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Gentrification and Racial Discrimination in the Housing Market

Judy Mulusa

Howard University1

Abstract

This paper looks at racial discrimination and gentrification in the housing market. The paper seeks to determine the effect of gentrification on housing options for African Americans, i.e. what is the extent of racial discrimination in the housing market and does gentrification exacerbate this effect and if it does is there a variation across households of different income groups. In the paper I modify a search model to incorporate effects of gentrification on search costs and use it to test various theories of displacement, segregation and gentrification. Using American Housing Survey data for the Washington DC metropolitan area this paper documents the decrease in housing options for African Americans as they are displaced from the inner cities by the process of gentrification and made to pay more for housing in the suburbs. The findings show that if sellers respond to racial prejudice housing is being purchased by blacks in black neighborhoods at higher prices and the maximum price differential is an increasing function of the fraction of white seller’s who are averse to dealing with blacks, hence an increasing function of gentrification in a neighborhood.

Keywords: Gentrification, Racial Discrimination, Housing options

1 Preliminary and Incomplete

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1 Introduction

Many studies have examined whether African Americans pay more for housing than whites in

U.S. cities and metropolitan areas. Ross, (2008) notes that studies from the 1960’s2 tend to find

evidence that blacks pay more for housing while studies from the 1970’s3 find little or no

evidence of housing being more costly for blacks. These findings are confirmed by Glaeser and

Vigdor (1999) who find a premium in the first half of this century that fell between 1950 and

1970 and completely reversed in the 1990’s. However several authors continue to question these

findings. Fischer (2003) argues that any decline in segregation identified in the aforementioned

studies is attributable to improvements in the inner cities but suburban segregation remains

persistent and this continues to impact housing options for minorities.

Apart from segregation, gentrification is another factor that has impacted the housing options for

different households particularly in the central city. Traditionally minorities have resided in the

central cities in most of the metropolitan areas of the US, hence the effects of gentrification

would definitely impact these households, however literature remains unclear on the direction of

this effect. Does gentrification lead to displacement of particular households? If it does this

would impact the housing options for these households in the central city. Studies on effects of

gentrification have given varied conclusions; Vigdor 2001, Mckinish et al 2010, Freeman and

Bracconi (2004), Newman and Wyly (2006) and Atkinson (2000).

Is racial segregation persistent and if so does the process of gentrification exacerbate the effects

of racial segregation to reduce housing options for minorities particularly the blacks? Using a

bid rent analysis and incorporating spatial regression analysis this paper determines the effect of

discrimination and gentrification on the prices of housing for different groups of black

households. The hypothesis is that blacks are made to pay more on the house prices in the

suburbs due to racial segregation and gentrification and this effect varies with the income group

of the black household. The research questions are:

(1) What is the extent and trend of the housing discrimination in the DC housing market

(2) Are there price differentials due to discrimination or prejudice 2 King and Mieszkowski 1973, Yinger 1978 3 Schnare, 1976, Follain and Malpezzi, 1981)

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(3) How does discrimination vary across the income groups

(4) Does gentrification exacerbate the effects of housing discrimination for minorities

(5) What is the extent of the spatial heterogeneity and dependence in the DC housing market

Research in the areas of racial segregation and gentrification has failed to come to a consensus

and this means that it is not clear what direction the policy in housing market should take. The

advantage of the bid rent analysis approach is that it incorporates the heterogeneous aspect of

housing stock into the analysis. Viewing housing as a bundle of housing attributes puts the

probable effects of housing market discrimination into perspective showing that discrimination

varies with housing attributes. Price determination can be heterogeneous. Heterogeneity can

originate from demand, supply, institutional barriers or discrimination each of which can cause

differentials across neighborhoods in the way housing attributes are valued by consumers and

house prices determined.

In contrast spatial dependence will lead to spatial autocorrelation due to contagion effects. “All

places are related but nearby places are more related than distant places”. The spatial

relationships may lead to; the bid rents in adjacent locations affecting each other or the bid rents

in one location may be affected by the value of one of the independent variables in a particular

location or the errors in estimates may be spatially correlated. A spatial regression model

includes relationships between variables and their neighboring values. Incorporating spatial

regression tells us if there is an unmeasured process that manifests itself in space that affects the

bid rent. Hence incorporation of spatial regression analysis in this study will lead to more

dependable coefficients as they are no longer biased or inconsistent.

The paper modifies a search cost model (Courant 1978) that shows how a sellers aversion to

blacks affects the search cost for housing. In this paper we alter the model so that it not only

shows the effect of sellers’ aversion but also shows the effect of increased gentrification on

search costs for housing. The implications of the model are:

Sellers’ aversion to blacks leads to price differentials resulting in blacks paying

more for housing in black neighborhoods

Gentrification increases price differentials between blacks and whites

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Price differentials are an increasing function of white sellers in a neighborhood

who are averse to dealing with blacks (the degree of discrimination is determined

by the white population share)

Price differentials lead to segmentation by neighborhood racial composition

To analyze these effects the paper determines the price differentials for blacks and whites using a

bid rent analysis. The price difference gives a measure of discrimination. The paper then uses a

logistic regression model to document the displacement of blacks from the central city. The

logistic model incorporates various factors that affect displacement of households including a

gentrification dummy that identifies gentrifying and non gentrifying zones.

The findings show that blacks are paying more for housing in the different household stratum

and that the process of gentrification raises the bid rent for black households. The logistic

regression showed that the log odds of being displaced increased for black households compared

to white households.

2 Literature Review

2.1 Gentrification

Gentrification has prompted a wide range of research investigations beginning with a debate over

the simple definition of the term. Others have asked about causes of this process, with some

writers emphasizing the role of developers seeking to benefit from closing the “rent gap”. Others

have attributed this form of neighborhood transition to the demand structure of prospective

gentrifiers, while others have focused on government policy as the facilitating intermediary

making gentrification happen. Often these studies implicitly suggest that the long-standing

residents lack voice, allowing developers, gentrifiers, and political elites to have their way with

the neighborhood. The third type of study examines the effects of the gentrification process on

the original residents and on the structure and culture of a gentrified community.

Gentrification is most often associated in the literature with displacement of long-standing

residents due to economic changes that such residents cannot afford such as higher rents or

higher property taxes (Freeman and Braconi 2002, Newman and Wyly 2006. More complex

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concepts of gentrification suggest that there is an indirect displacement effect due to cultural

shifts in the neighborhood. There remains an issue about whether in fact traditional residents are

displaced quickly and in large numbers.

Freeman and Braconi (2004) used two approaches to assess the degree of displacement; studies

of succession (Henig 1980 and Spain et al 19804) that show how the socioeconomic

characteristics of in movers differ from those of out-movers and surveys (Grier and Grier 1978,

Newman and Owen, 1982 and Lee and Hodge 19845) that ask residents why they moved from

their former residence. Both approaches have weaknesses. Succession studies can verify that

gentrification is underway but cannot demonstrate how the process occurs. Surveys on reasons

for moving fail to identify the previous residences of the respondents, making it hard to explain

whether moves were caused by gentrification or other factors. Freeman and Braconi (2004) try

to improve on these past techniques by examining the relationship between residence in a

gentrifying neighborhood in New York City and residential mobility among disadvantaged

households. They hypothesized that higher mobility rates would be observed among

disadvantaged households (based on income and education) residing in gentrifying

neighborhoods than among those residing elsewhere in the city. They used a multivariate model

to control for age, marital status, race, gender, income, employment status, educational

attainment, and characteristics of housing unit. Their results, contrary to their expectations,

showed lower rates of mobility among poor and less educated people in gentrifying

neighborhoods. Their two alternative explanations were that, perhaps, the poor lacked affordable

housing alternatives in nearby locations, or that as the neighborhood gentrified it was appreciated

more by both the disadvantaged and affluent residents reducing the former’s desire to move.

Their conclusion was that gentrification does not cause the displacement of low income

households, but they affirmed that the low income households are affected indirectly as the pool

of low rent houses is reduced by the arrival of middle income earners.

