Home » 2015 Categories » 2015 Technical Presentation » Mortgage Lending in Chicago and Los Angeles by Li Ding

Mortgage Lending in Chicago and Los Angeles by Li Ding

In “Mortgage Lending in Chicago and Los Angeles: a paired-testing study of the pre-application process”, Ross et al. (2008) used paired testing to measure discrimination against African-American and Hispanic homebuyers in the mortgage lending process. Many studies have provided evidence that minority buyers are less likely to receive mortgage loans than white buyers and, if successful, receive less favorable loan amounts and terms. There is debate, however, on how much of this outcome can be attributed to discrimination. Due to differences in creditworthiness, it is not typically straightforward to isolate the effects of differences in racial and ethnic treatment. Most work done on the topic of race in lending has used HMDA data which does not contain many important lender and loan attributes such as credit history and lending ratios.

Using data from a recent paired test study of discrimination in lending, Ross et al. examine the effects of race and ethnicity on mortgage lending. Using paired testing, two individuals, one white and one minority, separately pose as homebuyers with equal qualifications for borrowing. Both members of the pair ask about the availability and terms for the same home mortgage loan. Since the two borrowers are constructed to be equal in every regard other than race or ethnicity, differences in the responses received by the two can provide direct evidence for differing treatment of minorities. It should be noted that this methodology will only focus on the first part of the lending process, the pre-application stage (which involves a loan officer that can observe the race of the applicant) rather than the approval stage (with an underwriter who typically does not).

Paired Testing Methodology

The study included approximately 250 paired tests of a representative sample of mortgage lenders in Los Angeles and Chicago. Testers posed as first-time homebuyers with limited assets making general requests for information from lenders about their mortgage loan options. The testers were given profiles that qualified them for loans targeted towards A- credit quality borrowers in their respective housing markets. Each tester was assigned sufficient income to purchase a median-priced home in the area (with a 30 year fixed-rate loan and 5% down payment) and randomly assigned one or two minor credit issues, mostly late payments. Each pair was given almost identical financial and household characteristics with the minority in the pair receiving slightly better qualifications. These pairs, it should be noted, were not permanent—a tester could be paired with multiple partners if more than one partner was available that also generally matched in gender, age, and appearance.

Table 1 below provides data on the lending institutions in the study. The study looked only at lenders that reported under the Home Mortgage Disclosure act, accepted at least 90 loan applications in 1998, and had reasonably located offices for a first-time homebuyer. 67 lenders in Los Angeles and 106 lenders in Chicago qualified under these criteria, and in order to draw a market representative sample, lenders were selected (with replacement) with a probability of selection based on loan volume. This provided 35 lenders for black-white testing in Los Angeles, 34 for Hispanic-Anglo in LA, and so on as indicated in the table.

Li Ding - Table 1

The basic testing protocol involved five steps:

  1. Obtain an appointment – testers called to arrange in person visits with lenders
  2. Make the initial request – testers requested help in figuring out a price range of housing they could afford and an estimated loan amount that they would qualify for
  3. Exchange personal/financial information – testers provided all requested information on income, debts, assets, credit history, etc.
  4. Record information on recommendations – testers noted suggested home price range, estimated loan amount, and financing options recommended
  5. End the visit – testers thanked the lender and allowed them to suggest follow-up contact

The testers then completed a test report form which allowed the study to gather information on the following six questions:

  1. Did the testers receive the information they requested about loan amounts and house prices they could afford
  2. How much were testers told they could afford to borrow and/or buy?
  3. How many specific products were discussed with the tester?
  4. How much “coaching”, such as offers of advice on paying down debts, down payment assistance, or a prequalification letter, did testers receive to help them qualify for a loan?
  5. Did testers receive follow-up calls from lenders?
  6. Were testers encouraged to consider FHA loans as an option?

 Statistical Analysis Methodology

The paired tests each generate a series of treatments t for the white and minority testers, designated as Wit and Mit respectively. An incidence measure i is derived by comparing the experiences of the two testers and classifying the test as majority favored, equal treatment, or minority favored. For loan amounts or house prices, a test is considered favored one way or the other if a tester receives an estimate that is 5% higher than their counterpart. Gross majority favored treatment is defined as the fraction of tests classified as majority favored, and likewise for gross minority favored treatment. The net measure of adverse treatment, Nt is then defined as

Li Ding - Equation 1

which is gross majority favored treatment minus gross minority favored treatment. Probability (Pr) in this case is solely a measure of sample frequency. Also, a severity measure for a treatment is defined as the difference in the treatment experienced by the two testers

Li Ding - Equation 2

where the expected value (E) is captured by the sample mean of the difference of the two series of treatments. These two measures, Nt and St, are commonly used estimates of systematic discrimination towards minorities. Statistical tests are performed on these two variables to determine if they differ significantly from zero using a two sided test. While it would be very unlikely to find unfavorable treatment for whites based on past studies, the authors decided to use the two-sided test as it was more conservative.

