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

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Presentation on theme: "Do Demographics Predict Creditworthiness? Presented by Kelli Jones ECON 616 April 2, 2003."— Presentation transcript:

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

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

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

4 Introduction How are credit scores used? How are credit scores used? Credit applications Credit applications Mortgage loan applications Mortgage loan applications Insurance underwriting and/or pricing for personal auto and homeowners policies 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) 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 Data obtained from Equifax on 3.4 million individuals making up 2.5 million households income: 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 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 As median family income, median credit score

8 Race: Race: as the %age of minority households, median credit score as the %age of minority households, median credit score Education: Education: As the %age of high school graduates, median credit score As the %age of high school graduates, median credit score Location: Location: No statistically significant relationship shown between credit scores and urban/suburban/rural classification No statistically significant relationship shown between credit scores and urban/suburban/rural classification Age: Age: As the median age, median credit score 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 Comparison of family finances from data obtained from 1995 and 1998 Survey of Consumer Finances 1998 survey samples 4,309 households 1998 survey samples 4,309 households Income: Income: As income, the # of payments 60+ days past due As income, the # of payments 60+ days past due Age: Age: As age, the # of payments 60+ days past due As age, the # of payments 60+ days past due

10 Fair, Isaac (1997) Develops and markets credit scoring systems 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 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: Income: At a given credit score, the level of risk is the same regardless of income At a given credit score, the level of risk is the same regardless of income Race: Race: Distribution of credit scores differs between HMA and non-HMA populations Distribution of credit scores differs between HMA and non-HMA populations For HMAs, 25.3% have scores < 620 compared to 13.8 % for non-HMAs For HMAs, 25.3% have scores < 620 compared to 13.8 % for non-HMAs At any given score, the odds (ratio of good to bad accounts) are lower for HMAs; however, this difference seemed to be significant only at lower scores At any given score, the odds (ratio of good to bad accounts) are lower for HMAs; however, this difference seemed to be significant only at lower scores

12 Database 1998 Survey of Consumer Finances 1998 Survey of Consumer Finances Complete sample is 21,525 observations 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 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: Creditworthiness / credit score: Y = 1 if credit denied or approved for lower amount based on credit history 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 Y = 0 if approved for full amount or denied for reasons other than credit history Location: Location: No urban/suburban/rural classification No urban/suburban/rural classification 9 categories describing area of country (e.g. New England, Midatlantic) 9 categories describing area of country (e.g. New England, Midatlantic) Not available in 2001 public dataset Not available in 2001 public dataset

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

16 Income: Income: Continuous variable Continuous variable Age: Age: Continuous variable Continuous variable

17 Frequency Tables

18 VariableDescriptionFrequency% YCredit0good bad

19 VariableDescriptionFrequency% E Yrs. Of Education Base < H.S. diploma 1, , yrs. College 3, yrs. College 3, Grad. School 3,

20 VariableDescriptionFrequency% RRaceBaseWhite11, Black1, Hispanic Other

21 Table of Means

22 Variable Overall Mean Mean Y = 0 Mean Y = 1 E E E E R R R I402,414449,85687,303 A464740

23 OLS Regression (Linear Probability Model)

24 Model Y i = α + βX i + ε i Y i = α + βX i + ε i E(Y i ) = P i = P(Y = 1) = P( bad credit) = α hat + β hat X i E(Y i ) = P i = P(Y = 1) = P( bad credit) = α hat + β hat X i

25 Results

26 VariableDescription Parameter Estimate t Value Intercept < H.S. diploma E E yrs. College E3 4 yrs. College E4 Grad. School

27 VariableDescription Parameter Estimate t Value InterceptWhite R1Black R2Hispanic R3Other

28 VariableDescription Parameter Estimate t Value Intercept AAge Intercept IIncome E

29 Probit Model

30 Model Z i = α + βX i + ε i Z i = α + βX i + ε i Z ihat = α hat + β hat X i = F -1 (P ihat ) Z ihat = α hat + β hat X i = F -1 (P ihat ) P ihat = F(Z ihat ) where F is the normal distribution P ihat = F(Z ihat ) where F is the normal distribution Probability modeled is Y = 1 Probability modeled is Y = 1

31 Results

32 VariableDescription Parameter Estimate Chi- Square Intercept < H.S. diploma E E yrs. College E3 4 yrs. College E4 Grad. School

33 VariableDescription Parameter Estimate Chi- Square InterceptWhite R1Black R2Hispanic R3Other

34 VariableDescription Parameter Estimate Chi- Square Intercept IIncome Intercept AAge

35 Logit Model

36 Model Z i = α + βX i + ε i Z i = α + βX i + ε i Z ihat = α hat + β hat X i = ln (P ihat / (1 - P ihat )) Z ihat = α hat + β hat X i = ln (P ihat / (1 - P ihat )) P ihat = exp(Z ihat ) / (1 + exp(Z ihat ) ) P ihat = exp(Z ihat ) / (1 + exp(Z ihat ) ) Probability modeled is Y = 1 Probability modeled is Y = 1

37 Results

38 VariableDescription Parameter Estimate Wald Chi- Square Intercept < H.S. diploma E E yrs. College E3 4 yrs. College E4 Grad. School

39 VariableDescription Parameter Estimate Wald Chi- Square InterceptWhite R1Black R2Hispanic R3Other

40 VariableDescription Parameter Estimate Wald Chi- Square Intercept IIncome E Intercept AAge

41 Comparison of Results

42 Variable OLS OLS Sign Signif. Probit Probit Sign Signif. Logit Logit Sign Signif. E1-X-X-XE2-X-X-X E3-X-X-X E4-X-X-X R1+X+X+XR2+++ R3+++

43 Variable OLS OLS Sign Signif. Probit Probit Sign Signif. Logit Logit Sign Signif. I-X-X-X A-X-X-X

44 Comparison of P hat ECON 616 Comparison.xls ECON 616 Comparison.xls ECON 616 Comparison.xls ECON 616 Comparison.xls

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


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