Race/Ethnicity and Homeownership in an Emerging Immigrant Gateway of the US Southeast: A Neighborhood Scale Analysis

Abstract

Owning a home has become a distant, often unattainable dream for many Americans since the 2007–2009 recession. The shortage of homes has decreased affordability, forcing 43 million U.S. households to become renters rather than owners. Racially targeted policies and widespread discrimination, coupled with neoliberal urban renewal policies, have forced communities of color, especially immigrants and the foreign-born, at the greatest disadvantage in homeownership. This paper examines tract-scale disparities in homeownership across major racial/ethnic groups. Using the U.S. Census Office of Management and Budget’s (OMB) 2019 definition of the 13-county-metropolitan statistical area (MSA) of Nashville, Tennessee, as the study area, I use five-year American Community Survey (ACS) (2015–2019) data estimates to examine the spatial disparity in homeownership and its predictors. Nashville MSA is one of the fastest-growing southern gateways, and it is also the largest, most diverse, and most intermixed metropolis in Tennessee. It contains higher than the state’s overall share of foreign-born, and during 2019–2040, its share of immigrants is projected to grow by 40.7%, making it the best-suited laboratory for race/immigrant-focused research on housing. This analysis finds significant differences in race-based mean per-capita income, with Whites ($32,522) and Asians ($32,556) at the top, whereas Blacks ($25,062) and Hispanics ($20,091) are at the lowest. The ratio of race-based per-capita-income-versus-median housing values is the highest for Whites (15.19) and Asians (15.07) and the lowest for Blacks (11.49) and Hispanics (9.27), putting these two groups as the most disadvantaged regarding their affordability. Regression models suggest lower White homeownership in higher diversity tracts among foreign-born-not-citizens (FBNCs), whereas Black and Hispanic homeownerships are higher in tracts with higher diversity among FBNCs. Interestingly, Asian homeownership is high in tracts with high-income Black tracts, pointing toward the increasing significance of class.

1. A Brief Overview of Race/Ethnicity and Homeownership Disparity

1.1. Homeownership and the American Dream

Owning a home has become a distant dream for most Americans, especially since the recession of 2007–2009, followed by COVID-19-induced skyrocketed housing prices. Home prices had also started skyrocketing due to banking and finance-related policy changes that affected the housing market, making it more difficult for people of color. These policies attributed to the restricted allocation of new housing permits, reducing the overall supply of housing stock in the market. While the recession added to the problem of declining homeownership among minorities and socio-economically disadvantaged communities, homeownership has remained a challenging issue for several decades in the United States due to its racially targeted policies and widespread discrimination (Fields and Raymond 2021). Further, the neoliberal policies of gentrification-focused urban renewals have cumulatively forced communities of color from ownership into rentership (Chaney et al. 2018; Fields and Raymond 2021; Hightower and Fraser 2020; Thurber et al. 2021). The relatively lower supply of housing in the market at affordable prices made it very tough for almost everyone, but its impacts were felt unprecedently by the disadvantaged groups all over the country, with its severity falling disproportionately on the immigrants coming into the emerging immigrant gateways of the South, and Nashville is one of them (Chaney et al. 2018; Currier et al. 2018; Fields and Raymond 2021; Hightower and Fraser 2020; Thurber et al. 2021).

Homeownership in the USA remains a manifestation of a set of socio-economic, demographic/lifecycle, and cultural factors, including race/ethnicity, nationality, and immigration status. These processes eventually put people of color, especially immigrants and the foreign-born, many of whom made the US southern metropolises their new homes, at the greatest disadvantage in terms of both ownership and rentership in contemporary housing markets (Chaney et al. 2018; Currier et al. 2018; Hightower and Fraser 2020; Thurber et al. 2021). The rapidly rising share of immigrants, many of whom are still not citizens, are also deprived of many government programs and policies that would allow them to seek the benefits of the banking/financial institutions. All of these have exacerbated the issue of homeownership and wealth disparity across the communities of color, and those who are still not citizens often suffer from double penalties as they are discriminated against due to their pending legal status and non-White identities.

1.2. Determinants of Homeownership

Research on homeownership and wealth inequality has identified major categories of determinants: (i) Macro-level (structural) factors that comprise housing, stock market, assets, tax policies, racism, and (ii) Micro-level factors such as age, family structure, education, income, and inheritances (Freeman and Hamilton 2004; Keister and Moller 2000). Segregation tax—the indirect taxes relegated to minority-segregated clusters that attribute toward lower returns on homeownership investments by minorities (Faber and Ellen 2016; Flippen 2004; Shapiro 2006)—eventually puts minorities at a double disadvantage. It is also true that neighborhood demographics correlate with home values, although race and class both matter (Crowder 2001; Emerson et al. 2001; Galster 2008, 2019; Galster et al. 2018; Krysan and Farley 2001; Krysan et al. 2009; South and Crowder 1998). In this body of work, however, meso-level factors such as neighborhood characteristics and their relationship with wealth accumulation are missing in the literature (Keister and Moller 2000; Killewald et al. 2017). Also, while race and class are related to home values, class especially remains salient (Anacker 2010; Coate and Schwester 2011; Flippen 2004).

Conventional scholarship has also discussed how racist policies have created and continue to create inequities in the built environment, producing racially and ethnically segregated communities, poor housing conditions, unwalkable neighborhoods, and general disadvantage (Hall et al. 2015; Rugh et al. 2015; Rugh and Massey 2010; Yang et al. 2023). Racial/ethnic, and economic segregation, disinvestment, and discrimination created by redlining policies, etc., remain in several cities even today, albeit in a different style, and these policies have added to present-day disparities through health outcomes such as preterm births, asthma, mortality, sleep quality, heart and lung issues, etc. (Galster and Santiago 2017; Yang et al. 2023).

