credit-reporting agency records (which focuses the model on credit history). Return to text
8. Although most credit-scoring systems are based on statistically derived models, they need not be. For instance a creditor may use a rigidly implemented system of rule-based decisions in which the rules have not been statistically derived. More background information is in Robert A. Eisenbeis (1980), "Selection and Disclosure of Reasons for Adverse Action in Credit-Granting Systems," Federal Reserve Bulletin, vol. 66 (September), pp. 727-35. Return to text
9. "The first commercial [credit-scoring] systems were developed by Bill Fair and Earl Isaac in 1958 for American Investment, a finance company based in St. Louis; refer to Hollis Fishelson-Holstine (2004), "The Role of Credit Scoring in Increasing Homeownership for Underserved Populations," prepared for "Building Assets, Building Credit: A Symposium on Improving Financial Services in Low-Income Communities"; Working Paper Series BABC 04-12 (Cambridge, Mass.: Joint Center for Housing Studies, February). Return to text
10. For example, in 1994, Fannie Mae and Freddie Mac began to use the scores in their automated underwriting systems; refer to John W. Straka (2000), "A Shift in the Mortgage Landscape: The 1990s Move to Automated Credit Evaluations," Journal of Housing Research, vol. 11 (no. 2), pp. 207-32. Return to text
11. In 1958, Bank of America, based in San Francisco, issued BankAmericard, the first "revolving credit" card with widespread acceptance by merchants of all types. The revolving-credit feature allowed cardholders the option of paying their account balance in installments, with a monthly finance charge applied to the remaining balance. In 1966, Bank of America, through a subsidiary, began licensing banks outside of California to issue the cards to their customers. Return to text
12. Some of the items reported to the credit-reporting agencies are not comprehensive. For example, some reporters provide information only on delinquent accounts. Some items such as lawsuits are often not reported or collected from public entities. Consequently, some of the data include in the credit-reporting agency data are not fully representative of all credit-related activity or public records. For more information see, Robert B. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner (2003), "An Overview of Consumer Data and Credit Reporting," Federal Reserve Bulletin, vol. 89 (February), pp. 47-73. Return to text
13. Robert M. Hunt (2005), "A Century of Consumer Credit Reporting in America," Working Paper 05-13 (Philadelphia: Federal Reserve Bank of Philadelphia, June). Return to text
14. Information on each agency is available at www.equifax.com, www.experian.com, and www.transunion.com. Return to text
15. Free credit reports may be requested at www.annualcreditreport.com. State laws in Colorado, Georgia, Maine, Maryland, Massachusetts, New Jersey, and Vermont also require that their residents be allowed to obtain a copy of their credit report free of charge. Return to text
16. A detailed assessment of the contents of credit records is provided by Robert B. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner (2003), "An Overview of Consumer Data and Credit Reporting," Federal Reserve Bulletin, vol. 89 (February), pp. 47-73. Return to text
17. The CDIA (www.cdiaonline.org), the successor to the Associated Credit Bureaus, is the trade association for the credit-reporting industry.Return to text
18. A discussion of how the FCRA governs and encourages accurate credit reporting is in Michael E. Staten and Fred H. Cate (2004), "Does the Fair Credit Reporting Act Promote Accurate Credit Reporting?" prepared for "Building Assets, Building Credit: A Symposium on Improving Financial Services in Low-Income Communities"; Working Paper Series BABC 04-14 (Cambridge, Mass.: Joint Center for Housing Studies, February). Return to text
19. For example, the average number of accounts per credit record in general, and the number of mortgage accounts in particular, has increased substantially in the past decade or so. Also, in the past, each of the three national credit-reporting agencies tended to collect much of their information from a different specific region of the country. Regional differences have largely disappeared, as each of the companies now receives comprehensive information nationwide (Fishelson-Holstine, "The Role of Credit Scoring in Increasing Homeownership for Underserved Populations"). Return to text
20. Entities besides creditors, including public utilities and telecommunication firms, sometimes provide bill-payment information to the credit-reporting agencies, but most do not. Information on such bills tends to appear in credit records via reports from collection agencies on unpaid bills. Return to text
21. Address changes are very common; according to the 2000 census, about 15 percent of the U.S. population moves each year (http://factfinder.census.gov). Return to text
22. A discussion of these issues and references to the research are in Robert B. Avery, Paul S. Calem, and Glenn B. Canner (2004), "Credit Report Accuracy and Access to Credit," Federal Reserve Bulletin, vol. 90 (Summer), pp. 297-322. Return to text
23. Edward M. Lewis (1992), An Introduction to Credit Scoring (San Rafael, Calif.: Athena Press). Some other researchers recommend a minimum of 300 or 500 "bads" (refer to Gary Chandler, 1985, "Credit Scoring: A Feasibility Study," Credit Union Exec, vol. 25, pp. 8-12 or Elizabeth Mays (2004), Credit Scoring for Risk Managers: The Handbook for Lenders (Mason, Ohio: South-Western). Typically, many accounts cannot be straightforwardly identified as either "bad" or "good"; they are labeled "indeterminate" and eliminated from the estimation sample. For example, an account that is 30 days or 60 days in arrears may be treated as indeterminate while accounts that are 90 days or more in arrears may be considered bad. The rule of thumb of 1,500 "bads" may still be relevant for custom credit-scoring systems developed for small portfolios, but the most widely used consumer credit scores are estimated from samples with hundreds of thousands or even many millions of accounts and thus with numbers of "bads" far exceeding recommended minimums. Return to text
