Package Holidays - Germany

Germany

Highlights

Germany's Package Holidays market is predicted to experience a significant increase in revenue, with a projected amount of US$42.99bn by 2024.
Moreover, the market is expected to show an annual growth rate (CAGR 2024-2029) of 2.09%.
This growth will result in a projected market volume of US$47.67bn by 2029.
The number of users in this market is also expected to increase to 34.39m users by 2029.
In 2024, the user penetration rate is 38.3%, which is expected to reach 41.5% by 2029.
Additionally, the average revenue per user (ARPU) is predicted to be US$1.35k.
Online sales are expected to account for 90% of the total revenue in the Package Holidays market by 2029.
It is noteworthy that, globally, China is projected to generate the most revenue, with a predicted amount of US$49bn in 2024, in this market.
The German package holiday market is experiencing a shift towards sustainable and eco-friendly travel options.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Package Holidays market in Germany has been experiencing a significant growth in recent years.

Customer preferences: German consumers have shown a growing interest in hassle-free travel experiences, which has led to an increased demand for package holidays. Many travelers prefer the convenience of having all aspects of their vacation, including accommodation, transportation, and activities, organized by a single provider. This trend is in line with global preferences for all-inclusive and stress-free travel options.

Trends in the market: One notable trend in the German Package Holidays market is the rise of personalized and experiential packages. Travelers are looking for unique experiences that cater to their specific interests and preferences. As a result, tour operators and travel agencies are offering more specialized packages that focus on activities such as culinary tours, wellness retreats, and eco-friendly travel options. This trend reflects a broader shift in the travel industry towards more tailored and authentic experiences.

Local special circumstances: Germany's strong economy and high disposable income levels have contributed to the growth of the Package Holidays market. With a stable economy and low unemployment rates, many Germans have the financial means to invest in travel and leisure activities. Additionally, the country's strong emphasis on work-life balance has led to an increased desire for vacation options that allow for relaxation and rejuvenation.

Underlying macroeconomic factors: The robust performance of the German economy, coupled with low interest rates, has made travel more affordable for consumers. This has spurred an increase in consumer spending on leisure activities, including holidays. Additionally, the country's strong outbound tourism market has also supported the growth of the Package Holidays sector, as Germans continue to show a preference for traveling abroad for their vacations.

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.