Do rental agents discriminate against minority customers? Evidence from the 2000 Housing Discrimination Study

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Journal of Housing Economics 14 (2005) 1–26

HOUSING ECONOMICS www.elsevier.com/locate/jhe

Do rental agents discriminate against minority customers? Evidence from the 2000 Housing Discrimination Study q Seok Joon Choi, Jan Ondrich, John Yinger * Department of Economics, Maxwell School of Citizenship and Public Affairs, Syracuse University, USA Received 23 December 2004 Available online 5 March 2005

Abstract This study examines the incidence and causes of housing discrimination in qualitative treatment by rental agents, using national audit data from the 2000 Housing Discrimination Study. Using the fixed-effects logit method described by [Review of Economic Studies 47(1) (1980) 225–238], we control for unobservable factors that are shared by audit teammates and conduct hypothesis tests for the incidence and causes of discrimination. We find evidence that rental housing discrimination has declined since 1989 but continues to exist in several important types of housing agent behavior. We also find evidence that this discrimination is caused by agentsÕ own prejudice and by their response to the prejudice of white clients. Ó 2005 Elsevier Inc. All rights reserved. JEL Classifications: H39; R22; R38; R20; J71 Keywords: Housing discrimination; Audit; Fixed-effects logit

q The authors are, respectively, Ph.D. candidate in economics; Professor of Economics; Professor of Economics and Public Administration, Syracuse University. We are grateful to Stephen Ross, Associate Professor of Economics, University of Connecticut for his help with the data and to two anonymous referees for helpful comments. This research was supported by HUD Grant #H-21441RG. * Corresponding author. Fax: +1 315 443 1081. E-mail address: [email protected] (J. Yinger).

1051-1377/$ - see front matter Ó 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jhe.2005.02.001

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1. Introduction Housing discrimination occurs when a home seeker receives unfavorable treatment from a housing agent because of his or her race or ethnicity. This treatment may impose a financial loss on minority home seekers and may also restrict their access to local public goods, which are linked to residential location.1 Housing discrimination also may play a role in the labor market, because it may restrict minority householdsÕ access to housing near employment opportunities. Housing discrimination is, of course, illegal according to the Fair Housing Act in 1968, which was amended in 1988 to give the federal government more enforcement powers.2 Several articles use data from a technique called a fair housing audit to study racial discrimination in the sales housing market (see Galster, 1990; Newburger, 1989; Ondrich et al., 1998, 2003; Page, 1994; Roychoudhury and Goodman, 1996; Turner and Mickelsons, 1992; Yinger, 1986, 1995). A few other articles use similar data to examine discrimination in rental housing (Galster and Constantine, 1991; Ondrich et al., 1999; Page, 1994; Roychoudhury and Goodman, 1992). This study builds on this literature to examine the incidence and causes of housing discrimination in qualitative treatments by rental agents, such as showing an advertised apartment, using audit data from the 2000 Housing Discrimination Study (HDS 2000). The literature (Fix et al., 1993; Ondrich et al., 2000; Yinger, 1986) demonstrates the importance of controlling for unobserved factors shared by audit teammates in audit-based tests of discrimination. We address this issue using the fixed-effects logit technique derived by Chamberlain (1980). This paper makes several contributions. First, we examine the incidence and causes of discrimination across several types of rental agent behavior. Second, we explore new tests of hypotheses about the cases of discrimination. For example, we show how the treatment of black customers by Hispanic agents can be used to test for the discrimination based on both agent and customer prejudice. Third, we bring in several new variables that were not available in previous studies. Most importantly, we examine whether auditorsÕ true characteristics, not just the characteristics assigned to them for the purposes of the audit, influence the rental agentÕs relative treatment of minority auditors. Finally, we examine changes in the incidence and causes of discrimination between 1989 and 2000. The next section describes the 2000 Housing Discrimination Study (HDS 2000). Section 3 presents our econometric model for measuring the incidence of discrimination. Section 4 presents hypotheses concerning causes of discrimination. The estimation results are reported in Section 5. Changes in the incidence of discrimination are discussed in Section 6. Section 7 presents conclusions and a summary.

1

Yinger (1995) estimates that the 3-year cost of housing discrimination in the sales market is about $7.8 billion for blacks and $4.4 billion for Hispanics. 2 The 1968 Fair Housing Act and its 1988 amendments are described in Yinger (1995).

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2. The 2000 Housing Discrimination Study Since the 1970s the audit methodology has been used to examine the incidence and causes of discrimination in housing. Each audit consists of successive visits to the same housing agency by two audit teammates, one a non-Hispanic white and one a minority. The teammates are matched by sex and age and are assigned similar socioeconomic characteristics, such as marital status, number of children, and income. Advertised housing units are randomly drawn from a major metropolitan newspaper. Both teammates then visit, in random order, the agency placing an ad to inquire about the advertised unit. Each auditor independently records the behavior and characteristics of the agent that he or she meets. HDS 2000 is the third national audit study sponsored by the U.S. Department of Housing and Urban Development (HUD) to examine racial and ethnic discrimination in the U.S. housing market. The Housing Market Practices Study of 1977 (HMPS; Wienk et al., 1979) and the 1989 Housing Discrimination Study (HDS 1989; Yinger, 1995) found significant levels of discrimination in both the sales and rental markets. HDS 2000, which was conducted by the Urban Institute, was designed to determine whether minority home-seekers continue to receive differential treatment compared to white home seekers (see Turner et al., 2002; Ross and Turner, in press). The HDS 2000 rental market data include 1152 black-white and 731 Hispanic-white rental audits conducted in 2000.3 The black-white audits were conducted in 16 metropolitan areas4 and the Hispanic-white audits in 10 areas.5 Unlike previous housing audit studies, HDS 2000 collected data on auditorsÕ true characteristics, such as income, education, birth place, and previous audit experience. This information allows us to control for differences in auditor characteristics within a team as well as in shared characteristics across teams.

3. Econometric model Yinger (1986) explains that audit teammates going through the same training and visiting the same agency on the same day to ask about the same advertised unit will share values of unobservable variables, such as the conditions in the housing market or the policies of the agency. ChamberlainÕs (1980) fixed-effects logit framework can be used to account for the role of shared unobservable characteristics in the determination of qualitative dependent variables, such as whether the advertised unit is

3

HDS 1989 conducted 781 black-white and 767 Hispanic-white rental housing audits. HDS 2000 also conducted 226 Asian-white and 100 Native American-white audits, but we do not examine these data here. See Turner et al. (2002). 4 Los Angeles, New York, Chicago, Houston, Denver, Austin, Atlanta, Philadelphia, Detroit, Washington DC, New Orleans, Pittsburgh, Dayton-Springfield, Orlando, Macon, and Birmingham. 5 Los Angeles, New York, Chicago, Houston, Denver, Austin, San Antonio, Pueblo, San Diego, and Tucson.