1.3. Racial/Ethnic Disparity in Homeownership

Even though homeownership has long been symbolic of the American dream, for many non-Whites, it has been a dream deferred. Relatively lower incomes, the absence of family assets and wealth, and fewer financial resources, especially among Blacks and Hispanics, along with institutional and structural discriminatory practices, have kept them from attaining their American dream. Whether or not discrimination continues to be an important determinant of homeownership in 21st-century American cities has remained a topic of academic debate, and such practices have continued to impact minorities in numerous other ways (Sharma and Brown 2012; Sharma 2016a, 2016b; Yinger 1995).

Homes are a key source of wealth creation, and home values are closely related to neighborhood characteristics (Galster 2008; Sharma 2018). Neighborhoods also affect educational attainment, employment, labor, job types, income, and several other factors, and these contribute toward wealth accumulation gaps across various races/ethnicities (Levy 2022; Sharma 2018). In the US, homeownership contributes toward one-third of total household wealth and 40% of capital stock (Albouy and Zabek 2016). Research also suggests that homes in predominately Black neighborhoods across the US are valued at $48,000 lower than predominately White neighborhoods, and this contributes toward a cumulative loss in equity of approximately $156 billion. Also, Black adults, regardless of homeownership, are doubly disadvantaged in neighborhood–wealth relationships (Albouy and Zabek 2016). The latest report suggests that by 2020, the Black homeownership rate was 46.4% compared to 75.8% for White families and the US Census reports also indicated that in 1950, the Black homeownership rate was 34.5% compared to 55% for the overall population (Zhao 2024). As of the second quarter of 2024 reporting, the overall homeownership rate in the USA stood at 65.6%; these rates for Whites, Blacks, Hispanics, Asians, and All-others were at 74.4%, 45.3%, 48.5%, 62.8%, and 58.3%, respectively, pointing toward the long-held hierarchy with Whites at the top, and Hispanics and Blacks at the bottom (HTTP1 n.d., at https://fred.stlouisfed.org/release/tables?eid=784188&rid=296).

1.4. Structural Racism, Homeownership and Health Manifestations

Freeman and Hamilton (2004) suggested that even before the Civil Rights era, minorities, especially Blacks, were routinely discriminated against in almost all aspects of life, including buying and owning homes, and these occurred through routine practices by the real estate and financial institutions (steering, blockbusting, redlining, etc.). Structural and institutional racism has remained embedded in the past and present operations of the US housing market, and this has also contributed toward the largest share of racial health inequities among minorities (Lynch and Meier 2020). Structural racism, though a product of history, has adapted to these new contexts over time to recreate the conditions that give rise to poor health for racially minoritized populations (Williams et al. 2021). Structural racism permeates through the systems of education, housing, employment, healthcare, and criminal justice, impacting resource distribution and access to opportunity, which eventually reinforces unequal social, economic, and environmental conditions and, hence, poor health. Communities of color face a disproportionate burden of environmental hazards and reduced access to quality food, transportation, healthcare, educational and employment opportunities, as well as recreation and preventative health services (Lynch and Meier 2020).

1.5. Immigrants’ Experiences with Homeownership

Numerous immigration scholars have addressed the growth of immigrant gateways and the reasons for the attractiveness of such emerging and established immigrant cities (McDaniel et al. 2019a; McDaniel 2021; Singer et al. 2008). Immigration scholars thus far have discussed immigrants’ experiences with regard to social, economic, political, and institutional assimilation processes and the roles of policies and support measures taken by the state and federal programs that have made specific cities more receptive toward immigrants (for example Nashville in Tennessee has become an attractive destination for immigrants, largely driven by numerous policy changes) (see McDaniel et al. 2019b; McDaniel 2021). Yet, there are enormous discrepancies in immigrants’ experiences and pathways toward acceptance and integration with regard to immigrants’ housing experiences. There also exists significant disparities in housing experiences between documented and undocumented Hispanic immigrants compared to US-born Hispanics, Whites, and Blacks (Allen 2022; McConnell and Akresh 2010). In Los Angeles, for example, immigrants’ legal status was a better predictor of housing affordability among low-income households rather than their race or nativity (McConnell 2013; McConnell and Akresh 2010).

Nashville, the largest metropolis in Tennessee, is also known as a Nuevo southern metropolis due to its attractiveness toward diverse immigrant groups originating from Asian, African, Latin American, and Caribbean countries (Chaney 2015, 2022; Chaney et al. 2018; Chaney and Clark 2020; McDaniel 2021; Sharma 2016a, 2016b). Nashville—often referred to as a Hispanic hypergrowth metro area (Suro and Singer 2002), a pre-emerging immigrant gateway (Singer 2004), a 21st-century gateway (Singer et al. 2008), and a minor-emerging gateway (Singer 2015)—remains one such attractive destination for many groups, including Hispanic populations. Such unprecedented growth in a diverse population, however, has also raised numerous social and economic problems for immigrants, and housing is one such issue that affects all.

While a significant body of housing scholarship has focused on the largest cities and metropolises of the USA (e.g., McConnell 2013; McConnell and Akresh 2010), not much is known about housing-related issues in the emerging gateways of the South. Nashville, Tennessee, is a mid-sized emerging immigrant gateway that has attracted a significant share of immigrants and has seen a steep rise in its housing prices, but not much is known about its impacts on immigrant communities. Among those who have addressed housing research, they have consistently shown that minorities, especially Blacks, have suffered from lower levels of homeownership in major American metropolises, once again ignoring these effects in mid- and small-sized metropolises, especially those that have attracted significant diversity and immigrants over last two decades. Thus, the impacts of housing market issues on immigrant communities, especially in mid- and small-sized metropolises of the South that have a relatively higher share of immigrants and the foreign-born is still unknown. Based on the scholarship thus far, it is fair to expect that immigrants who have not yet become citizens are likely disadvantaged due to their restricted access to banking/financial and legal resources (see Chaney et al. 2018), making it more difficult to own homes; and this paper specifically explores these differences among various racial/ethnic groups along with those in FBNC category while focusing on the census tracts of Nashville metropolis. In doing so, this research also examines how the determinants of homeownership vary across major population groups and the FBNCs since the emerging gateway of Nashville has a significant share of its population who are immigrants (McDaniel et al. 2019a; McDaniel 2021). This intra-urban inspection of determinants will provide pathways for creating pro-immigrant-based policy measures that can help create an inclusive and equitable society.

