Examining the Effects of Scattered Site Supportive Housing on the Social and Economic Integration of Men Who Are Formerly Homeless and Primarily Black/African American

Abstract

Housing dispersal strategies (scattered sites) have its roots in efforts to construct public housing in more ethnically diverse and affluent communities as a result of the overrepresentation of Blacks/African Americans living in public housing developments with concentrated poverty and racial segregation. Today, the use of scattered site housing is more indicative of nonprofit housing providers seeking to end homelessness. Comparable with the demographics of public housing residents, Blacks/African Americans are severely overrepresented in homeless populations across America. This study examined the impact of scattered site supportive housing, located in an affluent and predominately White/European American community to determine any reductions in social isolation and any levels of improved socio-economic status of men who were formerly homeless and primarily Black/African American. This study used five variables identified from earlier studies thought to influence socio-economic well-being: resident satisfaction, a sense of community, impact on income, neighbor opposition or not-in-my-own backyard (NIMBY) attitudes, and impact of scattered site housing on property values. The results showed that the respondents were, on average, more satisfied with the buildings where they lived than their landlord relations or external neighbors. Furthermore, the study found very little neighbor opposition based on race or class and no negative impact on property values from nearby scattered site developments. Based on these findings, recommendations to continue implementing housing dispersal strategies are offered. In addition, directions for future research are provided.

Keywords

homelessness African Americans supportive housing NIMBY spatial location

Introduction

Housing dispersal strategies (scattered sites) have its roots in efforts to construct public housing in more ethnically diverse and affluent communities. The impetus behind this policy shift was the overrepresentation of Blacks/African Americans living in public housing developments with high poverty rates and racial segregation. Studying the patterns of racial and ethnic segregation as a result of the association between a group’s spatial position in society and their socio-economic well-being has long been a concern of social scientists (Gotham, 2003; Massey, 2001; Peterson & Krivo, 2009; Velez, Lyons, & Santoro, 2015; Wilson, 1987, 2012). Massey (2001) pointed out that individuals and families move spatially when their socio-economic situation improves, hoping to improve access to opportunities and benefits. Moreover, individuals and families seek to move into more ethnically diverse and economically viable communities to improve their chances of gaining greater access to residentially determined resources.

Most recently, nonprofit housing providers seeking to end homelessness use this same housing dispersal or scattered sites strategy. This is important because comparable with public housing residents when examining ethnic composition in homelessness, Blacks/African Americans are significantly overrepresented in the homeless population. The Institute for Children, Poverty & Homelessness (2012, para. 4) reported that statistics relative to Black/African Americans and homelessness paint a bleak picture: In 2010, one in 141 Black/African American family members stayed in a homeless shelter, a rate 7 times higher than for White/European American families. Furthermore, Black/African American members in families make up 12.1% of the U.S. family population, yet represented 38.8% of sheltered people in families in 2010. In comparison, 65.8% of people in families in the general population are White/European Americans while White/European American family members only occupied 28.6% of family shelter beds in 2010. The Institute further noted that homelessness is primarily a poverty issue, with nearly one quarter (23.3%) of Black/African American families in 2010 living in poverty, 3 times the rate of White/European American families (7.1%).

Burt (1992), Quigley and Raphael (2001), and Ji (2006) agreed that homelessness is a housing and economic issue. Ji (2006) studied four structural risk factors of homelessness in 52 metropolitan areas across the United States: poverty, economic conditions, lack of affordable housing, and the low levels or reduced real (entitlement) benefits. The study reported that only the poverty rate is strongly associated with the homelessness rate and determined to be a root issue that strongly affects the growth rate of homelessness among the studied variables. The study showed that poverty rates correlated with rates of homelessness because a lack of adequate income prevents people from being able to afford market rate housing. Thus, it is important that recent studies (Corporation for Supportive Housing, 2005; Furman Center for Real Estate and Urban Policy, 2008; Kresky-Wolff, Larson, O’Brien, & McGraw, 2010) continue on the impact of scattered site housing on socio-economic well-being. However, these recent studies have shifted from surveying public housing residents to individuals and families struggling with homelessness.

For homeless Blacks/African Americans, the same social and economic challenges exist as it did for public housing residents. Thus, measuring its effectiveness remains critical. The next two sections briefly review the relevant literature and identify the rationale behind this study’s use of the analyzing variables of resident satisfaction, sense of community, income, neighbor opposition, and property values.