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shown to a customer. ChamberlainÕs fixed-effects logit framework has been used by Whittington (1992), Christian et al. (1993), Korenman and Winship (1995), Fisman and Raturi (2003), and Anderson and Newell (2004). Ondrich et al. (1998, 1999) used the fixed-effects logit framework to analyze sales and rental audit data from HDS 1989; the present study follows their fixed-effects logit framework.6 3.1. A fixed-effects logit model for audit data Consider a favorable, qualitative action by a rental agent toward a customer, such as showing the customer an advertised unit. This action is measured by a variable Aav, which equals 1 when the action is taken during visit v of audit a. A standard logit model explains Aav as a function of the auditor and agent/agency characteristics associated with visit v of audit a, labeled Xav, and the minority status of the auditor, Wav, which equals 1 if the auditor is white. Because A is also influenced by unobserved factors shared by audit teammates, however, a standard logit may yield biased results. As shown by Ondrich et al. (1998, 1999), this problem can be solved using ChamberlainÕs fixed-effects logit procedure. More specifically, the Chamberlain procedure differences out the unobserved audit-level effects and produces a model that estimates the population parameters based on the sub-sample of audits in which teammates are treated differently. Although the order in which the white and minority auditors visited the agency was random, it simplifies the discussion to associate visit index value 1 with the minority auditor and visit index value 2 with the white auditor. With this notation, the fixed-effects logit model for the probability that the white auditor is treated more favorably than the minority auditor can be written PrðAa2
Aa1 ¼ 1jAa2 þ Aa1 ¼ 1; d; ½X a2
X a1 ; Z a2 ; b; c; aÞ w

¼ F ðdw þ b0 ½X a2
X a1  þ c0 ½Z a2
Z Þ:

ð1Þ

In this equation, F stands for the logistic cumulative distribution function. The X variables control for differences between teammates that are not eliminated by the audit methodology, such as differences in their age or in their true income or education. Unobservable factors shared by teammates, a, are differenced out. The Z variables are the subset of the X variables used to test hypotheses about the causes of discrimination. In a standard logit model they are interacted with the minority status indicator, W, so that only the value for the white auditor appears in the differenced version of the model. This model is specified so that the estimated intercept, dw, provides a consistent estimate of discrimination for the nation as a whole. The significance test for this intercept provides a test for the hypothesis that discrimination exists. This step is accomplished by expressing the Z variables as deviations from their weighted mean w values in the entire sample, Z , not just the sub-sample in which one teammate is 6 Preliminary versions of some of our fixed-effects logit models for HDS 2000 are presented in Turner et al. (2002).

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favored. The sampling weights used in this calculation are designed to yield nationw ally representative estimates. With this formulation dw equals d þ c0 Z , where d is the average difference in treatment between minority and white teammates at the average value of the ZÕs in the sub-sample used for estimation. In effect, this procedure ‘‘corrects’’ the constant term for differences in estimated discrimination that arise because the mean values of the Z variables in this sub-sample differ from the mean values in the population. See Ondrich et al. (1998) for a more formal derivation. Finally, the significance test for each c coefficient provides a test of the hypothesis that discrimination varies with the related Z variable, and hence provides a test of the hypothesis about the causes of discrimination with which this Z variable is linked. 3.2. The incidence of discrimination The intercept, dw, indicates the log-odds of discrimination in the audit data after accounting for observable differences between teammates. This estimate is particularly valuable for qualitative treatment variables in audit data, because it can be identified even when fixed-effects techniques are used. In this setting, fixed-effects techniques preserve the consistency of parameter estimates, but they do not yield group-specific measures of the probability of discrimination. With the classical linear regression model (CLRM), such measures can be obtained by using deviations from the group mean in place of the original variables. Because of the linearity of CLRM, the group-specific residual mean is an unbiased estimator of a groupÕs treatment. This approach does not work with a nonlinear model. With an added assumption, however, it is possible to estimate the incidence of discrimination from the results of a fixed-effects logit model (Ondrich et al., 1998). The odds ratio is defined as R¼

P W =ð1
P W Þ ; P M =ð1
P M Þ

ð2Þ

where PW is the probability that the white auditor is favored and PM is the probability that the minority auditor is favored. Because dw measures the log-odds of disw crimination, R ¼ ed measures the odds ratio of discrimination, that is, the odds ratio when values of the XÕs are identical for white and minority teammates. Now assume that PM always falls short of PW by a fixed absolute amount, d; that is, assume that PM = PW
d. Then d, a measure of the incidence of discrimination, equals d¼

P W ðR
1Þð1
P W Þ : P W þ Rð1
P W Þ

ð3Þ

This measure can be calculated using the estimated value of dw to determine R and the weighted share of audits in which the white auditor is favored to determine PW. Another way to estimate the incidence of discrimination is to subtract the weighted share of audits in which the minority is auditor is favored from the weighted share of audits in which the white auditor is favored. This difference is known as a simple net measure of discrimination. Note that d is also a net measure

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of discrimination; unlike the simple measure, however, it controls for teammate differences in the XÕs. Net measures of discrimination are based on the assumption that minority auditors are only favored for random reasons. Under this assumption it is appropriate to ‘‘net out’’ the share of audits in which the minority auditor is favored, which can be interpreted as a measure of random factors. An alternative assumption is that housing agents systematically favor white auditors under some circumstances and minority auditors under others. Under this assumption, it does not make sense to ‘‘net out’’ minority-favored audits, because systematic unfavorable treatment of minorities is in no way offset by systematic unfavorable treatment of whites. The simple net and gross measures provide rough bounds on the incidence of discrimination, and our logit-based net measure provides a more precise net measure that accounts for observable difference between teammates. For more on the distinction between gross and net measures, see Fix et al. (1993), Ondrich et al. (2000), and Turner et al. (2002).

4. Hypotheses about the causes of discrimination The hypotheses we want to test link the beliefs or perceptions of the rental agents with incentives that lead to discriminatory behavior. Hypotheses of this type can only be tested indirectly, however, because agentsÕ beliefs and perceptions are not observed. The previous audit literature tests hypotheses about the causes of discrimination by explaining how auditor, agent, or neighborhood characteristics, the ZÕs in Eq. (1), could be linked to agentsÕ incentives to discriminate and then by determining whether these variables have a significant impact on the probability that an agent discriminates (Newburger, 1989; Ondrich et al., 1998, 1999, 2003; Page, 1994). The hypotheses identified by these articles are not mutually exclusive; that is, any given type of discriminatory behavior could be influenced by more than one set of incentives. 4.1. The agent-prejudice hypothesis Discrimination may occur because an agent has a strong personal bias against racial or ethnic minorities (Becker, 1971). This hypothesis cannot be tested directly because audits do not measure an agentÕs prejudice, but it can be tested indirectly by determining whether agentsÕ treatment of minorities varies with characteristics that are likely to be related to agent prejudice, such as the race, age, and gender of the agent and the age and gender of the auditor (Schuman et al., 1985; Ondrich et al., 1998, 1999, 2003). Housing agents may also prefer to deal with minority customers who are married instead of single. We test the agent-prejudice hypothesis in two new ways. First, we explore the role of Hispanic prejudice against blacks. The available evidence indicates that Hispanic prejudice against blacks is quite high in some places. Surveys indicate that Hispanics have stronger prejudice against blacks than against whites or Asians (Bobo et al., 2000). Moreover, dark-skinned Hispanics are almost as segregated from whites as