Vigdor (2001) used a similar approach to assess whether gentrification caused displacement of

low income earners. He compared the poor and less educated residents in gentrifying and non-

gentrifying areas. Using the American Housing Survey data for the Boston Metropolitan area for

4 In Freeman and Bracconi (2004) 5 ibid

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the period 1985-1989, he employed a probit regression to test whether low status households

(defined by educational attainment) are more likely to exit housing units in gentrifying zones

relative to other parts of the Boston metropolitan area. The dependant variable was set to one if

a household observed in the initial year (1985) continues to reside in the same housing unit in the

final year (1989). If the housing unit is vacant, occupied by a different household, or has ceased

to be habitable the dependant variable is set equal to 0. The control variables were income level,

tenure status, whether the unit is subsidized, existence of rent control and whether it is part of a

public housing complex. Like Freeman and Braconi, he found that there was not a higher exit

rate for poor households living in a gentrified neighborhood.

McKinnish et al. (2010) used confidential census data (1990-2000) to study the demographic

processes underlying gentrification in low-income urban neighborhoods in the 1990s. Their aim

was to establish whether these demographic processes are consistent with the contention that

they cause displacement and harm to low-income and minority households. Their findings

indicate that gentrification is associated with disproportionate in-migration of college graduates,

particularly white college graduates. Examination of out-migration, however, gave no evidence

of a disproportionate exit of low-education or minority households. They conclude that, rather

than dislocating minority households, gentrification of predominantly black neighborhoods

creates neighborhoods attractive to middle-class blacks. They noted that because these

neighborhoods are experiencing income gains and are more racially diverse than established

middle class neighborhoods, they become a more desirable location for black middle-class

households.

Newman and Wyly (2006)undertook an evaluation of displacement in New York City using a

mixed method incorporating quantitative measurement of data and field interviews using the

same data set as Freeman and Bracconi (2004). Their findings gave slightly higher displacement

rates than Freeman and Braconi (2004) but they did agree that not all low income residents were

displaced by gentrification. They explained that some of the low income households are

protected from direct displacement by regulatory mechanisms. They note however that with

time the low income households may be forced out once the affordable housing protection is

done away with.

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Atkinson (2000) measured the extent of gentrification induced displacement in London using the

1981 and 1991 ward level census data. Gentrification was measured by changes in proportion of

professionals and managers while displacement was measured using decline in size of the

working class, the unskilled, renter households, unemployed, the elderly and the single parents.

His results showed a positive relationship with the displacement variables except for single

parents and unemployment. In a later paper Atkinson (2000) regressed the proxy variables for

gentrification against the displacement variables and his findings showed a high correlation

between gentrification and displacement. However, Hamnet (2003) carried out a longitudinal

study of inner London and negates the displacement effects of gentrification. He relates the

reduction in the working population in inner city London to a general long term reduction of

working class households in London as a whole. He argues that Atkinson ‘confuses’

replacement with displacement, asserting that what is taking place in London (at the time) is

replacement which is the result of long term industrial and occupational change and not a result

of gentrification.

Davidson (2008) criticizes what he describes as the singular conceptualization of gentrification.

He states that looking only at direct displacement without regard for the effect of indirect

displacement limits the understanding of how the transition process takes place in gentrifying

neighborhoods; the political, social and cultural neighborhood changes can induce displacement

as well. Lees (2008) questioned whether social mixing is positive. She critiques current policies

on social mixing (like HOPE VI) and suggests that social mixing policies are not likely to lead to

an inclusive urban renaissance. Davidson (2008) noted that the success of social mixing policy

may transform the socioeconomic status of deprived neighborhoods, leading them to gentrify

further and displace low income earners. He also notes the importance of indirect displacement.

2.2 Racial Discrimination and Housing

One additional factor that this essay seeks to incorporate is the effect of racial discrimination.

The basic urban model assumes racial discrimination has no important effects in welfare of black

households on in the functioning of urban housing markets. When housing is viewed as a bundle

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of housing attributes this suggests that racial discrimination varies in magnitude among housing

attributes and among bundles of different composition (Kain and Quigley 1975).

Distinguishing between price discrimination and prejudice, it is noted that price discrimination is

an action or behavior while prejudice is a feeling or attitude. Price discrimination is seen when

houses sold to non whites are priced higher than identical housing to whites while prejudice is

seen when house prices are impacted by a change in the racial compositions in the neighborhood.

Schafer (1979) discusses three theories of racial discrimination in the housing market. In the

first theory the pure racial price discrimination prices of identical housing units differ for whites

and blacks. This happens because landlords charge more to blacks than whites due to their own

racial prejudice. It has also been referred to as supplier price discrimination. This theory

predicts that blacks will pay more than whites for identical housing. If blacks and whites do not

live in the same neighborhood they may not consume identical housing, therefore to find

evidence of discrimination it is important to control for neighborhood effects. In a regression

inclusion of dummies for the race of the household head may capture this type of discrimination

though Bayer and Mcmillen (2008) notes that race is not completely capitalized into housing

prices due to what they describe as the endogeneity of neighborhood socio-demographics’.

The second theory is based on differences in preferences of blacks and whites for the racial

composition of their neighborhoods. The theory assumes that blacks prefer integration while

whites prefer segregation and is referred to as the prejudice theory. This results in whites paying

more to live far from blacks while blacks will pay more to live near the whites. The result is

white prices are high in the interior of white neighborhoods and lower at the white black

boundary. On the other hand black prices are higher at the black white boundary than in the

black interior. This theory results in prices differences at the neighborhood level rather than at

the individual level. Presence of prejudice will lower prices so that in a model a variable

representing percent in a neighborhood who are black is assumed negative. Since degree of

prejudice varies the magnitude of such a coefficient will vary across neighborhoods, Yinger

therefore suggests that in measuring prejudice the racial composition variable should be

interrelated with a dummy measuring whether a neighborhood is predominantly black or white.

This is because the racial composition coefficient tells about the effect of prejudice but does not

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divide the price differential into portions that are a result of black and white attitudes.

Interacting the variable with the dummy will separate these effects.

King and Mieszkowski (1973) note that differences in house prices for blacks and whites could

be a result of differences in the cost of providing housing. Blacks will tend to have larger family

sizes and this creates uncertainty of rental payments because of low and fluctuating incomes and

higher incidence of female headed households. Hence it is important to be able to establish how

much in the difference in price is due to discrimination and how much to due to higher costs.

This can be accomplished by use of a control for the varying cost of production using socio

economic characteristics. Data on family size can be used to test the probability that larger

families pay more rent because of their intensive se of housing. A racial variable in this case will

reflect any higher cost due to crowding and additional cost due to racial discrimination.

Education attainment can be used as a proxy for class status, since higher classes obtain units at

lower prices since they may cause less damage. In an imperfect market education may serve as a

pseudo supply variable representing knowledge of the market hence would be negatively related

to rent but in the case of demand it will be positively related to rent.

The final theory explains that blacks will pay a premium to add housing in the ghetto due to

collusive behavior by the white landlords. The collusive behavior that shuts blacks out of some

markets makes prices higher in black submarkets than for similar housing in white markets. This

is also referred to as segregation in literature and may be due to the first two theories as well as

due to non price discrimination for example redlining, steering of black home seekers to certain

neighborhoods, mortgage restrictions and even government confinements. This exclusion of

blacks leads to inter-neighborhood price differentials for instance with the blacks being confined

to the ghettos when their population increases the prices will go up and they end up paying more

than what whites are paying for similar housing. If segregation is due to supply price

discrimination (the first theory) one would expect to find blacks to pay more than whites for

housing in white neighborhoods while the housing in black neighborhoods will be priced higher

than that in white neighborhoods. If segregation is due to prejudice one would find housing

values fall with increases in the composition of blacks in a neighborhood but black

neighborhoods still cost more.

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The three theories have different implications. Supplier price discrimination predicts intra

neighborhood price differences while prejudice predicts inter neighborhood price differences

between neighborhoods of different racial composition. In this case to examine racial

differentials in housing price it is necessary to control for racial composition of neighborhoods

within which a house is located. Then it is possible to separate inter neighborhood and intra

neighborhood outcomes and obtain evidence about presence and nature of discrimination. The

key to understanding discrimination is to find out if blacks pay a different price from whites.

This requires controlling for characteristics of the house and surrounding neighborhoods.

Economic theory predicts discrimination can produce price differentials within a neighborhood

and prejudice and segregation produce price differentials between neighborhoods.

