Determinants of structural housing quality in Somaliland: a multilevel mixed-effects analysis of national survey data

Background

Globally, housing is recognized as a fundamental human requirement and a critical determinant of economic well-being, dignity, and overall quality of life (1, 2). Conceptually, structural housing quality refers to the physical integrity of a dwelling, typically measured by the durability of materials used for roofs, walls, and floors, as well as the adequacy of living space (2–4). At the global level, “adequate housing” must provide protection from environmental hazards, physical safety, and habitability as defined by Sustainable Development Goal (SDG) 11 (2). Regionally, in sub-Saharan Africa, this concept is often categorized by the distinction between “finished” materials (such as cement, tiles, or metal) and “natural/unfinished” materials (such as earth, dung, or thatch) (4). Nationally, in the Somaliland context, structural quality serves as a key indicator of living standards and is intrinsically linked to the multidimensional nature of poverty, where physical deficits in housing often coincide with limited access to basic infrastructure like electricity and financial services.

Historically, the global housing landscape has been transformed by unprecedented 21st-century urbanization, particularly in the Global South, where an estimated 1.6 billion people are expected to be added to urban areas by 2030 (2, 3). Regionally, sub-Saharan Africa has undergone a significant transition; between 2000 and 2015, the prevalence of housing built with finished materials nearly doubled from 32% to 51% (4). Despite this progress, millions still reside in makeshift structures due to a chronic supply-demand imbalance (2, 3). Nationally, Somaliland and the wider Somali region share a history of conflict and civil war that decimated social services and infrastructure, leaving the economy in a long-term state of recuperation (5). While there has been progress in governance, the housing sector continues to grapple with the legacy of displacement and a high prevalence of substandard shelters, particularly among nomadic and rural populations (5).

This study is anchored in Maslow's Hierarchy of Needs, which identifies adequate shelter as a fundamental physiological requirement that must be satisfied before higher-order social and psychological needs can be met (6). The study is also informed by the Actual Aspiration Gap Theory, which suggests that housing satisfaction depends on the extent to which actual housing conditions meet households' expectations and aspirations (6). In Somaliland, households with limited financial resources and restricted access to infrastructure may experience larger aspiration gaps because they are unable to obtain durable housing materials despite recognizing their importance. Together, these theories provide a framework for understanding how household socioeconomic characteristics and community context jointly influence structural housing quality.

Furthermore, Somaliland faces unique challenges where rapid urbanization and household formation have outpaced the provision of quality housing. Existing assessments often treat housing as a monolithic entity, failing to account for how individual, household, and community-level factors simultaneously re associated with structural quality (7, 8). This study identifies a critical gap: without understanding these hierarchical relations, policymakers cannot effectively target interventions to move the population toward the goal of “adequate housing for all” as envisioned in the 2030 Agenda for Sustainable Development (2).

This study is the first multilevel analysis of structural housing quality and its determinants in Somaliland based on the nationally representative SLDHS 2020 data, providing evidence that can inform housing policy, infrastructure planning, and public health interventions in the country.

Methods

Sampling technique

This study employs a quantitative, cross-sectional research design to profile the housing environment and identify its determinants. The sampling technique follows a stratified two-stage cluster sampling approach. In the first stage, Enumeration Areas (EAs) were selected from a national sampling frame using probability proportional to size. In the second stage, a fixed number of households were selected within each EA to ensure representativeness across urban, rural, and nomadic settlements.

The sample size was determined based on the Somaliland Demographic and Health Survey (SLDHS) 2020 dataset. To ensure statistical power for multivariate techniques, the final sample includes 6,074 households (unweighted) with complete data for structural quality indicators. Weighted analysis is applied to the sample to account for non-proportional distribution across regions and residence types, ensuring that the results are nationally representative.

Study variables

The primary outcome variable for this study was structural housing quality, a binary variable categorized as either “improved” or “unimproved.” Structural housing quality was derived from three housing components: floor material, wall material, and roof material. Each component was first classified as improved or unimproved according to the Somaliland Demographic and Health Survey (SLDHS) housing material classifications. Improved materials included durable construction materials such as cement, ceramic tiles, metal sheets, cement blocks, and stone with cement, whereas unimproved materials included natural or rudimentary materials such as earth, dung, grass, thatch, cloth, and mud-based materials. A household was classified as having improved structural housing quality only if all three components (floor, wall, and roof) were categorized as improved. Households with one or more unimproved components were classified as having unimproved structural housing quality.

