Housing Index in South Africa increased to 117.80 points in January from 117.30 points in December of 2024. This dataset provides - South Africa Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Residential Property Prices for South Africa (QZAN368BIS) from Q1 1967 to Q1 2025 about South Africa, residential, housing, and price.

As of 2022, the number of South African households living in formal housing reached more than 83 percent. Some twelve percent of South Africans, however, still inhabited informal dwellings, while 4.3 percent were living in traditional dwellings.

South Africa Residential Real Estate Market Report is Segmented by Property Type (Villas & Landed Houses, Apartments & Condominiums), by Price Band (Affordable Housing, Mid-Market, and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary (New-Build), and More), and by Key Cities (Cape Town, Johannesburg, and More). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.

Residential Property Prices in South Africa increased 5.20 percent in February of 2025 over the same month in the previous year. This dataset includes a chart with historical data for South Africa Residential Property Prices.

In 2023, the South Africa Real Estate Market reached a value of USD 60.9 million, and it is projected to surge to USD 98.4 million by 2030.

As of 2022, the number of households in South Africa increased and amounted to approximately 18.48 million, roughly 530,000 more than in the previous year. Between 2002 and 2022, the number of families in South Africa grew by around 65 percent. Looking at the number of households from a regional perspective , the Gauteng province (includes the city of Johannesburg) has the bulk of households, with almost 5.6 million residences. Although Gauteng is the smallest region in the country, it is highly urbanized and houses most of the population.

Households headed by women

The number of households headed by women averaged around 42 percent. Rural areas such as the Eastern Cape and Limpopo had a higher proportion of women in charge of their family unit. Urbanized regions, namely Gauteng and the Western Cape, were more likely to be headed by men.

Languages spoken in households

The most spoken language within and outside of South African households was isiZulu, with around 25 percent of the population utilizing it. The English language was the second most common language spoken outside of households, with a share of roughly 17 percent. However, within households, individuals preferred to speak other official languages such as isiXhosa and Afrikaans. South Africa has a diverse range of cultures, and language plays a crucial role in preserving these cultures.

The number of residential property sales in South Africa decreased for the second year in a row in 2020, reaching approximately ****** home sales. The strongest year for the housing market was 2018, when roughly ****** home sales took place. Over the whole observation period, properties in the luxury market segment of over *** million South African rands comprised the largest share of transactions.

Key information about South Africa Nominal Residential Property Price Index

• South Africa Nominal Residential Property Price Index was reported at 177.560 2010=100 in Sep 2024.
• This records an increase from the previous number of 177.480 2010=100 for Jun 2024.
• South Africa Nominal Residential Property Price Index data is updated quarterly, averaging 19.363 2010=100 from Mar 1966 to Sep 2024, with 235 observations.
• The data reached an all-time high of 178.093 2010=100 in Mar 2024 and a record low of 1.048 2010=100 in Mar 1966.
• South Africa Nominal Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Nominal Residential Property Price Index: 2010=100: Quarterly.

CPI Housing Utilities in South Africa increased to 100.70 points in May from 100.60 points in April of 2025. This dataset provides - South Africa Cpi Housing & Utilities- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cape Town was the most expensive metro to buy a home in South Africa in 2024. The average sales price of residential property was *** million South African rands in that year, which was roughly double the price paid in Port Elizabeth.

Residential properties under ******* South African rands comprised the largest share of the residential stock in South Africa in 2021. According to the source, there were over * million of these entry market properties. The affordable market segment refers to housing in the ******* to ******* South African rands price range, whereas properties up to ******* South African rands are considered part of the conventional market segment. High-end and luxury housing, on the other hand, is housing in the ******* to *** million South African rands price range and over *** million South African rands, respectively.

The South African residential real estate market size is valued at 19.89 million USD in 2025 and is projected to expand at a CAGR of 10.46% during the forecast period 2025-2033. The market size is expected to reach 36.89 million USD by 2033. The growth of the market is primarily driven by factors such as rising urbanization, increasing disposable income, and a growing middle-class population. The market is also witnessing a trend towards luxury and high-end properties, as well as an increasing demand for green and sustainable homes. Key market segments include villas and landed houses, condominiums and apartments, and key cities such as Johannesburg, Cape Town, Durban, Port Elizabeth, Bloemfontein, Pretoria, and Rest of South Africa. Major companies operating in the market include Reeflords, Renprop (Pty) Ltd, Pam Golding Properties, The Amdec Group, Kaan Development, Pipilo Projects, Devmark Property Group, RDC Properties, Harcourts International Ltd, and Legaro Property Development. The market is expected to face challenges such as rising interest rates, affordability concerns, and economic volatility. However, government initiatives aimed at promoting homeownership and the increasing popularity of alternative financing options are expected to support the growth of the market in the coming years. Recent developments include: July 2022- To improve access to affordable and sustainable housing in South Africa, IFC (International Finance Corporation) announced an investment to help South African residential property developer Alleyroads build over 1,000 rental apartments in the Johannesburg area., June 2022- Construction of a new mixed-use building, Rubik, began in Cape Town's CBD. The building will complement the city's growing skyline. Located on the corner of Roop and Rybeek Streets, the Rubik consists of luxury residential apartments above prime office and quality retail space. The architects have designed it in a very characteristic modern glass 'layered' building.. Key drivers for this market are: 4., Growing urbanisation in the countries4.; Increasing support of private sector to meet infrastructural growth in various sectors such as water, energy, transportation, and communications. Potential restraints include: 4., Lack of quality and quantity of infrastructure. Notable trends are: Increasing Demand for Sectional Title Living in South Africa.

