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

Do Demographics Do Demographics Predict Predict

Creditworthiness?Creditworthiness?Presented by Kelli JonesPresented by Kelli Jones

ECON 616ECON 616

April 2, 2003April 2, 2003

IntroductionIntroduction

What is a What is a credit scorecredit score ? ? Measure of relative creditworthiness / Measure of relative creditworthiness /

credit performancecredit performance Based on items from credit history such Based on items from credit history such

as bankruptcies, delinquent payments, as bankruptcies, delinquent payments, revolving credit balancesrevolving credit balances


How is a credit scoring system built?How is a credit scoring system built? It is determined how effective each risk It is determined how effective each risk

characteristic is in predicting credit characteristic is in predicting credit performanceperformance

Each element is given a weight depending on Each element is given a weight depending on that effectivenessthat effectiveness

The combination of each element and weight The combination of each element and weight results in the best predictor of credit results in the best predictor of credit performanceperformance

Generally, the higher the score, the better Generally, the higher the score, the better your credityour credit


How are credit scores used?How are credit scores used? Credit applicationsCredit applications Mortgage loan applicationsMortgage loan applications Insurance underwriting and/or pricing Insurance underwriting and/or pricing

for personal auto and homeowners for personal auto and homeowners policiespolicies

Purpose of ResearchPurpose of Research

To test whether certain demographic To test whether certain demographic groups have a tendency to have groups have a tendency to have worse credit (i.e. lower credit worse credit (i.e. lower credit scores)scores)

Literature ReviewLiterature Review

Avery, Bostic, Calem, Avery, Bostic, Calem, CannerCanner

(1996, 2000)(1996, 2000) Data obtained from Equifax on 3.4 Data obtained from Equifax on 3.4

million individuals making up 2.5 million individuals making up 2.5 million householdsmillion households

incomeincome: : 33% of households in lowest income range 33% of households in lowest income range

have low credit scores, compared to 23% of have low credit scores, compared to 23% of households overall and 17% of households households overall and 17% of households in the highest income range in the highest income range

As median family income ↑, median credit As median family income ↑, median credit score ↑score ↑

RaceRace: : as the %age of minority households ↑, as the %age of minority households ↑,

median credit score ↓median credit score ↓ EducationEducation::

As the %age of high school graduates As the %age of high school graduates ↑, median credit score ↑↑, median credit score ↑

LocationLocation:: No statistically significant relationship No statistically significant relationship

shown between credit scores and shown between credit scores and urban/suburban/rural classificationurban/suburban/rural classification

AgeAge:: As the median age ↑, median credit As the median age ↑, median credit

score ↑score ↑

Kennickell, Starr-McCluer, Kennickell, Starr-McCluer, SuretteSurette(2000)(2000)

Comparison of family finances from data Comparison of family finances from data obtained from 1995 and 1998 Survey of obtained from 1995 and 1998 Survey of Consumer FinancesConsumer Finances

1998 survey samples 4,309 households1998 survey samples 4,309 households IncomeIncome::

As income ↑, the # of payments 60+ days As income ↑, the # of payments 60+ days past due ↓past due ↓

AgeAge:: As age ↑, the # of payments 60+ days past As age ↑, the # of payments 60+ days past

due ↓due ↓

Fair, IsaacFair, Isaac(1997)(1997)

Develops and markets credit scoring Develops and markets credit scoring systemssystems

Provided research paper in response Provided research paper in response to concerns that the use of credit to concerns that the use of credit scores results in unfair treatment to scores results in unfair treatment to low-to-moderate-income (LMI) and low-to-moderate-income (LMI) and high-minority area (HMA) high-minority area (HMA) populationspopulations

IncomeIncome:: At a given credit score, the level of risk is At a given credit score, the level of risk is

the same regardless of incomethe same regardless of income RaceRace::

Distribution of credit scores differs Distribution of credit scores differs between HMA and non-HMA populationsbetween HMA and non-HMA populations

For HMAs, 25.3% have scores < 620 For HMAs, 25.3% have scores < 620 compared to 13.8 % for non-HMA’scompared to 13.8 % for non-HMA’s