24. In David J. Hand and Niall M. Adams (2000), "Defining Attributes for Scorecard Construction in Credit Scoring," Journal of Applied Statistics, vol. 27 (no. 5), pp. 527-40, is a discussion of empirical methods for determining the number of ranges and their appropriate end points.. Return to text
25. Federal Reserve, Regulation B, Equal Credit Opportunity, 12 CFR 202. The regulation implements title VII (Equal Credit Opportunity Act) of the Consumer Credit Protection Act. State and federal regulators (depending on jurisdiction) responsible for the safety and soundness of banking institutions specifically examine them to ensure that they are adhering to consumer protection laws, and the examinations include a review of credit-scoring systems. Nonbanking financial institutions, such as finance and mortgage companies, are subject to oversight variously by HUD, the FTC, the Department of Justice, and in many cases state regulators. Return to text
26. More information is available at www.myfico.com/CreditEducation/CreditInquiries.aspx. Return to text
27. Fair Isaac Corporation was founded in 1956; its credit-scoring systems were first used in 1958 and were based on custom models (www.fairisaac.com). Return to text
28. Trademarks, service marks, and brands referred to in this report are the property of their respective owners. Return to text
29. Refer to www.vantagescore.com. Return to text
30. An important aspect of the VantageScore is its "leveling" of the characteristics used in the model. Characteristic leveling ensures that the model interprets information from each of the credit-reporting agencies in the same manner. Return to text
31. A more detailed discussion of factors considered in credit evaluation, including the relative weights assigned to different factors, is available at a www.myfico.com. Refer also to Robert B. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner (1986), "Credit Risk, Credit Scoring, and the Performance of Home Mortgages," Federal Reserve Bulletin, vol. 82 (July), pp. 621-48. Return to text
32. A major-derogatory account, as used in this study, is any account that is delinquent 90 days or more or that is involved in a repossession or charge-off; a collection account involves a failure to pay a loan or non-credit-related bill; and a public record is a monetary-related public action such as bankruptcy. Return to text
33. Refer to www.myfico.com/CreditEducation/CreditInquiries.aspx. Return to text
34. Reasonableness often takes the form of imposing constraints on the relationship between characteristics and performance. One such constraint is "monotonicity," which requires that increasing values of a characteristic have either a consistently positive or negative relationship to the predicted outcome. Additional information on model estimation is in Lyn C. Thomas (2000), "A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers," International Journal of Forecasting, vol. 16 (no. 2), pp. 149-72; Fractal Analytics (2003), Comparative Analysis of Classification Techniques: A Fractal Non-Hispanic Whitepaper (Jersey City, N.J.: Fractal); Nick Ryman-Tubb (2003), "An Overview of Credit Scoring Techniques," Credit Control, vol. 21 (no. 1/2), pp. 39-45; David J. Hand and William E. Henley (1997), "Statistical Classification Methods in Consumer Credit Scoring: A Review," Journal of the Royal Statistical Society, Series A (Statistics in Society), vol. 160 (no. 3), pp. 523-41; Rosenberg and Gleit, "Quantitative Methods in Credit Management: A Survey"; David J. Hand (1994), "Deconstructing Statistical Questions," Journal of the Royal Statistical Society, Series A (Statistics in Society), vol. 157 (no. 3), pp. 317-56; and Hand and Adams, "Defining Attributes for Scorecard Construction in Credit Scoring." Return to text
35. For example, the NextGen FICO score ranges in value from 150 to 950, the Classic FICO score from 300 to 850, and the VantageScore from 501 to 990. Return to text
36. Refer, for example, to Alicia H. Munnell, Geoffrey M.B. Tootell, Lynn E. Browne, and James McEneaney (1996), "Mortgage Lending in Boston: Interpreting HMDA Data," American Economic Review, vol. 86 (March ), pp. 25-53. Return to text
37. Rate sheets provide information to loan officers on the relationship, for a given day, between underwriting factors (such as a credit score and loan-to-value ratio) and interest rates for a particular lender. Return to text
38. Steve Bergsman (2007), "The Thin-File Problem," Mortgage Banking, vol. 67 (March), pp. 32-41. Return to text
39. Bergsman, "The Thin-File Problem," p. 34. Return to text
40. Refer, for example, to Information Policy Institute (2005), Giving Underserved Consumers Better Access to the Credit System: The Promise of Non-Traditional Data, Political and Economic Research Council (New York: IPI); Michael A. Turner, Alyssa Stewart Lee, Ann Schnare, Robin Varghese, and Patrick D. Walker (2006), Give Credit Where Credit Is Due: Increasing Access to Affordable Mainstream Credit Using Alternative Data (Washington, D.C., and New York: Brookings Institution Urban Markets Initiative and Political and Economic Research Council); and Katy Jacob and Rachel Schneider (2006), Market Interest in Alternative Data Sources and Credit Scoring, Center for Financial Services Innovation, an Affiliate of ShoreBank Corporation (Chicago: CFSI). Return to text
41. For example, Fair Isaac offers the FICO Expansion Score, First American offers the Anthem Score (www.credco.com/anthem), and LexisNexis offers RiskView (www.lexisnexis.com/riskview). Return to text
42. For example, the firm Pay Rent, Build Credit, Inc. (www.prbc.com), is a credit-reporting agency that specializes in gathering information on payments for recurring expenses such as rent and utilities and on payments to payday lenders to establish an alternative database to support credit decisions. Return to text
43. Section 604(c) of the Fair Credit Reporting Act regulates how creditors and insurers may use credit report information to send unsolicited firm offers of credit or insurance. The law allows a credit-reporting agency to give lenders information only if all of the following three conditions are met: (1) "the transaction consists of a firm offer of credit or insurance," (2) prescreening is used solely to offer credit or insurance, and (3) the consumer has not elected to "opt out" of such solicitations. A more expansive discussion of marketing and solicitation practices and the legal framework governing such practices is provided in Board of Governors of the Federal Reserve System (2004), Report to the Congress on Further Restrictions on Unsolicited Written Offers of Credit and Insurance (463 KB PDF) (Washington: Board of Governors). Return to text
