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Abstract

This paper proposes a replicable, data-driven index to identify informal subdivisions and measure levels of peri-urban housing informality. Dallas (Texas) and Catawba (North Carolina) county assessment data, a review of the literature, and insights from qualitative research in North Texas yielded variables which were reduced to two factors through exploratory factor analysis. We identified 128 and fifty-three highly informal subdivisions in Dallas and Catawba counties, respectively. This work highlights the pervasiveness of peripheral informal subdivisions and helps guide planners’ efforts to locate and assist the most precarious subdivisions.

Keywords

housing informality index U.S. peri-urban informal subdivisions participatory housing upgrading Dallas Catawba

Introduction

Though initially believed to be a Global South planning issue, informal housing is now recognized as a prevalent phenomenon in the United States (Durst 2019; Herbert, Durst, and Nevárez Martínez 2022). Following Global South scholarship, U.S. scholars define informal housing in relation to state regulations (Harris 2018), meaning it occurs when dwellers transgress or are denied the protection of regulatory regimes, including property laws and zoning, land use, and building codes (Durst and Wegmann 2017). Working with this definition, U.S. informal housing appears to manifest in four ways: informal infill, informal subdivisions, repurposing of private property, and informal occupation of public space (Herbert, Durst, and Nevárez Martínez 2022). This article focuses on informal subdivisions, which typically arise in often unincorporated peri-urban and exurban spaces.

A recent national report drew attention to precarity in a massive informal subdivision in East Texas, where residents face exploitative financing practices and discriminatory policy enforcement (Goodman 2023). Despite such national news attention and growing academic recognition of the prevalence of informal subdivisions throughout the United States (Durst 2019; Herbert, Durst, and Nevárez Martínez 2022; Ward and Peters 2007), these communities receive little attention from planners and community development practitioners. In part, this is because informal subdivisions often lie in unincorporated communities that local governments underbound—or refuse to annex (Durst et al. 2023). While county governments have jurisdiction to enforce some regulations in unincorporated areas, the lack of official governance in these places makes them particularly vulnerable to housing and environmental injustices (Tippin 2021) and they become hotbeds for informal subdivisions characterized by poor-quality housing lacking easy access to piped water, sewage, and solid waste infrastructure (Durst and Ward 2016; Sullivan and Olmedo 2015). These precarities make clear that informal subdivisions have high need for upgrading assistance from planners and community development stakeholders. We propose a housing informality index to assist planners in upgrading previously overlooked informal subdivisions.

Recent literature highlighting the pervasiveness and precariousness of informal subdivisions has spurred attempts to locate them through geospatial analysis. Previous geospatial mapping at the national level (Durst 2019; Ward and Peters 2007) and at the Mexico-U.S. border (Durst 2016; Durst et al. 2021) identified informal subdivisions but the studies provided no way to prioritize planning interventions. Like Durst (2019), this paper emphasizes the need for further research on non-border informal subdivisions to understand their heterogeneity and inform creation of better targeted strategies for improving residents’ living conditions. It presents a methodology for developing an index that illuminates the spectrum of housing informality in peri-urban informal subdivisions. However, it does not include urban informal housing due to significant differences in regulations, housing markets, and physical forms (Herbert, Durst, and Nevárez Martínez 2022; Wegmann and Mawhorter 2017).

Though the U.S. literature lacks housing informality indices, the Global South literature has produced multiple slum severity indices that detect and measure concentrations of precarious informal communities to guide housing upgrade policies (Connolly 2009; Patel, Shah, and Beauregard 2020; Roy, Bernal, and Lees 2020). However, informality indicators used in this literature are country-specific and drawn from census data not collected in the United States. Furthermore, the variables were established by UN-Habitat (2003) specifically for Global South informal housing, which fundamentally differs in physical forms, scale, and visibility from that in the United States (Harris 2018; Herbert, Durst, and Nevárez Martínez 2022). Thus, this research draws on methodologies from the Global South while using measures of housing informality grounded in the United States.

To construct our index, we identified factors related to peripheral informal housing through a review of the literature on informal subdivisions along with previous qualitative research in North Texas informal subdivisions performed by two of the authors. We used housing and infrastructure variables from two county appraisal districts—Dallas County, Texas and Catawba County, North Carolina—and aggregated parcel data to the neighborhood level. We then used exploratory factor analysis (EFA) to create composite scores representing the degree of housing informality in neighborhoods and compared our results with satellite imagery (Durst 2016; Ward and Peters 2007). We relied on the literature and our qualitative fieldwork to refine the way we performed this analysis (Connolly 2009; Durst 2016).

