1 / 45

Do Demographics Predict Creditworthiness?

Do Demographics Predict Creditworthiness?. Presented by Kelli Jones ECON 616 April 2, 2003. Introduction. What is a credit score ? Measure of relative creditworthiness / credit performance

gautam
Download Presentation

Do Demographics Predict Creditworthiness?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

  18. Table of Means

  19. OLS Regression (Linear Probability Model)

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

  21. Results

  22. Probit Model

  23. 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

  24. Results

  25. Logit Model

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

  27. Results

  28. Comparison of Results

  29. Comparison of Phat • ECON 616 Comparison.xls

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

More Related