44. Refer to Board of Governors of the Federal Reserve System (2006), The Profitability of Credit Card Operations of Depository Institutions , annual report submitted pursuant to section 8 of the Fair Credit and Charge Card Act of 1988 (Washington: Board of Governors, June), www.federalreserve.gov/pubs/reports_other.htm. Return to text
45. Phillip Booth and Duncan Walsh (2001), "Cash Flow Models for Pricing Mortgages," IMA Journal of Management Mathematics, vol. 12 (no. 2), pp. 157-172, discuss the development of risk-based pricing models in the context of mortgages. Also refer to Wendy Edelberg (2003), "Risk-Based Pricing of Interest Rates in Household Loan Markets (2.40 MB PDF)," , Finance and Economics Discussion Series 2003-62 (Washington: Board of Governors of the Federal Reserve System, December); and Alan M. White (2004), "Risk-Based Mortgage Pricing: Present and Future Research," Housing Policy Debate, vol. 15 (no. 3), pp. 503-531. Return to text
46. See, for example, Margaret S. Trench, Shane P. Pederson, Edward T. Lau, Lizhi Ma, Hui Wang, and Suresh K. Nair, "Managing Credit Lines and Prices for Bank One Credit Cards," Interfaces 33 (5), 2003, pp. 4-21. Return to text
47. Lyn C. Thomas, J. Ho, and William T. Scherer, "Time Will Tell: Behavioural Scoring and the Dynamics of Consumer Credit Assessment," IMA Journal of Management Mathematics 12 (1), 2001, pp. 89-103; cite Mary A. Hopper and Edward M. Lewis, "Behaviour Scoring and Adaptive Control Systems," In Credit Scoring and Credit Control, eds. Lyn C. Thomas, Jonathan N. Crook and David B. Edelman, Oxford: Oxford University Press, 1992, pp. 257-276; and Helen McNab and Anthea Wynn, Principles and Practice of Consumer Credit Risk Management, Canterbury, England: Financial World Publishing, 2000 on how "behavioral scoring can be used for deciding how to deal with those in arrears. They advocate experimentation using a champion challenger approach. In this, one splits the customers randomly and applies different collection policies to each to find out which works best on which band of behavioral scores. One uses the existing policy (the champion) for the majority of the customers and tries the new policy (the challenger) on a much smaller subset until it is clear which is the more successful." Return to text
48. Refer especially to Amy C. Cutts and Richard K. Green (2005), "Innovative Servicing Technology: Smart Enough to Keep People in Their Houses?" in Nicholas P. Retsinas and Eric S. Belsky, eds., Building Assets, Building Credit: Creating Wealth in Low-Income Communities (Washington: JCHS/Brookings Press), who note that "automated credit scoring based servicing tools . . . emerged in wide use in the late 1990s. These tools risk-rank delinquent accounts to identify loans that are likely to benefit from early interventions to avoid foreclosure. The tools also are used to underwrite loan workouts, helping borrowers keep their homes." Cutts and Green, using data from delinquent loans scored with Freddie Mac's Early Indicator scoring system for mitigating losses, find empirical evidence that "the total population of delinquent borrowers, and among them low-to-moderate income borrowers and borrowers in underserved areas, are less likely to lose their home if they are in a repayment plan or other workout." Return to text
49. Among many recent discussions of the application of particular credit-scoring methods to fraud detection are Richard J. Bolton and David J. Hand (2002), "Statistical Fraud Detection: A Review," Statistical Science, vol. 17 (no. 3), pp. 235-55; José R. Dorronsoro, Francisco Ginel, Carmen Sánchez, and Carlos Santa Cruz (1997), "Neural Fraud Detection in Credit Card Operations," IEEE Transactions on Neural Networks, vol. 8 (no. 4), pp. 827-34; Richard Wheeler and Stuart Aitken (2000), "Multiple Algorithms for Fraud Detection," Knowledge-Based Systems, vol. 13 (nos. 2-3), pp. 93-99; and Phillip A. Chan, Wei Fan, Andreas L. Prodromidis, and Salvatore J. Stolfo (1999), "Distributed Data Mining in Credit Card Fraud Detection," IEEE Intelligent Systems, vol. 14 (no. 6), pp. 67-74. Return to text
50. David K. Musto and Nicholas S. Souleles (2005), "A Portfolio View of Consumer Credit," paper presented at the Carnegie-Rochester Conference on Public Policy, Columbia University, September, pp. 1-43. Return to text
51. Refer to Robert B. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner (1997), "Changes in the Distribution of Banking Offices," Federal Reserve Bulletin, vol. 83 (September), pp. 707-26. Return to text
52. A description of the uses of funds raised during cash-out refinancing and other forms of home equity borrowing is in Glenn Canner, Karen Dynan, and Wayne Passmore (2002), "Mortgage Refinancing in 2001 and Early 2002," Federal Reserve Bulletin, vol. 88 (December), pp. 469-81. Return to text
53. For example, one study notes that "the subjectivity of the approval and feedback process under manual underwriting makes [consumer] lending more vulnerable to fair lending violations, intended or otherwise [than under automated systems]" (Susan W. Gates, Vanessa G. Perry, and Peter M. Zorn, 2002, ""Automated Underwriting in Mortgage Lending: Good News for the Underserved?" Housing Policy Debate, vol. 13, no. 2, p. 373). Return to text
54. When the interest rate charged by a lender is appropriate for the average credit risk of a pool of prospective borrowers but is either too low or too high for some of the individual borrowers, the pool can suffer adverse selection, that is, a rise in the relative number of high-risk borrowers. High-risk borrowers-- those for whom the correct individual interest rate would be higher than the average interest rate--will perceive the single-rate offer as a good deal and accept the terms, perhaps borrowing more than they would if charged a rate more consistent with their risk profile. In contrast, lower-risk borrowers--those for whom the correct interest rate would be lower than the average interest rate--may be able to find credit on better terms from another lender and decline the terms offered. If credit at lower interest rates is not available to these lower-risk individuals, they may choose not to borrow or to borrow less than they would otherwise.