2. Research Design

2.1. Study Area

Based on my ongoing research on the rent burden faced by immigrants and racial/ethnic minorities in the Nashville metropolis (Sharma and Samarin 2024), this research attempts to expand our understanding of homeownership disparities faced by racial/ethnic groups and immigrants in the neighborhoods, defined by census tracts in the fastest growing immigrant hub of Nashville in Tennessee. This metropolis has attracted a significant share of immigrants from all over the world, including the regions that were not the conventional senders. These include African, Latin American, and Asian countries, all of which have attributed to the fast-increased multi-group diversity of Nashville (Chaney et al. 2018).1 The dynamism in this fastest emerging music capital of the country has also attracted several economic sectors, making Nashville one of the most expensive cities in the US, especially regarding its housing costs (Samarin and Sharma 2022). Due to its unaffordability in terms of housing prices and the legal and financial difficulties faced by immigrants, especially those not yet citizens, this paper explores homeownership disparities among racial/ethnic groups while also focusing on the FBNCs in the census tracts of the Nashville metropolis.

2.2. Data Sources

Based on the OMB’s 2019 definition, the Nashville MSA comprised 13 counties, namely Davidson, Sumner, Wilson, Cheatham, Dickson, Robertson, Rutherford, Williamson, Cannon, Macon, Smith, Trousdale, and Maury. I used tract-scale data from the National Historic Geographic Information Science (NHGIS)’s five-year estimates (2014–2019) to extract the demographic, socio-economic, and other relevant housing data (Manson et al. 2022).2 In Nashville, out of a total of 372 census tracts in these 13 counties, only 365 valid tracts with population were included in this analysis, and the remaining tracts with zero population were removed.

2.3. Methodological Steps

Computation of Six Types of Dependent (Y) Variables

Given the focus of this analysis, I computed six types of dependent variables that are later used in regression analyses to examine race-based homeownership disparities. These Y-variables are listed below:

Y1: Share of White Homeowners/Total Occupied Households;

Y2: Share of Black Homeowners/Total Occupied Households;

Y3: Share of Asians-with-Hawaiian and Pacific Islander Homeowners/Total Occupied Households

Y4: Share of Hispanic Homeowners/Total Occupied Households;

Y5: Share of non-Asian Minorities Homeowners/Total Occupied Households;

Y6: Share of Black and Hispanic-Combined Homeowners/Total Occupied Households

2.4. Identification of Explanatory (X) Variables

The Multi-Group Diversity Scores (DS) were calculated for all valid 365 census tracts of Nashville, using five major racial/ethnic groups (non-Hispanic Whites, non-Hispanic Blacks, non-Hispanic Asians including Hawaiian/Pacific Islanders, non-Hispanic American Indians along with All-Other groups, and Hispanics) for total tract population, using the following specification adopted from Theil and Finizza (1971):

$$DS={{\displaystyle \sum }}_{r=1}^{n{\displaystyle \sum }}(\mathit{Pr})\mathit{ln}(1/Pr)$$

(1)

where P_r refers to a particular racial/ethnic group’s proportion of the total population for a tract. All logarithmic calculations use a natural log (when the proportion of a particular group in a tract_(ri) is 0, then the log is set to 0; I prefer this procedure here, as the absence of a group(s) should result in a 0). The higher the value of DS, the more diverse an area is, and vice versa.

Likewise, using the same principles of computations, multi-group diversity scores were also computed for the foreign-born-not-citizens share of these five population groups (DS-_FBNC) for each tract. These two types of DS-Overall and DS-_FBNC are later used in correlations, cartographic mapping, and regression analyses. Having both types of DS helps us understand if being foreign-born and not having citizenship—our proxy for the immigrant population—might put immigrants at a greater disadvantage in terms of homeownership compared to others.

Thereafter, the income ratio variables were calculated for major population groups, including minorities, and other similar share variables were calculated. Major ones include educational attainment categories for major racial/ethnic groups, shares of homes in ownership and rentership categories for major races, shares of homes built in different year categories, shares of Foreign-born by their years of entry, and likewise. I also calculate the location quotients (LQs) for five major occupational categories for use as explanatory variables in regression models. To compute the LQ-values for five major occupations, I follow Moineddin et al. (2003)’s specifications:

LQi = (ei/e)/(Ei/E)

(2)

where ei is the employment in occupation i in the local tract; e is the total employment in the tract; Ei is the employment in occupation i at the metropolitan level, and E is the total employment at the entire Nashville metropolitan level (by adding total employment in all five major occupational categories taken together). These values of LQs provide an interesting overview of the under/over-representation of occupation types in specific tracts of Nashville.

Thereafter, I conducted various data exploratory analyses to gather a feel for the data and determine what variables would best serve as the explanatory variables. In this process, I also create various types of ratios and share variables, as they make better sense as explanatory variables for use in the regression models. Some basic descriptive analyses and bivariate correlation analyses were conducted using these variables so that the aspects of multi-collinearity could be examined and the best explanatory variables could be identified for use in the regression models. While I do not intend to discuss all these exploratory analyses at length, some basic understanding of the demographic composition and other major determinants is discussed below in the analysis and findings.

3. Analysis and Findings

3.1. Demographic Composition of Nashville Metropolis, 2019

Nashville has remained an important emerging immigrant gateway of the South due to its attractiveness as an economic hub. As noted in

Table 1. Racial/ethnic composition: USA, Tennessee, and Nashville, 2019.

