Do Demographics Predict Creditworthiness?

1. Do Demographics Predict Creditworthiness? Presented by Kelli Jones ECON 616 April 2, 2003

2. Introduction • What is a credit score ? • Measure of relative creditworthiness / credit performance • Based on items from credit history such as bankruptcies, delinquent payments, revolving credit balances

3. Introduction • How is a credit scoring system built? • It is determined how effective each risk characteristic is in predicting credit performance • Each element is given a weight depending on that effectiveness • The combination of each element and weight results in the best predictor of credit performance • Generally, the higher the score, the better your credit

4. Introduction • How are credit scores used? • Credit applications • Mortgage loan applications • Insurance underwriting and/or pricing for personal auto and homeowners policies

5. Purpose of Research • To test whether certain demographic groups have a tendency to have worse credit (i.e. lower credit scores)

6. Literature Review

7. Avery, Bostic, Calem, Canner(1996, 2000) • Data obtained from Equifax on 3.4 million individuals making up 2.5 million households • income: • 33% of households in lowest income range have low credit scores, compared to 23% of households overall and 17% of households in the highest income range • As median family income ↑, median credit score ↑

8. Race: • as the %age of minority households ↑, median credit score ↓ • Education: • As the %age of high school graduates ↑, median credit score ↑ • Location: • No statistically significant relationship shown between credit scores and urban/suburban/rural classification • Age: • As the median age ↑, median credit score ↑

9. Kennickell, Starr-McCluer, Surette(2000) • Comparison of family finances from data obtained from 1995 and 1998 Survey of Consumer Finances • 1998 survey samples 4,309 households • Income: • As income ↑, the # of payments 60+ days past due ↓ • Age: • As age ↑, the # of payments 60+ days past due ↓

10. Fair, Isaac(1997) • Develops and markets credit scoring systems • Provided research paper in response to concerns that the use of credit scores results in unfair treatment to low-to-moderate-income (LMI) and high-minority area (HMA) populations

11. Income: • At a given credit score, the level of risk is the same regardless of income • Race: • Distribution of credit scores differs between HMA and non-HMA populations • For HMAs, 25.3% have scores < 620 compared to 13.8 % for non-HMA’s • At any given score, the odds (ratio of good to bad accounts) are lower for HMA’s; however, this difference seemed to be significant only at lower scores

12. Database • 1998 Survey of Consumer Finances • Complete sample is 21,525 observations • Reduced sample used for my analysis of those who have applied for credit in the last 5 years consists of 13,664 observations

13. Description of Variables

14. Creditworthiness / credit score: • Y = 1 if credit denied or approved for lower amount based on credit history • Y = 0 if approved for full amount or denied for reasons other than credit history • Location: • No urban/suburban/rural classification • 9 categories describing area of country (e.g. New England, Midatlantic) • Not available in 2001 public dataset

15. Education: • 4 dummy variables to capture years of education • High school diploma • 1 – 3 years college • 4 years college • Graduate school • Having less than high school diploma is base case • Race: • 3 dummy variables • Black • Hispanic • Asian / Native American / Hawaiian / other • White is base case

16. Income: • Continuous variable • Age: • Continuous variable

17. Frequency Tables

21. Table of Means

23. OLS Regression (Linear Probability Model)

24. Model • Yi = α+ βXi + εi • E(Yi) = Pi = P(Y = 1) = P( bad credit) = αhat + βhat Xi

25. Results

29. Probit Model

30. Model • Zi = α+ βXi + εi • Zihat= αhat+ βhatXi = F-1(Pihat ) • Pihat = F(Zihat) where F is the normal distribution • Probability modeled is Y = 1

31. Results

35. Logit Model

36. Model • Zi = α+ βXi + εi • Zihat= αhat+ βhatXi = ln (Pihat / (1 - Pihat )) • Pihat = exp(Zihat) / (1 + exp(Zihat) ) • Probability modeled is Y = 1

37. Results

41. Comparison of Results

44. Comparison of Phat • ECON 616 Comparison.xls

45. Enhancements • Update data to 2001 SCF • Look at multivariate results • Analyze goodness of fit of models