Smart Home - South Africa

South Africa

The Smart Home market in South Africa is anticipated to experience significant growth in the coming years.
According to projections, the revenue in this market is expected to reach US$782.9m by 2024.
Moreover, the market is expected to witness a compound annual growth rate (CAGR 2024-2028) of 11.04%, leading to a projected market volume of US$1,190.0m by 2028.
In terms of household adoption, the number of active Smart Home market households in South Africa is estimated to reach [users_currentlayer_yearend] by 2028.
This indicates a substantial increase in household penetration, which is predicted to rise from 11.0% in 2024 to 20.6% by 2028.
Currently, the average revenue per installed Smart Home market in South Africa stands at US$369.90.
When comparing the global market, it is noteworthy that United States generates the highest revenue in the Smart Home market sector.
In 2024, the revenue generated United States is expected to amount to US$38,800.0m.
South Africa is experiencing a growing trend in the adoption of smart home technology, with an increasing number of households integrating automated devices for improved convenience and security.

Key regions: United States, United Kingdom, Germany, Europe, Norway

Market

Region

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Currency

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on the sales of smart home products, excluding taxes.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market segment. As a basis for evaluating markets, we use the Statista Global Consumer Survey, market data from independent databases and third-party sources, and Statista interviews with market experts. In addition, we use relevant key market indicators and data from country-specific associations, such as household internet penetration and consumer spending for households. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited for forecasting digital products and services due to the non-linear growth of technology adoption. The main drivers are GDP/capita, level of digitization, and consumer attitudes toward smart home integration.

Additional notes:

The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. The market is updated twice a year in case market dynamics change.