● Data errors in the explanatory variables,
● Misclassification in the dependent variable,
● Incorrect specification, and
● Endogenous explanatory variables.
Omitted Variables
Munnell et al. (1996) supplement the 1991 HMDA data for Boston with a survey of lenders that provides extensive information on an individual applicant’s credit history, among other things. The resulting data set includes all the loan applications by blacks and Hispanics in the Boston area that year plus a large random sample of the loan applications by whites. It has by far the most com- plete set of information on loan applications ever assembled. These data allow the authors to estimate a model of loan denial that depends on an extensive list of variables associated with the probability and cost of default, including variables to measure an individual’s credit history and dummy variables for the census tract in which the house is located and the lender to which the appli- cation is submitted. They also include a variable to indicate whether an appli- cant was black or Hispanic. The coefficient of this variable in their basic model indicates that the probability of denial is 8.2 percentage points higher for appli-
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cants in one of these groups than it is for a white applicant, controlling for loan, property, and applicant characteristics. This result, which indicates that equiv- alent minority and white applicants are not treated equally in the mortgage mar- ket, is what all the excitement is about.
Munnell et al. (1996) also estimate a number of alternative models that add additional control variables, including loan characteristics, such as term, fixed rate, and cosigner, and individual characteristics, such as age, gender, and mar- ital status. In addition, they replace the census tract dummies with explicit cen- sus tract characteristics, such as the percentage of units boarded up or vacant. The magnitude and significance of the minority-status coefficient is unaffected by these changes. Furthermore, the data set includes additional individual characteristics, such as years of job experience, education, and tenure in current job. The estimated minority-status coefficient is also unaffected by inclusion of these variables (Ross and Tootell 1998).
Many critics of the findings in Munnell et al. (1996), including Zandi (1993), Liebowitz (1993), Horne (1994), and Day and Liebowitz (1996), have argued that the study omits key explanatory variables. According to a well-known econometric theorem, the coefficient of one variable (in this case, minority status) will be biased if the estimating equation omits variables that are correlated with that variable and that help explain the dependent variable (loan denial). Moreover, if these omit- ted variables have a positive impact on loan denial (i.e., if higher values make loan denial more likely) and are positively correlated with minority status, then their omission will bias upward the coefficient of the minority status variable—or, to put it another way, will lead to an overstatement of discrimination. Because, on average, blacks and Hispanics have poorer credit qualifications, these critics con- clude that the Boston Fed Study probably exaggerates discrimination because of these omitted variables. Examples of the omitted variables these authors discuss are “presence of cosigner,” “loan amount,” “dollar amount of gifts,” “home equity,” “lender toughness,” “whether data could not be verified” (henceforth called “unable to verify”), and “whether the applicant’s credit history meets loan policy guidelines for approval” (henceforth called “meets guidelines”).6
The available evidence reveals that most of these variables are not a source of bias in the Boston Fed Study’s equations. As noted by Browne and Tootell (1995), “cosigner” and “loan amount” are in fact present in the Boston Fed Study’s data set, and their inclusion in a loan denial model has no effect on the estimated minority-status coefficient. This data set also includes “whether a gift was used for the down payment” and “whether the applicant is a first-time homebuyer.” In addition, the “net worth” variable included in the Boston Fed equation includes “home equity.” The “net worth” variable is included in the Munnell et al. equations and is insignificant. Inclusion of the “gift” or “first- time homebuyer” variables has no effect on the estimated minority-status coef- ficient (see Browne and Tootell 1995 or Tootell 1996b). It seems unlikely that including the (unavailable) “actual amount of the gift” or “home equity” vari- ables separately would have much influence on the minority-status coefficient, given that these related variables have no effect. In addition, Glennon and Stengel (1994) investigate many alternative sets of explanatory variables using the Boston Fed Study’s data. They find that the estimated minority-status coef- ficient is “remarkably” unaffected by their changes.
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The inclusion of the “unable to verify” and “meets guidelines” variables has a larger effect. With the Boston Fed Study’s data, the coefficients of the “unable to verify” and the “meets guidelines” dummy variables are statistically signifi- cant in a denial model. Moreover, according to Day and Liebowitz (1996), inclu- sion of “unable to verify” lowers the minority-status coefficient by 27 percent, and inclusion of both “unable to verify” and “meets guidelines” lowers the minority-status coefficient by 62 percent. Thus, including both variables sub- stantially reduces the magnitude and significance of the minority-status coeffi- cient; in fact, in some of Day and Liebowitz’s specifications, the minority-status coefficient is no longer statistically significant.
The effects of these variables on the minority-status coefficient are also examined by Carr and Megbolugbe (1993). They find that including “unable to verify” lowers the minority-status coefficient by 15 percent, and including both variables lowers the minority-status coefficient by 40 percent. However, even after including both variables, the minority-status coefficient is still statistically significant at the 1 percent level of confidence. The findings of Glennon and Stengel (1994), Browne and Tootell (1995), and Tootell (1996b) are similar to those of Carr and Megbolugbe.
Carr and Megbolugbe and Browne and Tootell argue that these variables, especially “meets guidelines,” should not be included in the denial equa- tion. These variables were not recorded in the original loan file. Rather, they involve the after-the-fact judgment of the individual completing the HMDA data forms. The “unable to verify” variable could reflect the fact that lenders make extra efforts to verify the information of white applicants, and the “meets guidelines” variable could simply reflect the lender’s loan denial deci- sion. In particular, Browne and Tootell contend that the “meets guidelines” question, which was answered a year after the loan approval decision, was interpreted by the respondents as whether “the sum total of applicant char- acteristics meet the institution’s credit guidelines for approval.” They note that some unsuccessful applications were coded as not meeting credit his- tory guidelines even though those applicants had no credit problems. Browne and Tootell test their claim by estimating a model of the “meets guidelines” variable. As predicted by their claim, they find that loan terms, such as housing-expense-to-income ratio, debt-to-income ratio, and loan-to-value ratio, help explain the “meets guidelines” variable—a result that does not make sense if the “meets guidelines” variable only concerns the quality of an applicant’s credit history.
Horne (1994) and Day and Liebowitz (1996) counter that the “meets guide- lines” variable is a suitable proxy for details of an individual’s credit history that were not collected for the Boston Fed Study. For example, Day and Liebowitz suggest that the “age of the credit problem” and the “size of the credit problem” should have been included in the analysis. They also suggest that standards may vary across lenders and claim that this is another justification for including the “meets guidelines” variable. Because credit history information often is provided by an outside agency, lenders also might determine whether an applicant meets their guidelines simply by comparing an external credit score to their internal standard. If so, then the “meets guidelines” variable can be considered exogenous and hence a legitimate explanatory variable.
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Finally, Day and Liebowitz (1996) develop a “lender toughness” variable by matching the Boston Fed Study’s data to HMDA data. They add both the “lender toughness” and the “unable to verify” variables to the denial equation. Including both variables lowers the minority-status coefficient by 27 percent, which is the same as the effect of including only “unable to verify.” Therefore, “lender toughness” alone probably has little effect on the minority-status coef- ficient. Moreover, the basic equation in Munnell et al. (1996) already includes a set of lender dummy variables, which capture, of course, lender toughness and other fixed lender characteristics that affect loan denial. These variables lower the minority-status coefficient by about 20 percent—an effect that is already included in Munnell et al.’s basic estimate.7