	USA	Tennessee	Nashville MSA	Percent-By-State’s-R/E-By-Total-Population
	Total (Percent)	Total (Percent)	Total (Percent)
Population (Total), 2019	328,016,242 (100)	6,709,356 (100)	1,871,903 (100)	27.89 (27.90)
Non-Hispanic—Total	266,260,953 (81.17)	6,345,182 (94.57)	1,735,263 (92.7)	27.35 (25.86)
Non-Hispanic–Whites	197,132,096 (60.10)	4,951,558 (73.8)	1,348,843 (72.06)	27.24 (20.10)
Non-Hispanic–Blacks	39,980,733 (12.19)	1,114,068 (16.61)	283,476 (15.14)	25.45 (4.23)
Non-Hispanic–American–Indians	2,160,496 (0.66)	15,553 (0.23)	4018 (0.22)	25.83 (0.06)
Non-Hispanic-Asians-with-Hawaiian & Pacific-Islanders	18,251,837 (5.56)	119,950 (1.79)	52,397 (2.8)	43.68 (0.78)
Non-Hispanic–All-Others	8,735,791 (2.66)	144,053 (2.15)	46,529 (2.49)	32.30 (0.69)
Hispanics	61,755,289 (18.83)	364,174 (5.43)	136,640 (7.3)	37.52 (2.04)

, Nashville’s is higher than the entire state’s share of the Hispanic population (7.30 percent versus 5.43 percent) but far lower than the nation’s share of 18.83 percent. However, Nashville contains 37.52 percent of Tennessee’s Hispanics—2.04 percent of the entire state’s population—making it an especially noted metropolitan area in terms of minority representation.

3.2. Foreign-Born and Immigrants of Nashville

An analysis of Nashville’s foreign-born (FB) population (

Table 2. Demographic portfolio of Nashville’s foreign-born population, 2019.

	Sum	Shr/T-FB	Share
A: Foreign Born and Nativity Status, 2019
Total Population 2019	1,871,903	100	1
Native Total, 2019	1,555,441	83.09	0.831
Total Foreign-Born	90,825	4.85	1
Native, Born in the State of Residence	968,399	51.73	0.517
Native, Born in Other US State	570,249	30.46	0.305
Native, Born Outside US	16,793	0.9	0.009
Native, Born Outside US, Puerto Rico	2643	0.14	0.001
Native, Born US Island Areas	702	0.04	0
Native, Born Abroad of American Parent(s)	13,448	0.72	0.007
B: Foreign Born Status by Race/Ethnicity, 2019
Foreign-Born	90,825	0.0485	1
Foreign-Born Naturalized Citizens	36,383	0.0194	0.4006
Foreign-Born Not-yet-Citizens (FBNCs)	54,442	0.0291	0.5994
White, FBNCs	8760	0.0047	0.0964
Black, FBNCs	4212	0.0023	0.0464
Asian-w-Hawaiian and Pacific Islanders, FBNCs	11,374	0.0061	0.1252
All-Others, FBNCs	10,335	0.0055	0.1138
Hispanics, FBNCs	29,059	0.0155	0.3199
C: Foreign Born Status by Region of Entry, 2019
FB—European Origin of Total FB Population	51,071	0.0273	0.5623
FB—Asian Origin of Total FB Population	28,110	0.015	0.3095
FB—Latin American Origin of Total FB Population	39,201	0.0209	0.4316
FB—Other Regions of Origin of Total FB Population	13,996	0.0075	0.1541
D: Foreign Born Status by Year of Entry, 2019
FB—Entered 2010/later, FBNCs	23,995	0.0128	0.2642
FB—Entered 2000–2009, FBNCs	19,948	0.0107	0.2196
FB—Entered 1990–1999, FBNCs	6936	0.0037	0.0764
FB—Entered Before 1990, FBNCs	3563	0.0019	0.0392

Note: Shr/T-FB = Share out of Total Foreign-Born; these basic descriptive statistics are calculated for each population sub-group from the entire dataset for Nashville MSA.

A) shows that they comprise 4.85% of its total population. Out of the total FB, about 40% are naturalized US citizens (Table 2B), whereas 59.94% are not. The latter cohort comprises an important focus of this research despite its small share of the total metropolitan population. When examining the race-based composition of the FB population (Table 2B), it is obvious that the Hispanics FBNC comprise the largest share—31.99% of the total FB—followed by Asians with Hawaiian/Pacific islanders at 12.52%, followed by All-Others (11.38%) and then Whites and Blacks.

Finally, out of the four major regions of immigrants’ origin (Table 2C), the largest share of foreign-born come from Europe (56.23%), followed by Latin America (43.16%), Asia (30.95%) and other regions (15.41%). Regarding years of entry into the USA (Table 2D), the largest share (26.42%) has entered after 2010, followed by the subsequent decades prior; this suggests that many FB who had entered decades ago have likely attained citizenship.

3.3. Education, Labor, and Socio-Economic Characteristics of Nashville

Almost 31.71% of 25 years/above population in Nashville (

Table 3. Educational attainment profile of Nashville’s 25 years/above population.