It is therefore noted that apart from higher prices an array of factors will deter blacks from

seeking housing outside the ghetto. This will mean that certain house bundles are altogether

unavailable to black households. This makes it difficult to impute a monetary cost for these

unobserved transactions hence making it difficult to empirically determine the magnitude of

discrimination.

Its against this background that this study seeks to establish the effects of gentrification and

racial discrimination in the housing market. Is there a chance that the two forces would

reinforce each other particularly in the case of African American households to reduce their

housing options?

3 Theoretical Model

The theoretical framework for this paper is a modification of a search model derived from

Courant (1978). This is a model in which the long run competitive equilibrium if there is

aversion among only some whites to living with and dealing with blacks may involve a

segmented market with all blacks receiving housing on terms inferior to those obtained by

whites. The model assumes that buyers know the distribution of housing available and their

utility functions. This is evaluated as:

( ) = 1 2.8.1

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V is the lowest utility and V̄ is the highest. The cumulative distribution of f(V) is F(V). Given

that buyers have different tastes over housing characteristics there will be a distribution of

housing characteristics available in the market and hence a non degenerate f(V) facing each

buyer. This distribution will not spike unless the market offers only one type of house. The cost

of looking for one house is constant at C. At any point in time the utility of looking for one more

house leads to utility such that

= max − + 1 − ( ) ( ≥ ) + ( )⁄ 2.8.2

In equation 2.8.2 V0 is the utility associated with the best house that has been found. The second

line is the expected utility after one more search, the terms in it are; C the cost of search, the

second term is the probability of finding a better house times the expected utility of better houses

conditional on finding such a house and the third term is the probability of not doing better times

the utility of not doing better which represents the utility of the best house thus far found. The

model assumes that there is no cost of reserving the best house found. A rational buyer stops

searching when the first line of the right hand side of the equation is equal to or greater than the

second line. The two lines are equal when

= ( − ) 2.8.3

Such that the expected gain in utility derived from further search will be just equal to the loss in

utility associated with the cost of search.

Sellers aversion is introduced in the model assuming there are n neighborhoods in a city, and for

each neighborhood in the city in which there are whites who have houses for sale, blacks

perceive that there is a nonzero probability γj (where j indexes the neighborhood) that whites in

that neighborhood will be unwilling to sell to them at current market prices. The probability that

blacks will be unwilling to sell to other blacks at current market prices is assumed zero. The

probability that a black searching in neighborhood j will find an averse-seller, is: = ∗

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Wj is the fraction of sellers who are white in neighborhood j. The j neighborhoods can be

ordered by their values of αj, assuming α1≤ α2…. ≤ αn. Utility of black households searching

will now be given by;

= − + (1 − )(1 − ( ) ( | ≥ ) + (1 − ) ( ) +− + (1 − )(1 − ( ) ( | ≥ ) + (1 − ) ( ) +...− + (1 − ) 1 − ( ) ( | ≥ ) + (1 − ) ( ) + 2.8.4

The effect of the sellers aversion is to take αj of the density fj(V) and put it at V=0. This reduces

expected benefits of search in neighborhood or alternatively increases costs. On average a black

searching in neighborhood j will have to look at 1/1-αj houses for every house he has the option

of purchasing. Cost of search for blacks is now given as;

= ( − ∗∗ ) 2.8.5

To incorporate the effect of gentrification I modified the search model such that: = ∗ ( )

Where Wj is the fraction of sellers who are white is now a function of amenities. Amenities in

this case refers to modern amenities6 that are identified with the process of gentrification as

opposed to traditional amenities7 seen in previous studies. Studies have shown a positive

relationship between amenities and composition of whites in a neighborhood hence the inclusion

of the amenity terms retains the ordering of neighborhoods such that α1 ≤ α2 ≤ α3. Including this

term will raise the search costs in cases when the amenities are high or increase compared to

neighborhoods where the amenities are low meaning that gentrification raises the search cost

increasing the price differentials between blacks and whites.

6 Bruckner et distinguish endogenous modern that are a result of gentrification from traditional amenities. 7 For example temperature, proximity to waterfront and school performance

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The amenity variable varies with racial composition and its justification is derived from

literature. Bruckner et al (1999) shows how an increase in modern/endogenous amenities leads

to reversal in spatial patterns. An increase in amenities in the inner cities as is the case with

gentrification leads to increase in whites in the inner city as the minority groups move further

out. Other studies show a link between amenities and racial composition. The pattern that

emerges is that an increase in the white population goes hand in hand with an increase in

amenities.

Boustan (2011) notes that racial sorting lowers the consumption of a wide variety of

neighborhood amenities by minority households. Increased consumption of amenities comes at

the expense of increased housing prices for high income households of each race. Development

of residential units in close proximity to commercial concentrations increases viability of the

units and attracts a ‘superior tenant mix’ (Gardner 2007) that increases the premium on the

residential units. Amenities are valued for the convenience they give to the tenants saving them

travel time and travel cost. They thus constitute a critical component of the urban experience

which adds value to an area. These increases in house prices are accompanied by sharp

decreases in the fraction of households of the same race for black and Hispanic households but

increases in the fraction of households of the same race for whites. Given segregating racial

preferences this implies that black and Hispanic households face a price for consuming these

neighborhood amenities that is implicitly higher than the price faced by white households. This

also applies to households in low income quartile they face an implicit price of neighborhood

amenities that exceeds the direct costs. Thus racial sorting in the housing market also raises the

implicit price of neighborhood amenities for low income blacks and Hispanics.

Waldfogel (2008) explores a related cause of black self segregation: shared preferences of local

private goods such as retail shops and restaurants. If blacks and whites have sufficiently

different tastes for local consumer goods, business that cater to a black clientele will be found

only in black neighborhoods a phenomenon he calls “preference externalities”. The resulting

concentration of black oriented retail establishments in black neighborhoods will in turn attract

more black household to locate in the area generating a self reinforcing segregated equilibrium.

Dixon (2006) explains how exclusionary amenities facilitate housing segregation, willingness to

pay for such goods functions as a proxy for race. Developers fix costly amenities in white

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neighborhood to the extent that such amenities have a tendency to deter middle and upper

income African Americans from joining the community. In his study Dixon8 (2006) showed the

example of building a golf club into residential communities. Other related studies have shown

various other amenities that have been used to ensure a homogenous residential community

including foregoing construction of inclusionary amenities such as basketball courts, subway

stops and soccer fields.

The search model highlights 4 factors:

(1) Sellers aversions is consistent with price differentials resulting in blacks paying more

for housing in black neighborhoods

(2) The price differential is an increasing function of the fraction of white sellers in a

neighborhood who are averse to dealing with blacks

(3) Price differentials will lead to market segmentation by neighborhood racial

composition

(4) Increase in gentrification (measured by the growth rate in modern/endogenous

amenities) increases price differentials between blacks and whites

4 Motivation for the Paper

For many years the city centre in many of America’s urban areas was left for lower income

minority groups as most of the middle income and upper income households moved to the

suburbs. In recent years however this pattern of spatial distribution has begun to change as

urban renewal programs have led to the process of gentrification that brings the higher income

households back to the city centre.

Demographic changes from 1998 to 2007 presented in table 9 and 10 point toward these racial

transitions taking place in DC. In table 9 we see marked differences in demographic transitions

across the zones in the central city of DC. Some zones have changes that closely conform to the

stylized characteristics of gentrification with an increase in the proportion of the population who

are white, an increase in median income and an increase in proportion of the population who are

college graduates while other zones are completely free of such changes.

8 Kim Dixon Costly Amenities may be Barrier to Gated Golf Communities The University of Chicago Chronicle Jan 19 2006 Vol 25 No. 8

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Examination of demographic characteristics of newcomers in the central city also exhibits

significant differences as shown in table 10. Some zones show that the proportion of blacks in

the newcomer population is low, while the proportion of college graduates in this population is

high. From the table a comparison of newcomers and longstanding residents shows that in some

zones the proportion of whites, and the median income is higher among newcomers than

longstanding residents. These differences in demographic transitions across zones in the DC

metropolitan area’s inner city points towards some forces at play that have stirred the movements

that result in the aforementioned changes. This paper seeks to document and analyze these

changes that have an impact on the housing market choices for African Americans in DC as

gentrification progresses in a number of zones in the city centre while assuming that racial

discrimination is persistent in the suburbs.