This study contributes to the literature in a number of ways. First, our study broadens the scope of U.S. informality literature by revealing how informal housing manifests within rapidly growing U.S. metropolitan areas, thus enabling future research aimed at understanding how different governance regimes can intervene in peripheral subdivisions in unincorporated communities across the United States (Anderson 2008). Second, our methodology integrates U.S.-based informal housing measures and data with Global South indexing methods to create a replicable peri-urban housing informality index. Finally, and more broadly, the housing informality index is useful to practitioners who can better target participatory housing upgrading policies through identification of precarious informal subdivisions where residents face the highest health and safety risks.

Literature Review

Informal housing operates through one of three mechanisms. Residents produce housing informality through non-compliance when they transgress property laws and land use and building regulations. This type of informality often occurs out of necessity and has received the most scholarly and public attention. Housing informality also occurs when the state fails to provide protections to residents. This can be local government failure to enforce regulations, partly because informal housing is often hidden from view, or it may be deregulation of sectors of the housing market (Durst and Wegmann 2017). Because they occur in local contexts, these transgressions or deficiencies vary in scale, coordination, and visibility (Harris 2018). Drawing on Harris (2018), we develop a spectrum of housing informality that moves beyond binary classification of communities as informal or not.

Informal housing varies not only in scale, coordination, and visibility but also in form. Herbert and colleagues (2022) identified four types of informal housing in the United States. First, informal infill occurs when people develop housing structures outside formal processes in dense, expensive, and highly regulated urban areas. For instance, in suburban Los Angeles, residents build accessory dwelling units lacking knowledge of or resources to obtain building permits, licensing, and inspection (Wegmann and Mawhorter 2017). Second, informal repurposing of private property occurs when property regulations are transgressed in areas with high rates of vacancy or abandonment; an example is squatting in cities like Detroit (Herbert 2021). Third, informal occupation of public space occurs in urban areas where regulations are not obeyed or enforced. Precarious homeless encampments in cities of the West Coast and Northeast are most representative of this type (Herbert, Durst, and Nevárez Martínez 2022). The fourth type, and the focus of this study, is informal subdivisions in peripheral areas where regulations do not hinder sprawl and development.

Informal Subdivisions in the United States

Early scholarship on U.S. informal subdivisions focused on colonias in the U.S.-Mexico borderlands. Although colonias provide affordable housing for low-income immigrants (Giusti and Olivares 2012), housing conditions are generally poor (Durst 2016; Durst and Ward 2014) and septic tanks and wells create financial burdens (Sullivan and Olmedo 2015). State policy solutions in colonias were largely underwhelming because local governments underbound these subdivisions to avoid infrastructural costs (Durst and Ward 2014). Even when policymakers enacted model subdivision rules and regularized lots, outcomes were minimal or negative (Durst and Ward 2016; Ward, de Souza, and Giusti 2004). The focus on colonias in policy and early informality literature (Larson 2002) overshadowed the existence of similar communities around the country, thus decreasing the potential for interventions in non-border informal subdivisions (Mukhija and Mason 2013; Mukhija and Monkkonen 2006). More recent research has uncovered the prevalence of precarious, non-border informal subdivisions housing impoverished residents of all races throughout the United States (Durst 2019).

While informal subdivisions expand low-income and minority land and home ownership (Durst and Sullivan 2019), they face multiple forms of precarity necessitating public assistance. Residents in informal subdivisions experience poor-quality housing and predatory practices because the financing, improvement, and transfer processes available in these communities significantly differ from formal housing development (Herbert, Durst, and Nevárez Martínez 2022). In informal subdivisions, impoverished buyers often pay developers directly at high interest rates instead of seeking mortgage loans (Durst and Ward 2014) for unserved lots without the protection of property regulations (Durst and Ward 2016; Olmedo and Ward 2016). While a small proportion of households in colonias improved their homes using nongovernmental organization (NGO) loans (Durst and Ward 2014) or self-help housing projects (Mukhija and Scott-Railton 2013), most residents in peri-urban informal subdivisions incrementally consolidate their housing over time using their own labor and scarce resources (Durst and Ward 2014; Giusti and Olivares 2012; Nevárez Martínez, Rendón, and Arroyo 2021). Incremental consolidation is a prolonged process that proceeds slowly over decades, so residents remain continuously exposed to precarious living conditions (Durst and Ward 2014).