The explanatory variables were organized into two hierarchical levels to account for the nested nature of the data: Level 1 (household-level) factors included the age and sex of the household head, household size, wealth index quintiles, livestock ownership, bank account ownership, and access to electricity; Level 2 (community-level) factors included the geographic region and the place of residence, categorized as urban, rural, or nomadic.

Data collection

This study utilized secondary data extracted from the Somaliland Demographic and Health Survey (SLDHS) 2020, a comprehensive national survey designed to provide reliable information on socio-demographic profiles, economic status, and living conditions across Somaliland. The primary data for the SLDHS were collected using a stratified two-stage cluster sampling design, which involved selecting Enumeration Areas (EAs) from a national sampling frame followed by the selection of households within those clusters to ensure representation across urban, rural, and nomadic populations. Data collection was conducted through face-to-face interviews by trained enumerators using structured and pretested questionnaires.

For the purposes of this mixed-effects analysis, specific variables were extracted from the dataset, including household-level socio-demographic characteristics (e.g., age and sex of the household head, household size), economic indicators (e.g., wealth index, livestock ownership, and access to bank accounts), and structural housing components (e.g., specific materials used for floors, walls, and roofs). The use of this weighted, nationally representative dataset allows for a statistically powerful analysis that accounts for the hierarchical nature of the data, where households are nested within community clusters

Data analysis

The data analysis was performed using Stata 17, following a multi-step approach modeled after the hierarchical analysis of the 2020 Somaliland Demographic and Health Survey (SLDHS). Missing data were assessed across all study variables before analysis. A complete-case analysis approach was employed, whereby observations with missing values on any variable included in the analysis were excluded. Only three observations were removed because of incomplete information, resulting in a final analytic sample of 6,074 households. Given the negligible proportion of excluded observations (<0.1% of the original sample), the potential for bias arising from missing data was considered minimal.

To account for the complex survey design of the SLDHS, sampling weights, clustering, and stratification were applied during the analysis. The individual sampling weight variable (V005) was normalized by dividing by 1,000,000 and applied to adjust for unequal probabilities of selection and non-response. Additionally, multicollinearity was assessed using variance inflation factors (VIF). All VIF values were below the commonly accepted threshold of 10, indicating no evidence of problematic multicollinearity.

Descriptive statistics were used to summarize the socio-demographic characteristics of households. Bivariate associations between explanatory variables and structural housing quality were assessed using Pearson's chi-square tests. Given the hierarchical structure of the data, with households nested within survey clusters (Enumeration Areas), multilevel mixed-effects logistic regression models were fitted to estimate both fixed and random effects.

A total of 151 survey clusters were included in the analysis. Four models were constructed: a null model (Model 0) containing no explanatory variables to assess cluster-level variation through the intraclass correlation coefficient (ICC); Model I, including household-level variables only; Model II, including community-level variables only; and Model III, including both household- and community-level variables. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood statistics. Results were presented as adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs), and statistical significance was declared at p < 0.05.

Ethical consideration

The study adheres to rigorous ethical standards for secondary data use. The original SLDHS 2020 data collection ensured informed consent from all participants, and the dataset used for this analysis is anonymized to protect respondent identity. Permission to access and analyze the data was obtained from the relevant health and statistical authorities,

Result

The socio-demographic characteristics of households in Somaliland are presented in Table 1. The majority of household heads were aged 36–50 years (36.04%), followed by those aged 20–35 years (32.55%) and above 50 years (30.45%), while less than 1% were younger than 20 years. Most households were headed by males (65.27%), whereas females accounted for 34.73% of household heads. Large households were common, with 80.71% comprising four or more members. Regarding socioeconomic status, nearly half of households (45.94%) belonged to the lowest wealth category, while 24.84% were classified as very high wealth, indicating substantial economic inequality. Regional distribution was uneven, with the largest proportion of households residing in Maroodi-jeex (34.48%), followed by Togdheer (20.68%) and Sanaag (16.94%), whereas Sahil accounted for only 5.25% of households. More than half of households resided in rural areas (51.36%), while 48.64% lived in urban settings. Approximately 48.73% of households reported livestock ownership, whereas only 3.94% had access to a bank account, reflecting limited financial inclusion. Access to electricity remained suboptimal, with 44.59% of households reporting electricity access and 55.41% lacking access.

