Hotels - Lithuania

Lithuania

Highlights

It is projected that the Hotels market in Lithuania will generate a revenue of US$266.60m by 2024.
The revenue is expected to grow at an annual rate of 3.19% between 2024 and 2029, resulting in a market volume of US$311.90m by 2029.
In terms of the number of users, the Hotels market is expected to have 1.16m users users by 2029.
In 2024, the user penetration is expected to be 38.6%, which is projected to increase to 45.0% by 2029.
The average revenue per user (ARPU) is expected to be US$256.10.
By 2029, 82% of the total revenue in the Hotels market will be generated through online sales.
It is noteworthy that in global comparison, United States is expected to generate the most revenue in the Hotels market, amounting to US$111bn by 2024.
Lithuania's hotel market is experiencing a rise in demand from budget-conscious tourists seeking unique and authentic experiences.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Over the past few years, the Hotels market in Lithuania has shown significant growth and development, reflecting the country's increasing popularity as a tourist destination.

Customer preferences: Travelers in Lithuania are increasingly seeking unique and authentic experiences, driving the demand for boutique and locally-owned hotels. This shift in preferences is influenced by a desire for personalized services and a more intimate setting compared to traditional chain hotels.

Trends in the market: One notable trend in the Lithuanian Hotels market is the rise of eco-friendly and sustainable accommodations. Travelers are becoming more environmentally conscious, leading to a growing demand for hotels that prioritize sustainability practices such as energy efficiency, waste reduction, and locally sourced materials. Another trend shaping the market is the integration of technology in hotel operations. From online booking platforms to in-room smart devices, hotels in Lithuania are embracing technology to enhance the guest experience and streamline operations.

Local special circumstances: Lithuania's rich cultural heritage and natural beauty make it a unique destination for travelers seeking history, art, and outdoor activities. This has led to the emergence of themed hotels that cater to specific interests such as history, art, or wellness, providing guests with a deeper immersion into the local culture. Additionally, the growing popularity of wellness tourism has influenced the development of spa resorts and wellness retreats in Lithuania. These establishments offer a range of holistic experiences aimed at promoting relaxation, rejuvenation, and overall well-being.

Underlying macroeconomic factors: The economic stability and steady growth of Lithuania's economy have contributed to the positive performance of the Hotels market. Increasing disposable incomes and a growing middle class have resulted in higher domestic travel rates and boosted the overall demand for accommodations in the country. Furthermore, government initiatives to promote tourism and improve infrastructure have enhanced Lithuania's attractiveness as a travel destination, attracting both domestic and international visitors to explore the country's diverse offerings.

Data coverage:

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

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.