Infrastructure is minimal or absent in informal subdivisions and prefabricated forms of housing are prevalent. Twenty percent or more of housing units in informal subdivisions are mobile homes (Durst 2019; Durst and Ward 2016). Informal subdivisions have unique physical characteristics, however, that differentiate them from manufactured housing communities such as mobile home parks. Informal subdivisions lie in peri-urban locations with low population densities, high vacancy rates, large individual lot sizes (Durst 2019; Durst et al. 2021; Ward and Peters 2007), and often lack piped water, sewers, paved roads, sidewalks, and streetlights (Durst 2016, 2019).

Mapping U.S. Informal Subdivisions

Aware of the precariousness of informal subdivisions, scholars have attempted to map informal communities in order to prioritize infrastructural improvements or extend policymaking beyond the borderlands (Durst 2019; Durst et al. 2021). Past studies of colonias used GIS along with the U.S. Geological Survey Colonia Health, Infrastructure, and Platting Status tool (Parcher and Humberson 2009) or Health Needs Assessments (Baig, Shaw-Ridley, and Munoz 2016) to assess infrastructure needs. However, these studies used data particular to colonias so their findings could not be generalized or replicated in peri-urban informal subdivisions.

Other studies used county and national data to identify informal subdivisions through geospatial analysis and satellite imagery inspection. Ward and Peters (2007) identified 447 peripheral informal subdivisions in Austin and San Antonio, Texas and Greensboro, North Carolina. Durst (2016, 2019) identified 796 non-colonia informal subdivisions in seven Texas border counties and 769 census-designated places throughout the United States containing one or more informal subdivisions. More recently, Durst and colleagues (2021) developed a method to identify and distinguish informal subdivisions from manufactured housing communities using national-level building footprint data. While these useful articles collectively identify previously unknown informal subdivisions, they do not include data that would give stakeholders guidance on prioritizing investments. Our research combines the goals of identification and prioritization to enhance local, state, and federal policymaking and development efforts.

Housing Informality Indices in the Global South

In contrast to the United States, research in the Global South has produced housing informality indices that identify and measure levels of precarity in informal settlements. Variables in these studies draw from the five indicators of informal housing developed by UN-Habitat (2003): inaccessible piped water and sewage, overcrowding, substandard structural quality, and tenure insecurity. For instance, assessments in various metropolitan areas in India (Patel, Shah, and Beauregard 2020) and Mexico City (Connolly 2009; Roy, Bernal, and Lees 2020) used UN-Habitat variables taken from census data to perform statistical analysis and geospatial mapping of informal housing. These studies also employed satellite imagery to validate findings (Roy, Bernal, and Lees 2020).

Although these studies used innovative methods, census data undermine assessment of informal housing quality at the parcel level because it is aggregated at the community or municipal level (Satterthwaite 2003). To overcome these limitations, Patel, Shah, and Beauregard (2020) used national survey data to conduct housing insecurity assessments. Other Global South scholars used supplementary qualitative methods: Connolly (2009) gathered insights from qualitative research to develop a housing quality model for Mexico City; Boateng and Adams (2023) collected survey data in Ghana’s informal communities; and Reyes, Sletto, and Caudillo (2024) conducted ethnographic research to inform their housing quality index.

Gaps in the Literature

While Global South informal housing indices have laid the groundwork for developing an index in the United States, the variables they relied on are only partially appropriate for the U.S. context. Previous studies used indicators from UN-Habitat (2003) but there is no consensus on the usefulness of these characteristics in different contexts due to the heterogeneity of informal housing across countries (Gilbert 2007). While some UN-Habitat indicators may identify precarious informal subdivisions in the United States, such as lack of piped water and poor structural quality, others do not apply. Scholars argue that U.S. housing informality is fundamentally different from the Global South (Harris 2018; Herbert, Durst, and Nevárez Martínez 2022). Peri-urban informal subdivisions in the United States are significantly less densely populated and feature higher levels of vacancy (Durst and Ward 2015), larger lot sizes, and pre-manufactured dwelling types (Durst and Ward 2014). Thus, a U.S. housing informality index must rely on variables different from those used in the Global South.

Our study sheds light on the data sources of variables necessary for uncovering peri-urban housing informality in the United States. U.S. data sources are different. Unlike the Global South, the U.S. Census Bureau does not collect relevant infrastructural and housing data. Instead, these data come from county appraisal districts as part of residential tax assessments (Durst 2016; Durst and Ward 2014; Durst et al. 2021). Although this means data are inconsistent across jurisdictions because it comes from decentralized sub-national governments, it is essential for constructing a U.S. housing informality index because it is collected at the parcel level and thus overcomes the limitations of census data.