Resident Satisfaction, Sense of Community, and Income

As of 1996, empirical studies focused on the benefits and satisfaction of the incoming tenants as a result of relocating to different communities. The works of Burby and Rohe (1989), Chandler (1992), Lord and Rent (1987), and Perloff, Davis, and Jones (1979) are relevant . These studies concurred that housing dispersal was successful based on residential satisfaction and indicated that those who are satisfied with their residence are also more likely to be integrated into the community than those who were less satisfied. Burby and Rohe (1989) concluded that residential satisfaction of one’s dwelling meant more than just the satisfaction with the physical structure. The study also indicated that satisfaction originates from the environmental setting and aesthetic factors. In a more recent study, Batson and Monnat (2015) put forward that satisfaction with one’s dwelling can act as a buffer against the effects of neighborhood distress including housing foreclosures, quality of life, and overall neighborhood satisfaction.

Additionally, research indicates that having a sense of community and related comparative aspects have a wide range of positive impacts on the outcomes of individuals and groups (Davidson & Cotter, 2006; McMillian & Chavis, 1986). These impacts may include connections, self-identify, and belonging, and are inherent in a sense of community. The lack thereof may lead to less positive outcomes and could lead to social isolation. Also, having a sense of community can mediate and moderate community-level socio-economic disadvantages, along with physical health problems (Browning & Cagney, 2003).

Finally, in studies before the 1980s, income was not strongly correlated with how scattered site units were evaluated by the neighbors. However, this conclusion is a paradox because an expected outcome in housing dispersal is to improve opportunities that help in reducing poverty (e.g., securing employment). Therefore, it is reasonable to measure whether this quality of life change has occurred as a result of the move. Although Perloff et al. (1979) was not specifically seeking changes in income, the study did find that the median income of the respondents increased from US$7,813 to US$13,355 (at the time) and unemployment rates dropped by more than 50%. Similarly, a survey of adults in the Gatreaux demonstration (the result of a court-ordered mandate in Chicago to relocate families to the suburbs in more diverse and affluent communities) concluded that the move increased the adults’ employment, but had no effect on pay or hours worked (Rosenbaum & Popkin, 1991).

Neighborhood Opposition and Property Values

Studies (Galster, 2007; Rosenbaum, 1995) on neighbor opposition to both scattered site units and the tenants have been conducted. Key findings indicated the importance of socio-economic factors in social mixing including resistance to change from lower income households, higher income households were more supportive, and variations in attitudes toward integration were associated with levels of education. Other results concluded that age, correlated with tolerance, and willingness to extend equity to other groups. Overall, age of the household, education, a balance of homes, and rental units seemed to be the mix that elicited approval from the neighbors.

Out of investigations from 1963 to 1986 on the controversial link between scattered site developments and property values, the collective findings indicated that only one study in Fairfax County, Virginia, reported that scattered site or other types of low-income housing impaired the values of adjacent properties (Babb, Pol, & Guy, 1984; Baird, 1980; Nourse, 1963; Rabiega, Ta-win, & Robinson, 1984; Varady, 1974; Wattenberg, 1974; Watts & Free, 1973). Later studies (Briggs, Darden, & Aidala, 1999; Galster, Tatian, & Pettit, 2004; Puryear & Hayes, 1990) concluded that scattered sites or other types of low-income housing did not impair the values of adjacent property values.

Theoretical Framework

In Wilson’s (1987) seminal work, The Truly Disadvantaged, the overarching premise regarding the impact of living in areas of concentrated poverty is his theory of “social isolation” (p. 256), an equally, if not more disturbing trend than racism. Wilson argued that social isolation deprives those living in locations with concentrated poverty, not only of social and economic resources, including conventional role models (e.g., seeing someone else go to work). He also contended that social isolation hinders cultural learning from external social networks that help “facilitate economic and social mobility in modern society” (p. 256). Social isolation has been studied by others (Basu, 2013; Delise, 1988; Fine & Spencer, 2009), since The Truly Disadvantaged, and determined to be a core component in the very idea of poverty. Basu (2013) argued that a person’s sense of belonging to a group or society is key to improving capability or supporting economic progression. Other empirical studies reported the correlation of living in a poor neighborhood and access to social resources. Tigges, Browne, and Green (1998) linked low income, greater isolation, and a lower sense of belonging, which also affects the perceptions and experiences of stigmatization and isolation for those who live on low income and the effects of social resources and different standards on economic outcomes (Grootaert, 1998).

