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The data set constructed by the Urban Institute for reanalysis differs signifi- cantly from NFHA’s original set of tests. First, NFHA’s analysis assessed treat- ment across all test parts within a test.9 _{The Urban Institute, in contrast,}

concentrates on differential treatment between any two testers in a test but not between three or more test parts at the same time.10_{For example, the original}

test might include a white tester in a white neighborhood, a minority tester in a minority neighborhood, and a minority tester in a white neighborhood. This is a three-part test. In this case, NFHA’s analysis would have looked at the treat- ment each tester received in relation to the other two. The Urban Institute reanalysis focuses on differences between the treatment of white testers in white neighborhoods and minority testers in minority neighborhoods.

In addition, the Urban Institute reanalysis uses only about one-third of the items on NFHA’s original form (a copy of the data extraction form is in annex C).11

Items were selected based on their importance in the test analysis and their suit- ability for statistical analysis. The Urban Institute reanalysis, therefore, does not use information from the open-ended questions and the comprehensive narra- tives written by the testers on their particular experiences including instances of coaching, encouragement, and underwriting exceptions made by loan offi- cers. This type of open-ended information is appropriate and powerful for case- by-case enforcement and is relied on heavily in presentations to legal professionals and juries making decisions about differential treatment in a judi- cial context. But narrative information is not well suited to research testing, which must rely on closed-end data elements that lend themselves to the stan-

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dardized comparisons based on large numbers of test pairs that are necessary to draw statistically valid conclusions.12

Because these portions of the NFHA test report form are not included in our reanalysis database, we did not construct any composite measure. We focused instead on individual treatment items (such as contact length and num- ber of quotes) and looked for potential patterns and forms of differential treat- ment with respect to those items. A fundamental aim of the Urban Institute reanalysis, in other words, is to compare treatment across standard elements without a narrative in order to see if differential treatment can be determined from statistical analysis alone.13

The core of the Urban Institute reanalysis data set consists of 150 two-part tests pairing an African American tester buying a home in an African American neighborhood with a white tester buying in a white neighborhood. Although other scenarios were used in some tests, the results presented here focus on this test structure because it is the most common type found in the data set.14_Six

cities are represented in the Urban Institute reanalysis data set.15_{Two-thirds of}

the tests were done in two of these cities, 58 in Chicago and 54 in Oakland. The other test numbers in the Urban Institute reanalysis are Richmond (14 tests), Atlanta (12 tests), Denver (8 tests), and Detroit (4 tests).16

Forty-three lenders are represented in the reanalysis data set, with the num- ber of tests per lender varying (see table 1). At one end of the spectrum, one- third of the tests were conducted at the same seven banks across different cities.17_{This is not surprising, given that lenders were deliberately retested}

when differential treatment was suspected in a previous test of that lender. At the other end of the spectrum, 12 of the 43 lenders in the sample were tested only once.

Results

The most basic measure of service in pre-application testing is whether a tester is seen by a lender and given access to information on loan products. Testers attempted to get “a quote,” defined as information about a loan product with an

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Table 1. Seven Lenders with the Most Tests by City

Atlanta Chicago Denver Detroit Oakland Richmond Total

Lender 1 1 2 0 2 3 3 11 Lender 2 0 0 2 0 5 3 10 Lender 3 0 3 0 1 4 0 8 Lender 4 0 2 1 0 3 1 7 Lender 5 0 1 2 0 0 3 6 Lender 6 2 1 0 0 0 3 6 Lender 7* 0 0 0 0 6 0 6 Total 3 9 5 3 21 13 54

estimate of a monthly mortgage payment and closing costs.18_{In most tests, both}

testers were given quotes. However, significant differences in treatment were detected in some cities (see table 2).

In four of the five cities in the data set, African American testers were more likely to be denied a quote than white testers; in two, these differences are sta- tistically significant. In both Chicago and Atlanta, minority testers were signif- icantly more likely to be denied a quote than their white counterparts.19 _The

denial of a quote includes both cases where a person is not allowed to speak to a loan officer (i.e., unless the person fills out an application or has a credit check) and cases where a tester speaks to a lender but is not given information on the basic question (“What type of loan do I qualify for and how much is it going to cost?”).