Study Areas

We selected Dallas County, Texas and Catawba County, North Carolina as cases for our index. Dallas County is the largest county by population and the urban center of the Dallas-Fort Worth metroplex. Its population rose 10.4 percent (3% more than the national average) to 2,613,539 residents between 2010 and 2020 (U.S. Census Bureau 2020). The county is 900.34 square miles in area and is composed of thirty-one cities—including Dallas, the ninth largest city in the United States—and fourteen unincorporated areas, which comprise 7.36 percent of its total area.

Catawba County is an exurban county in the Charlotte Combined Statistical Area (CSA). Catawba County’s population rose 4.1 percent to 160,610 residents between 2010 and 2020—compared to 6.56 percent in the CSA (U.S. Census Bureau 2020). Its 401.37 square miles encompass eight cities and twelve unincorporated areas—covering 82 percent of the county. The selection of Catawba County broadens the scope of previous research, which, except for one study (Ward and Peters 2007), has focused on Texas (Durst et al. 2021; Durst (2016). North Carolina has the second largest proportion of underbound low-income and minority communities in the country (Durst et al. 2023). North Carolina also has different annexation laws than Texas; for instance, municipalities can incorporate peripheral communities against their will. Thus, Catawba County provides a case where informal housing is more governed and regulated (Anderson 2008).

Materials and Methods

Developing Peripheral Informal Housing Criteria

We follow Durst (2016) by using residential parcel data from county appraisal districts. To narrow the scope of data collected from the Dallas County Appraisal District (DCAD) and Catawba County, we chose only variables representing informal housing characteristics gleaned from the literature on informal subdivisions and qualitative research performed by two of the authors. Though Dallas has a very large tax assessment database, we only retained variables common to both databases to ensure replicability in other U.S. counties.

The literature on informal subdivisions indicates that these neighborhoods have high long-term vacancy (Durst and Ward 2015), poor-quality housing materials, a lack of basic infrastructure, and prefabricated housing types (Durst 2016; 2019; Durst et al. 2021). The qualitative research in North Texas informal subdivisions—including observations of housing and infrastructure quality in multiple peripheral subdivisions as well as household surveys and in-depth interviews conducted in one Dallas County subdivision between 2021 and 2024—confirmed characteristics in the literature and provided a more nuanced understanding of subdivision conditions. For instance, we uniquely documented RVs as a predominant type of informal housing and detailed energy insecurity through lack of adequate insulation and inefficient heating and air conditioning technologies (Reyes, Newton, and Mistur, forthcoming).

Selecting Scope and Unit of Analysis

While U.S. informality research has previously used census geographies, this scale is geographically too large in peri-urban and rural areas (Durst 2019) and commonly combines multiple formal and informal subdivisions (Durst et al. 2021). Following Global South informal housing indices, we use the neighborhood scale for informal housing variables because upgrading policies are typically implemented at that level (Roy, Bernal and Lees 2020). We use DCAD neighborhood identifiers and Catawba County subdivision names to aggregate parcel data to an appropriate scale for housing policy and interventions.

Since our goal is to reveal peri-urban informal subdivisions, we used population density to identify peri-urban areas. It is difficult to use 2020 definitions to identify these areas because the current census only distinguishes between urban and rural areas (U.S. Census Bureau 2022). The 2010 U.S. Census, on the other hand, allows us to distinguish between higher density, higher population urban areas and lower population, lower density peri-urban areas (urban clusters). According to 2010 Census categories, urbanized areas contain at least 50,000 people and 1,000 people per square mile. We identified areas below these population and density criteria as peri-urban and included them in the study. This represented 22.9 percent of Dallas County and all of Catawba County, a peri-urban area of the Charlotte CSA.

Determining Housing Informality Variables

The variables for this study come from two sources: the 2023 DCAD and the 2023 Catawba County database. From these databases, we gathered twenty-seven initial variables associated with housing, infrastructure, and energy insecurity at the parcel level. See the first section in the Supplementary Appendix for more information.

We removed non-residential parcels and parcels in urbanized areas defined by the census. In addition, we used land use and zoning variables to remove two formal housing types that do not occur in informal subdivisions: multifamily buildings and mobile home parks (Durst et al. 2021). The removal of mobile home parks ensured that the mobile home variable identified only subdivisions where mobile homes contribute to informality. These steps focused the data set on peri-urban residential subdivisions where informality can possibly exist.

