Mapping changes in housing in sub-Saharan Africa from 2000 to 2015

Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10–12%) to 23% (21–25%). However, 53 (50–57) million urban Africans (47% (44–50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.

predicted to increase from 1.2 billion in 2015 to 2.5 billion by 2050 (an addition equivalent to the current population of India) 4 , which will necessitate hundreds of millions of new homes. Alongside increased housing demand, the existing housing stock is steadily transformingfor example, thatch roofs are being replaced by corrugated metal roofs, and mud walls by concrete and brick walls 5 . These changes present a powerful opportunity to improve human wellbeing, and they also demonstrate the urgent need for investment in housing infrastructure to ensure that vulnerable populations are not left behind 6 .
Reliable measurements of house types in Africa are critical for tracking changes and targeting interventions, but existing data on African housing are limited 7 . The primary housing indicator for the United Nations Millennium Development Goals and Sustainable Development Goals is the prevalence of urban slum housing, estimates of which are limited to urban areas only, derived from basic extrapolations from national survey data, restricted to specific years and not standardized across the continent at any subnational scale 8,9 . Other detailed records of African housing conditions are focused on housing costs and finance 10 . Here we conduct a standardized analysis using a geospatial framework to quantify the changing profile of housing in urban and rural sub-Saharan Africa during the era of the Millennium Development Goals. We show that African housing underwent a marked change between 2000 and 2015, but unimproved housing persists.
To quantify changes in housing across sub-Saharan Africa, we leveraged 62 georeferenced national household surveys, representing 661,945 unique households in 31 countries (Extended Data Fig. 1). We designed a geostatistical regression model to map house construction materials and overall house type at 5 × 5-km 2 resolution across sub-Saharan Africa. We categorized house construction materials into a binary variable that compared houses built from finished materials (for example, parquet, vinyl, tiled, cement or carpet flooring) to those built from natural or unfinished materials (for example, earth, sand,