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are blacks, and Hispanics with lighter skin often have higher social status (Massey and Denton, 1993). In addition, because of high immigration and high birth rates, Hispanics are the fastest growing minority group—a fact that may cause conflicts and competition with blacks, not only for economic benefits, such as employment and housing, but also for publicly provided resources, such as education, health care, and protective services.7 Thus, Hispanic agents may be even more prejudiced against blacks than are white agents. If so, the agent–prejudice hypothesis indicates that discrimination will be higher when the agent is Hispanic than when he or she is white. The specific variables used in this test are described in the next section. Second, we explore the possibility that the role of prejudice is different for rental brokers and property managers.8 The role of rental brokers is similar to that of real estate brokers. Rental brokers are hired by property owners to advertise available apartment and to show these units to prospective renters. These brokers receive their commissions from both the owner and the tenant once the unit is rented. In contrast, property managers are employed by property management companies or by landlords who own multiple apartment buildings. Their job is to deal with tenants and maintain the property. These job descriptions are linked to the agent prejudice hypothesis because property managers have continuing contacts with tenants, whereas rental brokers only have to deal with tenants during the housing search process. We hypothesize that prejudiced property managers want to avoid interacting with tenants who belong to a group against whom they are prejudiced, so that discrimination will be higher when the agent is a property manager instead of a rental broker. 4.2. The customer-prejudice hypothesis The customer-prejudice hypothesis states that rental agents may avoid renting to minority customers to protect their actual or potential business with prejudiced white customers. Agents may assume that whites feel uncomfortable when Hispanics or blacks move into their building or neighborhood and then cater to these perceived feelings by discriminating. The customer-prejudice hypothesis predicts that agents discriminate more against a minority customer if some of that customerÕs characteristics are particularly likely to upset their prejudiced white customers. Such characteristics may include low income and having children in the family (Schuman et al., 1985). The customer-prejudice hypothesis also predicts more discrimination when the agentÕs office is in a white

7 Bobo et al. (2000) document conflict and competition between Hispanics and blacks in metropolitan Los Angeles. 8 HDS 2000 does not directly identify the type of rental agent that testers meet. We identify the type of agent by comparing the addresses of the agentÕs office and the housing units. If the agent is a property manager, the addresses of office and housing units are identical or similar because the property manager office is usually located in the apartment complex or near its rental units. Additionally, if most of the units that the agent handles have identical addresses or are located near each other, we assume that the agent is a property management agent.

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neighborhood. Although we do not observe the location of the agentÕs office directly, the racial/ethnic composition of the neighborhood in which the advertised unit is located provides a proxy for the rental agentÕs current customer base, because the agentÕs office is likely to be at the same location or nearby (Ondrich et al., 1999).9 Our specification recognizes that these two predictions may interact with each other. To be specific, the discrimination blacks or Hispanics encounter in a largely white neighborhood may depend on their income. To explore this possibility, we interact the income level assigned to an audit and whether the unit is located in a largely minority neighborhood. More specifically, we define six categorical variables: (1) high-income minorities who seek housing in largely white areas; (2) high-income minorities who seek housing in largely minority areas; (3) middle-income minorities who seek housing in largely white areas; (4) middle-income minorities who seek housing in largely minority areas; (5) low-income minorities who seek housing in largely white areas; and (6) low-income minorities who seek their housing in largely minority areas. The sixth category is omitted in our regressions, so a positive sign for any of the other variables indicates that a minority household in that category encounters more discrimination than a low-income minority household seeking housing in a largely minority area. According to the customer-prejudice hypothesis, discrimination is higher in white than in minority neighborhoods and higher against low-income than against high-income households. Thus, this hypothesis predicts a negative sign for the second and fourth variables and a positive sign for the fifth variable. These two effects offset each other in the first and third variables, however, so the customer-prejudice hypothesis does not make a clear prediction about their signs. A positive sign for the first or third variable indicates that the effect of neighborhood on agent discrimination is stronger than the effect of income; a negative sign indicates the reverse. Another implication of the customer-prejudice hypothesis is that prejudiced white residents are more concerned about integration, and hence rental agents are more likely to discriminate, when those residents have a stronger attachment to the neighborhood. The rate of homeownership and average house values provide indicators of attachment. Thus, we test this prediction of the customer-prejudice hypothesis by determining whether discrimination is higher when the advertised unit located in a neighborhood with a higher level of homeownership or with a higher average house value. The customer-prejudice hypothesis is also linked to some characteristics of the rental agency (Ondrich et al., 1999; Yinger, 1995).10 Because larger agencies are more 9

In HDS 2000, as in HDS 1989, the vast majority of advertised apartments are in neighborhoods with a white majority. To ensure that a reasonable number of audits fall into the ‘‘minority neighborhood’’ category, we follow Ondrich et al. (1999) by defining a largely white area as one in which the minority composition is less than 20%. In the case of property managers, a more precise test of the customerprejudice hypothesis might come from the racial/ethnic composition of the building in which the advertised unit is located, but this information is not available. 10 Turner et al. (2002) discuss the possibility that larger firms have more experience serving customers in a range of different customer groups and are more likely to tailor their practices to fit their perceptions of each groupÕs preference.