Next, we disaggregated categorical variables into dummy variables that indicate informal housing. For instance, both Dallas and Catawba counties have heating type data for housing units that includes multiple categories ranging from central HVAC to no heating apparatus. Based on the literature and our qualitative research, we grouped these categories into two dummy variables indicating heating types found in formal and informal housing and repeated the same process for all other categorical variables.

Finally, we aggregated parcels into neighborhoods using DCAD neighborhood identifiers and Catawba County subdivision names to transform individual parcel data into neighborhood-scale data. For dummy variables, aggregation created continuous variables indicating the percentage of a neighborhood characterized by the variable. For monetary values—land, building, and total values—aggregation created median values at the neighborhood scale.

We narrowed the twenty-seven initial variables down to eleven by choosing only variables common to both data sets. Eight of these variables are characteristics of informal subdivisions and housing: vacancy, mobile homes, poor-quality exterior wall materials, heating type, no full bathroom, no half bathroom, no kitchen, and building age. Three variables indicate median dollar values of land, buildings, and parcel—a combination of land, buildings, and improvements. Later, we relied on statistical tests to eliminate variables that did not improve the model. Table 1 provides descriptive statistics of the six remaining variables.

The informal subdivisions and housing variables are concentrated in lower values with high positive skewness, while the median value variables have positive skewness and are concentrated in higher values. We controlled for skewness in the dollar values with logarithmic transformations. Due to differences in measurement scales, we transformed all variables in the model into standard normals.

Identifying an Analytical Method

U.S. scholars concur that informal subdivisions can be identified through data representing housing precarity and poor infrastructure (Durst 2016, 2019), but using all possible data within these categories would produce an overwhelming dataset of variables. High levels of dimensionality in data can convolute model-building processes and make data interpretation bewildering. Dimensionality reduction techniques such as principal component analysis (PCA) or EFA typically address this issue.

Global South scholars have relied on EFA to identify and measure housing informality. For example, in Mexico City, EFA helped Roy and colleagues (2020) develop a composite score for slum severity, and Reyes, Sletto, and Caudillo (2024) used EFA to develop a housing quality index in consolidated informal settlements. Following previous indices, we applied EFA to discover underlying, unobserved factors (latent variables) in the model and their structure in the data. EFA provides a more extensive framework to assess data suitability than similar techniques, including PCA. EFA determines not only the factors required to describe a complex construct but also a set of goodness of fit statistics to evaluate the final output. Each factor EFA produces receives a weight based on its contribution to explaining the total variation in the data (Gareth et al. 2023). We used these factor weights to create composite scores—based on the variation of variables—that construct a classification of housing informality in identified subdivisions. In the following sections, we describe the construction, results, and validation of our housing informality index.

Developing a Housing Informality Index for the United States

To begin, we performed several pre-estimation tests that showed our data is appropriate for EFA: the determinant of the correlation matrix was 0.025; the Bartlett test indicates all off-diagonal elements of the correlation matrix are not zero; and, based on a global Kaiser-Meyer-Olkin statistic above 0.5, we selected all variables suitable for the analysis. Furthermore, we chose how many factors we needed by conducting several more statistical tests: parallel analysis, minimum average partial, and the empirical Kaiser criterion. All tests indicated two factors were adequate to represent a high proportion of the variance in the data. The first factor summarized 40.2 percent and the second explained 30.3 percent of the variance.

Next, we applied factor analysis with minimum residual solution and varimax rotation for factor extraction. The model test results indicated that two factors were sufficient (chi_sq < 0.05) and that both the number of observations and harmonic mean fit were significant (p < .05). The fit indices also behaved well: the residuals’ root mean square was less than 0.05 and the reliability index (TLI [Tucker-Lewis index]) was 0.96. After varimax rotation, the first factor summarized 57 percent and the second explained 43 percent of the total variance. Table 1 shows the pattern matrix. The numbers in italics are the irrelevant loadings for each factor and the h2 column contains the total amount of variance explained by each variable. Although the proportion of mobile homes is the least relevant, we retained this variable on theoretical grounds, as it had proved to be highly relevant in other studies (Durst 2019; Durst and Ward 2016). The most important variables were the lack of a full bathroom in the first factor and median building value in the second factor. The standard measures of factor score adequacy are above 0.90. See Supplementary Appendix for complete output. The last row in Table 1 is the reliability index for each factor; values between 0.8 and 0.9 are good, suggesting each factor represents a different latent variable.

