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1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September 25, 2007 Confidential information; please do not circulate

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3 DIR Fair Lending Examination Support  Offsite screening: DIR developed and runs statistical screens using HMDA pricing data to identify banks that appear to be “at risk” in terms of disparities in mortgage loan pricing to racial/ethnic minorities or women  Analytical support for on-site exams: Conduct statistical analysis of loan data to investigate potential pricing disparities identified by FDIC screens or by DSC examiners  Statistical analysis  What measure of pricing will be examined (dependent variable)  What sample of loans to review  What variables to include in the model  What statistical tests to use

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Page 1: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

1

Fair Lending Pricing Analytics

Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance CorporationSeptember 25, 2007Confidential information; please do not circulate

Page 2: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

2

Fair Lending Analytics The Division of Insurance and Research provides analytical support for fair lending examinations conducted by the FDIC’s Division of Supervision and Consumer Protection

Statistical analysis to compare credit outcomes for a target group (a racial-ethnic minority group or females) with credit outcomes for a control group (NH whites or males)

Page 3: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

3

DIR Fair Lending Examination Support

Offsite screening: DIR developed and runs statistical screens using HMDA pricing data to identify banks that appear to be “at risk” in terms of disparities in mortgage loan pricing to racial/ethnic minorities or women

Analytical support for on-site exams: Conduct statistical analysis of loan data to investigate potential pricing disparities identified by FDIC screens or by DSC examiners

Statistical analysis What measure of pricing will be examined

(dependent variable) What sample of loans to review What variables to include in the model What statistical tests to use

Page 4: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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FDIC-Supervised Banks Many relatively small institutions Many in rural areas Examiner-identified non-mortgage pricing

cases Emphasis on assisting with preliminary

screening analysis: Use readily available data to evaluate disparity. If a HMDA outlier, does the suspicious pattern continue in recently collected HMDA data?

Page 5: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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HMDA Pricing Data Since 2004, Home Mortgage Disclosure Act Data

have included information on “higher-priced” loans Loans secured by a first lien having an APR of more

than 300 basis points above that of an equivalent-maturity Treasury security

Loans secured by subordinate lien having an APR of more than 500 basis points above the APR of an equivalent-maturity Treasury security

2002 Revisions to Reg C by the FRB: “gathering information about the higher-priced segment of the market;”

But, loan characteristics and the underlying interest rate environment affect the extent to which HMDA higher-priced loans capture loans in the subprime or near-prime market

Page 6: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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HMDA Pricing Data Pricing information reported

APR spread for a higher-priced loans: The APR of the higher-priced loan minus the APR of an equivalent maturity mortgage

HOEPA flag: indicates whether a loan is a HOEPA loan (TILA, reg Z) having rates or fees above a certain percentage or amount. (See the FFIEC website.) Rate-spread is more than 800 basis points (8 percent) for 1st-lien loans and more than 1,000 basis points (10 percent) for subordinate-lien loans. HOEPA points and fees threshold: Total points and fees paid exceed the greater of 8 percent of the total loan amount, or a dollar amount that is adjusted annually for inflation. For 2004, the dollar amount was $499.

Page 7: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Other information about application/loan characteristics was also added starting with the 2004 HMDA data Lien status, manufactured housing flag, Disposition of mortgage application (e.g.

accepted, denied, withdrawn) now includes information about preapprovals

HMDA Pricing Data

Page 8: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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FDIC HMDA Data Screens In 2005, DIR developed analytical “screens” to

flag institutions having the largest pricing disparities on loans

To particular racial/ethnic minorities (compared to the control group of non-Hispanic Whites) and

To females (compared to a control group of loans where a male applicant is present)

We analyze pricing disparities measured for specific categories of loans (loan product types), so that we are comparing “apples to apples”

Page 9: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Banks that have the largest, statistically significant, pricing disparities among FDIC-supervised in any particular loan product category for any target group are flagged by the FDIC screens and we provide DSC with lists of flagged institutions

The FDIC screens also flag institutions with large and significant denial rate disparities

FDIC HMDA Data Screens

Page 10: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Pricing Disparities ExaminedDisparities in incidence of higher-priced loans: The percent

of target group loans that are higher-priced loans minus the percent of control group loans that are higher-priced.

If 60 % of blacks received higher-priced loans, but only 30% of non-Hispanic whites received higher-priced loans, the disparity for the bank would be 30 percentage points

Disparities in average rate-spread on higher-priced loans: The average reported rate-spread on higher-priced target group loans minus the average rate spread reported on higher-priced loans to the control group

If the average rate spread on higher-priced loans to blacks is 190 basis points and the average rate spread on the higher-priced loans to non-Hispanic whites is 125 basis points; then the disparity in the average spread is 75 basis points

Disparities in incidence of HOEPA loans: The percent of target group loans flagged as HOEPA loans minus the percent of control group loans flagged as HOEPA loans

Page 11: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Loan Product Groups Examined Conventional 1st-lien home purchase, 1-4 family, owner-

occupied Government 1st-lien home purchase, 1-4 family, owner-

occupied Conventional 1st-lien home improvement, 1-4 family,

owner-occupied Conventional 1st-lien refinance, 1-4 family, owner-

occupied Government 1st-lien home improvement and refinance, 1-

4 family, owner-occupied All 1st-lien manufactured housing, owner-occupied

(conventional and government; home purchase, home improvement, and refinance)

All 2nd-lien home purchase, owner-occupied (conventional and government; 1-4 family and manufactured housing)