5 Methodology

5.1 Data

The paper used American Housing Survey data for 1998 and 2007 and data from the County

Business Patterns for the same years to estimate housing price discrimination in the Washington

DC metropolitan area. The variables used are shown in Table 3 in the Appendix. They are

income (Y), per mile cost of round trip to work (d), distance to work (u) and the housing

components (Xi) shown in the table below. The AHS data gives data on time spent commuting

to work this was used to obtain a proxy variable to measure commuting expenditure by

multiplying commuting time by wages per hour prior to this the time variable was adjusted to

reflect time in hours.

Other data used in the study include; income of the household, percent white population in a

zone, a black neighborhood variable showing neighborhoods with more than 70% black, a

gentrification dummy, a measure of amenities proxied for by the percentage growth rate in the

number of restaurants, museums and theaters (data obtained from County Business Patterns).

5.2 Model Estimation

5.2.1 A Bid rent Analysis

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The study uses a bid rent analysis to document the price differentials for whites and blacks in the

DC metropolitan area. Wheaton (1977) notes that the bid rent analysis is based on two

assumptions; firstly individuals who have similar demographic characteristics (i.e. education

attainment, family size, life cycle positions and occupation) have similar utility functions.

Secondly the housing market is assumed to be in equilibrium so that the first assumption is

fulfilled. The study uses both renters and owners. For renters the bid rent is the value of annual

rent and water and heating expenses, while for owners the study used the user cost approach to

get annual cost of owner occupied housing. This was described by Malpezzi and Green (2003)as

the cost to use a unit of housing capital each period given as;

R=(I + t+ d)V

Where i is the interest rate, t is the tax rate and d represents depreciation. In this study data on

depreciation was not available, the interest rate used was the annual average mortgage rate for

2007 given as 6.349 data was also obtained for annual cost on homeowners insurance, annual

cost on water and sewerage and annual real estate tax payment to compute the user cost of owner

occupied housing.

A household’s (i) bid rent is defined (R) as the payment to the owner of a housing unit at

location j (Rij)that would leave the household at a fixed level of utility(U01) given the housing

attributes of j. Wheaton (1977) represents this as;

Ui0=U(X, Yi-Rij-Tj) where X is the housing attributes, T is expenditure on commuting and Y is

income, this can be rewritten as

R ij=Yi-Tj-Ui-1(Xj, Ui

0)

Wheaton (1977) uses a Cobb Douglas model of the form given below to estimate the model

above, in his estimation the level of utility is estimated as the regression constant.

Log (Y-R-T)=U-∑I αi log Xi-ε

9 Freddie Mac 30 year fixed rate mortgage

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This study applies the same idea in the estimation of the bid rent. To attain constant utility the

study stratified the households based on three different criteria; race, position in life cycle,

education attainment and income. A further stratification by family size was attempted but the

resulting population groups were too small to warrant meaningful regression results hence the

family size is dropped as a stratification basis. Data limitations hindered any further

stratifications that would give more homogenous submarkets. The stratification was based on

the following values/levels

Age of head of household

o Group 1: 20-35 years

o Group 2 :36-55 years

o Group 3 :Over 55 years

Income was divided into 3 groups based on the income distribution of the DC

metropolitan area

o Low Income: those earning income below 25% of median income

o Middle Income: those Earning income between 25% and & 75% of median

income

o High Income: those earning over 75% of median income

Education Attainment

o Graduates: Have a university degree

o Non graduates: No university degree

Race

o Black

o White

Regressions are run for each strata to obtain the coefficients for the black stratum. These

coefficients are then applied to the mean values of the non white strata and then to the mean

values of the white strata to estimate what the various non white strata would be willing to bid

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for typically white occupied units. The divergence between such bids and prices actually paid by

whites will provide the measure of the existence of discrimination.

The regression residuals from these models are then investigated for spatial randomness if the

null hypothesis of no spatial relationships is rejected this means that the residuals that are close

to each other in location are close to each other in value i.e. the residuals are not independently

distributed. We used the Moran’s I, a measure of correlation between each residual and a

spatially weighted average of neighborhood residuals to test for spatial relationships. In addition

we used the LM test to ascertain whether spatial lags and errors are present. The robust LM test

enables us to choose between the spatial lag and error model as it shows whether addressing one

problem resolves the other. Incorporating spatial econometrics methods allows us to separate

racial preferences from unobserved spatially dependent amenities.

In the case of a spatial lag the assumption is that the weighted average of housing prices affects

the price of each house. This is an indirect effect in addition to the indirect effects associated

with the standard explanatory variables that capture structural effects of housing as well as

neighborhood attributes. Including the spatial lag variable is similar to detrending the time

series data(Anselin 2007), it captures the influence of unobserved neighborhood characteristics.

The spatial lag model is given as: = + +

~ (0, ) ρ is the coefficient on the spatial lag and W is an n*n spatial weight matrix that is constructed

using location coordinates for the neighborhoods. In W the diagonal and all elements outside a

given threshold are zero. Inside the threshold distance neighboring houses are assigned a row

normalized weight of equal magnitude.

The spatial error is a result of unmeasured neighborhood influences common to houses in close

proximity to each other but not captured in the model, it arises because of the spatial correlation

of unobserved attributed of the houses and neighborhoods. When both racial composition and

the unobserved amenities are positively spatially correlated then OLS errors are likely to be

19

biased downward leading to incorrectly rejecting null hypothesis on significance of coefficients.

The spatial error model incorporates a spatially weighted error term to cater for any omitted

variable that varies spatially or any regression variables in the model that are measured at

different geographical levels and are thus subject to measurement error. The spatial error model

is given as: = +

≡ +

~ (0, ) λ is a measure if the spatial autocorrelation.

A number of studies have shown the superiority of spatial approaches over traditional hedonic

regressions in terms of improving the explanatory power and the statistical properties of the

model (Basu and Thibodeau 1998, Bowen et al 2001, Farber 2004, Pace and Gilley 1997)

The spatial relationships are measured based on contiguity. If two census blocks touch then they

are defined as neighbors. The two most common cases of contiguity based matrices are the

queen and the rook. In the rook blocks are neighbors if they share an edge and in the queen they

are neighbors if they share and edge or a point. We will estimate two alternative econometric

models that incorporate spatial effects; a spatial lag and spatial error. In the spatial lags model

spatial effects are captured through a spatial multiplier. The spatial lag model adds the spatially

weighted average of house prices into the explanatory variable set. With spatial error, spatial

autocorrelation is assumed to arise from omitted variables that follow a spatial pattern.

5.2.2 Logistic Regression

The study also estimated a logistic regression to determine the log odds of blacks moving out of

the city centre. The bid rent analysis sought to document higher bid rents for black population.

This logistic regression will sought to determine displacement of blacks from the inner city.

Documenting a combination of these two effects points towards a reduction in housing options

for blacks as housing discrimination persists and gentrification sets in.

20

The spatial patterns have shown blacks are concentrated in the inner city but gentrification is

altering this spatial pattern and this logistic model will document any changes in spatial

distribution. This concept is borrowed from Vigdor (2001) who carries out a probit analysis of

household transitions based on socioeconomic status to determine whether low status households

are more likely to exit housing units in gentrifying zones relative to other parts of the city.

In this study I a logistic regression was run to analyze transitions based on race. The

dependent variable will be equal to one if the same household resided in the housing unit in the

previous enumeration period, 1998. If it’s a new household in the housing unit or the housing

unit is vacant the dependant variable is set equal to zero. This dependant variable is regressed

upon a set of control variables; a gentrification dummy variable, a dummy variable for low

income group (i.e. equal to 1 if the household falls in the low income category10 and equal to 0

otherwise), an interaction term representing blacks in gentrified zones, owner occupied housing

variable, rating of the neighborhood as a place to stay represented by two variables derived from

a principal component analysis11 of 6 variables describing the neighborhood and public housing.

To create the gentrification dummy variable the study relies on findings from previous research.