Educational Attainment by Gender, Total, 25 Years/Above	Total	Share
Male, Total 25 Years/above	524,810	0.4733
Female, Total 25 Years/above	583,911	0.5267
Total Population, 25 Years/above	1,108,721	1.0000
Male, No Schooling of Total 25 Years/above	4991	0.0045
Female, No Schooling of Total 25 Years/above	5299	0.0048
Total No Schooling of Total 25 Years/above	10,290	0.0093
Male, No High School Degree of Total 25 Years/above	62,765	0.0566
Female, No High School Degree of Total 25 Years/above	60,147	0.0542
Total No High School Degree, of Total 25 Years/above	122,912	0.1109
Male, High School Degree of Total 25 Years/above	172,235	0.1553
Female, High School Degree of Total 25 Years/above	179,308	0.1617
Total High School Degree of Total 25 Years/above	351,543	0.3171
Male, Some College/Assoc. Degree of Total 25 Years/above	141,407	0.1275
Female, Some College/Assoc. Degree of Total 25 Years/above	175,073	0.1579
Total Some College/Assoc. Degree of Total 25 Years/above	316,480	0.2854
Male, Bachelor’s Degree of Total 25 Years/above	91,503	0.0825
Female, Bachelor’s Degree of Total 25 Years/above	102,704	0.0926
Total Bachelor’s Degree of Total 25 Years/above	194,207	0.1752
Male, Master’s Degree of Total 25 Years/above	33,958	0.0306
Female, Master’s Degree of Total 25 Years/above	46,557	0.0420
Total Master’s Degree, of Total 25 Years/above	80,515	0.0726
Male, Professional Degree of Total 25 Years/above	9858	0.0089
Female, Professional Degree of Total 25 Years/above	8534	0.0077
Total Professional Degree, of Total 25 Years/above	18,392	0.0166
Male, Doctorate Degree, of Total 25 Years/above	8093	0.0073
Female, Doctorate Degree, of Total 25 Years/above	6289	0.0057
Total Doctorate Degree of Total 25 Years/above	14,382	0.0130

) hold a high school degree, followed by some college/associate degree (28.54%), Bachelor’s degree (17.5%), no high school (11%) and Master’s/Professional/Doctoral degrees (10.7%). There exists gender-based equal distribution (almost) in educational attainments.

Regarding other characteristics of Nashville’s population and labor (16 years/older), about 13% of Nashville’s total population live below poverty, whereas 43.35% of its total population are civilian labor force (16 years/older) (

Table 4. Labor force characteristics of Nashville (16 years/older), 2019.

Labor Force/Employment Characteristics	Total	Shr-MS-Pop	Shr-LF
Below Poverty Level	246,456	0.1317	N/A
Total Labor, 16 Years/Older	811,391	0.4335	N/A
In Labor Force, Armed Forces	9099	0.0049	0.0112
In Labor Force, Civilians	802,292	0.4286	0.9888
Employed, Civilian Labor Force	760,415	0.4062	0.9372
Unemployed, Civilian Labor Force	41,877	0.0224	0.0516
Employed in Management/Business/Science/Arts	215,817	0.1153	0.2660
Employed in Service	71,423	0.0382	0.0880
Employed in Sales/Office	115,110	0.0615	0.1419
Employed in Natural Resources/Const./Maintenance	52,427	0.0280	0.0646
Employed in Production/Transportation/Material-Moving	95,339	0.0509	0.1175

Note: Shr-MS-Pop (share of Nashville’s total population) and Shr-LF (share of total labor force).

). In terms of major employment types, 26.6% of the civilian labor is engaged in management/business/science/arts occupation, 14.19% is occupied in sales/office, 11.75% in production/transportation/material-moving, 8.8% is engaged in service, and 6.46% is engaged in natural resources/construction/maintenance-type occupations.

Further analysis of per capita income and median household incomes by race (

Table 5. Race-based per capita income and income vs. median housing value ratios.

Racial/Ethnic Group’s Per Capita Income (Inflation Adj.)	Mean Income ($)
Median HH Income	55,386
Overall Per Capita Income	28,609
White Per Capita Income	32,522
Black Per Capita Income	25,062
American Indians Per Capita Income	26,304
Asian Per Capita Income	32,556
Native Hawaiians Per Capita Income	17,716
Some Other Races Per Capita Income	20,569
Two or More Races Per Capita Income	20,239
Hispanic Per Capita Income	20,091
Ratios of Per Capita Income compared to Median Housing Value	Mean
Ratio, White Per Capita Income vs. Median Home Value	0.1519
Ratio, Black Per Capita Income vs. Median Home Value	0.1149
Ratio, Asian Per Capita Income vs. Median Home Value	0.1507
Ratio, Hispanic Per Capita Income vs. Median Home Value	0.0927

) highlights an important aspect of differentiation, and this helped us understand what might be attributing to homeownership differences. While the overall median household income in Nashville is $55,386, there exists a significant difference in race-based mean per capita income; these figures are $32,522 for non-Hispanic Whites, $25,062 for Blacks, $32,556 for Asians, and $20,091 for Hispanics. Further investigation of the ratios of race-based per capita income (mean values) versus overall median housing value—which provides an overall housing purchase stress and each race’s affordability toward buying homes—suggests that these ratio values are 15.19 for Whites, 15.07 for Asians, 11.49 for Blacks, and 9.27 for Hispanics, thence putting Hispanics as the most disadvantaged group in terms of housing affordability in Nashville.

3.4. Nashville’s Housing Composition and Housing Stock

A closer examination of race-based homeownership suggests wide racial gaps, with 47% homeownership for Whites (they comprise 72% of the total population), 6.85% for Blacks (comprise 15% of the total population), 1.44% for Hispanics (comprise 7.3% of the total population), and 0.76% for Asians (comprise 2.8% of the total population) (

Table 6. Housing stock by race-based ownership and year of built structure.

Total Housing Stock	Numbers	Share
Total Stock of Housing Units	720,041	1.000
Total Occupied Homes	633,749	0.880
Total Vacant Homes	86,292	0.120
Housing Units by Built Year
Built After 2014 of Total HH Stock	23,709	0.033
Built 2000–2013 of Total HH Stock	143,578	0.199
Built 1970–1999 of Total HH Stock	343,577	0.477
Built 1950–1969 of Total HH Stock	139,100	0.193
Built Before 1949 of Total HH Stock	70,077	0.097
Total Owner-Occupied Homes by Race	409,956	0.569
White-Alone	337,359	0.469
Black-Alone	49,304	0.068
American Indian	887	0.001
Asian-w-Haw and Pacific Islanders	5468	0.008
All-Other Races	6541	0.009
Hispanic	10,397	0.014

).

