Housing and ServicesHousing and Services

Housing type

Author/s: Katharine Hall
Date: October 2016


This indicator shows the number and proportion of children living in formal, informal and traditional housing. For the purposes of the indicator, “formal” housing is considered a proxy for adequate housing and consists of: dwellings or brick structures on separate stands; flats or apartments; town/cluster/semi-detached houses; units in retirement villages; rooms or flatlets on larger properties. “Informal” housing consists of: informal dwellings or shacks in backyards or informal settlements; dwellings or houses/flats/rooms in backyards; caravans or tents. “Traditional dwelling” is defined as a “traditional dwelling/hut/structure made of traditional materials”. These dwelling types are listed in the General Household Survey, which is the data source.


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Data Source Statistics South Africa (2003 - 2015) General Household Survey 2002 - 2014. Pretoria, Cape Town: Statistics South Africa.
Analysis by Katharine Hall & Winnie Sambu, Children’s Institute, University of Cape Town.
  1. Children are defined as persons aged 0 – 17 years.
  2. Population numbers have been rounded off to the nearest thousand.
  3. Sample surveys are always subject to error, and the proportions simply reflect the mid-point of a possible range. The confidence intervals (CIs) indicate the reliability of the estimate at the 95% level. This means that, if independent samples were repeatedly taken from the same population, we would expect the proportion to lie between upper and lower bounds of the CI 95% of the time. The wider the CI, the more uncertain the proportion. Where CIs overlap for different sub-populations or time periods we cannot be sure that there is a real difference in the proportion, even if the mid-points differ. CIs are represented in the bar graphs by vertical lines at the top of each bar.

Children’s right to adequate housing means that they should not have to live in informal dwellings. One of the seven elements of adequate housing identified by the UN Committee on Economic, Social and Cultural Rights is that it must be “habitable”. To be habitable, houses should have enough space to prevent overcrowding, and should be built in a way that ensures physical safety and protection from the weather.

Formal brick houses that meet the state’s standards for quality housing could be considered “habitable housing”, whereas informal dwellings such as shacks in informal settlements and backyards would not be considered habitable or adequate. Informal housing in backyards and informal settlements makes up the bulk of the housing backlog in South Africa. “Traditional” housing in rural areas is a third category, which is not necessarily adequate or inadequate. Some traditional dwellings are more habitable than new subsidy houses – they can be more spacious and better insulated, for example.

Access to services is another element of “adequate housing”. Children living in formal areas are more likely to have services on site than those living in informal or traditional dwellings. They are also more likely to live closer to facilities like schools, libraries, clinics and hospitals than those living in informal settlements or rural areas. Children living in informal settlements are more exposed to hazards such as shack fires and paraffin poisoning.

The environmental hazards associated with informal housing are exacerbated for very young children. The distribution of children in informal dwellings is slightly skewed towards younger children and babies: 41% of children in informal housing are in the 0 – 5-year age group. Of children under two years, 14% live in informal dwellings, after which the rate declines slightly with age. Nine percent of children over 10 years are informally housed. Given that this trend has remained consistent over a number of years, it seems likely that it is the result of child mobility or changing housing arrangements for children as they get older, rather than indicating an increase in informality over time.

In 2014, over 1.7 million children (9%) in South Africa lived in backyard dwellings or shacks in informal settlements. The number of children in informal housing has declined slightly from 2.3 million (12%) in 2002. The provinces with the highest proportion of informally-housed children are Gauteng (20% of children), North West (16%) Western Cape (16%) and the Free State (15%). Limpopo has the lowest proportion (4%) of children in informal housing and the highest proportion (94%) in formal dwellings. The Eastern Cape and KwaZulu-Natal have by far the largest proportions of children living in traditional dwellings (38% and 23% respectively).  

The distribution of children in formal, informal and traditional dwellings has remained fairly constant since 2002. But racial inequalities persist. Almost all White children (99%) live in formal housing, compared with only 75% of African children. Access to formal housing increases with income. Ninety seven percent of  children in the wealthiest 20% of households live in formal dwellings, compared with just over  two-thirds (70%) of children in the poorest quintile.

There are slight but statistically significant differencesin housing across age groups, with children in the older age group (12 -17 years.
South Africa’s housing policy has no clear or consistent definition of adequate housing since ‘adequate’ includes a range of attributes. Some of these are very technical, for instance minimum standards relating to the quality and size of the dwelling, type of wall and roof materials, provision of services, etc. There are also qualitative descriptors of ‘adequate’ housing, which refer to things like “reasonable living space and privacy” (RDP 2) as well as “habitability, accessibility, location and cultural adequacy” (National Housing Code 3). However, the main attribute used to determine the housing backlog is the type of dwelling.

