Vacation Rentals - Thailand

Thailand

Highlights

Thailand is expected to witness significant growth in the Vacation Rentals market in the coming years.
The projected revenue in this market is estimated to reach US$679.20m in 2024, with an annual growth rate of 6.62% (CAGR 2024-2029), resulting in a market volume of US$935.80m by 2029.
By that time, the number of users in this market is expected to reach 11.98m users.
Additionally, the user penetration rate is expected to increase from 14.1% in 2024 to 16.6% by 2029.
The average revenue per user (ARPU) is expected to be US$67.11.
It is projected that 73% of the total revenue will be generated through online sales by 2029.
Among all countries, United States is expected to generate the most revenue, with a projected revenue of US$20bn in 2024.
Thailand's Vacation Rentals market is thriving due to its stunning beaches, cultural experiences, and affordable luxury accommodations.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Thailand is experiencing significant growth and evolution, driven by various factors shaping the tourism industry in the country.

Customer preferences: Travelers in Thailand are increasingly seeking unique and personalized experiences, leading to a rising demand for vacation rentals over traditional hotel stays. The flexibility, space, and authenticity offered by vacation rentals appeal to a wide range of visitors, from budget-conscious travelers to luxury seekers.

Trends in the market: One notable trend in the Thai vacation rental market is the increasing popularity of eco-friendly and sustainable properties. Travelers are showing a preference for accommodations that prioritize environmental conservation and offer green amenities. Additionally, the integration of technology, such as online booking platforms and smart home features, is streamlining the booking process and enhancing the overall guest experience.

Local special circumstances: Thailand's diverse landscape and rich cultural heritage make it a prime destination for vacation rentals. From beachfront villas in Phuket to traditional Thai houses in Chiang Mai, the country offers a wide array of unique rental options to cater to different preferences. Moreover, the warm hospitality of the Thai people and the vibrant local cuisine contribute to the overall appeal of vacation rentals in the country.

Underlying macroeconomic factors: The growth of the vacation rental market in Thailand is also influenced by macroeconomic factors such as increasing disposable incomes, government initiatives to promote tourism, and the expanding middle-class population. As more travelers look for affordable yet comfortable accommodation options, vacation rentals present a lucrative opportunity for both property owners and tourists alike.

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.