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Racial Housing Price Differential and Racially Transitional Neighborhoods

by Carmen Augustine TP_AUGUSTINECARMEN

            The question of whether black renters and homeowners face a different set of housing prices than their white equivalents is addressed in several studies through the period of integration and beyond. Past studies have drawn conflicting conclusions, some pointing toward a discount for black housing and others suggesting there is a premium paid by blacks. The present study is a response to Follain and Malpezzi’s 1981 review of racial price differentials in Chicago from 1974-1976, which reported significant discounts to black renters and owners in a majority of regions in Chicago. The survey omitted racial composition of each region as well as neighborhood control variables, which Chambers believes may have a significant effect on price differentials. For example, it may be the case that black renters and owners more often live in older or otherwise less desirable neighborhoods, which are factors outside of race that would cause the price of housing to be reduced.

Chambers additionally distinguishes between three types of racial housing differentials. A household differential exists “if black households pay more than white households for the same housing in the same market.” (Chambers, 216) Another cause of differential may be exclusion of black families from certain neighborhoods, raising demand for housing (and with it prices) in neighborhoods supporting a larger black population. The third cause is white households that prefer areas of more homogenous racial composition, causing housing demand to go up in less integrated areas and with it prices.

In order to more thoroughly assess the racial price differential, Chambers uses data from the 1975 Annual Housing Survey for Chicago as well as linked data from the 1979 survey to look at the effect of housing structure, neighborhood characteristics, renter/owner racial characteristics and neighborhood racial characteristics on the price of housing. This is an extension of the 1981 Follain and Malpezzi model that excluded both racial and non-racial neighborhood characteristics.

Presentation of the Model

Chambers first presents the following model to describe changes in housing price, Ei:

Ei = f(Hi, Zj, BLACKi, RCBj)

Where H represents housing structure characteristics, Z represents non-racial neighborhood characteristics, BLACK represents race of the renter or owner (a dummy variable equal to 1 if head of the household is black) and RCB is the percentage of housing units occupied by a black household. The subscript i represents one housing unit and the subscript j represents one neighborhood. H is a vector that includes many variables describing housing—number of rooms, presence of amenities such as air-conditioning and heating, parking and age (for a complete list, see Chambers 228-230). Z is a vector that includes average aggregate variables for a neighborhood, including household income, education level of the household head, neighborhood quality, property tax rate and others (for a complete list, see Chambers 231).

The model is estimated as a linear semi-log equation:

(1) ln(Ei) = a + bHi + cZj + dBLACKi + eRCBj + u1

Where each of the variables b, c, d and e are a coefficient on one of the independent variables and u1 is an error term. In relation to the comparable Follain and Malpezzi equation:

ln(Ei) = a + bHi + dBLACKi + u2

It can easily be seen that omitting the variables Z and RCB would cause the error term, u2, to be much higher than u1 if Z and RCB are significant. This is because if those two variables do in fact affect housing price, their effect will turn up in u2. Additionally, if either Z or RCB is correlated with H or BLACK we can state that the independent variables are endogenous in the Follain and Malpezzi model, which violates an assumption of OLS estimation and implies that our estimated coefficients (b and d) are biased. Chambers further speculates that the effect of omitting Z and RCB is a downward bias on the coefficient d. Z and BLACK are negatively correlated, and we anticipate the coefficient on Z to be positive (as neighborhood quality improves, price increases) yielding a negative bias. Similarly, RCB and BLACK are positively correlated but we anticipate the coefficient on RCB to be negative (as percentage black increases price of housing decreases), causing an additional negative bias. Thus, we expect Follain and Malpezzi to have lower estimates of the effect of race on housing price than the complete model.

Using data available from both 1975 and 1979, Chambers estimates the restricted Follain and Malpezzi model as well as his revised model. He finds that omitted variables bias the coefficients on BLACK and RCB downward. In the complete specification, the effect of race on rental prices almost disappears (drops to 0.3% premium in 1975 and 0.2% in 1979) and the effect of race on housing prices for owners drops significantly in both years, where it was strongly negative in the incomplete model. The effect of RCB on rental and housing price is negative in all cases except that for renters in 1975, suggesting that as the percentage of units occupied by blacks increases cost of housing decreases.

