Vacation Rentals - Republic of the Congo

Republic of the Congo

Highlights

The projected revenue for the Republic of the Congo in the Vacation Rentals market is set to reach US$5.84m by 2024.
It is anticipated that the revenue will grow annually by 4.34% (CAGR 2024-2029) leading to a projected market volume of US$7.23m by 2029.
In terms of users, it is expected that the number of users in the Vacation Rentals market will reach 214.50k users by 2029.
The user penetration is expected to increase from 2.3% in 2024 to 3.1% by 2029.
Additionally, the average revenue per user (ARPU) is expected to be US$40.48.
By 2029, it is projected that 48% of the total revenue in the Vacation Rentals market will be generated through online sales.
In comparison to other countries, United States is expected to generate the most revenue in the Vacation Rentals market with US$20bn in 2024.
Vacation rentals in Republic of the Congo are limited and primarily geared towards high-end travelers seeking exclusive experiences.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Republic of the Congo is showing signs of growth and development.

Customer preferences: Travelers in Republic of the Congo are increasingly seeking unique and authentic experiences, leading to a rise in demand for vacation rentals over traditional hotel accommodations. Customers are looking for properties that offer a more personalized and local experience, such as homestays or eco-friendly lodges.

Trends in the market: One notable trend in the Vacation Rentals market in Republic of the Congo is the increasing popularity of nature-based accommodations, catering to the country's rich biodiversity and natural attractions. Properties located near national parks, wildlife reserves, or along the Congo River are in high demand among eco-conscious travelers and adventure seekers.

Local special circumstances: Republic of the Congo's unique selling points, such as its lush rainforests, diverse wildlife, and vibrant culture, are driving the growth of the Vacation Rentals market. The country's natural beauty and relatively untouched landscapes make it an attractive destination for travelers looking to immerse themselves in nature and experience off-the-beaten-path adventures.

Underlying macroeconomic factors: The growth of the Vacation Rentals market in Republic of the Congo can also be attributed to the increasing connectivity and infrastructure development in the region. Improved transportation links, such as new roads and airports, are making it easier for tourists to access remote areas and discover hidden gems within the country. Additionally, government initiatives to promote tourism and sustainable development are creating a favorable environment for the expansion of vacation rental businesses.

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.