Package Holidays - Southern Africa

Southern Africa

Highlights

The Package Holidays market in Southern Africa is expected to witness a significant growth in the coming years.
The revenue in this market is projected to reach US$701.00m by 2024, with an estimated annual growth rate of 6.05% (CAGR 2024-2029).
This growth rate is expected to result in a market volume of US$940.50m by 2029.
By this year, the number of users in the Package Holidays market is expected to increase to 3.48m users.
The user penetration rate in this market is expected to be 4.0% in 2024 and 4.7% by 2029.
The average revenue per user (ARPU) is expected to be US$248.60.
It is noteworthy that 74% of the total revenue in the Package Holidays market in Southern Africa is expected to be generated through online sales by 2029.
It is also interesting to note that in a global comparison, China is expected to generate the highest revenue in this market, with a projected revenue of US$49bn in 2024.
South Africa's package holiday market is on the rise, thanks to its diverse tourism offerings and favorable exchange rate.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Package Holidays market in Southern Africa has been experiencing significant growth and development in recent years.

Customer preferences: Customers in Southern Africa are increasingly seeking convenience and hassle-free travel experiences, which has led to a rise in the demand for package holidays. Tourists are looking for all-inclusive packages that offer a combination of accommodation, transportation, meals, and activities, allowing them to relax and enjoy their vacation without the stress of planning every detail.

Trends in the market: In South Africa, the largest economy in the region, there has been a noticeable shift towards domestic tourism, with more locals opting for package holidays to explore their own country. This trend has been further fueled by the global pandemic, which has led to travel restrictions and a greater focus on domestic travel. Additionally, an increase in disposable income among the middle class has made package holidays more accessible to a larger segment of the population.

Local special circumstances: Countries in Southern Africa boast diverse landscapes, rich cultures, and abundant wildlife, making them attractive destinations for tourists seeking unique experiences. The region offers a wide range of package holiday options, from safaris in national parks to beach resorts along the coast. This variety appeals to different customer preferences and contributes to the overall growth of the package holidays market in Southern Africa.

Underlying macroeconomic factors: The economic stability and growth in Southern Africa have played a crucial role in the development of the package holidays market. As disposable incomes rise and the middle class expands, more people are able to afford travel packages, driving demand in the market. Additionally, governments in the region have been investing in infrastructure and promoting tourism, further supporting the growth of the package holidays market.

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and sales channels of package holidays.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market. As a basis for evaluating markets, we use financial reports, the Global Consumer Survey, third-party studies and reports, data from industry associations (e.g., UNWTO), and price data of major players in respective markets. To estimate the number of users and bookings, we furthermore use data from the Statista Consumer Insigths Global survey. In addition, we use relevant key market indicators and data from country-specific associations, such as country-related GDP, demographic data (e.g., population), tourism spending, consumer spending, internet penetration, and device penetration. 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, ARIMA, which allows time series forecasts, accounting for stationarity of data and enabling short-term estimates. Additionally, simple linear regression, Holt-Winters forecast, and exponential trend smoothing methods are applied. A k-means cluster analysis allows for the estimation of similar countries. The main drivers are tourism GDP per capita and respective price indices.

Additional notes:

The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.