Table 1.

Descriptive Statistics and EFA Standardized Loadings.

Variable	M	Median	Max	SD	Median	MR1	MR2	h2	com
Mobile homes^a	0.025	0.000	1.000	0.122	0.000	0.547	-0.06	0.303	1.02
Poor exterior wall materials^a	0.025	0.000	1.000	0.115	0.000	0.82	-0.08	0.679	1.02
Absence of efficient heating^a	0.035	0.000	1.000	0.134	0.000	0.731	-0.135	0.553	1.07
No full bathroom^a	0.011	0.000	1.000	0.092	0.000	0.945	-0.087	0.9	1.02
Median building value^b	4.531	5.209	7.912	1.955	5.209	-0.1	0.96	0.932	1.02
Median total value^b	5.088	5.335	8.471	1.225	5.335	-0.063	0.928	0.865	1.01
Cronbach’s α						0.830	0.890

Proportion of housing. ^bLog transformed, thousands of dollars.

We name each factor according to the variables associated with it:

The first factor captures housing informality. The loadings pattern clearly indicates that as informal housing characteristics increase and housing values decrease, the first factor increases. This factor summarizes precarious features of housing and thus serves as the practical cornerstone of our research.

The second factor represents the formal housing elements in the data. As housing values increase and informal housing characteristics decrease, the second factor increases. The correct interpretation in terms of informality is negative; the lower this factor, the higher the informality.

Housing Informality Index Results

We focus on the first factor to map informality in the two counties. Due to high positive skewness, we used Fisher’s method to transform this factor and classified it into the five categories discussed later in this article, ranging from 1 (very low informality) to 5 (very high informality). Figure 1A and 1C shows the spatial pattern of the housing informality index in Dallas County and Catawba County, respectively. There are more neighborhoods with high and very high informality in Dallas than Catawba.

Figure 1.

Spatial pattern of the housing informality index in Dallas County (A) and Catawba (C). Local indicators of spatial association in Dallas County (B) and Catawba County (D).

Using Moran’s I global autocorrelation index, we compared spatial distribution to determine whether there is a pattern of co-location of similar values in space. For Dallas, Moran’s index reaches 0.19, which is not very high. Nevertheless, it is statistically significant and much higher than Catawba, which has a statistically significant value of 0.019. Figure 1B and 1D shows the local indicators of spatial association. The color codes represent the spatial autocorrelation classes. There are many high-high cluster cores concentrated in the southeast and northeast of Dallas County. In Catawba, there are a few scattered cluster cores but no homogeneous areas like in Dallas. In both cases, highly informal subdivisions are segregated from subdivisions with low informality.

Testing the Index with Satellite Imagery and Fieldwork Data

To test how the index classifies peripheral neighborhoods, we drew from morphology images of informal subdivisions developed by Durst (2016) and Durst and Sullivan (2019), as well as pictures and knowledge from our ethnographic research in North Texas informal subdivisions (see Figure 2) to evaluate satellite imagery. To evaluate the precision of different levels of informality, we inspected Google Earth images guided by the criteria of quality of infrastructure, lots, and housing.

Figure 2.

Images of peripheral subdivisions classified as highly informal in Catawba County (A, B) and Dallas County (C, D). Pictures of housing precarity (E, F, G, and H) collected from fieldwork.

To validate the index, we developed a random sample of 10 to 20 percent of the subdivisions in each category of informality. Two of the authors, who had also conducted qualitative fieldwork, tested the randomly selected subdivisions separately and independently validated their results. Our comparisons of the index with satellite imagery revealed that 91 and 87 percent of peri-urban subdivisions in Dallas County and Catawba County, respectively, were correctly classified. A few peripheral subdivisions were misclassified due to the presence of very few residential structures. Most incorrect classifications, however, were subdivisions classified one category below where they should be—for example, very low informality rather than low informality. The high precision of the housing informality index confirms its usefulness as a tool for identifying and distinguishing between informal neighborhoods.

Our analysis also allowed us to interpret the levels of informality. Classes 1 (very low informality) and 2 (low informality) identify formal peripheral subdivisions with high-quality housing, which is reflected in high building values. The distinguishing factors between the two classes seem to be levels of vacancy and quality of materials, where class 2 exhibits more vacancy and slightly lower quality materials than class 1. Class 3 identifies peripheral decaying subdivisions with aging and low-value buildings, and marks the beginning of subdivisions exhibiting housing informality. It identifies subdivisions with higher vacancy than the first two classes and a mix of land uses, namely housing interspersed with commercial or industrial uses. Classes 4 and 5 identify highly informal and precarious subdivisions that need upgrading. These classes show higher vacancy and land use mixes than class 3, and lower quality materials than previous classes. Class 4 exhibits a mix of formal and informal structures, which distinguishes it from the purely informal class 5.