Studies have used increases in professional managerial households, physical upgrading of

housing stock, changes in tenure, percentage change in income and percentage change in

poverty rate as measures of gentrification (Guerrieri et al 2010, Atkinson 2000, Mckinish and

Walsh 2008 and Wyly and Hammel 2004). These studies compare the changes in these variables

in neighborhoods and compare the changes to changes in the same variables at a broader

geographical area to establish presence of gentrification. Atkinson (2000) compares the rate of

occupational change in any single ward to the rate of increase in London as a whole to determine

if gentrification is taking place. This study used 4 measures done at the zone level compared to

the metropolitan area values;

Change in median income

Change in percentage white population

10 Low income group was identified as households falling in the 1st quartile of the income groups for the MSA 11 Variables used in PCA litter,junk, abandoned buildings, undesirable neighborhood/ property bothersome and poor county/city services bothersome

21

Change in age group 20-34 years as a percentage of total population

Change in educational attainment

The measure of amenities was proxied for by data from county business patterns. The study

used data for full service restaurants and snack and non alcoholic beverages ( NAICS codes

722110 and 722213) . This is adopted from Brueckner et al (1997) who make a distinction

between exogenous and endogenous amenities. The exogenous amenities are the traditional

amenities for example; proximity to water bodies and temperature or sunny days while the

endogenous amenities, they refer to as modern amenities. They explain that the modern

amenities depend on the current economic conditions in a neighborhood especially the income

level and they include increase in restaurants, theaters and museums.

6 Findings

In determining gentrification we made use of the exclusive and inclusive definitions of

gentrification given by Wyly and Hammel (1999) and Vigdor (2001), the exclusive definition

identifies the zones in the central city that are undergoing gentrification while the inclusive

definition identifies additional zones in the suburbs that show the signs of gentrification or have

similar characteristics to the gentrifying zones in the central city. In addition we construct a

gentrification dummy variable using the different characteristics of gentrification in isolation so

as to examine any variations when the definition of gentrification is altered.

To define the 24 zones in the DC metropolitan area we analyzed changes in income, racial

composition of the population and changes in median income. We were not able to capture

changes in age due to data limitations12. The findings from this analysis are presented in Table 1

while the findings using the characteristics are presented in Tables 12, 13 and 1413. We

identified 2 central city zones as undergoing gentrification, these are zone 614 and zone 315.

These two comprise the exclusive definition of gentrification. For the inclusive definition the

12 AHS data does not have data on householders age for the surveys prior to 2000 13 The models in this case were not tested for spatial regression as the aim is to see the significance of these definitions’ in explaining gentrification 14 Arlington neighborhood 15 Colombia Heights, Shaw, Howard university neighborhood

22

other zones included are; 102, 112, 114 and 11716. From Table 1 we note that these 5 zones had

above average changes in the 3 respective variables. These findings were used to generate the

gentrification dummy for the study. The dummy takes a value of 1 if the zone is gentrifying

otherwise it takes a value of 0.

The results of the spatial regression analysis are presented in Table 3. It shows that the bid rent

is significantly related to gentrification, household income, proportion of whites in the

neighborhood, size of the housing unit and rating of the neighborhood and the housing unit as a

place to live, amenities and the move variable which controls for utility levels for households

that moved recently relative to those that did not. The model shows that gentrification varies

directly with the bid rent, as gentrification increases then the bid rent will increase. The Moran’s

I test shows that there is significant spatial autocorrelation among the net incomes( income less

bid rent less commuting costs) of households and the error terms.

18 regressions were run to determine the bid rent for black strata, having stratified the data into 3

income levels, 3 age groups, white and black population and two education groups. Of the 18

regressions we picked 8 regressions that gave the best results, i.e. had more significant

coefficients with the required signs for the different variables. The output for the 8 equations is

represented in Table 3.

The signs for the main coefficients were as expected. The gentrification dummy, racial

composition variable, amenity variable, bedroom number and neighborhood and house ratings

sign gave a negative coefficient showing a direct relationship between the bid rent and each one

of these variables. In the same way the neighborhood and housing unit rating, size of housing

unit measured by the number of bedrooms increased the bid rent for the various strata.

The gentrification variable was significant in 6 of the 8 models and this emphasizes that the

process of gentrification does influence the bid rent in the neighborhood. The models where

16 Zone 102 and 114 are in Fairfax county in Virginia, 112 is just outside of the District of Columbia central business district in the neighborhood of Alexandria, while 114 borders Arlington encompassing the neighborhoods of Falls Church, Baileys Crossroads and Lincolnia. 117 is an expansive zone encompassing Prince William, Loudon and Stafford counties. Zone 102 is found in Maryland’s Montgomery (part of the county) county with neighborhoods like Olney, Rockville and Poolsville.

23

gentrification was defined based on variations in the 3 measures of gentrification in isolation

showed the gentrification variable in the case of education attainment and white population

growth to be significant in 3 and 2 models respectively and it had the expected sign in all of

these cases. However defining gentrification using just the change in median income yielded

only 1 significant model which had the wrong sign.

The racial composition variables were significant in at least 4 models. Showing that the

proportion of whites in a neighborhood raises the bid rent while the black neighborhood variable

which identified neighborhoods that were at least 70% black had different signs for different

household strata. Most notable was that in the high income stratum this variable had a positive

sign showing that in high income neighborhoods an increase in proportion of blacks beyond a

certain level decreases the bid rent. In 2 of the low income stratum the variable had a negative

coefficient and this meant that it served to raise the bid rent. To ensure accuracy of this finding

the model was adjusted to avoid any possible multicollinearity issues brought by the percent

white variable and the black neighborhood variable by using a racial composition variable and

including its quadratic term to determine if there is a threshold effect of race on bid rent. Table

11 shows the output when this model is applied to 3 of the stratum. The percent black variable is

negative while its quadratic term is positive.

Having derived the coefficients for the black strata to identify and measure racial discrimination

we used these coefficients to generate hypothetical bid rents by blacks for housing units with

white characteristics and compare these bids to the mean values of white bids. If the

hypothetical bids are larger than the white means that shows that blacks bid rent is larger than

whites bid rent for the same unit of housing. Table 5 shows the results for this analysis. The

table shows that except for one strata (MB2GA) all the other strata showed that the black

hypothetical bid rent is greater than the white bid. Comparison of the value that blacks are

willing to pay for white housing units shows discrimination in all income groups.

The effect of displacement of blacks from the central city is captured by the logistic regression

model. A variable measuring displacement versus non displacement was regressed against a

number of control variables to establish the extent to which displacement of blacks was

significant when there is gentrification. Results from the logistic regression show that the model

is significant with a likelihood ratio of 408.13 and probability of 0.000. Except for percentage

24

of public housing units in the zones all the other variables are significant at 90% confidence

interval of the coefficients.

7 Discussion of Findings

These findings show that to define or identify gentrification one needs an overlapping approach,

i.e. use a combination of the stylized characteristics of gentrification identified in literature the

models because isolating the causes of gentrification to define gentrified yielded models with

fewer significant variables. The amenity variable on the other hand was significant only in 4 of

the models, 3 of which were in the high income strata. This is not an unusual finding as amenity

consumption is more meaningful to higher income households and this demonstrates fewer

amenities (as defined in the study) in the neighborhoods with low and middle income groups.

The significance of the gentrification variable shows gentrification increases the bid rent. The

amenity variable which is also a measure of the effects of gentrification in a neighborhood also

shows that an increase in amenities in a neighborhood has the effect of increasing the bid rent

thus emphasizing the results from the gentrification dummy. The results show that in

gentrifying zones the net income ( household income less bid rent less commuting expenditure)

decreases by a value ranging from 219% (seen in the case of strata LB2G017) to 25.2% (HB3G1)

compared to a non gentrifying zone. Notable is the fact that the largest declines in the net

income, as a result of gentrification are seen in the case of the low income strata.

With regard to spatial regression analysis the results show that the non spatial model

overestimates the effect of gentrification and the other variables as well in bid rent analysis.

Hence in analyzing the effects of gentrification we are careful to incorporate spatial regression

analysis to control for spatial autocorrelation. The spatial regression analysis established that

there is market segmentation as the dependent variable showed presence of spatial clustering in 4

out of the 8 models selected. In 3 of the models the Moran’s I was positive showing that

17 Coding defined in the appendix table 4

25

households with a high net income will tend to cluster while those with low income will also

tend to cluster. Further analysis of the spatial regression showed that 2 of the models with spatial

autocorrelation were corrected for using a spatial lag model showing that the dependent variable

in this case is influenced by neighbors while the other 2 stratum with spatial autocorrelation

which were from the low income stratum were corrected for spatial autocorrelation using the

spatial error term meaning that the residuals in these models were influenced by neighbors.

