The Vacation Rentals market comprises of private accommodation bookings. This includes private holiday homes and houses, e.g., HomeAway, as well as short-term rental of private rooms or flats via portals such as Airbnb, in travel agencies or by telephone.
The main performance indicators of the Vacation Rentals market are revenues, average revenue per user (ARPU), users and user penetration rates. Additionally, online and offline sales channel shares display the distribution of online and offline bookings. The ARPU refers to the average revenue one user generates per year while the revenue represents the total booking volume. Revenues are generated through both online and offline sales channels and include exclusively B2C revenues. Users represent the aggregated number of guests. Each user is only counted once per year.
The booking volume includes all booked travels made by users from the selected region, independent of the departure and arrival. The scope includes domestic and outbound travel.
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Short-term rental of private rooms or flats via portals such as Airbnb or telephone
Hotels and professionally-run accommodation such as guest houses
Vacation Rentals are becoming increasingly popular. Rooms, apartments or vacation homes can often be rented at very short notice, which increasingly represents an attractive accommodation alternative for tourists, especially from the USA and Europe.
The data encompasses B2C enterprises. Figures are based on bookings, revenues, and sales channels of vacation rentals.
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.
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.
The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.