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diversified, that is, have a broader client base, they need not be as concerned as smaller agencies about the impact of their actions on their attractiveness to white customers and therefore may discriminate less. We include a proxy for agency size, namely, the maximum number of agents encountered by either teammate, to test this prediction. To distinguish the role of agency size from the number of rental units a particular agent has to work with, we also include a variable indicating whether a unit similar to the advertised unit was available to show to either teammate. This variable is not linked to a particular hypothesis, but indicates whether agents are more likely to discriminate when the advertised unit is the only one they have to show. The distinction between rental brokers and property managers is also linked to the customer-prejudice hypothesis, because these two types of agents draw on different customer pools. Rental brokers, like real estate brokers, attempt to build a reputation that will attract potential tenants and property owners to their office. Their objective, therefore, is to avoid transactions that harm their reputation with their main customer base. If this base consists of prejudiced whites, they have an incentive to discriminate against blacks and Hispanics. In contrast, property managers focus on the tenants in their buildings. If these tenants are prejudiced whites, then they may want to cater to these tenants by rejecting black and Hispanic applicants. We do not know which of these incentives is stronger, so this line of argument does not lead to a sign prediction for the coefficient of the rental broker variable. Instead, it provides one possible interpretation for this sign; a positive value indicates that the incentives associated with attracting clients are more powerful than those associated with pleasing current tenants. Hispanic agents who work in Hispanic neighborhoods may obtain most of their business from Hispanic customers, so these agents may be particularly concerned about the preferences of Hispanics. Given the evidence presented earlier that Hispanics are prejudiced against blacks, the customer-prejudice hypothesis indicates that Hispanic agents with a large Hispanic clientele may discriminate to preserve their business with Hispanic customers. The location of the advertised unit is a proxy for the location of an agentÕs business. Thus, the customer-prejudice hypothesis predicts that discrimination will be relatively high when the agent is Hispanic and the advertised apartment is in a largely Hispanic neighborhood. To distinguish the prejudice of Hispanic agents from the prejudice of their potential Hispanic customers, we create four variables based on whether the advertised unit is located in a Hispanic neighborhood and whether a black auditor meets a Hispanic agent.11 The agent-prejudice hypothesis predicts that discrimination will be higher when the agent is Hispanic, regardless of where the housing is located, whereas the customer-prejudice hypothesis predicts that discrimination will be higher only when the agent is Hispanic and the advertised unit is in a Hispanic neighborhood. Some predictions of the agent-prejudice and customer-prejudice hypotheses cannot be separated. Either housing agents or their potential white customers, for

11 In the black-white audits in HDS 2000, the average neighborhood is approximately 15% Hispanic. In our analysis, a census tract more than 15% Hispanic is classified as a largely Hispanic neighborhood.

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example, may have stronger prejudices against younger blacks or Hispanics than against older ones. 4.3. The statistical-discrimination hypothesis12 Statistical discrimination occurs when agents treat people in different groups differently because they believe that group membership is correlated with unobserved characteristics that affect the profitability of their actions. In the rental market, for example, a rental agent may use customersÕ race or ethnicity as a signal about their preferences for housing type or neighborhood. Like the customer-prejudice hypothesis, the statistical-discrimination hypothesis can be linked to the racial or ethnic composition of the neighborhood in which an apartment is located. Rental agents may believe, for example, that all households prefer to live with their own racial or ethnic group and that a housing rental is unlikely to be successful when a minority customer is matched to an advertised unit that is located in a largely white neighborhood area (Ondrich et al., 2003). If so, the statistical-discrimination hypothesis predicts that the probability of discrimination is higher when the advertised unit is located in a white instead of a minority neighborhood. This is, of course, the same as the prediction based on customer prejudice. Ondrich et al. (2003) find that real estate brokers discriminate more against higher-income black customers, apparently discounting statements by these customers (but not by their identically qualified white teammates) that they can afford expensive houses. A similar stereotype may be at work in the rental market. If so, agents may discriminate against high-income minorities to save themselves the time of showing units that they believe these minority customers cannot afford. More specifically, this hypothesis predicts that higher-income minority customers will encounter more discrimination, even in minority neighborhoods. Thus, the statistical-discrimination hypothesis predicts a positive sign for the first, third, and fifth of the interaction variables described in the previous section. Its predictions for the second and fourth variables are ambiguous, however, because these variables combine a higher income (more discrimination) with a minority neighborhood (less discrimination). A positive sign for these variables would suggest that impact of higher income on discrimination is stronger than the impact of location in a minority neighborhood. Ondrich et al. (2003) also find that real estate brokers are more likely to discriminate against blacks in neighborhoods with higher house values, even controlling for the value of the houses being shown. This result suggests that statistical discrimination is at work; brokers avoid showing blacks houses in wealthy neighborhoods because they believe blacks are unlikely to complete transactions in those

12

Several scholars have proposed a ‘‘perceived-preference’’ hypothesis that is equivalent to statistical discrimination (Ondrich et al., 2003).

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neighborhoods. Our variable measuring neighborhood house value tests for a similar effect in rental markets. Finally, rental agents may perceive that black customers are aware of, and wish to avoid, Hispanic prejudice against them. In this case, all rental agents, black, white, and Hispanic, will avoid marketing housing to black customers in Hispanic neighborhoods. To test this hypothesis, we include another variable to indicate cases in which the agent is not Hispanic and the advertised unit is in a Hispanic neighborhood. This hypothesis predicts a positive sign for this variable, as well as for the variable indicating a Hispanic agent in a Hispanic neighborhood. 4.4. Hypotheses concerning true auditor characteristics As noted earlier, some of auditorsÕ true characteristics are observed in HDS 2000. As discussed below, we explore the role of true auditor characteristics both as differences (X variables) and as levels (Z variables). Including the differences tightens the audit methodology by ruling out the possibility that teammates are treated differently because of differences in their true characteristics. Including the level variables helps us examine the possibility that our hypothesis tests about the causes of discrimination are biased when the true characteristics of auditors are omitted from the estimation. AuditorsÕ true characteristics are not explicitly revealed in an audit, but they might influence agentsÕ behavior in a variety of ways. An agent may evaluate customers based on their language or knowledge, for example, which are likely to reflect the amount of education they have received. Moreover, an auditor with actual experience searching for an apartment may phrase questions differently and therefore may be treated differently by a rental agent. In addition, an auditor born in a foreign country may have a distinctive accent that provides a clue about his country of origin. These clues may feed into an agentÕs perceptions about foreigners or new immigrants that influence the way he or she treats such customers.13

5. Estimation and test results 5.1. Gross and net incidence in HDS 2000 Table 1 presents the simple net and gross measures of the incidence of discrimination for several types of agent behavior. The top panel of Table 1 presents the results for discrimination against blacks, while the bottom panel presents the results for Hispanics. Recall that these measures do not correct for observed differences between teammates.

13 For example, Ondrich et al. (1999) show that a Hispanic with a heavy accent is more likely to receive unfavorable treatment.

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Table 1 Gross and net incidence, HDS 2000 Number of units recommendeda

Advertised unit inspected

Number of units inspectedb

Asked to fill out application

0.754 0.685

0.889 0.839

0.607 0.559

0.679 0.647

0.498 0.467

Probability of unfavorable treatment (relative to teammate) for Black (simple gross incidence) 0.140 White 0.090 Difference (simple net incidence) 0.050

0.285 0.226 0.059

0.159 0.110 0.049

0.235 0.161 0.074

0.185 0.161 0.024

Multivariate net incidencec

0.058

0.117

0.075

0.102

0.080

Advertised unit available

Number of units recommendeda

Similar unit inspected

Number of units inspectedb

0.785 0.671

0.856 0.823

0.152 0.132

0.671 0.621

Probability of unfavorable treatment (relative to teammate) for Hispanic (simple gross incidence) 0.150 White 0.072 Difference (simple net incidence) 0.078

0.316 0.226 0.090

0.093 0.073 0.020

0.220 0.145 0.075

Multivariate net incidencec

0.090

0.077

0.030

Black-white audits Probability of favorable action for White Black

Hispanic-white audits Probability of favorable action for White Hispanic

a b c

0.127

Favorable action equals positive number of recommendations; unfavorable treatment equals being recommended fewer units than teammate. Favorable action equals positive number of inspections; unfavorable treatment equals inspecting fewer units than teammate. This estimate is based on the results from our fixed-effects logit model.