The GHS instructs the fieldworker to record dwelling type for the main dwelling as well as any other dwelling that belongs to the household but is situated elsewhere. Only the main dwelling type is used in this indicator.

The data are derived from the General Household Survey4, a multi-purpose annual survey conducted by the national statistical agency, Statistics South Africa, to collect information on a range of topics from households in the country’s nine provinces. The survey uses a sample of 30,000 households. These are drawn from Census enumeration areas using multi-stage stratified sampling and probability proportional to size principles. The resulting estimates should be representative of all households in South Africa.
The GHS sample consists of households and does not cover other collective institutionalised living-quarters such as boarding schools, orphanages, students’ hostels, old age homes, hospitals, prisons, military barracks and workers’ hostels. These exclusions should not have a noticeable impact on the findings in respect of children.
Changes in sample frame and stratification
The current master sample was used for the first time in 2004, meaning that, for longitudinal analysis, 2002 and 2003 may not be easily comparable with later years as they are based on a different sampling frame. From 2006, the sample was stratified first by province and then by district council. Prior to 2006, the sample was stratified by province and then by urban and rural area. The change in stratification could affect the interpretation of results generated by these surveys when they are compared over time.
Provincial boundary changes
Provincial boundary changes occurred between 2002 and 2007, and slightly affect the provincial populations. Comparisons on provincial level should therefore be treated with some caution. The sample and reporting are based on the old provincial boundaries as defined in 2001 and do not represent the new boundaries as defined in December 2005.
Person and household weights are provided by Stats SA and are applied in Children Count – Abantwana Babalulekile analyses to give estimates at the provincial and national levels. Survey data are prone to sampling and reporting error. Some of the errors are difficult to estimate, while others can be identified. One way of checking for errors is by comparing the survey results with trusted estimates from elsewhere. Such a comparison can give an estimate of the robustness of the survey estimates. The GHS weights are derived from Stats SA’s mid-year population estimates. For this project, weighted GHS population numbers were compared with population projections from the Actuarial Society of South Africa’s ASSA2008 AIDS and Demographic model.

Analyses of the ten surveys from 2002 to 2011 suggest that some over- and under-estimation may have occurred in the weighting process: 

§  When comparing the weighted 2002 data with the ASSA2008 AIDS and Demographic model estimates, it seems that the number of children was under-estimated by 5% overall. The most severe under-estimation is in the youngest age group (0 – 9 years) where the weighted numbers of boys and girls yield under-estimations of 15% and 16% respectively. The next age group (5 – 9 years) is also under-estimated for both boys and girls, at around 7% each. The difference is reduced in the 10 – 14-year age group, although boys are still under-estimated by around 1% and girls by 3%. In contrast, the weighted data yield over-estimates of boys and girls in the upper age group (15 – 17 years), with the GHS over-counting these children by about 5%. The pattern is consistent for both sexes, resulting in fairly equal male-to-female ratios of 1.02, 1.01, 1.03 and 1.01 for the four age groups respectively. 

§  Similarly in 2003, there was considerable under-estimation of the youngest age groups (0 – 4 years and 5 – 9 years) and over-estimation of the oldest age group (15 – 17 years). The pattern is consistent for both sexes. Children in the youngest age group are under-estimated by as much as 16%, with under-estimates for babies below two years in the range 19 – 30%. The results also show that the over-estimation of males in the 15 – 17-year age group (9%) is much more severe than the over-estimation for females in this age range (1.4%), resulting in a male-to-female ratio of 1.09 in this age group, compared with ratios around 1.02 in the younger age groups. 

§  In the 2004 results, all child age groups seem to have been under-estimated, with the under-estimate being more severe in the upper age group (15 – 17 years). This is the result of severe under-estimation in the number of girls, which outweighs the slight over-estimation of boys in all age groups. Girls are under-estimated by around 6%, 8%, 8% and 12% respectively for the four age bands, while over-estimation in the boys’ age bands is in the range of 2 – 3%, with considerable variation in the individual years. This results in male-to-female ratios of 1.10, 1.11, 1.12 and 1.14 for the four age groups. 

§  In 2005, the GHS weights seem to have produced an over-estimate of the number of males and an under-estimate of the number of females within each five-year age group. The extent of under-estimation for girls (by 7% overall) exceeds that of the over-estimation for boys (at 2% overall). These patterns result in male-to-female ratios of 1.06, 1.13, 1.10 and 1.13 respectively for the four age groups covering children. 