Discussion

The EFA results produced a list of variables that align with the empirical research on U.S. housing informality. The two factors produced by the EFA summarize informality and formality in the data and represent 70.5 percent of the variance in the data. We found that lack of a complete bathroom most effectively signals informality in peripheral subdivisions. The lack of a complete bathroom represents sanitation injustices, which are well-documented as a significant factor of precarity in Global South informal settlements (Satterthwaite 2003) and U.S. colonias (Tippin 2021).

Low building values and poor-quality exterior wall materials are also significant variables tied to housing informality. These factors indicate low quality in housing structures, and the latter also impacts interior thermal comfort, exacerbating energy insecurity. Substandard housing is indicative of informal housing in both the Global South (Reyes, Sletto and Caudillo 2024) and the United States (Durst and Ward 2016). The absence of efficient heating technology also marks informal housing and energy insecurity in peripheral subdivisions. Some residents who lack proper insulation and efficient heating and air conditioning systems resort to inexpensive and inefficient devices that escalate energy burdens (Durst and Ward 2014). Other residents endure precarious indoor temperatures to reduce electricity use and protect household budgets. Energy insecurity is an understudied issue in informal subdivisions in the Global North (Reyes, Newton, and Mistur, forthcoming) and the Global South. Only Boateng and Adams (2023) included this factor in their index. Nonetheless, energy injustice is particularly relevant in regions with hot summers and cold winters, as exemplified by Dallas and Catawba counties.

Mobile homes and long-term vacancy (ten years or more), important variables in prior studies, were unimportant in our model. The discrepancy in long-term vacancy could signal differences between non-border informal subdivisions and the colonias of previous studies (Durst and Ward 2015). In fact, qualitative research in North Texas informal subdivisions performed by two of the authors found that long-term vacancies diminished over a three-year period due to in-migration. Short-term vacancies caused by evictions, county displacements, and out-migration were more prevalent and indicative of resident precarity. Vacancies may be unimportant for identifying informal subdivisions but these findings suggest that more fine-grained research on the dynamics of vacancy in these neighborhoods is needed.

Although mobile homes were used to identify informal subdivisions in previous studies (Durst 2019; Durst and Ward 2016), they had low explanatory value in our model. This is likely not because mobile homes are irrelevant to identifying informal subdivisions, but rather because of poor data collection on mobile homes by county and national officials. Another possible explanation from our previous qualitative research is that RVs are more indicative of informal subdivisions. Yet, data on RVs as residential structures are almost nonexistent. Data collection on prefabricated types of housing needs to be improved at all levels.

Our index complements innovative work of U.S. scholars of peri-urban housing informality by identifying 128 and fifty-three new peri-urban subdivisions with high and very high levels of informality in Dallas and Catawba counties, respectively (see Figure 1A and 1C). Highly informal subdivisions represent 17.78 percent of total peri-urban subdivisions in Dallas County and 5.32 percent of Catawba County peri-urban subdivisions. The difference may suggest that housing informality manifests more in Texas than North Carolina because of weaker county government regulatory authority (Anderson 2008). But it may also indicate that housing informality manifests more in highly urbanized areas, like Dallas County, where proximity to economic opportunities is greater than that found in exurban counties like Catawba. Global South research suggests informality is dominant on the immediate periphery of large metropolitan areas where there is weak regulatory enforcement, and accessible, affordable land for low-income residents (Harris 2018). More research is needed to test these hypotheses in the United States.

Like the research efforts in Mexico, India, and Ghana that identified and measured housing informality, our index should be used to inform public policy and planning interventions. As shown in Figure 2, the most severe concentrations of housing informality are in southeast Dallas County, with smaller concentrations scattered in the northeast. These findings are unsurprising, as these subdivisions are farthest from the county’s larger cities, such as Arlington, Dallas, Garland, Irving, and Plano. Nevertheless, the sheer number of highly informal neighborhoods in southeast and northeast Dallas County underscores the urgent need for community and economic development interventions (Krupala 2019). While Catawba does not have areas of high-density informality, some highly informal subdivisions appear to cluster in extra-territorial jurisdictions contiguous to municipal boundaries. This finding again underscores the importance of proximity to economic opportunities in urban areas but also indicates a need for reevaluation of underbounding practices by municipalities (Durst et al. 2023).

