Vacation Rentals - Tanzania

Tanzania

Highlights

By 2024, Tanzania's Vacation Rentals market is projected to earn a revenue of US$184.60m.
Moreover, it is expected to show a Compound Annual Growth Rate (CAGR) of 4.49% from 2024 to 2029, resulting in a projected market volume of US$229.90m by 2029.
In the same market, the number of users is predicted to reach 12.49m users by 2029, with user penetration being 9.0% in 2024 and expected to hit 15.7% by 2029.
The average revenue per user (ARPU) is expected to be US$29.69.
Furthermore, it is projected that 68% of the total revenue in the Vacation Rentals market will be generated through online sales by 2029.
Lastly, in comparison to the rest of the world, United States is predicted to generate the highest revenue, with US$20bn in 2024.
Tanzania's Vacation Rentals market offers unique experiences, from luxurious beachfront villas to rustic safari lodges.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Tanzania has been experiencing a significant growth in recent years, with a surge in demand for alternative accommodation options among travelers.

Customer preferences: Travelers in Tanzania are increasingly seeking unique and authentic experiences, leading to a growing preference for vacation rentals over traditional hotels. The desire to immerse oneself in the local culture and lifestyle has been a key driver behind this shift in consumer behavior.

Trends in the market: One noticeable trend in the Tanzanian vacation rental market is the rise of eco-friendly and sustainable properties. Travelers are becoming more conscious of their environmental impact and are actively seeking accommodations that align with their values. This trend has led to an increase in eco-lodges and off-grid vacation rentals across the country.

Local special circumstances: Tanzania's diverse landscape and rich wildlife make it a prime destination for nature lovers and adventure seekers. This unique selling point has contributed to the growth of vacation rentals, especially those situated in close proximity to national parks, beaches, and other natural attractions. Additionally, the warm hospitality and friendliness of the Tanzanian people have made vacation rentals a popular choice for those looking for a more personalized and authentic experience.

Underlying macroeconomic factors: The growth of the tourism industry in Tanzania, driven by government initiatives and investments in infrastructure, has had a positive impact on the vacation rental market. As more tourists flock to the country to experience its natural beauty and wildlife, the demand for alternative accommodation options continues to rise. Additionally, the increasing connectivity and accessibility within Tanzania have made it easier for travelers to explore different regions and opt for vacation rentals in remote or off-the-beaten-path locations.

Data coverage:

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

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.