Vacation Rentals - ASEAN

ASEAN

Highlights

The Vacation Rentals market in ASEAN is expected to experience a significant boost in revenue, with projections forecasting an increase of up to US$3.79bn by 2024.
Furthermore, the market volume is expected to grow at a Compound Annual Growth Rate (CAGR) of 5.64% between 2024 and 2029, reaching a projected US$4.99bn in the latter year.
The number of users in this market is also predicted to rise significantly, with an estimated 76.92m users users expected by 2029.
In 2024, user penetration in ASEAN is projected to be 8.6%, with a forecasted increase to 10.7% by 2029.
The average revenue per user (ARPU) is expected to amount to US$63.81, indicating a promising future for the Vacation Rentals market in the region.
Moreover, online sales will play a vital role in generating revenue in this market, with an estimated 76% of the total revenue expected to come from online sales by 2029.
Despite this, United States is forecasted to be the global leader in generating revenue in this market, with a projected revenue of US$20bn in 2024.
In ASEAN, vacation rentals are booming in Thailand with a wide range of affordable options and unique experiences for travelers.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in ASEAN is witnessing significant growth and evolution driven by changing customer preferences, emerging trends, and unique local circumstances.

Customer preferences: Travelers in ASEAN countries are increasingly seeking unique and personalized accommodation options that offer a more authentic and immersive experience compared to traditional hotels. The rise of the sharing economy has also influenced customer preferences, with many opting for vacation rentals that provide a sense of community and local connection.

Trends in the market: In Thailand, the popularity of vacation rentals in beach destinations such as Phuket and Koh Samui is on the rise, driven by the appeal of private villas with stunning ocean views. Similarly, in Indonesia, the demand for luxury villas in Bali has been increasing steadily, attracting high-end travelers looking for exclusive and secluded accommodation options.

Local special circumstances: Countries like Vietnam and Cambodia are experiencing a surge in vacation rental bookings, fueled by the growing tourism industry and government initiatives to promote sustainable travel. In Vietnam, the rising popularity of homestays in rural areas offers travelers a glimpse into the local way of life, while in Cambodia, the demand for eco-friendly accommodations near Angkor Wat is driving the development of unique lodging options.

Underlying macroeconomic factors: The overall economic growth in ASEAN countries, coupled with increasing disposable incomes and a growing middle class, is contributing to the expansion of the vacation rentals market. Additionally, the rise of digital platforms and online booking services has made it easier for property owners to list their accommodations and for travelers to discover and book unique rental properties across the region.

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.