All 2nd-lien home improvement and refinance, owner-occupied (conventional and government; 1-4 family and manufactured housing). Disparities in incidence of higher-priced loans

Page 12: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Examples: FDIC HMDA Examples: FDIC HMDA Racial-Ethnic ScreensRacial-Ethnic Screens

What is a large disparity? “Large” is measured relative to the mean disparity

evident for FDIC-supervised banks that make loans in the product area to the target group

Choose threshold for flagging banks (in terms of standard deviations from the mean)

Loan products Conventional first-lien home purchase loans (product 1) Conventional first-lien home refinance loans (product 4)

Pricing measure Incidence of higher-priced loan Mean of reported rate-spreads on higher priced loans

Page 13: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Confidential: Please do not circulate

Disparities in the Incidence of Higher-priced Conventional Home Purchase Loans

8.7 8.1

5.6

16.8

21.5

5.7

9.4

25.5

29.6

5.5

15.0

-0.3

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Hispanic Native American Asian Black

Data for FDIC-supervised banks extending loans of this product type to the indicated target group

Per

cent

age

poin

t dis

parit

y

Mean for FDIC banks in sampleStandard DeviationThreshold pricing disparity

Page 14: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Disparities in the Incidence of Higher-priced Conventional Home Loan Refinancings

4.53.7

11.211.2

8.9 8.8

14.915.7

12.5

8.3

26.1

-0.5

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Hispanic Native American Asian Black

Data for FDIC-supervised banks extending loans of this product type to the indicated target group

Per

cent

age

poin

t dis

parit

y

Mean for FDIC banks in sampleStandard DeviationThreshold pricing disparity

Confidential: Please do not circulate

Page 15: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Disparities in the Mean Spread on Higher-priced Conventional Home Purchase Loans

0.00.1

0.3

1.1

1.0

0.8

1.2

0.3 0.3

0.1

0.6

-0.1

-0.5

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

Hispanic Native American Asian Black

Data for FDIC-supervised banks extending higher-priced loans of this product type to the indicated target group

Per

cent

age

poin

t dis

parit

y

Mean for FDIC banks in sampleStandard DeviationThreshold pricing disparity

Confidential: Please do not circulate

Page 16: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Disparities in the Mean Spread on Higher-priced Conventional Home Loan Refinancings

0.10.1

0.4

0.9

0.8 0.8

1.2

0.40.3

0.1

0.7

0.0

-0.5

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

Hispanic Native American Asian Black

Data for FDIC-supervised banks extending higher-priced loans of this product type to the indicated target group

Per

cent

age

poin

t dis

parit

y

Mean for FDIC banks in sampleStandard DeviationThreshold pricing disparity

Confidential: Please do not circulate

Page 17: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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FDIC HMDA Outliers Statistical significance—observed differences in

outcomes for the target and the control group would be very unlikely to occur if the source of the differences were just random chance Harder to find statistical significance with small

samples; requires disparities of larger magnitude. Moreover, our screens do not control for any factor

except for the product characteristics that are used to classify loans in a particular product group:

lien status, loan purpose, site built versus manufactured housing, owner occupancy, conventional versus govt-lending program

Page 18: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Examples of Variables not used in FDIC MDA Screens

HMDA variables: Loan size, Borrower income (front-end DTI) Presence of co-applicant Race of applicant/co-applicant (in gender screens) Gender of applicant/co-applicant (in race screens) Market where the loan was made (MSA) Application was associated with a pre-approval

Legitimate factors used in pricing that are not included in HMDA, such as: credit history, debt service burden, house value (needed to compute LTV)

Page 19: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Analytical support for on-site exams:

In depth, bank-specific statistical analysis of loan data to investigate potential pricing disparities identified by FDIC screens or by DSC examiners

What measure of pricing will be examined (dependent variable)

What variables to include in the model What sample of loans to review What statistical tests to use

Analysis depends on pricing policies and realities Criterion Interviews conducted by DSC examiners

ascertain pricing policies Across lending units Across products offered Across markets Available data

Page 20: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Modeling Pricing Outcomes

Pricing policies and realities Are there clear non-discriminatory criteria for pricing? To what extent are outcomes automated (factors specified on rate sheets,) versus judgmental (factors considered in judgmental fashion—such as “customer relationship”)?

To what extent are rate sheet deviations permitted? What are the YSP compensation agreements? What are the bank’s markets and the “competitive factors” ?

Is reality consistent with bank policies? Is there documentation of factors used to price in loan files?

Are exceptions to rate sheets documented?

Page 21: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Modeling Pricing Outcomes Dependent variable—it depends

Note rate or APR Rate sheet deviation Yield spread premium Incidence of loans extended in high-price

unit Explanatory variables—it depends

Examiner criterion interview Information in electronic files or compiled

by DSC examiners from loan files Hard to include information if the bank

doesn’t have it on file

Page 22: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Examples of Pricing Factors Loan Amount Credit Score DTI LTV Deposit relationships Performance on past loans

Page 23: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Evaluating OutcomesStatistical significance—observed differences

in outcomes for the target and the control group would be very unlikely to occur if the source of the differences were just random chance Again, hard to find significance for small

samples Robustness checks

Economic significance—what is the magnitude of the pricing differential?

Page 24: 1 Fair Lending Pricing Analytics Katherine Samolyk, Senior Economist, Division of Insurance and Research, Federal Deposit Insurance Corporation September

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Questions?