Conceptual Framework for Analyzing the Effects of Scattered Site Supportive Housing

This study adopted the underlying assumption that “social isolation” is a result of concentrated poverty and residential segregation and hinders opportunities that can help community members secure jobs, access quality education, and build social capital and networks to help end poverty and homelessness. Further this study adopted Cruz-Saco (2008) and Jargowsky & Swanstrom (2009) definitions of social and economic integration as the antithesis of social isolation. Measuring social and economic integration is not easy because there is no universal definition of social and economic integration, but these two papers placed an emphasis on efforts to improve the quality of life for people of color. With this framework in place, this study sought to join the ongoing debate on spatial location and race and class effects to help address the plight of hundreds of thousands of Blacks/African Americans lingering in the current homeless system.

By combining five of the known analyzing variables (resident satisfaction, sense of community, income, neighbors’ opposition, and property values), thought to at minimum foster or hinder social and economic integration, this study also represented a different approach. Some studies looked only at neighborhood opposition and impact on property values, while some studies focused only on resident satisfaction, and then others only measured a sense of community and/or only income. It was this study’s intent that by combining the variables in one study, a clearer picture might emerge as to direction and predictors to help stakeholders achieve or improve current efforts of community social and economic integration.

Supporting Wilson’s (1987) theory that social isolation occurs in concentrated areas of poverty, this study proposed that social and economic integration can only be achieved depending on where scattered site housing is located, along with other key factors present that promote its occurrence. Thus, social and economic integration served as the dependent variable, and the location of scattered site housing served as the independent variable.

Scattered Site Supportive Housing

For the needs of this study, scattered site supportive housing is defined as any single room occupancy, studio, to any number of bedroom units in multi-family buildings dispersed throughout communities rather than concentrated in areas of poverty and segregation. Rental units are owned by a social service agency or private landlord where tenant-based rent subsidies exist. Governmental sources pay 70% of the rent at fair market rate (FMR) as determined by the area median income (AMI), and the tenant pays 30% of his or her income. Wrap-around supportive services, such as case management, on-site mental health care, and/or employment services, are an integral part of the program; hence, the “supportive” term is included in the supportive housing model.

Purpose of the Study

The purpose of this study, then, was to further the literature on the impact of spatial location and housing as it relates to efforts to reduce social isolation and achieve social and economic integration. Furthermore, the study hoped to add to identified best practices on how to achieve social and economic integration, specifically as it relates to Blacks/African Americans in the homeless system and living in poverty.

Research Questions and Hypothesis

This study’s overarching research question was

Research Question 1: Does the effects of scattered site supportive housing located in a more affluent and predominately White/European American community promote or lead to social and economic integration of men formerly homeless and primarily Black/African American?

Five sub-questions are listed below based on the identified analyzing variables:

Research Question 1a: Do men who are formerly homeless and housed in scattered site supportive housing report resident or tenant satisfaction with their housing options?

Research Question 1b: Do tenants in scattered site supportive housing feel socially integrated into the communities where they live? (identified as a sense of community)

Research Question 1c: Have tenants in scattered site supportive housing seen an increase in their levels of income from entry into the housing program, to time of study?

Research Question 1d: What are the neighbors’ perceptions of scattered site housing developments on the communities where tenants move into?

Research Question 1e: Is there a negative association between sales prices (property values) and scattered sites developed 3 years before and after development?

Because the respondents in this study had additional social supports (e.g., an employment program, case management and health care, the local advocacy coalition and the mayor) in the study community, this author hypothesized as follows:

Hypothesis 1: The respondents would primarily report positive perceptions and their neighbors would not be highly opposed.

Method

Participants

The current study included three sets of data. Group one participants (n = 86) included men who were formerly homeless at the time of the study and lived full-time in scattered site supportive housing. The second group of participants came from approximately 38 households located in the Chicago communities of Lakeview, Uptown, Rogers Park, the Near North, and an additional 48 from the Chicago metropolitan area (n = 86) which may or may not include the communities aforementioned, but had supportive housing units in their communities.

