Vacation Rentals - EMEA

  • EMEA
  • Revenue in the Vacation Rentals market is projected to reach US$42.17bn in 2024.
  • Revenue is expected to show an annual growth rate (CAGR 2024-2029) of 4.41%, resulting in a projected market volume of US$52.32bn by 2029.
  • In the Vacation Rentals market, the number of users is expected to amount to 438.10m users by 2029.
  • User penetration is projected to be 14.3% in 2024 and 16.8% by 2029.
  • The average revenue per user (ARPU) is expected to amount to US$120.60.
  • In the Vacation Rentals market, 79% of total revenue will be generated through online sales by 2029.
  • In global comparison, most revenue will be generated in the United States (US$20,270m in 2024).

Key regions: India, Vietnam, Saudi Arabia, Singapore, Germany

 
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Analyst Opinion

The Vacation Rentals market in EMEA is experiencing a surge in popularity, driven by shifting customer preferences, emerging trends, and unique local special circumstances.

Customer preferences:
Customers in EMEA are increasingly seeking unique and authentic travel experiences, opting for vacation rentals over traditional hotels. The desire for more space, privacy, and flexibility, especially among families and group travelers, is fueling the demand for vacation rentals across the region.

Trends in the market:
In Spain, the vacation rentals market is booming due to the country's diverse landscapes, rich cultural heritage, and favorable weather conditions. Coastal regions like Costa del Sol and Balearic Islands are particularly popular among tourists seeking beachfront villas and apartments. The rise of digital platforms and online booking services has also made it easier for property owners to list their rentals and for travelers to find the perfect accommodation.

Local special circumstances:
Italy, known for its historic cities, picturesque countryside, and world-renowned cuisine, is seeing a growing trend of travelers choosing vacation rentals for an immersive experience. Tourists are drawn to properties in Tuscany, Amalfi Coast, and Sicily, where they can live like a local and explore the hidden gems of the region. The Italian government's initiatives to promote sustainable tourism and preserve cultural heritage are further driving the growth of vacation rentals in the country.

Underlying macroeconomic factors:
In France, the vacation rentals market is benefiting from strong domestic tourism and international visitors seeking a taste of the country's art, fashion, and gastronomy. The regulatory environment in France has become more favorable for short-term rentals, allowing property owners to capitalize on the growing demand. Economic stability, improved infrastructure, and a growing sharing economy are supporting the expansion of the vacation rentals market in France and other EMEA countries.

Methodology

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.

Overview

  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • Global Comparison
  • Methodology
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