The Report on Housing Market South Africa is segmented By Type (Villas and Landed Houses, Condominiums, and Apartments) and By city (Johannesburg, Cape Town, Durban, Port Elizabeth, Bloemfontein, Pretoria, and the Rest of South Africa). The report offers the market size and forecasts in values (USD billion) for all the above segments.

Graph and download economic data for Housing Inventory: Median Days on Market in Lee County, FL (MEDDAYONMAR12071) from Jul 2016 to Feb 2025 about Lee County, FL; Cape Coral; FL; median; and USA.

Commercial Real Estate Market in South Africa Report is Segmented by Type (Office, Retail, Industrial and Logistics, and Hospitality) and Key Cities (Johannesburg, Cape Town, Durban, Port Elizabeth, and Other Key Cities). The Report Offers Market Sizes and Forecasts in Value (USD) for all the Above Segments.

Graph and download economic data for Housing Inventory: New Listing Count in Cape Coral-Fort Myers, FL (CBSA) (NEWLISCOU15980) from Jul 2016 to Feb 2025 about Cape Coral, FL, new, listing, and USA.

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 0.7 and eight percent. In 2023, house prices appreciated by 0.7 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.

[Keywords] Market include IBM, Cape Analytics, Baidu Inc., Engel & Völkers, Skyline AI

The global modular houses market is experiencing robust growth, driven by increasing demand for affordable, sustainable, and rapidly deployable housing solutions. The market size in 2025 is estimated at $50 billion, projecting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, shorter construction times compared to traditional methods significantly reduce project timelines and costs, making modular homes attractive to both developers and individual buyers. Secondly, the rising need for sustainable and environmentally friendly construction practices is boosting the adoption of modular homes, which often incorporate eco-friendly materials and energy-efficient designs. Furthermore, government initiatives promoting affordable housing and disaster relief efforts are contributing to the market's growth. The increasing urbanization and population growth, particularly in developing economies, also significantly contribute to the demand for efficient and cost-effective housing solutions. The market is segmented by house type (Ranch, Cape Cod, Others), floor area (various sizes), and region, offering diverse options to cater to varied customer preferences and needs. The market's growth is expected to be regionally diverse. North America, particularly the United States, currently holds a significant market share due to established infrastructure and consumer preference. However, rapidly developing economies in Asia Pacific and parts of Europe are projected to show significant growth in the coming years. Challenges to the market include overcoming public perception issues regarding modular homes’ aesthetics and perceived quality. Also, the relatively high initial investment in modular construction facilities and skilled labor can pose a barrier to entry for smaller companies. However, continuous innovation in design, materials, and construction technologies is expected to address these challenges, paving the way for sustained market growth in the long term. Key players are constantly innovating to stay ahead of the competition and capitalize on the growing opportunities in this dynamic market. This comprehensive report meticulously analyzes the burgeoning modular housing market, projected to reach a staggering $250 billion by 2030. We delve into key trends, regional disparities, and the competitive landscape, providing actionable insights for investors, manufacturers, and industry stakeholders. The report leverages rigorous market research and data analysis to offer a clear picture of this dynamic sector. This report uses high-search-volume keywords like "prefab homes," "modular construction," "offsite construction," "sustainable housing," and "factory-built homes" to ensure maximum online visibility.

This statistic shows the growth in prime property market in Cape Town, South Africa in 2018. Between June 2017 and June 2018, the luxury property market grew by 8.2 percent in Cape Town, South Africa.

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.