Credit rationing--not extending loans to individuals judged to pose higher credit risk--is a response to the result of adverse selection, which is an actual pool of loans with an average credit risk higher than appropriate for the interest rate charged. An alternative to credit rationing--raising the interest rate to reflect the average risk of the actual borrowers--is unlikely to help; indeed, it may worsen adverse selection, thereby further increasing the average level of risk of the remaining borrowers. A discussion of adverse selection and credit rationing is in Joseph E. Stiglitz and Andrew Weiss (1981), "Credit Rationing in Markets with Imperfect Information," American Economic Review, vol. 71 (June), pp. 393-410; Marco Pagano and Tullio Jappelli (1993), "Information Sharing in Credit Markets," Journal of Finance, vol. 48 (December), pp. 1693-718; and Dwight M. Jaffee and Thomas Russell (1976), "Imperfect Information, Uncertainty, and Credit Rationing," Symposium: The Economics of Information, Quarterly Journal of Economics, vol. 90 (November), pp. 651-66. Return to text
55. Gary G. Chandler and John Y. Coffman (1979), "A Comparative Analysis of Empirical vs. Judgmental Credit Evaluation," Journal of Retail Banking Services, vol. 1 (2), pp.15-26; Eric Rosenberg and Alan Gleit (1994), "Quantitative Methods in Credit Management: A Survey," Operations Research, vol. 42 (July-August), pp. 589-613; Lyn C. Thomas (2000), "A Survey of Credit and Behavioural Scoring: Forecasting Financial Risk of Lending to Consumers," International Journal of Forecasting, vol. 16 (April-June), pp. 149-72; and D.J. Hand and W.E. Henley (1997), "Statistical Classification Methods in Consumer Credit Scoring: A Review," Journal of the Royal Statistical Society, Series A: Statistics in Society, vol. 160 (3), pp. 523-41. Return to text
56. Public comment submitted in response to the February 28, 2005, Federal Register notice requesting comment on the present study; received December 4, 2006. Return to text
57. Public comment submitted by Fair Isaac Corporation on April 25, 2005, in response to the February 28, 2005, Federal Register notice requesting public comment on the present study, p. 5. Return to text
58. Hollis Fishelson-Holstine (2004), "The Role of Credit Scoring in Increasing Homeownership for Underserved Populations," Working Paper Series, Joint Center for Housing Studies, Harvard University, BABA 04-12, February; and Javier Martell, Paul Panichelli, Rich Strauch, and Sally Taylor-Shoff (1999), "The Effectiveness of Scoring on Low-to-Moderate-Income and High-Minority Area Populations" (San Rafael, Calif.: Fair Isaac). Return to text
59. John W. Straka (2000), "A Shift in the Mortgage Landscape: The 1990s Move to Automated Credit Evaluations," Journal of Housing Research, vol. 11 (no. 2), pp. 207-32. Return to text
60. Straka, "A Shift in the Mortgage Landscape," p. 210. Refer also to Thomas M. Holloway, Gregor D. MacDonald, and John W. Straka (1993), "Credit Scores, Early-Payment Mortgage Defaults, and Mortgage Loan Performance," paper presented at the American Real Estate and Urban Economics Association Mid-Year Meeting, June 2, Washington, D.C. Return to text
61. A comment letter submitted for the present study cites statistics indicating that upwards of 75 percent of mortgage evaluations are made within two or three minutes with automated underwriting systems (comment letter submitted by Experian Information Solutions on the FACT Act scoring study matter P044804, August 20, 2004, p. 6). Refer also to Straka, "A Shift in the Mortgage Landscape," p. 216. Return to text
62 Comment letter submitted by Experian Information Solutions, August 20, 2004, p. 7. Return to text
63 Straka, "A Shift in the Mortgage Landscape," p. 216. Return to text
64 Refer to public comment submitted for this study by the American Financial Services Association, dated April 25, 2005, pp. 7 and 8. Return to text
65. Hyung-Kwon Jeong (2003), "Screening Technology and Loan Portfolio Choice," Working Paper, Institute for Monetary and Economic Research, Bank of Korea. Return to text
66. Gates, Perry, and Zorn, "Automated Underwriting in Mortgage Lending," p. 369. Return to text
67. Public comment submitted in response to the February 28, 2005, Federal Register notice requesting comment on the present study, received December, 4, 2006. Return to text
68. Refer to Board of Governors of the Federal Reserve System (2006), Report to the Congress on Practices of the Consumer Credit Industry in Soliciting and Extending Credit and their Effects on Consumer Debt and Insolvency, submitted pursuant to section 1229 of the Bankruptcy Abuse and Consumer Protection Act of 2005 (Washington: Board of Governors, June), www.federalreserve.gov/pubs/reports_other.htm. Return to text
69. Brian K. Bucks, Arthur B. Kennickell, and Kevin B Moore (2006), "Recent Changes in U.S. Family Finances: Evidence from the 2001 and 2004 Survey of Consumer Finances," Federal Reserve Bulletin, vol. 92 (March 22), pp. A1-A38, www.federalreserve.gov/pubs/bulletin/2006/06index.htm. Return to text
70. The number of families surveyed in each year of the SCF ranges from 3,143 to 4,519. The 1986 survey was a limited telephone-only re-interview of a subset of households that had participated in the 1983 survey and is not used in the analysis. Bucks, Kennickell, and Moore, "Recent Changes in U.S. Family Finances," offer additional detail on the design of the SCF as well as an overview of results from the 2004 survey. Return to text