Table 1. Socio-demographic characteristics of households in Somaliland.

Figure 1 illustrates the distribution of structural housing quality among households in Somaliland. The findings indicate that a substantial majority of households (75.9%) were classified as having unimproved structural housing, while only 24.1% had improved housing conditions. This distribution highlights a pronounced burden of substandard housing, suggesting that a large proportion of households live in dwellings constructed with inadequate materials for roofs, walls, and floors. The relatively low proportion of improved housing underscores persistent structural and socioeconomic challenges affecting housing conditions in the study population. Overall, the figure demonstrates a clear imbalance in housing quality, with unimproved conditions overwhelmingly predominating.

Table 2 presents the bivariate associations between household characteristics and structural housing quality in Somaliland. Significant differences in housing quality were observed across all explanatory variables examined. The proportion of households living in improved housing increased significantly with the age of the household head, reaching its highest level among households headed by individuals aged 36–50 years (25.88%), compared with only 16.46% among those younger than 20 years (p = 0.006). Female-headed households exhibited a higher prevalence of improved housing than male-headed households (26.90% vs. 22.72%, p < 0.001). Likewise, larger households (≥4 members) were more likely to reside in improved housing compared with smaller households (25.53% vs. 18.57%, p < 0.001). A pronounced socioeconomic gradient was evident across wealth categories (p < 0.001). The prevalence of improved housing increased markedly from only 1.46% among households in the lowest wealth category to 36.88%, 58.39%, and 75.65% among middle-, high-, and very-high-wealth households, respectively. This finding highlights the strong relationship between household economic status and access to improved housing conditions.

Table 2. Bivariate analysis of factors associated with structural housing quality among households in Somaliland (SLDHS 2020).

Significant geographical disparities were also observed (p < 0.001). Households in Sanaag (30.51%) and Sahil (26.95%) reported the highest prevalence of improved housing, whereas those in Togdheer (17.92%) and Sool (19.05%) experienced comparatively lower levels of housing quality. Housing quality differed substantially by place of residence. More than half of urban households (53.28%) lived in improved housing, compared with only 11.82% of rural households (p < 0.001), underscoring the persistent urban–rural gap in housing conditions. Similarly, households without livestock ownership were considerably more likely to have improved housing than livestock-owning households (48.02% vs. 10.06%, p < 0.001), reflecting differences in livelihood patterns and settlement characteristics. Financial inclusion and infrastructure access were strongly associated with housing quality. Households with bank accounts were nearly three times more likely to reside in improved housing than those without bank accounts (67.86% vs. 23.28%, p < 0.001). Likewise, access to electricity showed one of the strongest associations with housing quality, with 61.76% of households with electricity living in improved housing compared with only 9.31% among households without electricity access (p < 0.001).

Table 3 presents the comparison of the multilevel mixed-effects logistic regression models fitted to identify factors associated with structural housing quality in Somaliland. The null model (Model 0), which contained no explanatory variables, yielded an intraclass correlation coefficient (ICC) of 0.891, indicating substantial clustering of housing quality across survey clusters. This finding suggests that households within the same cluster tend to share similar housing characteristics, supporting the use of a multilevel modeling framework. The inclusion of household-level variables in Model I substantially improved model performance, as reflected by the reduction in ICC from 0.891 to 0.215 and the marked decrease in AIC from 4,646.14 to 3,305.26. These findings indicate that household socioeconomic and demographic characteristics accounted for a considerable proportion of the variation in structural housing quality across clusters. Similarly, Model II, which incorporated community-level factors, improved model fit compared with the null model, yielding an ICC of 0.288 and lower AIC and BIC values. This suggests that contextual factors, including region and place of residence, contribute meaningfully to differences in housing quality across Somaliland. Among all fitted models, the full model (Model III), which simultaneously included household- and community-level variables, demonstrated the best overall fit to the data. This model yielded the lowest AIC (3,210.06) and BIC (3,324.21) values, along with the highest log-likelihood (−1,586.03), indicating superior explanatory performance compared with the other models. Furthermore, the ICC declined to 0.131, suggesting that a substantial proportion of the between-cluster variation was accounted for by the variables included in the final model. Based on these model fit statistics, Model III was selected as the most appropriate model for identifying factors associated with structural housing quality in Somaliland.

