Several authors have argued that the results in the Boston Fed Study are biased because the study’s data misclassify the outcome of many applications (see Horne 1994 and 1997; Day and Liebowitz 1996). The Boston Fed Study’s authors were themselves concerned about this issue and address several aspects
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of it (Munnell et al. 1996). Their basic model excludes applications that were withdrawn before the lender made a decision about them; they support this approach by showing that the factors determining withdrawals are very differ- ent from those determining loan denials. Moreover, they checked their final data set data carefully to remove “[r]efinancings, home improvement loans, and some business loans that institutions had mistakenly coded as mortgage origi- nations in their original filings” (p. 31).
Based on the FDIC review, Horne (1994, 1997) argues that many of the apparently rejected loan applications on the exceptions list should not be con- sidered “denied” applications. The FDIC reviewed 62 minority and 33 white loan application files. Out of these, Horne (1994) identifies 5 applications (3 minority and 2 white) that were actually approved; 8 applications (6 minor- ity and 2 white) that were withdrawn; 6 minority applications to which the bank made a counteroffer that was turned down by the applicant; 5 minority applications in which the applicant applied for a special program loan and was overqualified; 1 minority application that was rejected because the VA would not approve the loan; and 1 minority application that was rejected because PMI was denied.
Day and Liebowitz (1996) found that the removal of these 26 applications (plus two others they identify as being in a bank-specific special program) from the Boston Fed Study’s sample lowers the minority-status coefficient by 39 percent, from 0.0531 (3.96) to 0.0325 (2.45). Even after this step, however, the estimated coefficient is still significant at the 1 percent level. Day and Liebowitz also observe that, since the FDIC reviewed only a small number of applications, additional file reviews would certainly yield additional errors, and all these errors might account for the significance of the minority-status variable in the Boston Fed Study.
Horne (1997) also examines the effect of observations with a potentially mis- classified dependent variable using just the FDIC subsample of the Boston Fed Study’s data. First, Horne drops 111 observations because the application had one of the following characteristics: it was withdrawn, it involved a unit under con- struction, it involved refinancing, it was an investor application, it involved an applicant who was overqualified for a special program, or it had an LTV below 0.30. He also recoded four applications as approvals, arguing that they had been incor- rectly coded as denials. The minority-status coefficient for the entire FDIC sub- sample is 1.12 and is highly significant statistically. After deleting and recoding observations, the minority-status coefficient falls to 0.67 and is still significant at the 1 percent level. Next, Horne dropped 61 applications because he believed that the outcome is ambiguous. For example, counteroffers were made by some lenders and turned down by applicants, or applicants were denied PMI. The FDIC file reviews also uncovered instances in which the lender appeared to be willing to provide credit but the transaction was precluded by outside factors, such as title problems or housing code violations. The exclusion of these applications and some modifications to the model specification result in a minority-status coefficient of 0.35, which is no longer statistically significant at even the 10 percent level.
The Boston Fed Study’s authors follow the HMDA reporting requirements and consider accepted counteroffers to be approvals and denied counteroffers to be rejections. They argue that an accepted counteroffer implies that the
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lender provided credit based on the borrower’s application package and pref- erences. A denied counteroffer implies that the lender was not willing to pro- vide a loan at terms that were acceptable to the borrower. All counteroffers may not be equal. Some counteroffers may involve small changes to the terms of the loan, whereas others may involve dramatic changes. The data contain no information concerning the magnitude of the change proposed by lenders in their counteroffer, so, according to Munnell et al., the fact that the coun- teroffer was accepted is the best indicator that the change was minimal.
Horne (1994, 1997) proposes that all counteroffers be considered accep- tances because the lender is willing to provide credit. However, one could also argue that all counteroffers should be considered rejections because the lender was not willing to provide credit based on the terms in the application package, which are in fact the terms that are included in the Boston Fed Study’s data set. One possible way to deal with this difference of opinion would be to esti- mate a model based on three lender choices: approve, counteroffer, and deny.28
In this type of model, a difference in either the likelihood or the nature of coun- teroffers based on minority status also constitutes differential treatment.
A study of an alternative sample yields some insight into the issues raised by Horne (1994, 1997). Glennon and Stengel (1995) examine applications from three different lenders. They find, on average, a significant impact of minority status on loan denial.29They identified all applications in which the applicant
rejected the bank’s counteroffer and all files in which the applicants were overqualified for special loan programs. The deletion of all of these applications did not affect their findings. Moreover, they reviewed all denied files at one lender and detected 41 withdrawals that had been missed previously. The dele- tion of these withdrawals did not affect the estimated minority-status coeffi- cient for that bank.
