Package Holidays - EU-27

  • EU-27
  • It is estimated that the Package Holidays market in the EU-27 will generate a revenue of US$104.70bn by 2024.
  • It is projected to grow annually at a rate of 2.58% from 2024 to 2029, resulting in a market volume of US$118.90bn by 2029.
  • The number of users is expected to reach 105.50m users by 2029, with a user penetration of 21.7% in 2024 that is projected to increase to 23.8% by 2029.
  • The average revenue per user (ARPU) is expected to be US$1.08k.
  • By 2029, online sales are expected to generate 82% of the total revenue in the Package Holidays market.
  • In comparison to other countries, China is expected to generate the most revenue with US$49,250m in 2024.
  • Spain remains the most popular destination for package holidays among EU-27 countries, with its sunny beaches and vibrant culture attracting millions of visitors annually.

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

 
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Analyst Opinion

Over the past decade, the Package Holidays market in EU-27 has witnessed significant growth and evolution, driven by changing customer preferences, emerging trends, and local special circumstances in various countries across the region.

Customer preferences:
Customers in the EU-27 have shown a growing inclination towards convenience, affordability, and unique experiences when choosing their holiday packages. They prefer all-inclusive deals that save them time and effort in planning, as well as packages that offer personalized experiences tailored to their interests and preferences.

Trends in the market:
In countries like Spain and Italy, there has been a rise in demand for package holidays that include cultural tours, culinary experiences, and outdoor activities. Tour operators are adapting to these trends by offering specialized packages that cater to these interests, attracting a niche market of travelers looking for authentic and immersive experiences.

Local special circumstances:
Countries like Greece and Portugal, known for their picturesque landscapes and rich cultural heritage, have seen a surge in package holiday bookings from international tourists. The local special circumstances, such as favorable weather conditions, competitive pricing, and improved infrastructure, have contributed to the growth of the Package Holidays market in these destinations.

Underlying macroeconomic factors:
The overall economic stability and growth in the EU-27 region have also played a crucial role in the development of the Package Holidays market. With rising disposable incomes and consumer confidence, more people are willing to spend on travel and leisure, driving the demand for diverse and attractive holiday packages across different countries in the region.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and sales channels of package holidays.

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
  • Key Market Indicators
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