Table 3. Multilevel mixed-effects logistic regression model comparison for structural housing quality in Somaliland (SLDHS 2020).

Table 4 presents the results of the multilevel mixed-effects logistic regression analysis of factors associated with improved structural housing quality in Somaliland. After controlling for both household and community-level characteristics, household wealth emerged as the most influential factor associated with housing quality. Compared with households in the lowest wealth category, those in the middle wealth category had 26.61 times higher odds of residing in improved housing (AOR = 26.61, 95% CI: 18.76–37.75, p < 0.001). The odds increased substantially among households in the high wealth category (AOR = 95.72, 95% CI: 64.31–142.48, p < 0.001) and were highest among households in the very high wealth category, which had nearly 300 times greater odds of improved housing than households in the lowest wealth group (AOR = 298.19, 95% CI: 191.29–464.84, p < 0.001). These findings underscore the strong relationship between household economic status and access to improved housing conditions. Livestock ownership and electricity access were also significantly associated with housing quality. Households without livestock ownership had 38% higher odds of residing in improved housing compared with livestock-owning households (AOR = 1.38, 95% CI: 1.13–1.70, p = 0.002). Similarly, households without electricity access had significantly lower odds of residing in improved housing compared with households that had electricity access (AOR = 0.68, 95% CI: 0.51–0.89). This finding suggests that access to electricity is closely linked to improved housing conditions and may reflect broader differences in household socioeconomic status and infrastructure availability. Significant geographic disparities were observed across regions. Relative to households residing in Awdal, those in Maroodi-jeex (AOR = 0.40, 95% CI: 0.24–0.66, p < 0.001), Sahil (AOR = 0.52, 95% CI: 0.34–0.80, p = 0.003), and Togdheer (AOR = 0.62, 95% CI: 0.39–0.99, p = 0.047) had significantly lower odds of improved housing. However, no statistically significant differences were observed for households residing in Sool or Sanaag after adjustment for other explanatory variables. Place of residence remained independently associated with housing quality. Rural households had 47% lower odds of residing in improved housing compared with urban households (AOR = 0.53, 95% CI: 0.40–0.71, p < 0.001), indicating a persistent urban–rural disparity in housing conditions. In contrast, the age of the household head, sex of the household head, the household size, and bank account ownership were not significantly associated with improved structural housing quality in the fully adjusted model.

Table 4. Multilevel mixed-effects logistic regression analysis of factors associated with structural housing quality in Somaliland (SLDHS 2020).

Discussion

This study examined the factors associated with structural housing quality among households in Somaliland using data from the 2020 Somaliland Demographic and Health Survey. The findings demonstrated that only 24.1% of households lived in improved housing, while 75.9% resided in housing with at least one unimproved structural component. This finding indicates that a substantial proportion of households in Somaliland continue to experience inadequate housing conditions. The observed prevalence is lower than that reported in several low- and middle-income countries, where improvements in housing infrastructure have been supported through urban development programs, economic growth, and investments in basic services (9, 10). The relatively low level of improved housing in Somaliland may reflect persistent socioeconomic challenges, limited infrastructure development, rapid population growth, and constrained access to quality construction materials.

Household wealth emerged as the strongest determinant of structural housing quality. Compared with households in the lowest wealth category, those in the middle, high, and very high wealth categories had substantially greater odds of residing in improved housing. This finding is consistent with studies conducted in Sub-Saharan African countries, which have consistently identified household economic status as one of the most important predictors of housing quality (4). Wealthier households are more capable of purchasing durable construction materials, investing in home improvements, and accessing services that support safer and healthier living environments (11). The strong association between socioeconomic resources and housing quality supports Maslow's proposition that adequate shelter represents a foundational human need whose attainment depends on access to material resources. The extremely large odds ratios observed for wealth categories should be interpreted cautiously. Such magnitudes may partly reflect sparse data within some wealth–housing combinations, residual overlap between wealth measurement and housing characteristics, or model instability. Therefore, emphasis should be placed on the direction and consistency of the association rather than the exact magnitude of the odds ratios. These findings are also consistent with the Actual Aspiration Gap Theory, which proposes that housing outcomes reflect the gap between what households aspire to achieve and what they are able to attain. In Somaliland, wealthier households are better able to translate housing aspirations into durable construction materials, whereas poorer households face financial constraints that limit improvements in housing quality.