Measures

Measures in the study included an informed consent, a Sense of Community scale originally developed by McMillan and Chavis (1986) and adapted by Briggs et al., but three questions were added and showed a coefficient alpha of 94%. A Tenant Satisfaction Survey was used, containing 15 questions from a modified version of Community Science’s (2011) Likert-type scale, and a Neighbor’s Perception Survey, modified from Briggs et al. with two sections (a) homeowners (or renters) perceptions of neighborhood and plans to move by proximity to scattered sites and race/ethnicity and (b) a scale of opposition. The survey consisted of a total of 10 questions (five in each section). The Briggs et al. study used a 20-point scale; however, for this study, the scale ranged from 0 to 10 with 0 being the lowest point and 10 being the highest. The opposition section had a Cronbach’s alpha of .76 in the Briggs et al. study, but the largest score went to 20. A general demographic survey was also administered consisting of 10 general demographic questions (e.g., age and income). Finally, a pre/post design was used to determine the impact of scattered site supportive housing on property prices and followed the hedonic econometric–based model, where the independent variables are related to quality or attributes of a product (e.g., the characteristics of real property that one might purchase). The study identified three neighborhoods in the Lakeview community in Chicago where two areas contained supportive housing developments where property (single-family homes and multi-unit buildings) was located within one block to 1 square mile. A third neighborhood was used as the control neighborhood that had no scattered site developments and located further away from the scattered site supportive housing. The study measured the difference in home values at least 3 years before 2000 and the difference in the same home values after 2003. The year 2006 was designated as the cutoff year to control for the general housing market downtown that began in approximately 2007 (Baker, 2008). The median before value was then subtracted from the value (after 2003 but before 2006) and adjusted for inflation to find any differences.

Procedures

Data from the tenants (n = 86) were collected at housing providers administration and program offices in Lakeview and housing units in several other Chicago communities. All respondents entered into a raffle to win one of three US$25.00 gift cards from Target. Three-hundred households were recruited with approval from a list of emails that belonged to the housing provider. To increase the response rate among households and help reduce any potential bias among the email recruits, Survey Monkey, the online survey tool was requested in recruiting additional respondents. Survey Monkey was paid US$3.00 per respondent. Eligible respondents were 18 years of age and older, well-educated, homeowners or renters and lived in the same neighborhoods as the tenants as well as other neighborhoods with supportive housing units.

Study Community

The Lakeview community in Chicago was selected as the study community based on the number of scattered site supportive housing units in the community and its middle-class to more affluent status and percentage of Whites/European American residents (85%). Thirty-two (n = 32) initial properties were selected based on (a) their proximity to scattered site housing; (b) whether they were single-family homes, townhouses, or condominiums; and (c) whether they had pre and post sales prices (before and after scattered site development) available and all, or nearly all of the criteria for the study also available (e.g., square footage, year built, type of building structure, and number of bedrooms, among others). Dummy variables were added for proximity to lake front, churches and schools, street cleaning, and public transportation to indicate the presence of these community variables to complete the model: P = f(Structure, Neighborhoods, Local Services). These variables were expected to shift the outcome in the regression model (e.g., predict sales price).

Characteristics of Tenants

Table 1 highlights the characteristics of 82 of the 86 participants who responded to the General Demographic Survey.

Table 1.

Demographic Characteristics of Sample (Tenants).

	Tenants(n = 82)
Demographic characteristics	n	%
Ethnicity
African American	49	60
European American	22	27
Latino	6	7
Other	5	6
Current income
Yes, I have income	50	63
I sometimes have income	11	14
No, I don’t have income	18	23
Housing contributed to income status
Yes, housing helped	30	38
Somewhat housing helped	12	15
Not sure if housing helped	5	6
No, housing did not help	31	40
Community contributed to income status
Yes, community helped	20	27
Don’t know if community helped	12	16
Maybe community helped	6	8
No, community did not help	37	49