71. In contrast to the credit-score data used elsewhere in this report, most data in the Survey of Consumer Finances are collected at the family level. Families are classified in the tables on the basis of the characteristics of the head of the family. An exception is for race and ethnicity, which is reported by the survey respondent, who may not be the head of the family as defined by the SCF. Return to text
72. Specifically, we model ownership of each type of debt separately by cells defined by year and several age ranges. The regressions control for family income, age, and whether the head is single or is married or living with a partner. For cells with fewer than fifty families and cells for which all or no families have a given type of debt, the predicted value is equal to the average percent within the cell. The within-cell average ownership rate is also used to estimate the counterfactual rate of owning only store or gas cards, which is difficult to model in a regression framework, in part because it is a rare outcome, particularly in later years. Return to text
73. The insensitivity, to this and the subsequent adjustment, of the estimated probabilities of owning only store or gas cards is likely due in large measure to the comparatively simple model used. Return to text
74. Pooling years of the SCF, 44 percent of black families have income in the bottom quartile, compared with 37 percent of Hispanics and 20 percent of non-Hispanic whites. Thirty-seven percent of Hispanic family heads are younger than 35, compared with 28 percent of black and 23 percent white, non-Hispanic family heads. Return to text
75. The percentile cutoffs that determine the income categories are calculated within years. Return to text
76. The regressions underlying the adjustments are estimated separately within income groups by year and category of debt. The models control for age, age squared, indicators for whether the family head was married/living with a partner, and indicators for whether the head was either black or Hispanic. Regressions for ownership of only store- or gas-type cards control only for age. Return to text
77. The counterfactuals are predicted based on separate regressions for each year for blacks and Hispanics on the one hand, and for non-Hispanic whites, Asians, and other racial categories on the other. The regressions control for income, age, whether the head is single or married/living with a partner, and whether the head had any college education (including a college degree). For ownership of only store or gas cards, the regressions control for age. Return to text
78. Under the Home Mortgage Disclosure Act of 1975, as amended in 1989, covered lenders are required to collect and disclose information about the race or ethnicity and sex of individuals applying for mortgages covered by the law. Return to text
79. Race, color, religion, sex, and national origin are prohibited bases under the FHA, as under ECOA. Additional prohibited bases under the FHA are handicap and family status, but, unlike under ECOA, not age and marital status. Return to text
80. Courts and agencies have sometimes referred to certain forms of particularly blatant discriminatory treatment on a prohibited basis as "overt discrimination." Return to text
81. Refer, for example, to Janet Sonntag (1995), "The Debate About Credit Scoring," Mortgage Banking (November), pp. 46-52; and Warren L. Dennis (1995), "Fair Lending and Credit Scoring," Mortgage Banking (November), pp. 55-58. Return to text
82. Regulation B, 12 CFR 202.2(p) and (t). Return to text
83. More information is available at www.ffiec.gov/ffiecinfobase/resources/retail/occ-bl-97-24_credit_scor_models.pdf (387 KB PDF). Return to text
84. Refer to Gregory E. Elliehausen and Thomas A. Durkin (1989), "Theory and Evidence of the Impact of Equal Credit Opportunity: An Agnostic Review of the Literature," Journal of Financial Services Research, vol. 2 (no. 2), pp. 89-114; Straka, "A Shift in the Mortgage Landscape"; Elaine Fortowsky and Michael LaCour-Little (2001), "Credit Scoring and Disparate Impact," Working Paper, Wells Fargo Home Mortgage; and M. Cary Collins, Keith D. Harvey, and Peter J. Nigro (2002), "The Influence of Bureau Scores, Customized Scores and Judgmental Review on the Bank Underwriting Decision Making Process," Journal of Real Estate Research, vol. 24 (no. 2), pp. 129-52. Return to text
85. Martell, Panichelli, Strauch, and Taylor-Shoff, "The Effectiveness of Scoring on Low-to-Moderate-Income and High-Minority Area Populations." Return to text
86. Agency files include personal identifying information that allows the credit-reporting agencies to distinguish among individuals and construct a full record of each individual's credit-related activities. Files include the individual's name, current and previous addresses, and Social Security number. Other demographic characteristics sometimes found in credit files include date of birth, telephone numbers, name of spouse, number of dependents, income, and employment information. Except for date of birth, such information was removed from the sample for this study. Return to text
87. An additional sample of 15,743 individuals with credit records established after June 30, 2003, was obtained by the Federal Reserve to achieve a representative sample of individuals with credit records as of December 31, 2004. The data on these individuals were used only in the robustness analysis. Return to text
88. The credit-account information was provided by 92,000 reporters, 23,000 of which were reporting at the time the sample was drawn. Return to text
89. The credit characteristics were those created by TransUnion as of June 2003. Since that time, they may have expanded the number of characteristics available to model builders. Model builders may also create their own characteristics from the raw credit records. Return to text