Geographic location was another important determinant of structural housing quality. Households residing in Maroodi-jeex, Sahil, and Togdheer had significantly lower odds of improved housing compared with households in Awdal. These findings suggest that considerable regional inequalities exist in housing conditions across Somaliland. Similar geographic disparities have been reported in studies conducted across sub-Saharan Africa, where differences in infrastructure development, economic opportunities, access to construction materials, and public investment contribute to variations in housing quality (12). The regional differences observed in this study may reflect unequal distribution of development projects, variations in urbanization patterns, and disparities in access to housing-related services. These findings indicate that housing improvement programs should adopt geographically targeted approaches that prioritize regions with the greatest housing deficits.

Place of residence also showed a significant association with structural housing quality. Rural households were significantly less likely to reside in improved housing than urban households. This finding is consistent with previous studies conducted in low- and middle-income countries, which have consistently reported poorer housing conditions among rural populations (13). Rural households often face challenges related to limited access to infrastructure, transportation networks, electricity, and quality construction materials. In many rural communities, housing construction continues to rely heavily on locally available traditional materials, which are frequently classified as unimproved according to international housing standards. From the perspective of the Actual Aspiration Gap Theory, rural households may experience larger aspiration gaps because limited access to infrastructure, markets, and construction materials reduces their ability to achieve the housing standards they desire. The persistent urban-rural gap identified in this study highlights the need for targeted rural housing and infrastructure investments to reduce inequalities in living conditions and promote equitable development.

Electricity access was also associated with structural housing quality. Households with access to electricity generally exhibited better housing conditions than those without access. This finding may reflect the broader relationship between infrastructure development and household socioeconomic status. Access to electricity is often concentrated among households living in more developed areas with better housing infrastructure and greater access to public services. Similar associations between housing quality and access to infrastructure have been documented in studies from low and middle-income countries, where improvements in basic services frequently occur alongside improvements in housing conditions (12).

The study also found that livestock ownership was associated with structural housing quality. Households without livestock ownership had higher odds of residing in improved housing compared with livestock-owning households. One possible explanation is that livestock ownership in Somaliland is more common among rural and pastoral households, which often rely on traditional housing structures and locally sourced construction materials. In contrast, households that are less dependent on livestock-based livelihoods may be more likely to reside in urban settings and possess greater access to improved housing infrastructure. This finding highlights the importance of considering livelihood patterns when designing housing improvement interventions.

Overall, the findings of this study demonstrate that structural housing quality in Somaliland is strongly associated with socioeconomic status, geographic location, place of residence, access to infrastructure, and livelihood characteristics. These results highlight the need for comprehensive housing policies that address both household-level economic constraints and broader regional inequalities. Policy efforts should prioritize improving access to affordable housing materials, expanding rural infrastructure, promoting equitable regional development, and integrating housing improvement initiatives into broader public health and sustainable development strategies. Such interventions would contribute directly to Sustainable Development Goal 11, which aims to ensure access to adequate, safe, and affordable housing for all.

Conclusion

This study examined the determinants of structural housing quality in Somaliland using nationally representative data from the 2020 Somaliland Demographic and Health Survey and a multilevel mixed-effects logistic regression approach. The findings revealed that only one-quarter of households resided in improved housing, indicating substantial deficits in housing conditions across the country. Household wealth emerged as the strongest determinant of improved housing quality, while geographic region, place of residence, electricity access, and livestock ownership were also significantly associated with housing conditions. Furthermore, considerable clustering of housing quality was observed across communities, highlighting the importance of both household-level and contextual factors.