The results suggest that the observed black household discount is likely a result of racial composition and other non-racial amenities rather than race of renter or owner.

Addition of Racial Submarkets

Chambers breaks down housing price further by racial submarkets, speculating that each region may have an independent average price of housing and thus regions should not be aggregated across the entire Chicago area. Submarkets are divided into black ghetto with an average of 81% black occupied housing, black border with an average 27% black occupancy, Spanish submarket which is predominately Spanish but has 3% black occupancy and white interior submarket with 2% average black occupancy.

The updated model includes housing unit i, neighborhood j and submarket k as follows:

(2) ln(Ei) = a + bHik + cZj + dBLACKik + eRCBj + fRCBSQj + gSUBk + u3

The terms added in this iteration of the model include RCBSQ, which is a quadratic term reflecting racial composition, and SUB which is a dummy variable for each submarket. The coefficient g on SUB displays price differentials between submarkets.

The 1975 results suggest that rental prices are higher for black renters than white renters in the ghetto and Spanish submarkets, and lower for black renters in the black border and white interior submarkets. This is somewhat aligned with the theory that increased demand in areas supporting a larger black population cause prices to rise. In 1979 there is no black renter premium in the ghetto submarket, but a premium emerges in the black border submarket, which may reflect an “entrance fee” into communities bordering on racial transition.

For owners, the findings suggest that black households are given a discount in ghetto, black border, and white interior submarkets in both 1975 and 1979. This is not consistent with the “entrance fee” hypothesis, and may instead be a result of black owners purchasing homes in racially transitional neighborhoods within a submarket that are in lower demand.

The relationship between racial composition of the neighborhood and price was inconsistent across years for both owners and renters based on both the RCB and RCBSQ terms. Additionally it is not clear that a price differential exists between submarkets, as evidenced by the changing sign of coefficients on the dummy variables GHETTO and BLKBORD, representing homes in ghetto or black border submarkets.

Addition of Racial Transition Factor

To take into account the hypothesized discount due to racial transitioning within a neighborhood, Chambers adds a factor RTB to model (2) based on change in percentage of neighborhood population that is black from 1975 to 1979.

(3) ln(Ei) = a + bHik + cZj + dBLACKik + eRCBj + fRCBSQj + gSUBk hRTBj + iRTSj+ u3

The coefficient on RTB is negative and statistically significant, indicating that there is a discount on housing based on the level of racial transition within the neighborhood—neighborhoods experiencing more transition over the period had overall lower housing prices for both renters and owners. Looking at the same transition in percent occupied by Spanish residents (RTS), there is also a significant discount in housing price in neighborhoods with higher RTS.

Implications and Extensions

The study suggests that racial housing differentials are overstated in models that fail to include racial and non-racial neighborhood characteristics. The addition of a racial transition factor specifies that neighborhoods experiencing higher rates of racial transition offer an overall price discount, which may account for some of the observed price discount to black households.

The discount in racially transitioning areas implies lower demand for housing, which in turn suggests that integration of black and white households may be encountering some social barriers—whether consciously or not, households prefer to be in racially homogenous areas. One obvious extension of the present model is to look at racial price differentials in more recent years, as well as the 90s and the changes over the entire life of the study (1975-present). Additionally, it would be interesting to extend the survey beyond Chicago and look at other racially integrated urban areas and the difference between cities—for example, whether integration has been more complete in the northern or southern US and whether certain cities are more open to integration than others. I would also be interested in seeing the application of model (3) to Asian minorities in cities receiving a larger volume of Asian immigrants.


D.M. Chambers. 1992 (September). “The racial housing price differential in racially transitional neighborhoods.” Journal of Urban Economics: 214-232.

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