90. More information about the model is available at www.vantagescore.com/pressreleases.html. Return to text
91. The specific information in the credit records of an individual in the sample used to develop VantageScore may differ across the three agencies, primarily because the agencies do not always receive the same data from reporters, they receive data at different times, and reporters do not all furnish information to all three agencies. Return to text
92. The application form for a Social Security card is form SS-5 (05-2006), www.ssa.gov/online/ss-5.html. Return to text
93. Individuals may have applied multiple times for a Social Security card for several reasons, including loss of the original card or a change in legal name. Individuals are allowed to obtain up to three cards in a year and up to ten over a lifetime except for applications in response to a change in legal name, which are unlimited. Individuals always receive the same Social Security number when they make additional applications. Return to text
94. A census-block group is a cluster of census blocks (up to nine) within the same census tract. Census blocks vary in size, often relatively small in urbanized areas but much larger in rural areas. Census-block groups, which generally contain between 600 and 3,000 individuals, have an optimum size of about 1,500. Census tracts typically include about 4,000 individuals (www.census.gov). No specific addresses of individuals in the sample of credit records used for this study were provided to the Federal Reserve. Return to text
95. It was determined that information on religion, national origin and ethnicity, and language preference was derived mainly from the individual's name and not from a primary source. Consequently, these demographic categories were not used except to help impute race or ethnicity for the SSA data as described below. Return to text
96. To be included in the study sample, an individual must have had a credit record as of June 30, 2003. Individuals who were, for example, younger than 15 years of age are highly unlikely to have had credit records. Consequently, such an age for individuals with credit records likely represents a mismatch between the credit-reporting agency records and the SSA records. Return to text
97. An alternative would have been to use the validation sample--those who filed in both time periods--for the model estimation. An advantage to this approach would have been the ability to estimate a separate model for each available response (white, black, and other) for those who applied in both periods. Ultimately this approach was rejected because the number of observations available for estimation was too small, for example, only 4,187 individuals classified themselves as "other" before 1981 and subsequently refiled in the later period. An additional concern was that those pre-1981 individuals who subsequently refiled might not be representative of the broader pre-1981 population.
Specifically, the prediction process was conducted as follows. The estimation sample was divided into cells by age (two groups: one older than 30 and the other 30 or younger), marital status, and sex. A set of dichotomous indicator variables were generated on the basis of an individual's reported SSA race or ethnicity selection. White was the excluded category for the estimation. Each nonwhite SSA race choice was then regressed using a logistic model form on a combination of variables relevant to the race in question. These variables included ethnic background, foreign-born status, language preference, religion, a measure of racial and ethnic composition in the individual's census block or census tract, and this measure of composition interacted with the individual's ethnicity and language preference. The variables involving racial and ethnic concentration were capped at 0.001 and 0.999 and then log-odds transformed. In cells for which logistic regression was impossible, a linear probability model was used. These models were used to predict the racial or ethnic choice that would have been made by individuals whose only SSA application was earlier than 1981. After all five probabilities were generated, they were normalized to sum to 1. Return to text
98. Nineteen individuals in the sample were missing the TransRisk Score but were assigned a VantageScore; 17,533 were missing the VantageScore but had a TransRisk Score; 51,517 were missing both credit scores. Return to text
99. Racial and ethnic identity is not available (except for mortgage) in the data used to develop credit scores. Consequently, the locational approach has been used in previous studies that examine the relationship between credit scores and race or ethnicity. In the locational approach, the adult racial or ethnic composition of the individual's census block (available for about 85 percent of the individuals) or census tract is used as an approximation of the individual's race or ethnicity. The proportion of the block belonging to each racial or ethnic group can be viewed as the probability that a random adult drawn from the block will have that race or ethnicity. The probability is used as a weight in forming the tables presented in this section and for analytic work presented later. Return to text
100. Census tracts were placed into four income groups--low, moderate, middle, and high--according to the median family income in the tract relative to the median in the metropolitan statistical area (MSA) or nonmetropolitan portion of the state in which the tract is located: In a low-income tract, the median family income is less than 50 percent of the median in the wider area; in a moderate-income tract it is 50-79 percent; middle income is 80-119 percent; and high income is 120 percent or more.