To address the potential issue of bias arising from using the normal distribution for small sample sizes, the authors use Fisher’s exact (permutation) tests, writing the null hypothesis for Nt as

Li Ding - Equation 3

For Sthe null hypothesis is

Li Ding - Equation 4

Empirical Results

Table 5 below summarizes the patterns of findings. Significant differences between the white favored and minority favored are indicated, with * representing significance at the 5% level and ** for the 1% level. The last row of the table shows that in Chicago, Hispanics and blacks received significant differential treatment from whites in three and four of the six categories, respectively. For both minority groups in Chicago, this leads to a rejection of the null hypothesis of equal treatment for whites and minorities at the 0.01 level. In Los Angeles, the data taken as a whole is consistent with the null hypothesis of equal treatment.

Li Ding - Table 2

In summary, the paper finds strong evidence of adverse treatment of Hispanics and blacks compared to whites in Chicago in the pre-application stages of the mortgage lending process. In the study, Hispanics were quoted lower loan amounts and house prices, were given less information about products, and received less coaching. African Americans were provided less information, received information about fewer products, received less coaching, and were less likely to experience follow-up contact. Los Angeles, on the other hand, showed no statistically significant differences in overall treatment of its white and minority borrowers. While minorities received worse treatment in some specific categories, this was not indicative of an overall pattern in LA.

Discriminatory treatment at this early stage in the mortgage lending process, though subtle,can have effects on the rest of the mortgage application. Minority homeseekers may be discouraged from applying for a mortgage due to their treatment by a lender, either abandoning their search completely or applying through the costlier subprime mortgage market instead. Also, loan officers provide more support and information to white applicants in certain circumstances which gives them a better chance of acceptance than a similarly qualified minority applicant.

Federal law, through the Equal Credit Opportunity Act (ECOA) and Fair Housing Act (FHA), forbids credit discrimination and real-estate related discrimination. The results from this study show that discrimination in these aspects is an unfortunate reality for minorities seeking home mortgage loans. Further study could be done on the reasons behind the different levels of discrimination found in Chicago and Los Angeles in the study. This research could then be used to help implement policies and effect change on a broader scale to help fight against unfair lending treatments and practices.

Referenced Paper

Stephen Ross, Margery Austin Turner, Erin Godfrey, and Robin Smith, 2008, “Mortgage lending in Chicago and Los Angeles: a paired-testing study of the pre-application process,” Journal of Urban Economics 63: 902-919.


Tables 3 and 4 below provide information on the proportions of each test that were favored for white or minority testers.

Li Ding - Table 3

Li Ding - Table 4


  1. Li,

    Insightful look at the lending market. I had always heard about discrimination by financial institutions, and I think we even talked in this class or another econ class about how minorities learned to try setting up meetings through other means besides in person, but did not think that the numbers would prove the discrimination factor to be significant at such high levels. It’s also interesting to see how the numbers for how Blacks and Hispanics were treated vary by city. Definitely an expected outcome given the location of each city and L.A.’s proximity to Mexico, but interesting nonetheless.

    My question for the people that did this study would be: Are there differences on how people get treated based on the race of the loan officer as well? This would add another subcategory into their study, so they would probably have to add more test subjects to make their sample size big enough — once again getting into the issue of this being frowned upon/illegal since they’re falsely taking up the loan officer’s working day. But, it would be interesting to see if other races are partial in a different manner, especially in cities as diverse as these two.

  2. Li, this is a very fascinating comparison between the two cities and how lenders in those cities treat the minority population. The difference in the treatment of minorities between the cities is a reflection of the entire differences in culture in each city. I am very familiar with both cities having grown up in the Chicago area and with family in the LA area. It’s interesting to think about what caused or continues to promote the differences in lending treatment and doing so requires analysis of other racial trends in each city. One big factor that comes to mind to explain Chicago is the unfortunate degree of racial neighborhood segregation. Neighborhoods within Chicago are so incredibly racially pronounced that seeing racist practices take hold in the lending industry is not surprising. Seeing the lack of racist practices in LA is also not surprising because of the cultural acceptance of immigrants/minorities, especially with Hispanics due their strong community and historical relationship with California.

    Considering these cultural differences, an interesting extension of the paper could include the degree of racial segregation among the neighborhoods in each city. I think it can be hypothesized that when a city’s neighborhoods are more racially defined, discriminatory treatment in the lending industry would tend to be higher than in a city with more diverse neighborhoods. Proving or disproving that hypothesis with more city data would make for a really fascinating paper.

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