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Advertised unit available

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In HDS 2000, the simple gross measure of discrimination against blacks (row three) ranges from 14% for ‘‘advertised unit available’’ to 28.5% for ‘‘number of units recommended.’’ The simple net measure (row five) ranges from 2.4% for ‘‘asked to fill out application’’ to 7.4% for ‘‘number of units inspected.’’ In the case of Hispanics, the gross measure of discrimination ranges from 9.0% for ‘‘similar unit inspected’’ to 31.6% for ‘‘number of units recommended.’’ The net measure ranges from 2.0% for ‘‘similar unit inspected’’ to 9.0% for ‘‘number of units recommended.’’ To keep these results in perspective, Table 1 also presents our multivariate net incidence measure, the d defined by Eq. (3). This measure corrects for observed differences between teammates, including differences in their true characteristics, but requires the assumption that there is a fixed absolute gap between the white and minority probabilities of unfavorable treatment (Ondrich et al., 1998). In every case except one (‘‘number of units inspected’’ in the Hispanic-white audits), this measure equals or exceeds the simple net measure. In some cases, such as ‘‘number of units recommended’’ and ‘‘asked to fill out application’’ in the black-white audits and ‘‘advertised unit available’’ in the Hispanic-white audits, the multivariate net incidence measure is considerably larger than the simple net measure, but the multivariate net measure never reaches the simple gross measure. These results suggest that simple net measures provide conservative estimates of the incidence of discrimination. 5.2. The existence of discrimination The first major question to be addressed is whether discrimination exists. As explained earlier, a positive and significant value for the intercept, dw, in our fixed-effects logit estimation supports the hypothesis that discrimination exists. The test results for the black-white and Hispanic-white audits are presented in Table 2 for each type of behavior. The first row in each panel shows the number of audits in which a difference in treatment occurred. The second and third rows present estimates of dw and the associated t statistic. The fourth row gives the estimated white-minority odds ratio for receiving favorable treatment from the rental agent, defined as exp (dw). The final row repeats our multivariate net incidence measure, which is based on the estimated dw. In the black-white audits, the null hypothesis of no discrimination can be rejected at the 5% level (one-tailed test) for all behaviors except ‘‘advertised unit available,’’ which is significant only at the 10% level. In the Hispanic-white audits, ‘‘advertised unit available’’ and ‘‘similar unit inspected’’ are the only behaviors for which discrimination is significant (in both cases at the 5% level). Overall, these results attest to frequent and statistically significant discrimination against minorities in many types of rental agent behavior. The types of agent treatment with relatively high probabilities of discrimination include those with a great impact on access to rental housing, such as making the advertised unit available and showing the advertised unit.

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Black-white audits Number of observations Estimate of discrimination (dw) t statistic Odds ratio Multivariate net incidence

Hispanic-white audits Number of observations Estimate of discrimination (dw) t statistic Odds ratio Multivariate net incidence

Advertised unit available

Number of units recommended

Advertised unit inspected

Number of units inspected

Asked to fill out application

259 0.606 1.502 1.834 0.058

576 0.684 2.816 1.982 0.117

303 0.736 1.906 2.088 0.075

446 0.732 2.464 2.061 0.102

390 0.668 2.567 1.951 0.080

Advertised unit available

Number of units recommended

Similar unit inspected

Number of units inspected

157 2.020 4.240 7.538 0.127

384 0.455 1.919 1.576 0.090

118 0.521 0.780 1.683 0.077

259 0.186 0.789 1.204 0.030

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Table 2 Test of the hypothesis that discrimination exists, HDS 2000

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

15

5.3. The causes of discrimination The dependent variables for the fixed-effects logits are the discrete treatments presented in Table 1. Brief descriptions of the explanatory variables, summary statistics, and expected signs for the coefficients (of the Z variables) in the fixed-effect logits are presented in Table 3. The results for the black-white audits are presented in Table 4 and the results for the Hispanic-white audits are presented in Table 5. A positive coefficient for a variable in Tables 4 and 5 indicates that the variable increases the probability that the white auditor receives more favorable treatment than the minority auditor. Coefficient estimates for difference variables (the XÕs) are not reported. Unless otherwise indicated, we focus on results that are significant at the 5% level based on a two-tailed test. 5.4. Tests of the agent-prejudice hypothesis 5.4.1. Black-white audits Three results support the agent-prejudice hypothesis in the black-white audits. Specifically, in the ‘‘advertised unit available’’ regression, we find less discrimination if the assigned income is high and the unit is in a minority neighborhood; in the ‘‘advertised unit inspected’’ estimation, we find significantly less discrimination when the agent is black; and in the ‘‘asked to fill out application regression’’ we find less discrimination against black females than against black males. We also find, however, that some results in the ‘‘asked to fill out application’’ regression are not consistent with the agent-prejudice hypothesis. In particular, we find that discrimination is higher when the agent is a broker, when the auditor is older, and when both the assigned incomes are high and the advertised unit is in a minority neighborhood.14 As discussed earlier, the agent prejudice hypothesis predicts negative signs in all of these cases. Agent prejudice obviously does not have a straightforward impact on this particular behavior. 5.4.2. Hispanic-white audits Two results support the agent-prejudice hypothesis in the Hispanic-white audits. In the ‘‘advertised unit available’’ regression, female Hispanics encounter less discrimination than male Hispanics, and in the ‘‘similar unit inspected’’ regression, female agents are significantly less likely to discriminate than are male agents. One result also contradicts this hypothesis: older agents are less likely to discriminate in ‘‘number of units recommended.’’ This result suggests that the role of experience in preventing discrimination may dominate the role of higher prejudice among older cohorts of agents.

14

As explained earlier, this result for the agent broker variable is consistent with the customer-prejudice hypothesis, but not a clear test of it. The result suggests that the incentives associated with attracting clients are more powerful than those associated with pleasing current tenants.