At any given score, the odds (ratio of At any given score, the odds (ratio of good to bad accounts) are lower for good to bad accounts) are lower for HMA’s; however, this difference seemed HMA’s; however, this difference seemed to be significant only at lower scoresto be significant only at lower scores

DatabaseDatabase

1998 Survey of Consumer Finances1998 Survey of Consumer Finances Complete sample is 21,525 Complete sample is 21,525

observationsobservations Reduced sample used for my Reduced sample used for my

analysis of those who have applied analysis of those who have applied for credit in the last 5 years consists for credit in the last 5 years consists of 13,664 observationsof 13,664 observations

Description of VariablesDescription of Variables

Creditworthiness / credit scoreCreditworthiness / credit score:: Y = 1 if credit denied or approved for Y = 1 if credit denied or approved for

lower amount based on credit history lower amount based on credit history Y = 0 if approved for full amount or Y = 0 if approved for full amount or

denied for reasons other than credit denied for reasons other than credit history history

LocationLocation: : No urban/suburban/rural classificationNo urban/suburban/rural classification 9 categories describing area of country 9 categories describing area of country

(e.g. New England, Midatlantic)(e.g. New England, Midatlantic) Not available in 2001 public datasetNot available in 2001 public dataset

EducationEducation:: 4 dummy variables to capture years of education4 dummy variables to capture years of education

High school diplomaHigh school diploma 1 – 3 years college1 – 3 years college 4 years college4 years college Graduate schoolGraduate school

Having less than high school diploma is base Having less than high school diploma is base casecase

RaceRace:: 3 dummy variables3 dummy variables

BlackBlack HispanicHispanic Asian / Native American / Hawaiian / otherAsian / Native American / Hawaiian / other

White is base caseWhite is base case

IncomeIncome:: Continuous variableContinuous variable

Age:Age: Continuous variableContinuous variable

Frequency TablesFrequency Tables

VariabVariablele

DescriptiDescriptionon

FrequeFrequencyncy

%%

YY CreditCredit

00 goodgood 1187611876 86.986.911

11 badbad 17881788 13.013.099

VariabVariablele


FrequeFrequencyncy

%%

EE Yrs. Of Yrs. Of EducatioEducationn

BaseBase < H.S. < H.S. diplomadiploma

1,3121,312 9.609.60

11 1212 3,0263,026 22.122.155

22 1 -3 yrs. 1 -3 yrs. CollegeCollege

3,1983,198 23.423.400

33 4 yrs. 4 yrs. CollegeCollege

3,1223,122 22.822.855

44 Grad. Grad. SchoolSchool

3,0063,006 22.022.000

VariablVariablee


FrequeFrequencyncy

%%

RR RaceRace

BaseBase WhiteWhite 11,44411,444 83.783.755

11 BlackBlack 1,0871,087 7.967.96

22 HispanicHispanic 691691 5.065.06

33 OtherOther 442442 3.233.23

Table of MeansTable of Means

VariaVariableble

Overall Overall MeanMean

Mean Y = Mean Y = 00

Mean Y = Mean Y = 11

E1E1 .221.221 .216.216 .257.257

E2E2 .234.234 .224.224 .304.304

E3E3 .228.228 .235.235 .183.183

E4E4 .22.22 .24.24 .087.087

R1R1 .080.080 .066.066 .169.169

R2R2 .051.051 .05.05 .054.054

R3R3 .032.032 .032.032 .036.036

II 402,414402,414 449,856449,856 87,30387,303

AA 4646 4747 4040

OLS Regression OLS Regression (Linear Probability Model)(Linear Probability Model)

ModelModel

YYi i = = αα + + ββXXii + + εεii

E(YE(Yii) = P) = Pii = P(Y = 1) = P( bad credit) = P(Y = 1) = P( bad credit) = = ααhathat + + ββhat hat XXii