Resident or Tenant Satisfaction

A composite descriptive analysis was conducted using all 14 of the questions to test resident satisfaction after a reliability analysis was performed. The 14 items measuring resident satisfaction of the supportive housing formed a reliable composite (α = .78). Results indicated that the total composite scores ranged from 24 to 54 with a mean score of 40.3, where SD = 7.5. Thus, on average, respondents fell between somewhat and mostly satisfied, while only one respondent was closer to being completely satisfied with a maximum score of 54. To further analyze tenant satisfaction, 11 questions on the Tenant Satisfaction Scale (TSS) were grouped into subcategories: (a) Building, which comprised five questions that related to the actual unit or building where the tenants lived; (b) Neighborhood (e.g., safety) had three questions; and (c) Landlord Relations had three questions as well. The alpha for Building was (α = .61) indicating that it is somewhat less than the acceptable .70. Neighborhood’s alpha was .61 as well, and Building’s alpha was .80. An ANOVA was conducted to examine whether there were statistically significant differences among the respondents’ responses relative to resident satisfaction as indicated Building, Neighborhood, and Landlord Relations. The results revealed statistically significant differences among the three subcategories of resident satisfaction: Building, Neighborhood, and Landlord Relations such that F(1.782, 131.89 = 3.9) p < .05. Table 2 shows the means.

Table 2.

Means and Standard Deviations for Subcategories of Tenant Satisfaction Scale.

Subcategory	n	M	SD
Building	80	2.9	.68
Neighborhood	80	3.0	.77
Landlord Relations	79	3.1	.80

Table 3 shows the pairwise comparison using Bonferroni as a post hoc test.

Table 3.

Repeated Measures ANOVA With Greenhouse-Geisser Correction.

(I) RSS	(J) RSS	M difference(I − J)	SE	Significance^a	95% CI for difference^a
1. Building	2	−.14	.08	.19	[−.33, .04]
1. Building	3	−.26^*	.10	.05	[−.51, −.00]
2. Neighborhood	1	.14	.08	.19	[−.04, .33]
3. Landlord Relations	1	.26^*	.10	.05	[.00, .51]
3. Landlord Relations	2	.12	.10	.70	[−.12, .35]

On average, the 82 (n = 82) participants who took the Tenant Satisfaction Survey were more satisfied with the actual buildings where they lived than the neighborhoods or landlord relations. Finally, a simple linear regression analysis was conducted with the three subcategories, Building, Neighborhood, and Landlord Relations of the Resident Satisfaction Survey, and ethnicity as predictors of a sense of community. The results indicated that there was a statistically significant difference in the combination of variables as shown in Table 4.

Table 4.

Building, Neighborhood, Landlord Relations, and Ethnicity Predicting SOC.

Model	RSS	df	MS	F	Significance
Regression	1,811	4	453	8.1	.000ª
Residual	1,385	63	51
Total	4,996	67

Note. SOC = sense of community.

Upon further review of the regression analysis, only the “Building” significantly contributed to the equation as shown in the coefficients Table 5.

Table 5.

Coefficients Table Indicating Building as Predictor Variable of Sense of Community.

Model	Unstandardized coefficients		Standardized coefficients	t	Significance
Model	B	SE	β	t	Significance
(Constant)	16.	4.4		3.6	.00
Building	4.4	1.6	.36	2.9	.01
Neighborhood	2.0	1.6	.19	1.3	.19
Landlord Relations	1.3	1.2	.12	1.0	.31
Ethnicity	1.2	0.1000	.13	1.2	.24

Sense of Community

The respondents also took the Sense of Community Index–2 (SOCI-2) to determine any levels of connectedness in the community, which is expected to foster social and economic integration. The 15 items were merged to form a composite score. As in the original SOCI, the Alpha was strong with (α = .73) and no items were excluded. The results showed that the totals ranged from 18 to 60 (of which one respondent had 60, meaning he checked “completely agree” on all 15 questions). The mean total responses on the scale was 41 where SD = 8.6 indicating a wide range of the scores further away from the mean. Thus, on average, the respondents fell somewhat in the middle of somewhat or mostly having a sense of community. In comparison on the same items in survey, 50% of the respondents felt the neighborhood was completely a good place to live, 44% felt completely at home in the neighborhood, and 31% felt they completely expected to live in the community for a long time.