16

Table 3 Variables used in testing the causes of discrimination and expected signs Variable

a b c d e f g

White auditor in Hispanic-white audits

Predicted sign (by hypothesis)

Mean

Standard deviation

Mean

Standard deviation

Agent prejudice

0.341

0.039 0.018 0.033 0.128 1.994 0.616 1.357 0.437 35.74 0.568 0.023 0.070 0.122 0.671 0.016 0.324 0.556 0.367 1.564

0.194 0.132 0.181 0.334 0.895 0.482 0.643 0.494 11.52 0.495 0.151 0.252 0.322 0.478 0.123 0.464 0.492 0.265 1.371

(
) (+) (+) (0) (+) (
)

0.134

2.001 0.603 1.454 0.461 32.35 0.537 0.035 0.038 0.279 0.510 0.009 0.345 0.613 0.358 1.601

0.863 0.484 0.723 0.481 9.500 0.498 0.323 0.192 0.441 0.501 0.091 0.472 0.486 0.277 1.431

(+) (
) (
)

Customer prejudice

Statistical discrimination

(+) (0) (0)

(+) (0) (+)

(
) (?) (
) (?) (
) (?) (
) (+) (+)

(+) (?) (+) (?) (+)

(
) (+) (+)

Hispanic neighborhood is a dummy equal to one if the advertised unitÕs neighborhood is at least 15% Hispanic. Non-Hispanic neighborhood is a dummy equal to one if the advertised unitÕs neighborhood is less than 15% Hispanic. High income is a dummy equal to one if teammatesÕ assigned monthly income is greater than $7500. White neighborhood is a dummy equal to one if the minority composition of the advertised unitÕs neighborhood is less than 20%. Minority neighborhood is a dummy equal to one if the minority composition of the advertised unitÕs neighborhood is at least 20%. Middle income is a dummy equal to one if teammatesÕ assigned monthly income is between $2500 and $7500. Low income is a dummy equal to one if teammatesÕ assigned monthly income is less than $2500.

(+)

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Agent black Hispanic agent * Hispanic neighborhooda Hispanic agent * non-Hispanic neighborhoodb Non-Hispanic agent * Hispanic neighborhooda Agent age Agent female Agency size Agent broker Auditor age Auditor female High income * white neighborhoodc,d High income * minority neighborhoodc,e Middle income * white neighborhoodd,f Middle income * minority neighborhoode,f Low income * white neighborhoodd,g Auditor parent Auditor married Percent home-ownership Median house value ($100K)

White auditor in black-white audits

Table 4 Estimates of the causes of discrimination, black-white audits, HDS 2000 Variable

Number of units recommended

Advertised unit inspected

Number of units inspected

Asked to fill out applicationa

Estimate

t statistic

Estimate

t statistic

Estimate

t statistic

Estimate

t statistic

Estimate

t statistic

0.566 2.067

1.083 1.605

0.032 1.941

0.095 2.276


1.401 2.458


2.656 1.964


0.504 1.516


1.254 1.699

0.353 0.287

0.672 0.270


0.114


0.133

0.887

1.585


1.172


1.210


0.441


0.650

0.365

0.391

0.459

0.896


0.116


0.351

0.672

1.423

0.186

0.495


0.190


0.358

0.008
0.136
0.126
1.064 0.583 0.521 0.018
1.135
2.221
0.642
0.346
0.909
0.276 0.297 0.442 1.350

0.036
0.334
0.555
2.878 1.455 0.726 0.051
1.294
2.244
0.850
0.497
0.509
0.755 0.788 0.536 0.945


0.074
0.114 0.249
0.522 0.214
0.282
0.046 0.375
0.029 0.401 0.632 0.924 0.306
0.197
0.210 0.182


0.563
0.478 1.705
2.025 0.956
0.711
0.224 0.708
0.046 0.930 1.496 1.467 1.395
0.030
0.982 0.443


0.014
0.250 0.622
0.973 0.151 0.754
0.150 0.340
0.767
0.202
0.537 0.098
0.204
0.030
0.982
1.710


0.068
0.659 2.507
2.979 0.439 1.167
0.457 0.350 0.776
0.289
0.905 0.093
0.610
0.166
1.269
0.841

0.126
0.135 0.502
0.791 0.299
0.072
0.114
0.615
0.022
0.145
0.057 1.136 0.059
0.037
0.395 2.477

0.796
0.468 2.814
2.903 1.111
0.169
0.459
0.971
0.026
0.281
0.113 1.547 0.224
0.144
0.773 2.018

0.263
0.075
0.179
0.106 1.075 1.800
0.693 0.260 2.969 0.214 0.210
0.980
0.013
0.240
0.439
2.840

1.405
0.225
0.859
0.332 3.127 2.425
2.000 0.301 2.579 0.379 0.337
1.027
0.045
0.780
0.623
1.802

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Agent black Hispanic agent * Hispanic neighborhood Hispanic agent * Non-Hispanic neighborhood Non-Hispanic agent * Hispanic neighborhood Agent age Agent female Agency size Similar unit available Agent broker Log auditor age Auditor female High income * white neighborhood High income * minority neighborhood Middle income * white neighborhood Middle income * minority neighborhood Low income * white neighborhood Auditor parent Auditor married Percent homeownership Median house value/100,000

Advertised unit available

Note. Estimated with a fixed-effects logit model; all regressions also control for observed differences in teammate characteristics. a This regression also includes auditorsÕ true characteristics; coefficient estimates are reported in Table 6. 17

18

Table 5 Estimates of the causes of discrimination, Hispanic-white audits, HDS 2000 Number of units recommended

Similar unit inspected

Number of units inspected

Estimate

t statistic

Estimate

t statistic

Estimate

t statistic

Estimate

t statistic

0.350 1.534
0.526
0.956
0.040 0.445
0.325 0.229
0.887 1.004 0.787
0.294 1.028
1.032
0.003 0.317
1.300 1.407

0.164 1.600
1.447
1.513
0.118 0.870
0.519 0.191
1.966 0.661 0.376
0.261 1.043
0.603
0.001 0.569
1.261 0.687


0.393
0.593
0.420
0.201
0.037
0.070
0.347 0.053
0.286
0.183 0.522
0.325 0.071
1.510
0.363 0.228 1.200 2.946


0.459
1.390
2.502
0.697
0.224
0.195
1.121 0.091
1.282 0.223 0.612
0.482 0.166
1.043
1.264 0.823 2.049 2.127

2.528
1.331
0.075
2.565
1.043
0.948 0.738
2.547 0.857
3.334 1.862
2.527 1.985 —
1.790
0.186
0.360 1.300

1.063
0.891
0.153
2.692
2.285
0.766 0.772
1.427 1.234
1.214 0.672
1.157 1.194 —
0.942
0.195
0.193 2.376


0.420 0.351
0.190
0.009
0.178 0.349 0.135
0.538
0.259
0.857
0.751
0.880
0.258 — 0.385
0.334 0.545 4.688


0.451 0.700
0.910
0.026
0.814 0.840 0.351
0.702
0.770
0.791
0.701
1.047
0.352 — 1.026
0.972 0.732 2.340

Note. Estimated with a fixed-effects logit model; all regressions also control for observed differences in teammate characteristics.