The residential property market in South Africa has grown year-on-year between 2001 and 2023, except for 2008. Since 2009, the annual house price increase varied between *** and eight percent. In 2023, house prices appreciated by *** percent. While the market has been growing, the growth rate was much lower than during the period before the global financial crisis. Meanwhile, rental growth in South Africa has accelerated since 2021.

Abstract

The GHS is an annual household survey which measures the living circumstances of South African households. The GHS collects data on education, health, and social development, housing, access to services and facilities, food security, and agriculture.

Geographic coverage

The General Household Survey has national coverage.

Analysis unit

Households and individuals

Universe

The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa, and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons, and military barracks.

Kind of data

Sample survey data

Sampling procedure

From 2015 the General Household Survey (GHS) uses a Master Sample (MS) frame developed in 2013 as a general-purpose sampling frame to be used for all Stats SA household-based surveys. This MS has design requirements that are reasonably compatible with the GHS. The 2013 Master Sample is based on information collected during the 2011 Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs, were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the Master Sample, since they covered the entire country, and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the Master Sample, with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current Master Sample (3 324) reflect an 8,0% increase in the size of the Master Sample compared to the previous (2008) Master Sample (which had 3 080 PSUs). The larger Master Sample of PSUs was selected to improve the precision (smaller coefficients of variation, known as CVs) of the GHS estimates. The Master Sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types are Urban, Tribal and Farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro.

The sample for the GHS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.After allocating the sample to the provinces, the sample was further stratified by geography (primary stratification), and by population attributes using Census 2011 data (secondary stratification).

Mode of data collection

Computer Assisted Personal Interview

Research instrument

Data was collected with a household questionnaire and a questionnaire administered to a household member to elicit information on household members.

Data appraisal

Since 2019, the questionnaire for the GHS series changed and the variables were also renamed. For correspondence between old names (GHS pre-2019) and new name (GHS post-2019), see the document ghs-2019-variables-renamed.

South Africa Consumer Price Index (CPI): Western Cape: Housing and Utilities: Owners Equivalent Rent data was reported at 112.100 Dec2021=100 in Dec 2024. This records an increase from the previous number of 111.100 Dec2021=100 for Nov 2024. South Africa Consumer Price Index (CPI): Western Cape: Housing and Utilities: Owners Equivalent Rent data is updated monthly, averaging 73.700 Dec2021=100 from Jan 2008 (Median) to Dec 2024, with 204 observations. The data reached an all-time high of 112.100 Dec2021=100 in Dec 2024 and a record low of 46.000 Dec2021=100 in Feb 2008. South Africa Consumer Price Index (CPI): Western Cape: Housing and Utilities: Owners Equivalent Rent data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.I032: Consumer Price Index: by Region: Dec2021=100.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

Key information about South Africa Gold Production

• South Africa Real Residential Property Price Index was reported at 87.122 2010=100 in Sep 2024.
• This records a decrease from the previous number of 87.636 2010=100 for Jun 2024.
• South Africa Real Residential Property Price Index data is updated quarterly, averaging 75.341 2010=100 from Mar 1966 to Sep 2024, with 235 observations.
• The data reached an all-time high of 123.390 2010=100 in Sep 2007 and a record low of 44.268 2010=100 in Mar 1997.
• South Africa Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

Abstract

The 1991 South African population census was an enumeration of the population and housing in South Africa.The census collected data on dwellings and individuals' demographic, family and employment details.

Geographic coverage

The South African Census 1991 covered the whole of South Africa. The "homelands" of Transkei, Bophuthatswana, Venda and Ciskei were enumerated separately and the dataset contains data files for Bophuthatswana, Venda and Ciskei. The dataset does not include a data file for the Transkei as this was never provided by Statistics South Africa.

Analysis unit

Households and individuals

Universe

The 1991 Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 7 and 8 March 1991) were therefore enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were enumerated and included in the figures. The Diplomatic and Consular Corps of foreign countries were not included. Crews and passengers of ships were also not enumerated, except those who were present at the harbours of the RSA on census night. Similarly, residents of the RSA who were absent from the night were not enumerated. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census enumeration data

Sampling deviation

As a result of the unplanned and unstructured nature of certain residential areas, as well as the inaccessibility of certain areas during the preparations for the enumeration of census, comprehensive door-to-door surveys were not possible. The Human Sciences Research Council had to enumerate these areas by means of sample surveys. 88 areas country-wide were enumerated on this basis.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1991 Population Census questionnaire covered particulars of households: dwelling type, ownership type, type of area (rural/urban) and particulars of individuals: relationship within household, sex, age, marital status, population group, birthplace, citizenship, duration of residency, religion, education level, language, literacy,employment status, occupation, economic sector and income.

South Africa Consumer Price Index (CPI): Weights: Housing and Utilities: Owners Equivalent Rent data was reported at 11.720 Per 100 in 2024. This stayed constant from the previous number of 11.720 Per 100 for 2023. South Africa Consumer Price Index (CPI): Weights: Housing and Utilities: Owners Equivalent Rent data is updated yearly, averaging 11.485 Per 100 from Jan 2009 (Median) to 2024, with 16 observations. The data reached an all-time high of 11.930 Per 100 in 2021 and a record low of 10.950 Per 100 in 2016. South Africa Consumer Price Index (CPI): Weights: Housing and Utilities: Owners Equivalent Rent data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.I035: Consumer Price Index: Weights (Old Classification).