Impact of Scattered Site Supportive Housing on Income

Eighty men (n = 80) responded to the question of, “do you have income now,” and the question of, “did you have income before securing the housing they have now.” Fifty-two percent reported that they had income before finding the housing they lived in and 23% shared they sometimes had income before they were placed in scattered site supportive housing. Sixty-one-percent of the respondents shared they had income (at the time of the study). Of those with income, percentages increased by 9% after being housed, however; 52% of the respondents who had income before being housed are a much larger number that anticipated. To investigate whether participants with current income had income before housing to determine whether having housing had an effect on income, a chi-square analysis was conducted. A Fisher’s Exact Test indicated that even though the expected and observed counts differed, there was no statistically significant difference between the observed and expected counts.

Neighbors’ Perceptions on Scattered Site Supportive Housing

The study then sought to understand the perceptions (not-in-my-own backyard [NIMBY] attitudes) of the existing neighbors on the incoming tenants and scattered site housing, specifically when those tenants were Blacks/African Americans. The characteristics of the neighbors are presented in Table 6.

Table 6.

Demographic Characteristics of Sample (Neighbors).

Demographic characteristics	Neighbors(n = 86)
Demographic characteristics	n	%
Ethnicity^a
African American	17	24
European American	47	66
Latino	2	3
Other	5	7
Education level^b
Graduate degree	14	29
Associate or bachelor’s degree	20	42
Some college	10	21
High school diploma	4	8

71 neighbors responded to ethnicity.

46 were asked their education levels via the online versions of the Survey.

Two important items on the scale of neighbors’ perceptions (with the option of selecting from 0 to 10, where 0 = not at all worried and 10 = completely worried) that was used to determine any imminent “flight” from the community were as follows: (1) If the respondent was seeking to change residence, and (2) if so why? Out of the (n = 66) who responded, 18% indicated they would like to move out of the present neighborhood, 9% indicated they would move out of Chicago, 24% said they would move out of state and 46% reported they would move into the same neighborhood. It should be noted that there was not an option on the survey for respondents to say no. When asked why the respondents would change residences, 21% reported they would change residences because of declining property values, 11% shared they would move because of tenants with low income moving in, while 42% shared they would move because existing property taxes were too high. Finally, 52% reported they would be seeking a better climate. In regard to worrying about the neighborhood tipping, n = 12 respondents had scores over the midpoint which was 5. Of those, nine (75%) had a score of 6, one had 7, one had 8, and one respondent checked a 9, which indicated they were highly worried about tipping.

Property Values

Table 7 shows the mean sales prices and standard deviation in the Lakeview East, Lakeview Southeast, and Central Lakeview neighborhoods before 2000 and 2003 to 2006.

Table 7.

Sales Prices Before and After SSH Before 2000 and 2003 to 2006.

Variable	n	M	SD
Sales prices before SSH	23	US$243,000	1.5
Sales prices after SSH	23	US$280,000	1.6

Note. SSH = scattered site housing.

A repeated measures ANCOVA was run to determine whether there were any statistically significantly differences in sales prices before and after scattered site housing based on proximity to scattered site housing. The results indicated that there were no statistically significant difference in the pre or post sales price as a result of the study properties’ proximity to scattered site housing, F(1, 15) = 1.0, p = .33. This result aligned with Briggs et al. The means are shown in Table 8.

Table 8.

Means for Pre and Post Sales Prices, and Proximity to Scattered Site Housing as Covariate.

	n	M	Proximity
			Close	Far
			M	M
Sales—Pre	17	1.5	1.7	1.4
Sales—Post	17	1.5	1.7	1.4

A linear regression analysis was conducted with the year the structure was built, type of home (single-family vs. multi-unit), and proximity to scattered site housing predicting sales price change scores after adjusting for inflation. As shown in Table 9, only the year the property was built significantly contributed to the model, P = f(Structural factors, Neighborhood, Local Services) but this is only when the year the property was built was included in combination with all the variables such that F(3, 20) = 3.45, p < .05.

Table 9.

Linear Regression of Year Built, Property Type and Proximity to Supportive Housing Predicting Sales Price With Change Score After Adjusting for Inflation.

Model	Unstandardized coefficients		Standardized coefficients	t	Significance
Model	B	SE	β	t	Significance
(Constant)	5.0	2.1		2.4	.02
Year property built	−2.0	.86	−.44	−2.4	.03
Property type	.68	.38	.33	1.0	.09
Proximity to SSH	.45	.58	.14	.77	.45

Note. SSH = scattered site housing.