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Letter reSeArCH dung or palm flooring) (Extended Data Table 1). We based our categorization of house type on the Millennium Development Goal and Sustainable Development Goal definition of slum housing. We considered 'unimproved' housing to have at least one of four characteristics: (1) unimproved water supply; (2) unimproved sanitation; (3) more than three people per bedroom; and (4) house made of natural or unfinished materials (Supplementary Methods). We considered houses that had none of these characteristics as 'improved' .
The independent variables (covariates) used in our model were aridity 11 , urbanicity 12 , accessibility 13 , travel friction 13 , night-time lights 14 and irrigation 15 , which are commonly used in Africa-focused geostatistical models 16 ; we also included space and time to account for autocorrelated residual effects. Our geostatistical model utilizes the random Fourier feature approach 17 in which a nonlinear, interacting function is defined through high-dimensional feature spaces computed in explicit form in a feature map. The feature map that characterizes this relationship φ → X H ( : ) induces a measure of similarity-the kernel function Expanding on a previous study 17 , our feature map takes the Fourier spectral form , with spectral measure ω r . Rather than assuming a specific spectral distribution ω r , we obtain this Lebesgue measure directly from the data 18 . Given a response variable (for example, wall material), we used a beta-binomial likelihood function p([y + , y total ] | x, ω, φ) = BetaBinomial(z(x|ω), φ) that enabled overdispersion in the data while simultaneously accounting for sample size variation (from y total y total ). We performed regularization using dropout 19 . Approximate posterior confidence intervals were estimated using the weighted likelihood bootstrap 20 . Fitting was performed using ADAM stochastic gradient descent 21 . Cross-validation was performed to check for model fit and to assess the predictive accuracy of the model (Supplementary Text, Extended Data Figs. 2,9). Populationweighted prevalences of people living in different house types in urban and rural areas were calculated using yearly population data from the WorldPop project 22 and a static urban-rural definition from the Global Urban Footprint project 23 .
Our analysis revealed a marked transformation of housing in sub-Saharan Africa from 2000 to 2015. Across all sub-Saharan countries (excluding South Africa, Comoros and desert areas), the prevalence of houses that were built with finished materials increased from 32% (29-33%) in 2000 to 51% (49-54%) in 2015 (Table 1 and Figs. 1, 2). Our analysis suggests a widespread pattern of incremental modifications to the roof, then the walls and finally to the floor of houses (Extended Data Fig. 3). Overall, the predicted prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubled from 11% (10-12%) in 2000 to 23% (21-25%) in 2015 (Table 1), with prevalences ranging from 5% (5-6%) in rural Results are derived from a geospatial model fitted to 62 surveys that represent 661,945 households (house construction materials) and 59 surveys that represent 629,298 households (house type). Houses were classified as improved if they had all of the following characteristics: improved water supply, improved sanitation, three or fewer people per bedroom and house made of finished materials (Extended Data Table 1 and Supplementary Methods). Maps were produced using the raster package (version 2.6-7) in R. The images were plotted using the rasterVis package (version 3.4).
Houses built with nished materials (%)  To examine the links between housing and socioeconomic factors, we quantified the association between house type and household characteristics in 51 national surveys, representing 588,892 households (Supplementary Table 2). For each survey, controlling for cluster-level variation, we jointly estimated the odds of improved housing in relation to education level of the household head, household wealth and age of the household head. We found that the odds of improved housing were 80% higher in more educated households (adjusted odds ratio, 1.80; 95% confidence interval, 1.68-1.93; P < 0.001; Fig. 3a), more than double in the wealthiest households (adjusted odds ratio, 2.53; 95% confidence interval, 2.28-2.82; P < 0.001; Fig. 3b) and 31% higher with increased age of the household head (adjusted odds ratio, 1.31; 95% confidence interval, 1.24-1.39; Extended Data Fig. 4). We also observed a higher prevalence of improved housing in urban survey clusters than rural survey clusters (Extended Data Fig. 5). Across all surveys, a 10% increase in the prevalence of urban clusters (including small rural towns that have grown from villages and account for much of sub-Saharan Africa's urban growth) was associated with a 7.5% increase in improved housing (Extended Data Fig. 6).
Here we quantified housing conditions across urban and rural sub-Saharan Africa during the era of the Millenium Development Goals, and provided a detailed baseline measurement for the Sustainable Development Goals. By applying a geospatial approach to empirical observations, we have built considerably on existing measurements of African housing, which are limited to urban areas, not standardized at any subnational scale and are derived from more simplistic extrapolations from survey data. We show that the prevalence of improved housing (defined as housing with improved water and sanitation, sufficient living area and durable construction) doubled during 2000-2015, but that an unacceptably large proportion of people still live in unimproved housing in urban areas.
Our findings are consistent with continent-wide changes to African housing being driven by economic growth 24 . Increasing household spending is likely to have led people to invest more in their homes and, indeed, we found a clear increase in the prevalence of houses built with finished materials since 2000. Furthermore, house types changed the most in countries with the highest baseline prevalence of improved housing (Extended Data Fig. 7) and house type was clearly associated at the household level with education, wealth and age of the household head. In urban areas, the changes may also have been driven by a lack of traditional materials and the commodification of housing. In the future, continued population and urban growth in sub-Saharan Africa      Table 2).
Letter reSeArCH may help to sustain housing demand and incremental housing changes. In turn, 'healthy urbanization' has been recognized as important for maintaining economic productivity and growth 24 .
Our study has important implications for international goals, which have sought to address housing inequalities by achieving a substantial improvement in the lives of 100 million people living in slums by 2020 3 and universal access to adequate, safe and affordable housing by 2030 2 . We show a considerable reduction in the prevalence of urban unimproved housing across sub-Saharan Africa from 68% (65-71%) in 2000 to 47% (44-50%) in 2015, similar to the equivalent estimates from the United Nations of 65% in 2000 and 55% in 2014 25 . However, nearly half of Africa's urban population still lives in unimproved conditions, which is partly explained by widespread unimproved sanitation-the most common housing deprivation in 75% (52 out of 69) of surveys analysed (Extended Data Fig. 8). These findings highlight the urgent need for governments to improve water and sanitation infrastructure as households continue to spend individually on their homes.
Housing is a central pillar of human security and wellbeing and is increasingly vital in the context of Africa's urbanization and population growth. For example, house design is integral to Sustainable Development Goal 3 through a myriad of associated health outcomes, including mental health, respiratory disease, soil-transmitted helminths, diarrhoeal disease, leishmaniasis and malaria [26][27][28] . As towns and cities in sub-Saharan Africa grow, rapid development of luxury housing in major urban centres is occurring alongside the expansion of informal settlements that lack basic infrastructure. In addition, formal housing investment typically lags behind urbanization and the continent's major urban growth is concentrated in smaller urban centres that have limited capacity to organize construction 6 . Addressing the housing needs of a growing population is key for sustainable urban development and the health and wellbeing of millions of Africans 29 , and will facilitate faster attainment of the Sustainable Development Goals. Our maps provide a critical mechanism to guide intervention and the measurement of change.

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Any methods, additional references, Nature Research reporting summaries, source data, statements of data availability and associated accession codes are available at https://doi.org/10.1038/s41586-019-1050-5. Fig. 4 | Association between house type and age of the household head. The pooled increase in odds of living in an improved house when the age of the household head is over 55 years, compared to 55 years or less, is shown to the right of the vertical line representing the null value (no difference between groups). Odds ratios are adjusted for wealth index, education level of the household head and geographical cluster. Error bars show 95% confidence intervals. Data are from 48 DHS, 2 MIS and 1 AIS conducted between 1996 and 2015 (Supplementary Table 2). Corresponding author(s): Lucy Tusting, Samir Bhatt Last updated by author(s): Dec 11, 2018 Reporting Summary Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, see Authors & Referees and the Editorial Policy Checklist.

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Study description
A geostatistical analysis was done to quantify changes in housing in sub-Saharan Africa from 2000 to 2015. Further analyses were done to explore the relationship between housing and socioeconomic factors.

Research sample
Data from the Demographic and Health Survey program were analyzed. We included all available surveys from https://dhsprogram.com/ with data on housing variables and that were georeferenced, a total of 62 national household surveys representing 661,945 unique households in 31 countries.

Sampling strategy
All available surveys and data points were included in the analysis.

Data collection
We performed a secondary analysis of survey data.

Timing
The data analysed were from a series of national household surveys conducted over several decades.

Data exclusions
No data were excluded from the analysis.