Further analysis of Nashville’s total housing stock (Table 6) suggests that about 56.9% of total housing stock in Nashville was owner-occupied; of these, 46.9% were owned by Whites, whereas Black, Hispanic, and Asian owners comprised 6.8%, 0.8% and 1.4% of the total housing stock. The year-built break-up suggests that a large share of the total housing stock was constructed during 1970–1999, followed by those built during 2000–2013 and 1950–1969, and a small share was built most recently, after 2014. Figure 1 illustrates the tract-scale spatial distribution of homeownerships for four major groups in Nashville.

3.5. Regression Analysis of Homeownership by Race/Ethnicity

I proceed ahead with conducting bivariate correlation analyses of select variables to identify the best explanatory variables for regression models. Based upon these relationships and my educated understanding of housing scholarship, I proceeded with select variables of diversity scores, educational attainment categories, shares of foreign-born by years of entry, ratios of race-based income vs. median household incomes, shares of housing structure built by various years, and the location quotients (LQs) for five major occupational categories; these regression models are illustrated in

Table 7. Regression models for White and Black homeownership in Nashville, TN.

	Y1: White Homeowners					Y2: Black Homeowners
Predictors	B	Beta	t	Sig.	VIF	B	Beta	t	Sig.	VIF
(Constant)	0.736		2.619	0.010		−0.375		−2.792	0.006
Diversity Score, Overall	x	x	x	x	x	−0.062	−0.203	−2.865	0.005	1.051
Diversity Score, FB Not Citizen	−0.335	−0.262	−3.656	0.000	1.559	0.120	0.232	2.708	0.008	1.535
Share, FB Entered 2010/later, FB Not Citizen	−0.269	−0.204	−2.740	0.007	1.679	−0.145	−0.271	−3.031	0.003	1.678
Share, FB Entered 2000–2009, FB Not Citizen	−0.187	−0.105	−1.273	0.206	2.063	0.061	0.085	0.862	0.391	2.038
Share, No High School Diploma and No Schooling	−0.901	−0.258	−2.029	0.045	4.916	−0.229	−0.162	−1.132	0.260	4.313
Share, Some College/Ass. Degree	−0.615	−0.232	−2.068	0.041	3.831	0.229	0.214	2.032	0.045	2.327
Share, Master’s, Professional, and Doctorate Degree	−0.676	−0.262	−2.039	0.044	5.005	0.177	0.170	1.125	0.263	4.783
Ratio, Black vs. Overall Per Capita Income	0.051	0.072	1.171	0.244	1.161	0.018	0.062	0.820	0.414	1.203
Ratio, White vs. Overall Per Capita Income	−0.179	−0.145	−2.008	0.047	1.589	0.181	0.363	4.225	0.000	1.551
Ratio, Asian vs. Overall Per Capita Income	−0.034	−0.074	−1.166	0.246	1.220	0.010	0.055	0.737	0.463	1.185
Ratio, Hispanic vs. Overall Per Capita Income	−0.061	−0.113	−1.863	0.065	1.116	0.038	0.172	2.380	0.019	1.102
Share, Employed in Labor, Civilians	0.781	0.268	3.962	0.000	1.392	0.130	0.110	1.388	0.168	1.318
LQ-Management/Business/Science/Arts	−0.083	−0.244	−2.535	0.013	2.829	x	x	x	x	x
LQ-Service	−0.117	−0.134	−2.106	0.038	1.227	x	x	x	x	x
LQ-Sales/Office	0.063	0.136	1.811	0.073	1.714	x	x	x	x	x
LQ-Natural Resources/Construction/Maintenance	−0.130	−0.250	−2.486	0.015	3.082	−0.022	−0.118	−1.319	0.190	1.680
LQ-Production/Transp./Material-Moving	x	x	x	x	x	0.089	0.425	3.773	0.000	2.668
Share, Built Before 1970, of Total HH Stock	x	x	x	x	x	−0.029	−0.070	−0.740	0.461	1.849
R-Value	0.813					0.714
R-Square Value	0.661					0.509
Adj. R-Square Value	0.612					0.438

Note: x in the cells implies those variables got dropped during stepwise regressions, and hence they have no coefficients.

,

Table 8. Regression models for Hispanic and Asian homeownership in Nashville, TN.

	Y3: Hispanic Homeowners					Y4: Asian Homeowners
Predictors	B	Beta	t-Value	Sig.	VIF	B	Beta	t-Value	Sig.	VIF
(Constant)	−0.0001		−2.3401	0.0212		−0.014		−0.471	0.638
Diversity Score, Overall	0.0000	0.0645	0.7308	0.4665	1.0614	−0.011	−0.193	−2.432	0.017	1.039
Diversity Score, FB Not Citizen	x	x	x	x	x	0.018	0.191	2.186	0.031	1.257
Share, FB Entered 2010/later, FB Not Citizen	0.0000	−0.1285	−1.2369	0.2189	1.4697	−0.037	−0.381	−4.221	0.000	1.346
Share, FB Entered 2000–2009, FB Not Citizen	0.0000	0.2085	1.9150	0.0582	1.6146	x	x	x	x	x
Share, FB Entered Vefore 1990, FB Not Citizen	0.0001	0.1952	2.1598	0.0331	1.1126	−0.044	−0.108	−1.289	0.200	1.156
Share, No High School Diploma and No Schooling	0.0001	0.2602	1.4051	0.1630	4.6696	x	x	x	x	x
Share, Some College/Ass. Degree	0.0001	0.4299	2.4991	0.0140	4.0311	0.023	0.116	0.976	0.331	2.345
Share, Bachelor’s Degree	0.0001	0.5620	2.6121	0.0103	6.3052	0.054	0.324	2.243	0.027	3.454
Share, Master’s, Professional and Doctorate Degree	x	x	x	x	x	x	x	x	x	x
Ratio, Black vs. Overall Per Capita Income	x	x	x	x	x	0.009	0.178	2.072	0.041	1.219
Ratio, White vs. Overall Per Capita Income	x	x	x	x	x	0.005	0.053	0.597	0.552	1.313
Ratio, Asian vs. Overall Per Capita Income	0.0000	−0.0853	−0.9277	0.3557	1.1525	0.004	0.113	1.360	0.177	1.147
Ratio, Hispanic vs. Overall Per Capita Income	0.0000	0.1212	1.3251	0.1880	1.1387	0.003	0.082	1.020	0.310	1.065
Share, Employed in Labor, Civilians	x	x	x	x	x	0.015	0.069	0.767	0.445	1.336
LQ-Management/Business/Science/Arts	x	x	x	x	x	x	x	x	x	x
LQ-Service	0.0000	−0.1645	−1.2450	0.2159	2.3776	x	x	x	x	x
LQ- Sales/Office	0.0000	0.0854	0.9243	0.3575	1.1641	−0.002	−0.037	−0.449	0.654	1.102
LQ-Natural Resources/Construction/Maintenance	0.0000	0.2466	2.1291	0.0356	1.8269	−0.003	−0.096	−0.923	0.358	1.804
LQ- Production/Transp./Material-Moving	x	x	x	x	x	x	x	x	x	x
Share, Built After 2014, of Total HH Stock	0.0000	−0.1017	−0.8539	0.3951	1.9326	x	x	x	x	x
Share, Built 2000–2013, of Total HH Stock	0.0000	−0.0716	−0.5808	0.5626	2.0696	x	x	x	x	x
Share, Built 1970–1999, of Total HH Stock	x	x	x	x	x	x	x	x	x	x
Share, Built Before 1970, of Total HH Stock	x	x	x	x	x	−0.014	−0.183	−1.721	0.088	1.859
R-Value	0.486					0.609
R-Square Value	0.236					0.371
Adj. R-Square Value	0.134					0.286

