Vacation Rentals - Southeast Asia

Southeast Asia

Highlights

The Vacation Rentals market in Southeast Asia is expected to experience significant growth in revenue, with a projected revenue of US$3.80bn by 2024.
The market is also expected to exhibit an annual growth rate of 5.64%, leading to a projected market volume of US$5.00bn by 2029.
Furthermore, the number of users in the region is expected to reach 77.03m users by 2029, with a user penetration rate of 8.6% in 2024 and an expected increase to 10.7% by 2029.
Moreover, the average revenue per user (ARPU) is estimated to be US$63.80.
In terms of revenue generation, about 76% of the total revenue is expected to come from online sales by 2029.
It is noteworthy that United States is projected to generate the highest revenue in the global comparison, with a revenue of US$20bn in 2024.
In Southeast Asia, vacation rentals in Indonesia are becoming increasingly popular due to the country's diverse range of natural attractions and cultural experiences.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Southeast Asia is experiencing a surge in popularity, with an increasing number of travelers opting for alternative accommodation options over traditional hotels.

Customer preferences: Travelers in Southeast Asia are increasingly seeking unique and authentic experiences, driving the demand for vacation rentals. They prefer the flexibility, space, and privacy that vacation rentals offer, especially for family or group travel. Additionally, the rise of digital platforms has made it easier for travelers to discover and book vacation rental properties that suit their preferences.

Trends in the market: In Thailand, the vacation rental market is booming, particularly in popular tourist destinations such as Phuket and Bangkok. The country's diverse offerings, from beachfront villas to urban apartments, cater to a wide range of travelers. Property owners are also capitalizing on the trend by investing in vacation rental properties to meet the growing demand.

Local special circumstances: Indonesia, with its stunning beaches and cultural attractions, has seen a significant increase in vacation rental bookings. The rise of digital nomads and remote workers has also contributed to the growth of the vacation rental market in Bali and Jakarta. Property owners are adapting to this trend by offering amenities such as high-speed internet and co-working spaces to attract long-term renters.

Underlying macroeconomic factors: The economic growth in countries like Vietnam and Malaysia has led to a burgeoning middle class with disposable income, driving domestic travel and the demand for vacation rentals. Additionally, government initiatives to promote tourism and improve infrastructure have made Southeast Asia an attractive destination for both domestic and international travelers, further fueling the growth of the vacation rental market.

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.