These results from the racial composition variables point toward presence of prejudice in the

housing market. The prejudice theory shows blacks pay more for housing to live with whites i.e.

the white/black boundary, shown by the increase in bid rent as the percentage of whites increase

in the neighborhood. When the proportion of blacks increases beyond a certain threshold the bid

rent declines as blacks increase this is what is referred to in the prejudice theory as the black

interior where bid rent will be lower than in the black white boundary. This shows that at low

levels of blacks in a neighborhood the bid rent will increase with an increase in black population

but after a certain fraction of blacks in the neighborhood the bid rent begins to decline. This

finding was corroborated by the model where percent white and black neighborhood were used

as measures of racial composition. The fact that the black neighborhood variable gave negative

values in the high income stratum and a positive sign in the low income stratum shows that in

certain neighborhoods (high income with more whites) having more blacks raises bid rent while

in others (low incomes and more blacks) having more blacks lowers bid rent. This confirms the

idea behind the prejudice theory.

The results for the bid rent paid by the 2 races point toward discrimination in the DC MSA

housing markets as blacks are forced to give a larger bid for housing similar to that of whites.

This corroborates findings from other studies that find discrimination particularly in the suburbs.

Due to data limitations however this study was not able to carry out separate bid rent analysis for

the suburbs and the central city to establish the difference in bid rents and discrimination for the

suburbs as compared to the central city.

In this study the findings show that the over 60 age group increases the probability of staying in

the same housing unit. To see the difference in different age groups we created 3 age group

26

dummies and used the first age group (20-34) as the reference point. The results show that

probability of displacement increases for age group 2 (35-59) compared to age group 1 while the

probability of displacement decreases for age group 3 (60 and over) compared to age group 1.

The key finding however is that the inclusion of the black dummy shows that displacement

increases for housing units where the householder is black. This shows that as gentrification

proceeds in the central city especially, the blacks are being pushed out of the central city.

The findings from the logistic regression show that being black, having gentrification, head of

household in age group 2 (35-59 years) as opposed to age group 1 (20-34) and earning income

which is within the first quartile for the DC MSA, reduces the probability of staying in the same

housing unit hence increases the probability of displacement. On the other hand owner occupied

housing, increase in quality of neighborhood , increase in education attainment and household

head being in age group 3 (over 60) as opposed to age group 1 (20-34) will reduce the chances of

displacement. This shows that gentrification in a neighborhood increases the possibility of

displacement for black households compared to white households.

8 Conclusion

The results show pure racial discrimination as blacks are made to pay more for housing identical

to that of whites while on the other hand there is displacement from the central city due to

gentrification. They also show presence of market segmentation which is a result of prejudice

leading to blacks paying more for housing in black neighborhoods and in white neighborhoods as

seen by the sign of the percent white variable.

The implications of these findings is that the urban renewal programs that lead to gentrification

are pushing blacks out of the city centre. On the other hand persistent racial discrimination

means they are made to pay more for housing in the suburbs and now in the inner cities as well

as the inner city zones are becoming more white and as they become more white the probability

of racial discrimination coming back to the central city increases. This further exacerbates the

housing choice problem for black. Based on these findings the recommendation is for

metropolitan areas to ensure adequate measures are put in place to protect minority households

against the displacement pressures of gentrification.

27

Data limitations however reduced further analysis as it limited the probability of creating further

homogenous groups of white and black populations with similar preferences. In addition the

study would have benefited further from different regression analysis for suburbs and the central

city, to distinguish the house price premiums for blacks in the central city and the suburbs. This

would have shown the greater discrimination levels in the suburbs compared to the central city.

However the study relies on findings of more discrimination against blacks in the suburbs from

other previously conducted studies and combined with these findings arrives at the conclusion of

fewer housing choices for African Americans.

28

Table 1: Gentrifying Zones in DC MSA

Zone

% change in Education Attainment

% change in Median Income

% Change in White Population

1 2.15 -12.28 27.91 2 14.05 4.16 7.67 3 10.02 54.71 40.28 4 -34.83 0 48.05 5 1.19 122.53 0 6 10.02 19.93 37.13 7 -8.59 32 27.13 8 8.19 0.11 21.78 9 -3.17 28 30.45 10 -2.4 39.6 12.4 11 -0.11 -20 21.11 12 -1.53 39.41 -0.84 13 -7.88 26.80 29.42 14 5.55 -2.88 -2.82 15 -6.26 68.25 1.95 16 -12.91 4.93 4.94 17 -13.78 33.49 26.02 18 0.73 68.75 23.58 19 2.72 30.48 17.02 20 -1.34 30.24 22.74 21 -4.43 65.03 6.95 22 -0.21 19.68 16.5 23 -13.74 38.46 34.32 24 -23.77 -1.86 46.46

Highlighted zones are those identified as gentrified zones

29

Table 2: Spatial Regression:

Dependent variable=Income-bid rent-commuting cost

OLS Estimate Std Error t/z value P

Hown -0.0307 0.0989 -0.310 0.756

Howhd 0.194 0.119 1.623 0.104

Bedrms 0.006 0.0251 0.249 0.802

Zinc 7.06333e-006 2.575105e-007 27.429 0.000

Rest_gr -0.0004 0.006 -0.678 0.497

Crime -0.051 0.059 -0.860 0.389

Pctwht -0.0016 0.001 -1.467 0.142

Ghetto 0.0672 0.155 0.431 0.000

Gent -0.910 0.108 -8.405 0.000

Dmove 0.167 0.048 3.431 0.000

Constant 10.06 0.144 69.563 0.000

F11 1168=110.324 P =0.000 R2=0.488 Log Likelihood=-1375.46

Spatial Model

Hown 0.013 0.098 0.131 .895

Howhd 0.167 0.119 1.398 .161

Bedrm -0.003 0.025 -0.121 .903

Zinc 0.000007 0.00000025 27.842 0.000

Rest -0.00036 0.00055 -0.659 .509

Crime -0.0344 0.0594 -0.579 .562

Pctwht -0.001 0.0019 -0.710 .477

Ghetto 0.083 0.162 0.516 .605

Gent -0.713 0.136 -5.229 0.000

Dmove 0.149 0.0049 3.046 0.000

Lambda 0.706 0.153 4.613 0.000

Constant 10.04 0.181 55.333 0.000

AIC= 2765.53

30

Table3: Variables in Bid Rent Regression

Name Definition

Zinc2 Household Income

Rest_gr Growth rate for Restaurants, museums and movie theatres

Bedrms Number of bedrooms in unit

Lbid Natural log of net bid, net bid was calculated as income less user cost of housing

dmove Dummy variable=1 if household moved into the unit after 1999 otherwise =0

Crimey Neighborhood has Neighborhood crime calculated as a percentage at the zone level

Pctwhite Percent of population that is white calculated at the zone level

Bnbhd Black neighborhood equal to 1 if the zone has over 70% black otherwise =0

Com_exp Annual Commuting expenses calculated as time taken to work times hourly wage

Gent Gentrification dummy equal to 1 if the zone is gentrified otherwise=0

hownd Dummy variable equal to 1 if rating of neighborhood as a place to live >5 otherwise =0

howhd Dummy variable =1 if rating of housing unit as a place to live >5 otherwise =0

Gentinc Gentrification dummy using only median income to define gentrified zones

Gentrace Gentrification dummy using only white population proportion to define gentrified zones

gentgr Gentrification dummy using only education attainment to define a gentrified zone

31

Table 4: Regression Estimates

hb2g1 lb2ga mb3g1 mb2ga hb2ga hb3g1 lb2g0 lb3g0

hownd -0.548 0.694** -0.151 0.047 -0.695 -0.057

(-1.24) (2.88) (-1.41) (0.703) (-1.56) (-0.25)

howhd 0.0943 -0.615 -0.920** 0.00955 0.091 -0.825**

(0.96) (-1.78) (-3.23) (0.07) (0.951) (-2.1)

bedrms -0.0897*** -0.147 -0.102** -0.0793*** -0.116*** -0.004 0.163 -0.195***

(-5.33) (-1.39) (-2.89) (-3.81) (-9.488) (-0.315) (-1.46) (-2.79)

zinc2 0.00000236***0.0000737*** 0.0000182***0.0000206*** 0.00000245***0.0000048*** 0.000073*** 0.000052***