Note: x in the cells implies those variables got dropped during stepwise regressions, and hence they have no coefficients.

and

Table 9. Regression models for non-Asian minorities and Blacks and Hispanics combined homeowners.

	Y5: Non-Asian Minority Homeowners					Y6: Black + Hispanics (Combined)
Predictors	B	Beta	t-Value	Sig.	VIF	B	Beta	t-Value	Sig.	VIF
(Constant)	−0.334		−3.649	0.000		−0.375		−2.502	0.014
Diversity Score, Overall	−0.074	−0.208	−3.034	0.003	1.042	−0.066	−0.196	−2.847	0.005	1.052
Diversity Score, FB Not Citizen	0.187	0.309	3.743	0.000	1.511	0.150	0.264	3.160	0.002	1.539
Share, FB Entered 2010/later, FB Not Citizen	−0.203	−0.325	−3.924	0.000	1.521	−0.176	−0.299	−3.421	0.001	1.685
Share, FB Entered 2000–2009, FB Not Citizen	0.071	0.084	0.948	0.345	1.741	0.089	0.112	1.162	0.248	2.040
Share, FB Entered before 1990, FB Not Citizen	x	x	x	x	x	x	x	x	x	x
Share, No High School Diploma and No Schooling	x	x	x	x	x	−0.258	−0.166	−1.184	0.239	4.360
Share, Some College/Ass. Degree	0.413	0.330	3.364	0.001	2.134	0.259	0.219	2.119	0.037	2.370
Share, Bachelor’s Degree	x	x	x	x	x	x	x	x	x	x
Share, Master’s, Professional and Doctorate Degree	0.308	0.253	2.025	0.045	3.456	0.201	0.175	1.188	0.237	4.790
Ratio, Black vs. Overall Per Capita Income	0.029	0.088	1.201	0.233	1.188	0.024	0.078	1.053	0.295	1.204
Ratio, White vs. Overall Per Capita Income	0.184	0.316	3.733	0.000	1.580	0.177	0.323	3.845	0.000	1.559
Ratio, Asian vs. Overall Per Capita Income	x	x	x	x	x	0.012	0.060	0.822	0.413	1.185
Ratio, Hispanic vs. Overall Per Capita Income	0.052	0.203	2.872	0.005	1.101	0.048	0.197	2.784	0.006	1.110
Share, Employed in Labor, Civilians	x	x	x	x	x	0.130	0.100	1.271	0.207	1.370
LQ-Management/Business/Science/Arts	x	x	x	x	x	x	x	x	x	x
LQ-Service	−0.022	−0.136	−1.366	0.175	2.198	x	x	x	x	x
LQ- Sales/Office	x	x	x	x	x	x	x	x	x	x
LQ-Natural Resources/Construction/Maintenance	−0.023	−0.104	−1.226	0.223	1.594	−0.015	−0.074	−0.849	0.398	1.680
LQ- Production/Transp./Material-Moving	0.085	0.349	3.126	0.002	2.753	0.097	0.420	3.819	0.000	2.670
Share, Built After 2014, of Total HH Stock	0.150	0.072	0.805	0.422	1.785	x	x	x	x	x
Share, Built 2000–2013, of Total HH Stock	x	x	x	x	x	−0.015	−0.023	−0.266	0.791	1.676
Share, Built 1970–1999, of Total HH Stock	0.058	0.106	1.260	0.211	1.557	x	x	x	x	x
Share, Built before 1970, of Total HH Stock	x	x	x	x	x	−0.051	−0.110	−1.056	0.294	2.394
R-Value	0.728					0.734
R-Square Value	0.530					0.538
Adj. R-Square Value	0.466					0.466

Note: x in the cells implies those variables got dropped during stepwise regressions, and hence they have no coefficients.

. I ran several reiterative models using stepwise backward regressions to derive the best-fit models, and the variables that were not significant dropped out of the final model. Major findings for homeownership by race are discussed below.