The census tracts were also placed into four groups according to the proportion of their population that was minority, that is, nonwhite or Hispanic: less than 10 percent, 10-49 percent, 50-79 percent, and 80 percent or more. Urban census tracts are those within MSAs as of June 2003; the remainder are rural census tracts. Return to text
101. The sample was drawn as a systemic sample where individuals were ordered by location. The sampling rate was about 1 out of 657. Return to text
102. Robert B. Avery, Paul S. Calem, and Glenn B. Canner (2004), "Credit Report Accuracy and Access to Credit," Federal Reserve Bulletin, vol. 90 (Summer), pp. 297-322. Return to text
103. Accounts that met this requirement but that also showed evidence of activity before July 2003 were excluded from the performance measure. Return to text
104. Following industry practice, collections, tradelines, and public records involving alimony or child support and collection agency accounts for amounts of less than $100 were not included in the measure of performance. Return to text
105. For the definitions of major-derogatory account, collection account, and public record, refer to note 32. Return to text
106. The credit characteristics were those created by TransUnion as of June 2003. Since that time, they may have expanded the number of characteristics available to model builders. Return to text
107. The definition of best prediction is the minimum sum of squared residuals, in which the residual for a given individual is the difference between the individual's performance (bad or good) and the mean performance of all individuals on that scorecard with the same attribute. Return to text
108. Attributes were also combined to avoid perfect collinearity, which could arise if two attributes of two different characteristics had the same values for each individual. Return to text
109. Different thresholds were evaluated; the 0.75 percent level was selected because it resulted in scorecards with numbers of credit characteristics consistent with industry practice. Return to text
110. The "divergence statistic" measures how well a scorecard separates good and bad distributions of outcomes, such as performance on loans. The distribution of bads and goods in loan performance can be measured by the percentage of loans that either pay on time or are seriously delinquent or default at different credit-score ranges. Ideally, a credit-scoring model will assign worse scores to loans that eventually go bad and better scores to loans that perform well. The further apart the distributions of good and bad loans, the better the credit-scoring model is doing in predicting outcomes. The divergence statistic is calculated as the square of the difference of the mean of the goods and the mean of the bads, divided by the average variance of the score distributions. Return to text
111. Some industry models are developed with a rolling sample, that is, a sample of individuals drawn over a period rather than at one point in time. For example, rather than selecting the entire sample of credit records on a given date, a rolling sample would consist of subsamples drawn successively a few months apart. This approach is intended to minimize any seasonality in the use of credit that could distort estimation. Return to text
112. Although not used throughout the process, the FRB base model was reestimated with a logistic model form as a robustness check. The correlation between the scores constructed using the two methods is greater than 0.99. Differences were almost entirely in the extremes of the distributions, that is, individuals in the top and bottom deciles of the score distribution. The two different scores tended to rank order individuals within these two deciles somewhat differently. Between these two extremes, rank orders were virtually identical. Return to text
113. The national distribution of scores generated by the FICO model is at www.myfico.com/CreditEducation/CreditScores.aspx. The distributions of scores generated by other credit-scoring models may differ from the distribution of FICO scores. Return to text
114. These credit-score patterns by race or ethnicity are consistent with those presented in an analysis of consumer perceptions of creditworthiness. Refer to Marsha Courchane, Adam Gailey, and Peter Zorn (2007), "Consumer Credit Literacy: What Price Perception (273 KB PDF)," paper presented at Federal Reserve System Conference, Financing Community Development: Learning from the Past, Looking to the Future, Washington, March 29-30. Return to text
115. The wider range of scores for the VantageScore likely stems from the choice of performance measure used to estimate the model rather than from any particular treatment of age-related characteristics. Return to text
116. The term "unexplained" as used here is a statistical concept. The unexplained difference is defined as the difference in average scores in the scorable sample after other factors included in the multivariate regressions are accounted for. Thus, the size of the unexplained component depends on what other factors are included in the model. Adding or subtracting factors to the model will affect the size of the unexplained differences. Return to text
117. The mean TransRisk Scores by census tract were normalized in the same manner as the TransRisk Score for the sample individuals. Return to text
118. Assessments of the importance of trigger events and other factors influencing loan performance are in Scott Fay, Erik Hurst, and Michelle J. White (2002), "The Household Bankruptcy Decision," American Economic Review, vol. 92 (June) pp. 706-18; and Li Gan and Tarun Sabarwal (2005), "A Simple Test of Adverse Events and Strategic Timing Theories of Consumer Bankruptcy," NBER Working Paper Series 11763 (Cambridge, Mass.: National Bureau of Economic Research, November). Return to text
119. Prediction residuals for populations with extremely small sample sizes, such as the Native American group, and for those with unknown census tracts should be viewed with caution because the performance estimates have large standard errors. Return to text
120. Consistent with this view, the major differences between the VantageScore and the other two scores are among the individuals on the FRB thin-file scorecard. Return to text
121. Interest rates are not included in credit-record data. However, for closed-end loans, one can estimate the current interest rate on the basis of items in the data, including the size of the monthly payment, the amount borrowed, and the term of the loan. Such estimates have been made for installment and mortgage loans and assume that the loans are fully amortizing. Return to text
122. Inquiries in the absence of new credit is obviously an imperfect proxy for denials, as the lack of new credit may reflect a decision by a prospective borrower not to borrow (for example, by withdrawing the loan application) rather than a denial of credit. Further, the inquiry might be associated with a loan taken out at a later time. Return to text
123. Regressions for other new installment loans were estimated but are not presented. This loan category was quite heterogeneous, and estimation results were not robust. Return to text