There are some limitations in data granularity at the community level. Data collection on mobile homes and RVs at the county and national level is inadequate. Manufactured housing is rapidly proliferating in peripheral informal subdivisions (Durst 2019), but counties have not updated their data collection methods. Previous scholarly work (Durst and Sullivan 2019) relied on the American Housing Survey to collect the most accurate data on manufactured housing; however, the survey lacks sufficient resolution to inform housing informality indices. Thus, data collection at the county and national level must improve.

County differences in data collection practices present a possible limitation. Dallas County collects more extensive data than other counties we examined across the United States. In fact, smaller counties with few resources could learn from the best practices of data collection in Dallas County. In the meantime, replication of the index could be more time intensive in some U.S. counties. For instance, in counties that do not produce neighborhood identifier codes and have limited data on substandard housing and energy injustices, planners might need to manually group parcels and construct data to build the index. Despite these limitations, our housing informality index is very precise and should serve as a reference point to identify and measure peri-urban informal neighborhoods in other U.S. counties, especially in states with state-wide appraisal data, like Florida and Alabama.

Implications for the Global Study of Informal Housing

This study complements previous work in the United States (Durst 2019; Ward and Peters 2007) that revealed precarious informal subdivisions beyond the Mexico-U.S. borderlands. Our combined studies show that U.S. informal housing is not a spillover effect from Mexico but a widespread phenomenon requiring the attention of municipal, regional, state, and federal planners and policymakers. Historically, U.S. policymakers have failed to acknowledge the significance of housing informality, as evidenced by absence of participation in UN-Habitat conferences that develop housing upgrading policies (Durst and Wegmann 2017; Wegmann and Mawhorter 2017).

Planners and community development stakeholders in the United States can learn from the insights of housing upgrading policies implemented in the Global South (Bredenoord, van Lindert, and Smets 2010), especially participatory approaches that foster democratic partnerships between residents, local governments, and community-based organizations (Kiefer and Ranganathan 2020). Participatory projects that assist the most vulnerable families are a well-documented aspect of effective informal housing upgrading in California (Mukhija and Scott-Railton 2013) and slum upgrading projects in the Global South (Kiefer and Ranganathan 2020). Similar projects in the United States use solutions like weatherization and rainwater harvesting to address water and sanitation deficiencies (Sullivan and Ward 2012). We call on planners and other stakeholders to empower residents, particularly the most vulnerable, through participation in affordable housing upgrading projects that can be sustained over time. Our study offers a replicable methodology to identify and measure precarious informal subdivisions and thus enable counties throughout the United States to prioritize participatory housing upgrading policies and programs.

Supplemental Material

sj-docx-1-jpe-10.1177_0739456X241294232 – Supplemental material for An Index to Identify and Classify the Spectrum of U.S. Peri-Urban Informal Subdivisions

Supplemental material, sj-docx-1-jpe-10.1177_0739456X241294232 for An Index to Identify and Classify the Spectrum of U.S. Peri-Urban Informal Subdivisions by Ariadna Reyes, Josh Newton, Camilo Caudillo and Subham Kharel in Journal of Planning Education and Research

Footnotes

Acknowledgements

We thank Seyedsoheil Sharifiasl and Luis Macias Barrientos for their research support. We also thank Jake Wegmann and Bjørn Sletto, and the anonymous reviewers, for their insightful comments to improve this article.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research for this article was funded by the Andrew W. Mellon Foundation Grant from the Crossing Latinidades Humanities Research Initiative in addition to the Research Enhancement Program from the University of Texas at Arlington and the Geisel Grant from the College of Architecture Planning and Public Affairs, and the Center of Mexican American Studies.

ORCID iDs

Ariadna Reyes

Camilo Caudillo

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Ariadna Reyes is an Assistant Professor at the University of Texas at Arlington. Her research interests include informal housing, transportation poverty, and climate justice in the United States and Mexico.

Josh Newton is a Postdoctoral fellow at the University of California, San Diego. His research interests include philanthropic community development, informal housing, and urban theory.

Camilo Caudillo is an Associate Professor at Centro Geo, Mexico City. His research interests include urban mobility, residential segregation, spatial analysis, and the detection of spatial and spatiotemporal patterns.

Subham Kharel is a PhD student at the University of Texas at Arlington. His research interests include transportation planning, urban informality, and economics.

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