Finding only one of the predictor variables that contributes to the model, P = f(Structural factors, Neighborhood, Local Services) may be the result of a much smaller sample size, unlike Briggs et al. (1999) who found all three variables influenced sales price before and after scattered site housing. This study further examined the sales prices 3 years before and 3 years after scattered site was developed in three neighborhoods in Lakeview: Lakeview East, Lakeview Southeast, and Central Lakeview to determine whether there was an effect of sales prices. The difference-in-differences measures was obtained by subtracting the median home values before 1996 to 2000 and the 2003 to 2006 median home values differences such that the model derived is shown:

V = (ASHP - BSHP) (IFH),

where V = value of property, ASHP = After Supportive Housing Program, BSHP = Before Supportive Housing Program, IFH = Inflation Factor for Housing (3.3% ).

Table 10 shows the median sales price before and after scattered site supportive housing programs were established.

Table 10.

Median Sales Prices Before and After Scattered Site Supportive Housing Developments.

Variable	n	Mdn
Median sales prices before SSH	23	US$248,500
Median sales prices after SSH	23	US$395,000

Note. SSH = scattered site housing.

Taking the median sales price after SSH was developed and subtracting the sales price before SSH was developed (multiplied by .967) such that, (US$395,000 − US$248,500) (.967) = US$141,666. This result indicate that property values actually increased by US$141,666, taking into account for inflation (.967) after scattered site supportive housing was developed in Lakeview.

Discussion

This study examined the effects of scattered site supportive housing on the social and economic integration of men who were formerly homeless, of which 60% are Blacks/African Americans. By using five analyzing variables the study explored the following: (a) tenant satisfaction, (b) sense of community, (c) income, (d) neighbor opposition, and (e) impact on property values. Wilson (1987, 2012) suggested that living in neighborhoods of concentrated poverty results in social isolation, while living in less ethnically homogeneous and more affluent communities provides the social connectedness and human interaction needed that allow people to enhance their capability and support economic progress. The study’s analysis of the five key elements not only suggests that social and economic integration may indeed be connected to where a person lives but also suggests the increasing importance of the built environment, no matter the location. The phenomena of “white flight” may or may not happen in a changing community, and scattered site supportive or what is sometimes called affordable or low-income housing does not diminish property values. The following sections give a brief discussion on each variable tested.

Resident or Tenant Satisfaction

The findings in this section support the existing literature by showing that there were no respondents who were 100% satisfied with their units in new socio-economic communities (Chandler, 1992; Hogan, 1996; Perloff et al., 1979). This result is not unusual as it is reasonable for tenants or homeowners not to like everything about the physical structure of their dwelling, neighborhood or landlords. Most satisfaction surveys do not show the same level of satisfaction among the respondents. Yet, this study revealed that the respondents were, on average, more satisfied with the building than their landlord relations, or their external (outside the building) neighbors. This finding was also not unusual because the respondents’ internal (inside the building) neighbors were likely men experiencing former homelessness. Furthermore, this study suggests the growing importance of the built environment although spatial location cannot be excluded from efforts to foster social and economic integration. There are a number of implications related to satisfaction with the physical environment such as it serves to fill the need for privacy and social interaction, it has a positive impact on mental and physical health, and is used to predict based on resident perceptions of neighborhood safety and employment. In addition, for Blacks/African Americans, the built environment is substantively related to the quality of health (Gordon-Larsen, Nelson, Page, & Popkin, 2006; Massey, 1990; Ross & Mirowsky, 2001; Wilson, 1996).

Income

Income plays a vast role in obtaining and retaining housing stability, which is key in preventing and ending homelessness (Corporation for Supportive Housing, 2005). The general demographics survey reported that a loss of income was the primary reason the respondents had initially become homeless. This is consistent with Ji’s (2011) premise that homelessness is a housing and economic problem. As mentioned, the findings from analyzing income suggest that housing in a more affluent neighborhood, has some influence on the increase of income (9%). In this study, a number of the respondents had access to an employment program where they were directly employed. The program was managed by the respondent’s housing provider, and funding was afforded through Special Service Area (SSA) dollars, which are localized property tax levies within a contiguous area. The inclusion of an employment program provided by a city contract suggests the benefit of living in a community where this type of amenity is available.

