Mortgage Discrimination ECN741: Urban Economics, Professor Yinger

Mortgage Discrimination Outline of Class  Formal Definitions of Discrimination  The Boston Fed Study  The Importance of Disparate-Impact Discrimination  Blending Loan Characteristics and Loan Performance Data to Obtain More Precise Estimates of Discrimination

Mortgage Discrimination A Hint of Trouble  The Home Mortgage Disclosure Act (HMDA) provides information on virtually all mortgage applications in the country.  These data reveal that for many years, blacks and Hispanic applicants have been far less likely than white applicants to have their applications approved.  This does not prove that discrimination exists, but it is a difficult regularity to explain without discrimination.

Mortgage Discrimination

Definition of Discrimination  The Behavior  Lenders receive applications for mortgage loans and decide whether to approve them (and what terms to offer) based on the characteristics of the applicant, the loan, and the property.  This underwriting process may involve automated underwriting tools, including credit scores.  It results in both an approval decision and, if the loan is approved, a set of terms and conditions.

Mortgage Discrimination Discrimination in Mortgage Lending  Disparate-Treatment Discrimination  Using different criteria for evaluating the loan applications (or setting terms) of people in different groups.  Disparate-Impact Discrimination  Using underwriting standards that have a disparate, negative impact on people in protected classes without being justified on the basis of “business necessity.”  Business Necessity  To meet the business necessity test, a standard must be directly linked to the profits a lender could make in a competitive, group-neutral market.

Mortgage Discrimination The Loan-Approval Decision  Definitions  L = characteristics of the loan, such as its interest rate and loan-to-value ratio;  A = characteristics of the applicant, such as his or her past credit problems;  P = characteristics of the property, such as whether it is in a declining neighborhood; and  π = loan profitability  Decision rule based on best available information

Mortgage Discrimination The Loan-Approval Decision, 2  Decision rule based on incomplete statistical model  or

Mortgage Discrimination Formal Definitions of Discrimination  Disparate-Treatment Discrimination  This type of discrimination exists if membership in a protected class (= M ) affects the accept/reject decision after controlling for L, A, and P.

Mortgage Discrimination  Disparate-Impact Discrimination  This type of discrimination is buried in the difference between π E and π.  A difference between π E and π implies that the lender is using criteria that, to some degree, are not related to loan profitability.  For disparate-impact discrimination to exist, the criteria leading to this difference must be particularly unfavorable to applicants in a protected class.

Mortgage Discrimination The Boston Fed Study (1990 Data)   CONTROL VARIABLES IN THE BOSTON FED STUDY  Risk of DefaultLoan Characteristics Housing Expense/Income Two- to Four-Family House Total Debt Payments/Income Lender ID Net Wealth Consumer Credit History Mortgage Credit History Public Record History Probability of Unemployment Self-Employed Loan/Appraised Value Is Low Loan/Appraised Value Is Medium Loan/Appraised Value Is High  Costs of DefaultPersonal Characteristics Denied Private Mortgage Insurance Black or Hispanic Census Tract Dummies

Mortgage Discrimination Criticisms of Boston Fed Study’s Methodology  Potential flaws in the Boston Fed Study include:  omitted variables,  data errors in the explanatory variables,  mis-classification in the dependent variable,  incorrect specification, and  endogenous explanatory variables.

Mortgage Discrimination Ross and Yinger, The Color of Credit (2002)  R/Y investigate each of these potential flaws.  No legitimate data-cleaning exercise or revised statistical procedure has a significant impact on the estimated minority status coefficient (or its statistical significance)  They confirm the Boston Fed’s conclusion: Blacks and Hispanics are 82% more likely than whites to be turned down for a loan, controlling for L, A, and P.

Mortgage Discrimination R/Y’s Re-Interpretation of Boston Fed Study  The Boston Fed Study’s results could reflect  Disparate-Treatment Discrimination  If all lenders use the same underwriting standards, any group- based difference in treatment implies that the standards are applied differentially across groups.  Or

Mortgage Discrimination  Disparate-Impact Discrimination  A significant minority-status coefficient could arise if different lenders use different underwriting standards and deviations from average standards cannot be justified on the basis of business necessity.  Legitimate Variation in Underwriting Standards Across Lenders  A significant minority status coefficient could reflect non- discriminatory, cost-based differences in underwriting standards across lenders.

Mortgage Discrimination Accounting for Variation in Underwriting Standards  R/Y re-estimate the Boston Fed Study’s equations to account for potential variation in underwriting standards across lenders.  Some regressions have interactions with characteristics of lenders’ portfolios.  Some regressions allow different coefficients for different lenders.

Mortgage Discrimination  All methods yield the same two main results:  Underwriting weights differ significantly across lenders.  Accounting for this variation in underwriting weights has no significant impact on the estimated minority-white disparity in loan approval.  Unless legitimate variation in underwriting weights is not linked to a lender’s portfolio, these results indicate that either disparate-treatment or disparate-impact discrimination exists.

Mortgage Discrimination Subsequent Literature  The Boston Fed Study has not been replicated  Later studies include  Studies of loan approval with a small sample of lenders.  Studies of discrimination in “overages” for a small sample of lenders.  Explorations of the HMDA data, which cannot be used to study discrimination due to lack of controls.