An alternative approach is to see if the Boston Fed Study’s results are driven by a few “influential” observations. If so, a few data errors or misclassifica- tions might drive the results. Rodda and Wallace (1996) rank applications by their influence on the minority-status coefficient. Most of the highly influen- tial applications are minority denials, and the elimination of the 23 most influ- ential minority denials causes the minority-status coefficient to become insignificant. Rodda and Wallace conduct this analysis to determine which applications should be subject to file review—not to determine whether esti- mates of discrimination are flawed. Moreover, they point out that this finding is driven predominantly by sample size; there are nearly four times as many white applications as minority applications, and most applications are approved. A related approach that focuses on outliers in a broader sense is provided by Carr and Megbolugbe (1993), who calculate the influence of every observation on the minority-status coefficient and on all other coefficients. They exclude 27 appli- cations that are highly influential on either measure and find that the minority- status coefficient does not change.
Evaluation
In the Boston Fed Study’s data, as in any large, complex data set, it is reasonable to explore the accuracy and interpretation of the information, as Horne (1994,
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1997) and Day and Liebowitz (1996) have done. However, many of the cases they identify as “errors” are in fact cases that raise issues of interpretation about which reasonable people may disagree. For example, we find the interpreta- tion of counteroffers by Munnell et al. (1996) to be entirely reasonable, although not the only interpretation possible. There is obviously room for more research on this topic. A model of three choices—accept, reject, and counteroffer, for example—might help to determine whether counteroffers are a relatively benign phenomenon, as some scholars claim, or are instead another type of lender behavior that involves differential treatment.
Moreover, the selection process used by the FDIC to identify files within the Boston Fed Study’s sample for review was designed to help bank examiners— not to shed light on a loan denial equation. For example, the FDIC did not review files at lenders that did not reject any minority applications. As a result, two-thirds of the files reviewed are minority rejections, despite that fact that more than half of the rejections in the Boston Fed sample are applications from whites. This feature of the FDIC exceptions list makes it inappropriate for use in a loan denial equation. The FDIC’s exclusive focus on rejected applications also is problematical for estimation purposes; after all, some, even many, denied or withdrawn applications could have been miscoded as approvals. Similarly, the Boston Fed Study’s data may contain underqualified minority applicants who were approved because they applied for a special program. Overall, Day and Liebowitz’s (1996) and Horne’s (1994, 1997) filtering of the data and the resulting conclusions must be rejected because they are not based on a random sample of the applications in the Boston Fed Study’s data. Indeed, alternative approaches to misclassification that are neutral with respect to minority status, such as the one in Glennon and Stengel (1995), find that mis- classification has little or no impact on the relationship between minority status and loan denial.
Finally, studies such as Rodda and Wallace (1996) that identify and drop “influential” observations also do not shed much light on data errors or mis- classification, although they might be useful for other purposes. In particu- lar, it is not surprising that minority denials have the most influence on the minority-status coefficient in the Boston Fed Study; after all, this coefficient is supposed to determine whether applications with this outcome are treated differently from comparable white applications. Nor is it surprising that the list of influential observations did not include any white approvals, the effects of which are “watered down” by the presence of so many similar observa- tions. As a result, this type of analysis sheds no light whatsoever on the cred- ibility of the Boston Fed Study’s result. The study by Carr and Megbolugbe (1993) is more to the point, because it defines “influential” in a way that is rel- atively neutral with respect to minority status, but it also does not provide a compelling conceptual or methodological argument for dropping influential observations. We conclude that any procedure for identifying and dropping influential observations in a loan denial study, particularly one that defines influence by impact on the minority-status coefficient, is not appropriate for evaluating the role of errors or of misclassification in a study of mortgage lending discrimination.
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Reanalysis
The public-use data from the Boston Fed Study contain some information to help shed light on the issues raised by Horne (1994, 1997) and by the FDIC file reviews. In particular, these data contain variables to indicate “whether an application for PMI was denied” and “whether a mortgage application was made for a special program.” We find that dropping all applications coded affir- mative for “PMI denied” or “application for special program” actually increases the impact of minority status on loan denial. For our three models and the full sample, the effects of minority status are 4.1, 5.6, and 7.7 percentage points. When these applications are dropped, the remaining sample contains 2,336 applications, the effects of minority status are 6.4, 6.4, and 9.0 percentage points, and the estimated minority-status coefficient is highly significant in all three models (see table 1).
Conclusions
Although the available data do not permit a definitive conclusion concerning the impact of misclassification on the Boston Fed Study’s results, we find that these results are not affected by many of the misclassification problems dis- cussed by Horne (1994, 1997) and Day and Liebowitz (1996). Moreover, even the strongest claims about bias due to misclassification are not compelling because they are based on a nonrandom sample of applications. There currently exists no evidence indicating that elimination from the data set of misclassi- fied observations identified on a minority status–neutral basis has any sub- stantial influence on the estimated minority-status coefficient.