(29.46) (6.52) (10.84) (14.59) (35.43) (22.08) (6.06) (7.18)

rest_gr -0.00185*** -0.00525 -0.000871 -0.000169 -0.0014*** -0.0006* -0.11** 0.0025

(-5.87) (-1.60) (-0.75) (-0.38) (-4.951) (-1.85) (-3.04) (1.48)

crimey 0.0823* -1.019*** 0.142 0.0372 0.0693** 0.019 -1.38*** 0.53***

(2.28) (-3.98) (1.63) (0.86) (1.99) (0.525) (-5.29) (4.05)

pctwhite1 0.00608*** -0.0222** -0.00855** 0.000152 0.00064 -0.018* -0.016** -0.018***

(5.50) (-3.23) (-3.25) (0.15) (0.528) (-1.72) (-1.98) (-4.31)

dmove -0.00617 -0.0910 0.214* 0.00438 0.03 -0.015 -0.56* 0.37**

(-0.25) (-0.38) (2.31) (0.12) (1.345) (-0.68) (-1.94) (2.18)

bnbhd 0.284*** -0.698* -0.0211 0.0699 0.101** -0.041 -1.14*** 0.76***

(5.97) (-2.08) (-0.23) (1.38) (2.059) (-1.05) (-3.19) (5.04)

gent -0.191 -1.753*** -0.361* 0.0663 -0.252** -0.149** -2.19*** -0.63***

(-1.78) (-4.43) (-2.32) (0.59) (2.399) (2.51) (-4.3) (-2.76)

_cons 11.43*** 10.43*** 10.29*** 9.611*** 5.83*** 7.86*** 11.59*** 8.4***

(78.46) (13.90) (41.52) (56.88) (4.69) (4.94) (12.89) (18.31)

lbid_lag 0.493*** 0.27**

(4.64) (1.99)

Lambda 0.62*** 0.845***

(3.7) (12.83)

Moran's I -0.564 2.948*** -0.578 -0.529 -1.718* 4.163*** 6.3*** 3.53***

N 154 109 70 161 184 62 91 53

t statistics in parentheses

***p<0.01

**p<0.05*p<0.1

32

Table 5: Bids for White and Black Population Strata

Population Strata Actual Mean White Bid Hypothetical Bids by Blacks

LB3G0 16489.49 26866.42

MB2GA 28812 25981.91

MB3G1 29194 33235.57

LB2GA 19805.57 24550.68

LB2G0 16088.60 37665.66

HB2G1 47856.58 50618.13

HB3G1 55308.02 203642.7

HB2Ga 45463.17 67847.51

Table6: Description of Codes used in Stratification

Code Description of Code

LB3G0 Low income blacks age group 3 not university graduates

MB2GA Middle income blacks age group 3 (no education attainment stratification)

MB3G1 Middle income blacks age group3 graduates

LB2GA Low income blacks age group 2 (no education attainment stratification)

LB2G0 Low income blacks age group 2 non graduates

HB2G1 High income blacks age group2 graduates

HB3G1 High income black age group 3 graduates

HB2Ga High income blacks age group 2 (no education attainment stratification)

33

Table 7: Variables in Logistic Regression

Name Definition

Black Dummy variable=1 when householder is black otherwise=0

Gent Dummy variable for gentrification equals 1 if the zone is gentrifying and 0 otherwise

Own Dummy for owner occupied housing =1 otherwise=0

PCA Index for quality of neighborhood derived from PCA

Pctproj Percentage housing units that comprise public housing in zone

Age2 Dummy variable for age group=1 if >35 years and less than 60 otherwise =0

Age3 Dummy variable for age group=1 if >59 years otherwise =0

Pctgrad Percentage of college graduates in zone

Inc1 Dummy variable for households with income within bottom quartile=1 otherwise=0

Table 8: Results For Logisitic Regression/Summary Statistics

coefficient Std. Error Mean Std. Dev Min Max

black -0.1199486** 4.54E-02 0.2067332 0.4049967 0 1

gent -0.0693948* 3.63E-02 0.3380139 0.4730733 0 1

own 0.1955608** 4.18E-02 0.6817797 0.4658249 0 1

pca -0.099247** 1.03E-02 -7.09E-10 1.754167 -2.97499 6.524633

Pctproj -1.402408 1.64E+00 0.0090675 0.0106535 0 0.037004

age2 -1.313467** 4.24E-02 5.32E-01 0.4989915 0 1

age3 0.6261595** 5.89E-02 1.41E-01 0.3477976 0 1

Pctgrad 0.25847549* 1.33E-01 0.502511 0.1322096 0.2627119 0.797386

inc1 -0.2442427** 4.37E-02 0.2206057 0.4146901 0 1

constant -0.3838473** 7.98E-02

LR chi2(9) 408.13**

34

Table 9: CBD Demographic Changes in DC Central City 2007 1998 Zones

% white % age 20-34

% Coll. Grad

median income % white

%age 20-34

% Coll. Grad

Median income

1 67.65 28.23 65.32 94000 63.36 19.31 60 83000 2 12.24 13.08 37.69 32760 6.39 14.67 27.41 35000 3 66.95 23.93 74.85 67725 42.67 29.75 54.84 36600 4 48.05 27.55 59.18 47500 29.52 13.9 47.55 30000 5 0 15 28 32000 1.82 15.6 19.27 18000 6 93.89 22.75 55.45 90905 77.43 33.06 46.36 56344

Table 10: DC Demographics for Newcomers in 2007

Newcomers Longstanding Residents

Zones Median income

% College Graduates

% Black % White % Age 20-35

% Black % White Median income

1 85091 64.91 19.3 71.93 52.63 33.33 62.22 125453 2 32000 21.82 72.73 21.82 20 97.67 0 32760 3 69000 68.75 22.5 70 40.74 34.21 60.53 51000 4 47500 48.05 50.65 48.5 35.06 * * 51000 5 35000 9.3 100 0 20.93 97.06 0 25243 6 77250 47.5 0 94.17 35 1.67 93.33 94000

35

Table 11: Bid Rent Regression Using quadratic Racial Composition Term.

(1) (2) (3) MB3G1 LB2GA HB2G1 hownd 0.267 -0.786 (1.24) (-1.85) howhd -0.585* -0.858* 0.0425 (-2.12) (-2.31) (0.39) bedrms -0.131** -0.132 -0.104*** (-3.24) (-1.23) (-5.70) zinc2 0.0000168*** 0.0000894*** 0.00000256*** (9.95) (7.98) (31.78) rest_gr -0.00367* -0.00585 -0.00126*** (-2.26) (-1.70) (-3.48) crimey 0.0716 -0.988*** 0.146*** (0.74) (-3.81) (3.81) pctblack1 -0.0128* 0.0457** -0.00359 (-2.34) (2.67) (-1.95) dmove 0.151 -0.221 0.0303 (1.61) (-0.90) (1.11) gent -0.376* -1.128** -0.0821 (-2.30) (-3.20) (-0.70) pctbsq 0.000108* -0.000384* 0.0000321* (2.41) (-2.59) (2.17) _cons 10.48*** 7.971*** 11.92*** (31.41) (12.20) (83.04) N 70 109 154

36

Table 12: Bid Rent with Education Attainment as Measure of Gentrification hb2g1 hb2ga hb3g1 lb2g0 lb2ga lb3g0 mb3g1 mb2gahownd 0 -0.105 0 -0.490 -0.464 0.245* 0.406*** 0.107 (.) (-1.72) (.) (-1.21) (-1.38) (2.03) (5.17) (1.71)howhd 0.193** 0.160* 0 -0.767* -0.636* 0 -0.415*** -0.0220 (3.13) (2.41) (.) (-2.25) (-2.32) (.) (-4.46) (-0.28)

rest_gr

-0.00157***

-0.00164***

-0.000481** -0.00107 -0.000151

0.00225**

0.00158*** -0.000119

(-5.68) (-5.80) (-3.25) (-0.34) (-0.06) (2.73) (4.25) (-0.47)

zinc2

-0.00000423***

-0.00000298***

-0.00000124**

0.0000128

0.0000185

0.00000202

-0.00000427***

-0.00000413**

(-5.21) (-6.11) (-3.04) (0.76) (1.60) (0.55) (-4.18) (-2.64)crimey 0.0284 0.0453 -0.0112 -0.664* -0.628** -0.0246 0.152*** 0.00204 (0.93) (1.40) (-0.81) (-2.60) (-2.91) (-0.35) (5.55) (0.08)pctwhite1