ResultsResults

VariableVariable DescriptionDescription Parameter Parameter EstimateEstimate

t Valuet Value

InterceptIntercept < H.S. < H.S. diplomadiploma

.230.230 25.0425.04

E1E1 1212 - .078- .078 - 7.10- 7.10

E2E2 1 -3 yrs. 1 -3 yrs. CollegeCollege

- .060- .060 - 5.53- 5.53

E3E3 4 yrs. College4 yrs. College - .125- .125 - 11.45- 11.45

E4E4 Grad. SchoolGrad. School - .178- .178 - 16.18- 16.18


t Valuet Value

InterceptIntercept WhiteWhite .116.116 37.0137.01

R1R1 BlackBlack .163.163 15.3615.36

R2R2 HispanicHispanic .023.023 1.771.77

R3R3 OtherOther .031.031 1.931.93


t Valuet Value

InterceptIntercept .326.326 32.9432.94

AA AgeAge - .004- .004 - 20.58- 20.58

InterceptIntercept .132.132 45.5245.52

II IncomeIncome - 2.857 E-9- 2.857 E-9 - 3.76- 3.76

Probit ModelProbit Model

ModelModel

ZZi i = = αα + + ββXXii + + εεii

ZZiihathat = = ααhathat + + ββhathatXXii = F = F-1-1(P(Piihat hat ))

PPiihathat = F( = F(ZZiihathat ) where F is the normal ) where F is the normal distributiondistribution

Probability modeled is Y = 1Probability modeled is Y = 1

ResultsResults


Chi-Chi-SquareSquare


- .738- .738 372.39372.39

E1E1 1212 - .290- .290 37.5537.55


- .217- .217 21.8221.82

E3E3 4 yrs. College4 yrs. College - .517- .517 112.43112.43

E4E4 Grad. SchoolGrad. School - .889- .889 270.93270.93



InterceptIntercept WhiteWhite - 1.197- 1.197 6088.06088.044

R1R1 BlackBlack .610.610 198.64198.64


R3R3 OtherOther .148.148 3.903.90



InterceptIntercept - 1.045- 1.045 5066.95066.900

II IncomeIncome - 0.000- 0.000 120.68120.68

InterceptIntercept - .154- .154 10.1310.13

AA AgeAge - .022- .022 410.12410.12

Logit ModelLogit Model

ModelModel

ZZi i = = αα + + ββXXii + + εεii

ZZiihathat = = ααhathat + + ββhathatXXii = ln (P = ln (Piihat hat / (1 - / (1 - PPiihat hat ))))

PPiihathat = exp( = exp(ZZiihathat) / (1 + ) / (1 + exp(exp(ZZiihathat) )) ) Probability modeled is Y = 1Probability modeled is Y = 1

ResultsResults


Wald Wald Chi-Chi-SquareSquare


- 1.207- 1.207 338.85338.85

E1E1 1212 - .512- .512 38.1338.13


- .380- .380 22.1322.13

E3E3 4 yrs. College4 yrs. College - .938- .938 114.09114.09

E4E4 Grad. SchoolGrad. School - 1.698- 1.698 260.61260.61



InterceptIntercept WhiteWhite - 2.034- 2.034 4843.14843.199

R1R1 BlackBlack 1.0831.083 216.12216.12


R3R3 OtherOther .276.276 4.034.03



InterceptIntercept - 1.678- 1.678 3302.73302.700

II IncomeIncome - 1.55 E-6- 1.55 E-6 99.7199.71

InterceptIntercept - .111- .111 1.591.59

AA AgeAge - .042- .042 395.87395.87

Comparison of ResultsComparison of Results

VariablVariablee

OLSOLS

Sign Sign Signif.Signif.

ProbitProbit


LogitLogit


E1E1 -- XX -- XX -- XX

E2E2 -- XX -- XX -- XX

E3E3 -- XX -- XX -- XX

E4E4 -- XX -- XX -- XX

R1R1 ++ XX ++ XX ++ XX

R2R2 ++ ++ ++

R3R3 ++ ++ ++

VariablVariablee

OLSOLS


ProbitProbit


LogitLogit


II -- XX -- XX -- XX

AA -- XX -- XX -- XX

Comparison of PComparison of Phathat

ECON 616 Comparison.ECON 616 Comparison.xlsxls

EnhancementsEnhancements

Update data to 2001 SCFUpdate data to 2001 SCF Look at multivariate resultsLook at multivariate results Analyze goodness of fit of modelsAnalyze goodness of fit of models

Documents

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