Besides getting in the door, how much time a tester is allowed with a loan officer may provide information on differential treatment. Presumably, lenders providing coaching and extra information will spend more time, on average, with a prospective applicant. However, the information gathered in the Urban Institute reanalysis does not reveal the content of the conversation a lender had with a tester, or whether more time means more chatting, coaching, or some other type of interaction.

In four of the five test cities, lenders spent more time with white testers than they did with their minority partners (see table 3). For Atlanta this difference is statistically significant.20_{In Atlanta, lenders spent an average of almost 30 more}

minutes with white than with minority testers. However, in Oakland, lenders spent more time with minority (47 minutes) than with white (41 minutes) testers. Lenders did not only spend more time with whites. In most cities they also provided more quantifiable information to whites. Whereas table 2 looked at who received an estimate of monthly loan payments and who was denied such information, table 4 looks at who got more quotes in tests where both testers received quotes.

White testers received significantly more quotes than their minority partners in Atlanta, Chicago, and Denver—all cities where more time was also spent with whites. Lenders in Richmond, in contrast, were slightly more likely to give minority testers more quotes, although they did not spend more time with them. It would be interesting to know more about the types of additional quotes testers received, because multiple quotes may represent either different prod- ucts or different interest rates or points on the same product.21_{It is also not clear}

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Table 2. Who Gets Quotes?

N White/Not Black Black/Not White Both Neither t-statistic

Chicago** 58 16% 3% 76% 5% –1.82

Oakland 54 11% 13% 70% 6% 0

Atlanta** 12 25% 0% 75% 0% –2.35

Denver 8 13% 0% 87% 0% 0 Richmond 14 14% 7% 79% 0% –0.56

Using a one-tailed t-test because the direction of unfavorable treatment is unambiguous: *Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

how the simple fact of more quotes should be interpreted. On the one hand, the number of quotes provides a proxy for the level of service, with more quotes possibly representing more information and more choice. On the other hand, more quotes could reflect other elements of a transaction, such as product steer- ing. If both testers are told about conventional, fixed-rate loan products, but only one is also recommended to the FHA (Federal Housing Administration) program, is this favorable or unfavorable treatment? The second tester may ben- efit from additional information or may be steered to a less desirable program or product.

The Urban Institute reanalysis data set does include useful information on whether a lender discussed FHA (see table 5). Fewer than half of all testers were told about FHA loans, and there are dramatic differences by city in how often FHA was discussed with at least one of the testers. In Denver, someone in every test pair was told about FHA. In Oakland, no one was told about FHA in 88 percent of the tests.22_{In Chicago, African American testers were significantly}

more likely than their white partners to be told about FHA.23_{In Atlanta, Denver,}

and Richmond, white testers were more likely to hear about FHA than minor- ity testers, although the differences are not statistically significant.

These differences within and across cities speak to the different uses and value placed on FHA as a loan product. In some markets, FHA may be viewed as a less desirable loan product or reserved for more risky clients. In other mar- kets, FHA may be viewed as advantageous because of features such as low down payment requirements. It is known that black borrowers are substantially

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Table 3. Contact Length Differences

N White Longer Black Longer Equal t-statistic Chicago 55 40% 35% 25% 0.18

Oakland* 48 33% 46% 21% –1.95

Atlanta*** 10 80% 0% 20% 3.47

Denver 7 57% 14% 29% 1.04 Richmond 13 54% 46% 0% 0.50

Using a two-tailed t-test: * Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

Note: For this analysis, we used a range of ± 5 minutes to determine contact length differences given that the average contact

time for both testers was around 45 minutes.

Table 4. Who Gets More Quotes?

N White Black Equal t-statistic

Chicago** 58 31% 14% 55% 1.93

Oakland 54 24% 24% 52% –0.17

Atlanta** 12 50% 17% 33% 1.86

Denver*** 8 63% 0% 37% 3.00

Richmond 14 28% 36% 36% –0.25

Using a one-tailed t-test: *Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

more likely to use the FHA program, but it is unclear why this disparity exists and what role lenders play in the process (Gabriel 1996). Although the results presented here are mixed, the Chicago results suggest that lender behavior may play a role in some cities.