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Agent black Hispanic agent Agent age Agent female Agency size Similar unit available Agent broker Log auditor age Auditor female High income * white neighborhood High income * minority neighborhood Middle income * white neighborhood Middle income * minority neighborhood Low income * white neighborhood Auditor parent Auditor married Percent homeownership Median house value

Advertised unit available

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

19

5.5. Tests of the customer-prejudice hypothesis 5.5.1. Black-white audits Several results support the customer-prejudice hypothesis in the black-white audits. First, the pattern of signs for the three variables interacting Hispanic agent with Hispanic neighborhood supports the customer-prejudice hypothesis over the agent-prejudice and statistical-discrimination hypotheses. The variable for a Hispanic agent in a Hispanic neighborhood is positive and significant at the 5% level in two regressions (‘‘number of units recommended’’ and ‘‘advertised unit inspected’’) and at the 10% level in a third (‘‘number of units inspected’’) and just above the 10% level in a fourth (‘‘advertised unit available’’). As this hypothesis predicts, neither of the other two interaction terms is ever significant. In other words, we find no support for the positive sign predicted by the agent-prejudice hypothesis for the Hispanic agent/non-Hispanic neighborhood variable or for the positive sign predicted by the statistical-discrimination hypothesis for the non-Hispanic agent/Hispanic neighborhood variable. The customer-prejudice hypothesis is also supported by our finding that there is more discrimination in ‘‘number of units inspected’’ in high-value neighborhoods, and that discrimination is lower for ‘‘advertised unit available’’ when the auditors have high income and the advertised unit is in a minority neighborhood. Once again, our results for ‘‘asked to fill out application’’ provide different signals, as the high income/minority neighborhood variable is positive and significant in this case, thereby contradicting this hypothesis. This provides further evidence that the incentives influencing discrimination in this type of behavior are quite different from the incentives influencing discrimination in the provision of information about available housing. In contrast, the customer-prejudice hypothesis is contradicted by our finding that larger agencies discriminate more in the two ‘‘inspection’’ regressions. At the very least, the broader client base of larger agencies does not adequately explain the link between agency size and discrimination. Recall that this agency size result does not arise because larger agencies have more units to show. In fact, our results for all types of agent behavior except ‘‘asked to fill out application’’ indicate that agencies with more units to show are less likely to discriminate. This result does not support any hypothesis about the causes of discrimination but does indicate that the agents with only one unit to show are the most likely to withhold housing from black customers. 5.5.2. Hispanic-white audits Several of our results for the neighborhood variables support the customerprejudice hypothesis in the Hispanic-white audits. First, as predicted by the customer-prejudice hypothesis, discrimination is higher in more desirable neighborhoods. Median house value is positive and significant in every regression except ‘‘advertised unit available’’ and the homeownership variable is positive and significant in the ‘‘number of units recommended’’ regression. Second, larger agencies are less likely to discriminate in showing similar units, controlling for

20

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

the number of units available. This result is the opposite of the one obtained for the black-white audits and suggests that the link between agency size and discrimination may be different in different metropolitan areas or for different ethnic groups. 5.6. Tests for statistical discrimination 5.6.1. Black–white audits Only one result in the black-white audits provides support for the statistical-discrimination hypothesis, namely, the positive, significant coefficient for median house value in the ‘‘number of units inspected’’ regression. This result is also consistent with the customer-prejudice hypothesis, but it suggests that agents may sometimes withhold apartments because they believe, contrary to the auditorÕs initial request, that a minority customer is unlikely to be willing or able to live in a wealthy neighborhood. One other result is consistent with statistical discrimination, but not a clear test of it. To be specific, the finding of higher discrimination when auditorsÕ incomes are high and the advertised unit is in a minority neighborhood is consistent with statistical discrimination (but not with customer prejudice) if the impact of customer income on agentsÕ incentive to discriminate is greater than the impact of neighborhood minority composition. 5.6.2. Hispanic-white audits As discussed earlier, the coefficient for median house value is positive and significant in three regressions. Like the comparable result for the black-white audits, this result supports the statistical-discrimination hypothesis (as well as the customer-prejudice hypothesis). 5.7. Results for auditorsÕ true characteristics In preliminary analysis, the null hypothesis of no impact generally could not be rejected for the true auditor characteristics, whether included in the X variables to account for differences between teammates, or in the Z variables, which explore variation in discriminatory behavior. In fact, the only estimation in which this null hypothesis could not be rejected was the ‘‘asked to fill out application’’ treatment in the black-white audits. Moreover, including true auditor characteristics among the X and Z variables has virtually no impact on either the multivariate net incidence measure or the tests of hypotheses about the causes of discrimination. Thus, in the interest of parsimony, none of the estimations except for ‘‘asked to fill out application’’ include auditorsÕ true characteristics. The results for these variables in the ‘‘asked to fill out application’’ regression are presented in Table 6. Even though several of the variables in Table 6 are statistically significant, including them in the regression still has little impact on either the estimated incidence of discrimination or on the results of hypothesis tests concerning the causes of discrimination.

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

21

Table 6 Coefficient estimates for auditorÕs true characteristics, HDS 2000 Variable

Asked to fill out application (black-white audits) Differences

Currently employed Home owner Audit experienced Highly educated Lives in metropolitan area Searching for home Real income Born in foreign country

Levels

Estimate

t statistic

Estimate

t statistic


0.859 0.447 0.035
0.007
0.620 0.093
0.150 1.762


2.211 1.189 0.081
0.080
0.724 0.234
1.129 3.243

1.289 0.051
0.114
0.080 0.775 1.551
0.183
2.676

2.188 0.091
0.172
0.599 0.648 1.979
1.038
2.880

5.8. Summary Like most previous studies, we find evidence that discrimination in rental housing has more than one cause. Several results support the view that agent prejudice sometimes leads to discrimination, and several others support the view that discrimination sometimes arises from agentÕs efforts to please their prejudiced actual or potential customers. A few of our results are also consistent with the existence of statistical discrimination. Our new variables tend to support the customer-prejudice hypothesis. In most of the types of behavior we study, discrimination against blacks is higher when the agent is a Hispanic working in a Hispanic neighborhood, where he or she is likely to cater to prejudiced Hispanic customers, than when the agent is white or black or the neighborhood non-Hispanic. Also as predicted by this hypothesis, discrimination against blacks in making the advertised unit available is relatively low when the auditorsÕ assigned income is high and the advertised housing unit is in a minority neighborhood. Another new variable, whether the rental agent is a broker, is positive and significant in one case, namely, whether the auditor was asked to fill out an application. This result contradicts the prediction of the agent-prejudice hypothesis, but is consistent with (if not a strong test for) the customer-prejudice hypothesis. In the case of the Hispanic-white audits, the clearest result is that for most types of agent behavior we examine, discrimination is higher in wealthier neighborhoods. For the number of units recommended, discrimination is also higher in neighborhoods with a higher homeownership rate. These results are consistent with the customerprejudice hypothesis, and the first one, at least, is consistent with the existence of statistical discrimination as well.