Concerning White homeownership (Table 7: Y1), many variables are significant (at 80% confidence level), and many are significant even at 95% and 90% levels. The strongest predictors for White homeownership have negative coefficients, with Master’s (Beta = −0.262) being the strongest, followed by no schooling/no high school diploma (Beta = −0.258), some college/associate degree (Beta = −0.232); other variables with negative coefficients include DS-FBNC (Beta = −0.262), share-FB-entered 2010 or later (Beta = −0.204), the ratio of Hispanic income compared to overall (Beta = −0.113); other variables in LQ-various categories of occupation are significant at 90%/above levels and all have negative coefficients, except LQ-Sales/Office (Beta = 0.136, significant at 90%), indicating higher level of White homeownership in tracts where labor is engaged in sales/office-type occupations. It is obvious from these values that White homeownership is lower in tracts with higher diversity among the FBNC and those with higher presence of recently-entered FB population. This model has an R-square value of 66.1%.

Regarding Black homeownership (Table 7: Y2), it is higher in tracts with high DS-_FBNC (Beta = 0.120) and lower (Beta = −0.203) in tracts with overall diversity (DS-_Overall); it is also negative in the tracts with higher presence of recently entered FB. Interestingly, Black homeownership is higher in tracts with some college/associate degree (Beta = 0.229) as well as those where White (Beta = 0.181) and Hispanic incomes (Beta = 0.038, significant at 80%) are higher compared to overall income—which is interesting, and points toward the greater role of class rather than race. Black homeownership is also higher in tracts where a higher share of labor is engaged in production/transportation/material-moving is higher (Beta = 0.425). This model has an R-square value of 50.92%.

Regarding Hispanic homeownership (Table 8: Y3), positive predictors include FBNCs who entered during 2000–2009 (Beta = 0.2085), FBNCs who entered before 1990 (Beta = 0.1952), share of those with some college/associate degree (Beta = 0.4299) and Bachelors degree (Beta = 0.562), and share of those engaged in natural resources/construction/maintenance type occupations; none of the variables have negative Betas. The R-square value is lower at only 23.6%.

In contrast, Asian homeownership (Table 8: Y4) predictors include significant positive relationship with DS-_FBNC (Beta = 0.191) and share of those with Bachelor’s degree (Beta = 0.324), which is also one of the strongest predictors; negative Betas load on overall diversity (Beta = −0.193) and the share of FBNC who entered 2010/later (Beta = −0.381); other positive Betas load on Black per capita income compared to overall income (Beta = 0.324) and negatively with the share of housing built before 1970 (Beta = −0.183). These values indicate that Asian homeownership is better in the tracts with a greater presence of diverse FB groups but not in tracts that have a significant presence of overall diversity or larger shares of recently arrived FB; they have lower homeownership in tracts that have older homes, built before 1970. Tracts with better relative income for Blacks have higher Asian homeownerships as well, though the relative income of other groups has no impact. The R-square value is better at 37.1%.

Finally, I also conducted regression analyses for two other minority groups–non-Asian Minorities (this included Blacks, Hispanics, American-Indians, and All-Others together as one group, (Table 9: Y5) and Hispanics and Blacks combined together as one group (Table 9: Y6) because these two comprise the most disadvantaged social group in most American metropolises, and I wanted to examine their status in the emerging metropolis of Nashville.

The models derived in this analysis suggest that the variables that show up as significant at various levels of confidence are almost quite similar, except that the presence of a Master’s degree is associated with a higher level of non-Asian Minority homeownership (Beta = 0.253) whereas this variable is not significant in the Black and Hispanic combined model. Other common variables with positive betas include DS-_FBNC, some college/associate degrees, and the relative income of Whites and Hispanics compared to overall income and tracts with higher LQs for production/transportation/material moving. Those with negative Betas include DS-_Overall and the recently arrived FBNCs (2010/later).

4. Conclusions and Policy Implications

When examining these six models that explain homeownership differences across major racial/ethnic groups, some major patterns emerge. White homeownership is lower in the tracts with a higher presence of diverse FBNCs and recently-entered FB populations. In contrast, tracts with higher LQs for sales/office-type occupations obtain positive Betas, whereas other occupation-types associate with negative Betas, suggesting greater White homeownership in the former. However, for almost all models for homeownership among minority groups, tracts with higher LQs for production/transportation/material-moving have higher/positive homeownership for Black Nashvillians as well as other non-Asian-minorities and for Blacks and Hispanics combined. For Hispanic homeownership, tracts with higher LQs for natural resources/construction/maintenance-type occupations (largely blue-collar occupations) are associated with higher homeownership, whereas none of these matter for Asians. These also indicate that engagement in the blue-collar economic sector has remained a better predictor of economic well-being, especially among the minority populations, and it has indeed propelled a sense of homeownership, and this could also be likely due to the better pay structure in manufacturing and other types of blue-collar economic activities. Research suggests Nashville’s gain in car assembly plants and other related manufacturing jobs over the last few decades, and these have likely contributed to improved homeownership. This finding has a policy implication that to improve the economic condition of people in general, it helps to attract more manufacturing and local-based economies as they improve resident’s purchasing power and local well-being. Regarding the age of the built-in structure, Asian homeownership is lower in older tracts (i.e., homes built before 1970); other groups do not show any specific pattern.

Finally, while the above models collectively illustrate neighborhood-scale characteristics that associate positively or negatively with homeownership for various population groups, what is not obvious is that the level of non-citizenship in each census tract is difficult to extract and model statistically since their numbers are very small in each tract, making it difficult to create robust models. This requires more in-depth field-based investigation as that could add rich contexts into race and citizenship-based differences in homeownership.

This research also points toward an important aspect of the built environment. The availability of newly built homes provides a larger pool of housing, which eventually helps people buy homes. It also helps specific racial/ethnic groups if the housing pool comprises mixed-plan communities, offering homes in various price ranges (see Sharma 2016b). The availability of mixed-plan communities helps increase diversity and affordability across communities, and this is much needed in Nashville, which remains a magnet metropolis that attracts people of diverse races and cultures. These are also critical to maintaining the overall pool of built-environment and urban spaces in one of the fastest-growing metropolises of Nashville. This has important policy implications as more investment in the housing market would likely propel economic development in basic and non-basic industries—which are essential in the long-term growth and sustainability of urban spaces.