The Urban Institute reanalysis captures information from the NFHA test report form on the questions lenders asked testers. Two questions of particular interest are about income and debts (see tables 6 and 7). Not asking for such information may indicate that a lender is not treating a person seriously as a potential applicant. If a lender does not have information on a prospective bor- rower’s income and debts, in other words, it is hard to accept any quote that such a person receives as tailored specifically for that person because a lender needs that information to determine if the person qualifies for a loan.

Only in Chicago was the difference between the test partners statistically significant in how often both of these questions were asked. Lenders there were more likely to ask white testers about both income and debts. This is particu- larly interesting given how often minority testers were recommended to FHA in Chicago. In 21 percent of the Chicago tests, for example, lenders did not get income and debt information from minority testers but they did discuss FHA with them. This may imply that the lenders tested in Chicago steered minority testers to FHA based on race as opposed to income and debt calculations.

As discussed previously, the NFHA test report information on the type of loans discussed with testers is difficult to interpret without background infor- mation on lenders’ products and programs. Although our ability to analyze the specific quotes received by testers is limited, some analysis is possible using

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Table 5. FHA Discussed

N White/Not Black Black/Not White Both Neither t-statistic

Chicago*** 58 2% 22% 12% 64% 3.51

Oakland 54 4% 6% 2% 88% 0.44 Atlanta 12 33% 17% 25% 25% –0.80 Denver 8 25% 0% 75% 0% –1.53 Richmond 14 36% 21% 36% 7% –0.69

Using a two-tailed t-test: *Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

Table 6. Lender Requests for Income Amount

N White/Not Black Black/Not White Both Neither t-statistic

Chicago* 57 19% 7% 58% 16% 1.85

Oakland 51 20% 8% 63% 9% 1.63 Atlanta 12 17% 8% 67% 8% 0.56 Denver 8 25% 0% 75% 0% 1.53 Richmond 13 0% 15% 85% 0% –1.48

data from 66 tests where both testers were quoted a product with a 30-year term.24 _{While it is not known if the loan products quoted are identical, we}

assume that both tests refer to conventional, fixed-rate loan products with a 30-year term. Differences between testers in the monthly payment costs quoted in these 30-year loans sometimes favor whites and sometimes favor blacks.25

The only statistically significant disparity—more expensive monthly payments quoted to African American testers in Chicago—favors whites (see table 8).

These mixed results on quoted loan terms reinforce the need to know more about the type of loan product and the specific details of a quote’s individual parts (interest rate, points, tax rate, insurance assumptions). It could be argued that information and encouragement are more important at the pre-application stage than the financial details of the quotes.26_{Even so, providing a meaning-}

ful quote is part of the “encouragement” process, and this key piece of infor- mation may determine whether prospective borrowers think they can afford to purchase a home.

A baseline comparison of the interest rates quoted to each test partner in the tests with a 30-year term reveals that African American testers were more likely to receive a higher rate quote than their white partners in three of the five cities, a difference that is statistically significant in Atlanta (see table 9).

A complete analysis of interest rates would require more information on the timing and sequencing of test pairs because the frequent and legitimate changes in mortgage interest rates could lead to test partners being legitimately quoted different rates. However, as long as test management does not introduce bias into the process (for example, if all minority testers visited in a particular week

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Table 7. Lender Requests for Debt Information

N White/Not Black Black/Not White Both Neither t-statistic

Chicago*** 58 26% 5% 57% 12% 3.02

Oakland 54 13% 15% 59% 13% –0.26 Atlanta 12 17% 8% 67% 8% 0.56 Denver 8 25% 0% 75% 0% 1.53 Richmond 13 8% 8% 84% 0% 0

Using a two-tailed t-test: *Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

Table 8. Total Monthly Payment Cost per $10,000 of Loan Amount

N White Greater Black Greater Equal t-statistic

Chicago* 24 0% 8% 92% –1.75

Oakland 25 16% 8% 76% 1.58 Atlanta 6 0% 67% 33% –0.99 Denver 5 20% 20% 60% 0.10 Richmond 8 25% 0% 75% 1.27

when higher rates are posted), such market fluctuations should cancel one another out across multiple test pairs.27

In document EJECUCIÓN DE LAUDOS ARBITRALES, SUS LIMITACIONES EN LA JUSTICIA ORDINARIA (página 170-200)