§  The 2006 weighting process yields different patterns from other years when compared to population estimates for the same year derived from ASSA2008, in that it yielded an under-estimation of both females and males. The under-estimation of females is greatest in the 0 – 4 and 5 – 9-year age groups, while the under-estimation of males is in the range 3 – 10% in the 5 – 9 age group and 1 – 6% in the 10 – 14-year age group. This results in male-to-female ratios of 1.09, 0.99, 0.96 and 1.00 respectively for the four age groups covering children. 

§  The 2007 weighting process produced an over-estimation for boys and an under-estimation for girls. The under-estimation of females is in the range of 4 – 8% while the over-estimation for boys is in the range of 1 – 5%. This results in male-to-female ratios of 1.07, 1.06, 1.08 and 1.06 respectively for the four age groups covering children. 

§  In 2008, the GHS weighted population numbers when compared with ASSA2008 over-estimated the number of boys aged 10 and over, in the range of 3% for the 10 – 14 age group, and 8% for the 15 – 17 age group. The total weighted number of girls is similar to the ASSA population estimate for girls, but this belies an under-estimate of female babies below two years (by 7 – 8%), and an over-estimate of young teenage girls. The GHS 2008 suggests a male-to-female ratio of 1.03 for children aged 0 – 4 years, which is higher than that of the ASSA2008 model. 

§  A comparison of the GHS and ASSA for 2009 suggests a continuation of the general pattern from previous years, which is that GHS weights result in an under-estimation of children in the 0 – 4 age group (especially infants), and an over-estimate of older children. In 2009 the under-estimation in the 0 – 4 age group ranges up to 4% for boys and 5% for girls. In the 15 – 17 age group, the GHS-weighted data produce population numbers that are 7% higher than ASSA for boys, and 3% higher for girls. The male-to-female ratios in 2009 are in keeping with those in ASSA2008, with the exception of the 15 – 17 age group where the GHS-derived ratio is higher, at 1.08, compared to 1.00 in ASSA. 

§  In 2010, the GHS weights again produce an underestimation of children in the 0 – 4 age group and an over-estimate of children aged 15 – 17 years. For the middle age groups, and for the child age group as a whole, there is less than 1% difference in the estimates from the two sources. For the 0 – 4 age group the under-estimate is lower than previously, at 2%, but for the oldest age group there is an over-estimate of 5%. The male-to-female ratios are similar across the two sources, although the ratio is 1.00 for all but the 0 – 4 age group in ASSA as against 1.01 for the youngest age group in ASSA and for all age groups in the GHS. 

§  A comparison of the GHS2011 to ASSA2008 (projected to 2011) suggests an under-estimation of children below two years and an over-estimation of children aged 14 – 17 years in the Stats SA survey. This pattern holds for both boys and girls. The under-estimation is particularly pronounced for babies under a year, at 8%. The male-to-female ratio for all children under 17 is 1.00 in ASSA, and 1.01 in the GHS. 

The apparent discrepancies in the ten years of data may slightly affect the accuracy of the Children Count – Abantwana Babalulekile estimates. From 2005 to 2008, consistently distorted male- to-female ratios means that the total estimates for certain characteristics would be somewhat slanted toward the male pattern. This effect is reduced from 2009, where more even ratios are produced, in line with the modelled estimates. A similar slanting will occur where the pattern for 10 – 14-year-olds, for example, differs from that of other age groups. Furthermore, there are likely to be different patterns across population groups.

Statistics South Africa suggests caution when attempting to interpret data generated at low level disaggregation. The population estimates are benchmarked at the national level in terms of age, sex and population group while at provincial level, benchmarking is by population group only. This could mean that estimates derived from any further disaggregation of the provincial data below the population group may not be robust enough.
Reporting error
Error may be present due to the methodology used, ie the questionnaire is administered to only one respondent in the household who is expected to provide information about all other members of the household. Not all respondents will have accurate information about all children in the household. In instances where the respondent did not or could not provide an answer, this was recorded as “unspecified” (no response) or “don’t know” (the respondent stated that they didn’t know the answer).
1Office of the United Nations High Commissioner for Human Rights (1991) The Right to Adequate Housing (art.11 (1)): 13/12/91. CESCR general comment 4. Geneva: United Nations.

Office of the President (1994) Reconstruction and Development Programme White Paper. Pretoria: Government Printer.

Department of Housing (2000) Housing Code. Viewed 9 March 2009: http://www.housing.gov.za/Content/The%20Housing%20Code/Index.htm

Statistics South Africa (2003-2015). General Household Survey 2002-2014 Metadata. Cape Town, Pretoria: Statistics South Africa.

Hall K (2005) Accommodating the poor? A review of the Housing Subsidy Scheme and its implications for children. In: Leatt A & Rosa S (eds) Towards a Means to Live: Targeted poverty alleviation to make children's rights real. Cape Town: Children's Institute, University of Cape Town.