Sense of Community

Research indicates that having a sense of community and relative aspects have a wide range of positive impacts on the outcomes of individuals and groups (Davidson & Cotter, 2006; McMillian & Chavis, 1986). These impacts may include connections, self-identify, and belonging and are inherent in possessing a sense of community. A sense of community is likely fostered by informal social contact with the respondents’ neighbors (other tenants living in the building).

Neighbors’ Perceptions

The premise that community members are averse to low-income or minority residents as neighbors did not seem to be the case in the Lakeview community. NIMBY attitudes have been a concern for years (Corporation for Supportive Housing, 2006; Young, 2012) in attempts to integrate communities. These attitudes have ranged from concerns over the possibility of the neighborhood tipping (becoming predominately Black/African American), increased crime to diminished property values. The finding that the neighbors supported the premise that government should do more to increase the numbers of subsidized housing could be taken to indicate support for scattered site developments in their neighborhoods. Furthermore, the neighbors were more inclined to move because of high property taxes than Blacks/African Americans with low income moving into the community. This finding also suggests either no or nominal opposition to the scattered sites. These findings are also consistent with existing research that the fears of Whites/European African Americans regarding Blacks/African Americans and/or lower income individuals and families moving into their neighborhood are far more myth than reality (DeGenova, Goodwin, Moriarty, & Robitaille, 2009).

Finally, it is worth noting that an assumption regarding this finding may be related to the fact that the study community is comprised of a high percentage of lesbian, gay, bisexual, and transgender (LGBT) members and Chicago’s first openly gay mayor. Thus, community members may have more tolerant attitudes toward differences in ethnicity, race, gender, and other socio-economic demographics.

Scattered Sites and Impact on Property Values

The final test of the study was to examine property values before and after scattered site supportive housing was developed to determine whether nearby single-family homes and other real estate were negatively affected by the close proximity of the scattered site developments. Results of the property value analysis revealed that scattered site supportive housing does not negatively affect property values in a growth housing market, and most likely does not when the market is in a decline. This finding is consistent with other studies (Babb et al., 1984; Briggs et al., 1999).

Limitations of the Study

While this study provided strong support for the positive impact of scattered site housing on the socio-economic well-being of those with low-incomes, homeless and Blacks/African Americans, there were a number of limitations to be considered. First, the sample of both tenants (n = 86) and neighbors (n = 86) was small, as well as the small numbers of properties analyzed for impact of property values relative to the larger numbers of scattered site supportive housing. Second, as with any examination of neighborhood characteristics relative to property values, there is no infallible way to account for every variable that may affect prices. Finally, gathering reliable data for property listings is an unwieldy process, and often data are not available and may skew results because of omitted variable bias.

Conclusion and Future Directions in Research

Taking these findings together, the study appeared to demonstrate support, albeit small, of the type of housing and variables that influence social and economic integration of individuals homeless, specifically Blacks/African Americans. Findings from this study and findings from other research indicate the need to continue involving both residents and existing neighbors as respondents in studies of this type. Various quantitative studies have documented more resident satisfaction results, but surveying the neighbors of incoming tenants is equally important. As this study showed, it is probable that studies can continue to refute and even end the myth of the overwhelming presence of NIMBY attitudes. Also, considering previous research results and the current findings, it is proposed that nonprofit housing providers and affordable housing developers continue to use housing dispersal strategies to improve the socio-economic status of Blacks/African Americans and citizens with low income.

Overall, it is recommended that more studies (a) include the existing neighbors, (b) continue to focus on the importance of the built environment and what fosters a sense of community, (c) increase employment services and programs or income increasing services (e.g., assisting clients with obtaining mainstream benefits such as social security disability), and (d) continue to assess the impact of low-income developments on property values in new neighborhoods.

Footnotes

Acknowledgements

Much thanks to Dr. Judah Viola, Dr. Bradley Olson, and Dr. Suzette Fromm-Reed, Community Psychology Department, National Louis University

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Author Biography

Geraldine L. Palmer, PhD, holds a doctorate degree in Community Psychology from National Louis University, Chicago, IL. She is the Executive Director of South Suburban PADS, a nonprofit housing provider, and adjunct professor at Adler University, Chicago, IL. Her current research interests are housing, housing policy, homelessness, African American culture and community life, and power disparities.

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