0.00584***

0.00508*** -0.000151 -0.0148* -0.0111* -0.000295 -0.000242 -0.000106

(5.84) (5.00) (-0.44) (-2.29) (-2.22) (-0.18) (-0.27) (-0.17)dmove 0.0334 0.0405 -0.0138 -0.0383 -0.139 0.245** -0.0210 0.0142 (1.52) (1.82) (-1.57) (-0.16) (-0.74) (2.73) (-0.68) (0.67)

bid3 0.00000697***

0.00000567***

0.00000625***

0.0000854***

0.0000824***

0.0000697***

0.0000239***

0.0000260***

(7.78) (10.66) (14.97) (4.98) (6.19) (15.20) (25.14) (18.11)

bnbhd 0.307*** 0.240*** -0.0419** -0.735* -0.560* 0.208** 0.0674* -0.0424

(7.34) (5.53) (-3.11) (-2.22) (-2.01) (2.83) (2.39) (-1.45)gentgr -0.0372 -0.0423 -0.0320* -0.602 -0.658* -0.122 -0.0966* 0.0457 (-1.35) (-1.46) (-2.27) (-1.78) (-2.25) (-1.36) (-2.26) (1.20)

_cons 11.12*** 11.28*** 11.26*** 9.885*** 9.402*** 7.878*** 9.763*** 9.715***

(121.14) (100.85) (322.55) (14.02) (17.24) (44.47) (139.89) (102.96)

37

Table 13: Bid Rent Analysis Using Median Income as Measure of Gentrification hb2g1 hb2ga hb3g1 lb2g0 lb2ga lb3g0 mb3g1 mb2gaBid Rent Analysis Using Income as the measure for Gentrificationhownd 0 -0.124* 0 -0.438 -0.436 0.202 0.354*** 0.117 (.) (-2.16) (.) (-0.99) (-1.23) (1.65) (4.55) (1.85)

howhd 0.244*** 0.262*** 0 -0.719* -0.640* 0 -0.377*** -0.0220

(3.97) (4.17) (.) (-2.06) (-2.28) (.) (-3.95) (-0.28)

rest_gr

-0.00168*** -0.00194*** -0.000372* 0.000590 0.00143 0.00192* 0.00150*** -0.000132

(-5.84) (-7.01) (-2.51) (0.15) (0.48) (2.39) (3.52) (-0.51)

zinc2

-0.00000401***

-0.00000275***

-0.00000181*** 0.00000470 0.0000127 0.00000314

-0.00000478***

-0.00000494**

(-4.86) (-5.93) (-4.93) (0.28) (1.07) (0.83) (-4.46) (-3.19)crimey 0.00446 -0.0163 -0.0222 -0.616* -0.555* -0.0126 0.134*** 0.0145 (0.13) (-0.49) (-1.65) (-2.25) (-2.38) (-0.18) (4.66) (0.60)

pctwhite1 0.00633*** 0.00666*** -0.0000672 -0.0134* -0.00826 0.00143 0.000235 -0.0000136

(5.99) (6.48) (-0.19) (-2.03) (-1.62) (1.27) (0.25) (-0.02)dmove 0.0335 0.0333 -0.00948 -0.00977 -0.146 0.278** -0.0309 0.00324 (1.53) (1.58) (-1.00) (-0.04) (-0.76) (3.05) (-0.98) (0.15)

bid3 0.00000674***

0.00000539***

0.00000682***

0.000103***

0.0000999***

0.0000656***

0.0000243***

0.0000266***

(7.39) (10.65) (18.20) (7.30) (8.99) (14.69) (24.78) (18.84)

bnbhd 0.336*** 0.331*** -0.0408** -0.905** -0.748** 0.157* 0.0488 -0.0250

(7.36) (7.22) (-2.81) (-2.80) (-2.70) (2.10) (1.69) (-0.73)gentinc 0.0441 0.106*** -0.0113 0.0781 0.0239 -0.0472 0.0159 0.0293 (1.77) (4.67) (-1.06) (0.24) (0.09) (-0.72) (0.52) (1.12)

_cons 11.02*** 11.07*** 11.27*** 9.515*** 8.999*** 7.906*** 9.773*** 9.718***

(108.50) (97.86) (301.88) (13.87) (16.60) (41.49) (134.78) (103.16)N 154 184 62 91 109 53 70 161 hb2g1 hb2ga hb3g1 lb2g0 lb2ga lb3g0 mb3g1 mb2gahownd 0 -0.124* 0 -0.438 -0.436 0.202 0.354*** 0.117 (.) (-2.16) (.) (-0.99) (-1.23) (1.65) (4.55) (1.85)

howhd 0.244*** 0.262*** 0 -0.719* -0.640* 0 -0.377*** -0.0220

(3.97) (4.17) (.) (-2.06) (-2.28) (.) (-3.95) (-0.28)

rest_gr

-0.00168*** -0.00194*** -0.000372* 0.000590 0.00143 0.00192* 0.00150*** -0.000132

(-5.84) (-7.01) (-2.51) (0.15) (0.48) (2.39) (3.52) (-0.51)

38

Table 14: Bid Rent Analysis with White Population Growth rate as the measure of gentrification hb2g1 hb2ga hb3g1 lb2g0 lb2ga lb3g0 mb3g1 mb2gahownd 0 -0.117 0 -0.542 -0.425 0.222 0.348*** 0.105 (.) (-1.92) (.) (-1.42) (-1.31) -1.83 -4.48 -1.67howhd 0.220*** 0.197** 0 -0.42 -0.296 0 -0.366*** -0.0145 -3.6 -2.99 (.) (-1.27) (-1.05) (.) (-3.88) (-0.18)rest_gr -0.00156*** -0.00169*** -0.00022 0.0059 0.00676* 0.00181* 0.00153*** 5.98E-05 (-5.29) (-5.50) (-1.29) -1.94 -2.55 -2.15 -3.87 -0.2

zinc2

-0.00000433***

-0.00000285***

-0.00000150*** -1.3E-05 -8.1E-06 2.01E-06

-0.00000463***

-0.00000472**

(-5.31) (-5.68) (-4.00) (-0.81) (-0.66) -0.51 (-4.43) (-3.09)crimey 0.0286 0.0434 -0.00657 -0.578* -0.502* -0.0237 0.134*** 0.0129 -0.93 -1.33 (-0.41) (-2.42) (-2.44) (-0.30) -4.72 -0.53pctwhite1 0.00558*** 0.00481*** 0.000601 -0.0131* -0.00909 0.000973 -9.1E-05 -7.4E-05 -5.51 -4.68 -1.22 (-2.14) (-1.94) -0.71 (-0.10) (-0.11)dmove 0.0365 0.0449* -0.00938 -0.0563 -0.141 0.269** -0.0371 0.00898 -1.65 -2.01 (-1.03) (-0.24) (-0.78) -2.99 (-1.13) -0.44

bid3 0.00000710***

0.00000554***

0.00000652***

0.000116***

0.000115***

0.0000676*** 0.0000243***

0.0000264***

-7.91 -10.15 -16.93 -8.65 -10.23 -15.29 -24.91 -18.82bnbhd 0.302*** 0.237*** -0.0131 -1.319*** -1.078*** 0.178* 0.0476 -0.0419 -7.21 -5.44 (-0.71) (-4.11) (-3.97) -2.51 -1.72 (-1.42)gentrace 0.0101 0.0216 -0.0299 -1.049*** -0.928*** 0.0378 0.0223 -0.0229 -0.4 -0.81 (-1.79) (-3.56) (-3.53) -0.5 -0.76 (-0.75)_cons 11.10*** 11.25*** 11.21*** 10.27*** 9.571*** 7.869*** 9.769*** 9.735*** -120.8 -100.63 -245.91 -15.28 -18.27 -43.53 -134.96 -104.89N 154 184 62 91 109 53 70 161 hb2g1 hb2ga hb3g1 lb2g0 lb2ga lb3g0 mb3g1 mb2ga

39

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