Mortgage Discrimination Subsequent Literature, 2  A recent article in the Federal Reserve Bulletin looks at the probability that a borrower will have a high-priced loan, controlling for borrower creditworthiness (= credit score) and other things.  The study uses HMDA data from  The methodology of this study is not airtight, but the results are intriguing:

Mortgage Discrimination Source: Bhuttan and Ringo, Federal Reserve Bulletin

Mortgage Discrimination More on Interest Rate Gaps  Ping Cheng & Zhenguo Lin & Yingchun Liu “Racial Discrepancy in Mortgage Interest Rates” J Real Estate Finan Econ (2015) 51:101–120  Using data from three waves of the U.S. Survey of Consumer Finance, our results suggest that black borrowers on average pay about 29 basis points more than comparable white borrowers. We also find that rate disparity mainly occurs to young borrowers with low education as well as those borrowers whose income and credit disqualify them for prime lending rates. Furthermore, among borrowers in the higher rate groups, black women seem to receive much more disparate treatment than black men.

Mortgage Discrimination Data Problems  Scholars have not been able to get ahold of the necessary data for more convincing studies.  Problems include:  Lenders will not release the data to scholars.  Regulators will not provide the data to scholars.  Loans are difficult to follow because they are sold and re- sold, so samples with approval and performance data (more on this later) are very hard to put together.

Mortgage Discrimination The Need to Study Disparate-Impact Discrimination  People who want to practice disparate-treatment discrimination (but are prevented from doing so) can achieve virtually the same outcomes using disparate-impact discrimination.  A firm can rely on traits that are correlated with race to “predict” a person’s race and then to construct rules that are unfavorable for people in a certain racial group.  This is particularly true in lending, because so many financial variables are highly correlated with race.

Mortgage Discrimination Disparate Impact in Automated Underwriting  An automated or statistically based underwriting system (or “scoring scheme”) uses sample of outstanding loans to explain loan performance, P (=default probability or loan profitability) on the basis of credit characteristics, X (the same as L, A, P from earlier slides).  Then it predicts loan performance for applicants based on their characteristics and this analysis.  No scheme is supposed to use membership in a protected class, M.

Mortgage Discrimination A Full-Information Scoring Scheme  A full-information scheme estimates  Then calculates S 1 = full-information loan score  Whenever γ is significant, this scoring scheme involves disparate-treatment discrimination because it considers M.

Mortgage Discrimination A Non-Discriminatory Scoring Scheme  A non-discriminatory scoring scheme is: where is the share of loans held by minority households.  This scheme is based on the same performance regression as S 1, but it does not differentiate between minority and white applicants.

Mortgage Discrimination A Seemingly “Group-Neutral” Scoring Scheme  Suppose a credit-scoring company tries to be race- neutral by estimating  and using the “group-neutral” scoring scheme:

Mortgage Discrimination Bias in the “Group-Neutral” Scheme  Using expected values, it is easy to show that this performance equation is biased by the omission of M. where b i is the correlation between M and X i.  This is an application of the standard formula for omitted variable bias.

Mortgage Discrimination The Trouble with Lawyers  This “group-neutral” scheme changes the weight placed on each X, not on the basis of a business necessity, but instead on the basis of correlation with minority status.  Lawyers seem to conclude that this approach is neutral.  But, in fact, this is disparate-impact discrimination.

Mortgage Discrimination Blending Performance and Time-of-Application Data  This analysis leads to new, more precise tests for discrimination, which, with one exception discussed below, have not yet been implemented, due to lack of data.  The most straightforward application is to loan approval, but the same logic applies to automated underwriting or credit scoring schemes  This approach picks up both disparate-treatment and disparate-impact discrimination.

Mortgage Discrimination Analysis of Automated Underwriting and Credit Scores  I have not seen a scholarly evaluation of an automated underwriting scheme along these lines.  A thorough evaluation of credit scores by scholars at the Federal Reserve Board found no sign of discrimination. 

Mortgage Discrimination A New Test for Discrimination (from R/Y)  Step 1:  Obtain a sample of applications to many lenders; observe the number of approvals ( A ).  Step 2:  Obtain a sample of approved loans drawn from the same pool; observe their performance over time; estimate a loan- performance model using the best method available.

Mortgage Discrimination A New Test for Discrimination, 2  Step 3:  Use the model from step 2 to obtain a loan-performance score for each application in step 1.  Rank the applications in step 1 by their loan-performance score; identify the highest-ranking A applications.

Mortgage Discrimination A New Test for Discrimination,3  Step 4:  Compare the minority composition of the approved applications in step 1 with the minority composition of the highest-ranking applications (which might not be approved).  This difference is a measure of discrimination (and can be evaluated with a difference-of-means test).  It cannot separate disparate-treatment and disparate-impact discrimination, but picks up both.

Mortgage Discrimination Accounting for Legitimate Variation Across Lenders  There are two ways to account for legitimate variation in underwriting standards across lenders:  1. Implement the above procedure with data for a single lender.  2. Estimate the above model with data pooled across lenders, with lender dummies, and with variables to describe a lender’s portfolio (so long as they predict within-group performance).

Mortgage Discrimination Extension to Loan Terms  This approach can be extended to cover loan terms, which is an important extension in a world with automated underwriting.  Estimate a model of loan performance as before.  Predict performance for a sample of new loans.  Regress interest rate (including points and fees) on predicted performance and group membership; the coefficients of the group membership variables are tests for discrimination.

Mortgage Discrimination Extension to Loan Terms, 2  One study that uses the R/Y approach for a large sample of loans from Florida and California in 2005 recently appeared:  A. C. Ghent, R. Hernandez-Murillo, and M. T. Owyang, 2014, “Differences in Subprime Loan Pricing across Races and Neighborhoods,” RSUE 48, pp  This study finds evidence of discrimination in loan pricing, especially for loans originated by mortgage brokers.

Mortgage Discrimination Extension to Loan Terms, 3  The impact of discrimination on blacks and Hispanics in this study is up to 29 basis points, which translates into an increase in the monthly payment of about $60 or increase in the mortgage amount of about $20,000.  They also find evidence of redlining against minority neighborhoods, which is also illegal.