Vacation Rentals - China

China

Highlights

According to the latest projections, the Vacation Rentals market in China is expected to generate a revenue of US$11.05bn in 2024.
Moreover, the market is projected to expand with an annual growth rate (CAGR 2024-2029) of 2.64%, resulting in a market volume of US$12.59bn by 2029.
The number of users in this market is expected to reach 166.30m users by 2029, with a user penetration of 9.3% in 2024, which is projected to increase to 11.7% by 2029.
The average revenue per user (ARPU) is expected to be US$83.31.
Additionally, it is expected that 70% of the total revenue in the Vacation Rentals market will be generated through online sales by 2029.
In comparison to other countries, United States is expected to generate the highest revenue, with a projected revenue of US$20bn in 2024.
China's Vacation Rentals market is seeing a shift towards luxury properties and personalized services to cater to the country's growing middle and upper classes.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in China is experiencing a significant growth trajectory driven by changing consumer preferences and evolving market dynamics.

Customer preferences: Chinese consumers are increasingly seeking unique and personalized travel experiences, opting for vacation rentals that offer a more authentic and local experience compared to traditional hotels. The demand for vacation rentals is also fueled by the growing popularity of domestic travel as more Chinese travelers explore their own country.

Trends in the market: In China, there is a noticeable trend towards the use of online platforms and mobile apps for booking vacation rentals, making it easier for travelers to find and secure accommodation. Additionally, the rise of short-term rental platforms and the sharing economy has led to a proliferation of vacation rental listings across various cities in China.

Local special circumstances: The regulatory environment in China plays a crucial role in shaping the vacation rental market. Government policies and restrictions on short-term rentals in certain cities have influenced the operations of vacation rental hosts and platforms. Additionally, cultural factors such as the importance of family travel and group accommodations impact the types of vacation rentals preferred by Chinese travelers.

Underlying macroeconomic factors: The rapid urbanization and expanding middle-class population in China have contributed to the growth of the vacation rental market. As disposable incomes increase, more Chinese consumers are willing to spend on travel experiences, including accommodation options beyond traditional hotels. Moreover, the rise of domestic tourism and the development of transportation infrastructure have made travel more accessible, driving the demand for vacation rentals across the country.

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.