6. Trends in rental housing discrimination To shed light on recent trends in rental housing discrimination, we now compare the results for HDS 2000 and HDS 1989. This paper and Ondrich et al. (1999) share

22

dw (Standard error)

Agent behavior

2000 c

Advertised unit available

Number of units recommendedd Advertised unit inspectedc Number of units inspectedd a

0.606 (0.403) 0.684** (0.243) 0.736* (0.386) 0.723** (0.293)

Simple net incidencea

1989 **

0.572 (0.207) 1.084** (0.169) 0.938** (0.211) 1.211** (0.200)

Multivariate net incidenceb

2000

1989

2000–1989

2000

1989

2000–1989

0.050

0.070


0.020

0.058

0.119


0.061

0.059

0.155


0.096

0.117

0.212


0.095

0.049

0.125


0.076

0.075

0.224


0.149

0.074

0.161


0.087

0.102

0.206


0.104

The share of audits in which white auditor receives favorable treatment minus the share of audits in which black auditor receives favorable treatment. Net incidence measure that accounts for observable differences between teammates, based on the assumption that the probability that a black customer is treated favorably falls short of the probability that a white customer is treated fairly by a fixed amount. c The HDS 1989 results come from Tables 1 and 2 of Ondrich et al. (1999). d Estimation for 1989 performed by authors. * Significant at the one-tailed 5% level. ** Significant at the one-tailed 1% level. b

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Table 7 Comparing the HDS 2000 results with the HDS 1989 results, black-white audits

dw (Standard error)

Agent behavior

2000 c

Advertised unit available

Number of units recommendedd Similar unit inspectedc Number of units inspectedd a

Simple net incidencea

1989 **

2.020 (0.467) 0.455* (0.205) 0.521 (0.737) 0.186 (0.235)

**

0.830 (0.235) 0.459** (0.130)
0.098 (0.206) 0.390** (0.157)

Multivariate net incidenceb

2000

1989

2000–1989

2000

1989

2000–1989

0.078

0.090


0.012

0.127

0.152


0.025

0.090

0.112


0.022

0.090

0.096


0.006

0.020

0.019

0.001

0.077


0.018

0.095

0.075

0.100


0.025

0.030

0.069


0.039

The share of audits in which white auditor receives favorable treatment minus the share of audits in which black auditor receives favorable treatment. Net incidence measure that accounts for observable differences between teammates, based on the assumption that the probability that a black customer is treated favorably falls short of the probability that a white customer is treated fairly by a fixed amount. c The HDS 1989 results come from Tables 1 and 2 of Ondrich et al. (1999). d Estimation for 1989 performed by authors. * Significant at the one-tailed 5% level. ** Significant at the one-tailed 1% level. b

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

Table 8 Comparing the HDS 2000 results with the HDS 1989 results, Hispanic-white audits

23

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S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

four common dependent variables in both the black-white audits and the Hispanicwhite audits. These variables, which are listed in Tables 7 and 8, provide an overview of treatments concerning housing availability and sales effort. Because Ondrich et al. (1999) and the present study share econometric methodology and are based on similar data collection efforts, the results of the two studies are comparable. Tables 7 and 8 present the estimates of dw, significance tests for dw, and both simple and multivariate net incidence measures of discrimination.15 Table 7 presents the results for discrimination against blacks, while Table 8 presents the results for Hispanics. As explained earlier, a significance test for dw is a test of the hypothesis that discrimination exists. For the black-white audits, HDS 2000 uncovers discrimination in three of the four treatments (two at the 1% level and one at the 5% level), while HDS 1989 found discrimination in all four treatments at the 1% level. In the black-white audits, the null of no discrimination for ‘‘advertised unit available’’ is rejected at the 1% level in HDS 1989 but is not rejected in HDS 2000. For the Hispanic-white audits, both HDS 2000 and HDS 1989 uncover discrimination in ‘‘advertised unit available’’ and ‘‘number of units recommended’’ at the 5% significance level or above. The null of no discrimination for ‘‘number of units inspected’’ can be rejected in HDS 1989, but not in HDS 2000. In HDS 2000, the multivariate net incidence measure for discrimination against blacks ranges from 5.8% for ‘‘advertised unit available’’ to 11.7% for ‘‘number of units recommended.’’ These results are substantially lower than the corresponding measures from HDS 1989, which range from 11.9% for ‘‘advertised unit available’’ to 22.4% for ‘‘advertised unit inspected.’’ The drops in this incidence measure range from 6.1% points for ‘‘advertised unit available’’ to 14.9% points for ‘‘advertised unit inspected.’’ Taken together, these results suggest that discrimination against blacks has declined significantly over the last 10 years, but that considerable discrimination still exists. Changes in discrimination are considerably smaller in the case of Hispanics. Between HDS 1989 and HDS 2000, for example, the multivariate net incidence measure hardly changed for ‘‘number of units recommended’’ or ‘‘advertised units available,’’ and the probability of discrimination remains at roughly 10% in both cases. The largest change occurred for ‘‘number of units inspected,’’ which saw a decrease in the multivariate net incidence measure of 3.9% points over this period, and now shows no significant discrimination. The multivariate net incidence measure actually increased by 9.5% points for ‘‘similar units inspected,’’ but we cannot reject the null of no discrimination in either HDS 1989 or HDS 2000.

7. Conclusion Using HDS 2000 data, we analyze the incidence and the causes of rental housing discrimination against blacks and Hispanics. These data make it possible to update

15 To calculate net incidence, we exclude non-newspaper advertised samples from HDS 2000, because in HDS 1989 only newspaper advertised units were used. See Turner et al. (2002).

S.J. Choi et al. / Journal of Housing Economics 14 (2005) 1–26

25

results from HDS 1989 and, unlike earlier audit data, they contain information on auditorÕs true characteristics. We find evidence of discrimination against blacks in 2000 for several key types of behavior by rental agents, but the incidence of discrimination against black customers decreased significantly between 1989 and 2000. We also find that agents discriminate against Hispanics in making advertised units available and that since 1989, discrimination against Hispanics has declined for some types of behavior, but not by very much. Like previous studies, we find that discrimination in rental housing has several causes and that these causes vary across types of agent behavior. Our results are essentially unaffected by controls for auditorÕs true characteristics. This paper sheds light on several new aspects of the causes of discrimination. We discover that rental brokers are sometimes more likely than property managers to discriminate against black customers, but this result does not have a clear link to any hypothesis about the causes of discrimination. In most types of treatment in blackwhite audits, we find that Hispanic agents discriminate against blacks when they seek rental housing in largely Hispanic areas. This result is consistent with the view that Hispanic agents cater to the preferences of a prejudiced Hispanic clientele. Thanks to some combination of social changes, changes in rental housing markets, and a stronger fair housing enforcement system, discrimination in rental housing has declined in recent years. Nevertheless, the evidence presented in this paper shows that discrimination in rental housing continues to restrict the opportunities of black and Hispanic households and continues to be supported by systematic factors that influence the incentives of rental agents. Both scholars and policy makers should continue to investigate discrimination in rental housing and its causes.

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