These findings support both Maslow's Hierarchy of Needs and the Actual Aspiration Gap Theory by demonstrating that structural housing quality is shaped by households' economic capacity as well as the contextual opportunities available within their communities. The findings also underscore the persistent socioeconomic and geographic inequalities in housing quality in Somaliland.

Efforts to improve housing conditions should prioritize economically disadvantaged households, underserved regions, and rural communities, while simultaneously expanding access to infrastructure and basic services. Policies aimed at increasing the affordability of durable construction materials, strengthening rural development programs, and promoting equitable regional investment may contribute to improving housing quality and reducing housing-related inequalities. Integrating housing improvement initiatives within broader public health and sustainable development strategies could play a critical role in advancing living conditions and supporting progress toward Sustainable Development Goal 11, which seeks to ensure access to adequate, safe, resilient, and affordable housing for all.

Policy implications

The findings of this study have important implications for housing policy, public health, and sustainable development in Somaliland. Given the strong association between household wealth and housing quality, policies aimed at improving housing conditions should prioritize low-income households through targeted housing support programs, subsidized construction materials, and affordable housing financing mechanisms. Such interventions could help reduce the substantial socioeconomic inequalities observed in access to improved housing.

The significant regional and urban rural disparities identified in this study suggest the need for geographically targeted interventions. Government agencies, development partners, and local authorities should prioritize investments in underserved regions and rural communities where households face greater barriers to accessing improved housing. Expanding rural infrastructure, including electricity, transportation networks, and basic public services, may create enabling conditions for housing improvement and broader community development.

The findings also highlight the importance of integrating housing improvement initiatives within public health and development policies. Improved housing is not only a marker of socioeconomic well-being but also a critical determinant of health, influencing exposure to environmental hazards, infectious diseases, and overall quality of life. Therefore, housing programs should be incorporated into national strategies aimed at improving population health, reducing inequalities, and achieving the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being).

Finally, the substantial community-level variation observed in housing quality indicates that housing challenges extend beyond individual households and require context-specific solutions. Future housing policies should adopt a multisectoral approach involving the housing, health, infrastructure, finance, and local government sectors to ensure sustainable and equitable improvements in living conditions across Somaliland.

Strength and limitations

The study presents several significant strengths that contribute to the academic understanding of residential conditions in the Horn of Africa. By focusing on Somaliland, the research addresses an important and historically under-researched context characterized by post-conflict recovery and rapid urbanization.

A primary methodological merit is the use of nationally representative survey data from the 2020 Somaliland Demographic and Health Survey (SLDHS), which utilizes a stratified two-stage cluster sampling approach to ensure the inclusion of urban, rural, and nomadic populations. Furthermore, the study applies multilevel mixed-effects modeling, which is essential for capturing the nested nature of the data where households are clustered within communities. This technique revealed that the null-model ICC of 0.891 suggests substantial clustering of housing quality across survey clusters. However, such a high value is unusual in household survey research and may partly reflect the highly uneven distribution of housing quality across communities or model instability arising from the binary outcome structure. Therefore, the ICC should be interpreted cautiously. Finally, the research aligns with Sustainable Development Goal (SDG) 11, specifically the target of ensuring access to adequate, safe, and affordable housing, thereby providing a critical evidence base for policymakers aiming to meet international development standards in a fragile state.

However, the study also has certain limitations that should be considered. First, it utilizes a cross-sectional study design, which identifies associations between variables but cannot definitively establish temporal or causal relationships. Second, the measurement of structural housing quality is limited to a binary classification (improved versus unimproved) based on three physical components: floors, walls, and roofs. While this provides a clear metric for durability, it may not encompass the full spectrum of “adequate housing”, potentially overlooking aspects such as tenure security, affordability, or overcrowding. Additionally, the analysis relies on secondary data and employs a complete-case approach; while the missing data was minimal (less than 0.1%), this method inherently excludes observations with incomplete information. Lastly, although the study accounts for nomadic populations, the variables used such as livestock ownership and electricity access may not fully capture the unique, transient nature of nomadic dwellings compared to settled rural or urban structures.

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Footnotes

Edited by: Jiangtao Du, University of Liverpool, United Kingdom

Reviewed by: Subrata Haldar, Lovely Professional University, India

Muhamad Munawar Hussain, Higher Education Department, Pakistan