124. As noted, the interest rate analysis conducted here is limited to the data included in credit records and consequently does not account for all factors creditors consider in pricing credit (for example, debt-to-income ratios, loan-to-value ratios, and collateral status). Return to text
125. An additional analysis was conducted using the amount borrowed, rather than the interest rate of the loan, as the dependent variable. All new loans could be used in that analysis because balances were reported for all loans. Results, not shown in the tables, indicate little difference across groups in the amounts borrowed once credit score and the type of loan and lender are taken into account. Return to text
126. Most of the data in the SCF are reported at the family level. Families are classified in the tables on the basis of the characteristics of the head of the family, except for race or ethnicity, which is reported by the survey respondent, who may not be the family head as defined by the SCF. Return to text
127. Refer, for example, to Dev Strischek (2000), "The Quotable Five C's," Journal of Lending and Credit Risk Management, vol. 82 (April). pp. 47-49. Return to text
128. Differences in income across racial and ethnic groups are also evident in census data. Importantly for the present study, which shows that significant performance residual differences persist between blacks and non-Hispanic whites even when census-tract location is accounted for, the census data show that a substantial portion of the difference between blacks and non-Hispanic whites are within tract. Specifically, for black families, mean income in 2000 was $38,700; for non-Hispanic white families, $56,870; and for Hispanic families, $42,800. The dollar difference in mean income between blacks and non-Hispanic whites is reduced to $9,800 when census-tract location and age of family head are controlled for. The roughly $14,000 difference in mean incomes between non-Hispanic whites and Hispanics is reduced to $7,600 when census-tract location and age are taken into account. Return to text
129. An additional difficulty in calculating correlations between credit characteristics and demographic characteristics is that some credit characteristics include missing information or take only categorical values. For example, those individuals who have never had a delinquent account would not have values for the characteristic "months since the most recent account delinquency." To account for these difficulties, a regression equation was estimated by regressing the demographic characteristic against two variables--a dichotomous indicator variable representing missing values for the credit characteristic and a continuous variable representing the credit characteristic when it was available. A similar approach was followed when the demographic characteristic had a small number of discrete categorical values, with the indicator variable used in the regression to represent the different values of the demographic characteristic. In both of these circumstances, the correlation coefficient was the square root of the r-squared of the regression. Return to text
130. Changing the characteristics on one scorecard can change the scores of individuals on other scorecards even though their estimated probability of going bad remains unchanged. The spillover effect occurs because the score, as we have used it here, is a rank-order score. Thus, a change of probability estimates on one scorecard can have effects on the rank-order of the whole population. In practice, the spillover effects are minor and are thus ignored in this presentation although not in the analysis. Return to text
131. These accounts include those assigned a code in the credit-record data indicating "finance company," although they may also include some other types of creditors. Return to text
132. Two additional attribute weight re-estimations were conducted in "sex-neutral" environments. One model was estimated using only the males in the sample and the other was estimated using only the females. The mean credit scores produced by these attribute weight re-estimations were very similar to those produced using the FRB base model for each demographic group, seldom varying by more than 0.25 points. These results confirm what the earlier analysis suggested, that the FRB base model does not embed a differential effect as a result of credit characteristics proxying for sex. Return to text
133. The choice of the population group (in this case non-Hispanic whites) was driven by sample size considerations alone. In principle, any group could serve as the base population for estimating a model. The non-Hispanic white population was the only population in the sample of sufficient size to provide a basis for model estimation Return to text
134. In the sample used here, about 30 percent of recent immigrants are Asian and about 28 percent Hispanic. For the broader foreign-born population, the majority of individuals are non-Hispanic white. Return to text
135. It is possible that this might not be a sufficient test for differential effect arising from excluded credit characteristics. The presence of a large differential effect could alter the way in which attributes aredefined or credit characteristics selected for a model. Consequently, as a robustness check, two more racially neutral models were estimated. Here, the entire process of model development--including attribute construction, selection of credit characteristics, and the estimation of attribute weights--was conducted using the white-only sample and separately with racial-indicator variables. Otherwise, the models were estimated using the same approach employed in the construction of the FRB base model.
Credit characteristics and attributes for these models developed in racially neutral environments did differ some from those selected for the FRB base model. However, this does not appear to arise from differential effect, but rather from differences in the sample and from the fact that controlling for race and ethnicity slightly alters the correlations among the credit characteristics. The high degree of correlation among credit characteristics implies that virtually any change in the model development process will affect the specific credit characteristics and attributes selected for the model. None of these changes, however, suggests evidence of differential effect or that a credit characteristic that would have appeared in a racially neutral model was left out of the FRB base model. The same process was followed for the age-neutral evaluations. Results were similar. Return to text
136. Most of the changes in the scores for older individuals occur for those in the top three quintiles in the credit-score distribution. Score changes in this region of the score distribution imply very small differences in expected performance and are unlikely to effect access to credit. Return to text
137. Another indication that results regarding the absence of differential effects with respect to race or ethnicity and sex found in the FRB base model may generalize to other credit scores is the fact that performance residuals for race and sex as calculated with the FRB base model are virtually the same as those calculated with the VantageScore and the TransRisk Score. Return to text