Package Holidays - Brunei Darussalam

Brunei Darussalam

Highlights

The Package Holidays market in Brunei Darussalam is poised for growth, with revenue projected to reach US$11.31m in 2024.
Furthermore, the market is expected to exhibit an annual growth rate of 4.29% between 2024-2029, resulting in a market volume of US$13.95m by 2029.
The number of users in this market is projected to amount to 43.66k users by 2029, with user penetration expected to hit 9.3% by the same year.
In 2024, user penetration is expected to be 8.1%.
The average revenue per user (ARPU) is forecasted to be US$306.10.
By 2029, 70% of total revenue in the Package Holidays market is expected to be generated through online sales.
It is noteworthy that China, the Package Holidays market is expected to generate the most revenue (US$49bn in 2024) in the global comparison.
Despite limited options for domestic package holidays, Brunei Darussalam's tourism industry is expected to grow with increased investment in infrastructure and promotion of cultural experiences.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Package Holidays market in Brunei Darussalam has been experiencing a steady growth in recent years, driven by various factors that cater to the preferences of local consumers.

Customer preferences: Bruneian consumers are increasingly seeking convenience and hassle-free travel experiences, which has led to a growing demand for package holidays. The all-inclusive nature of package holidays, which typically include accommodation, meals, and activities, appeals to customers looking for a seamless vacation experience without the need to plan every detail themselves.

Trends in the market: One notable trend in the Brunei Darussalam package holidays market is the rising interest in eco-friendly and sustainable travel options. As awareness of environmental issues grows globally, Bruneian consumers are showing a preference for package holidays that promote responsible tourism practices, such as supporting local communities and minimizing carbon footprints.

Local special circumstances: Brunei Darussalam's unique cultural heritage and natural beauty make it an attractive destination for both domestic and international travelers. The country's rich history, pristine beaches, and lush rainforests offer a diverse range of experiences for tourists, contributing to the popularity of package holidays that showcase the best of what the destination has to offer.

Underlying macroeconomic factors: The stable economic growth and increasing disposable income levels in Brunei Darussalam have also played a role in driving the growth of the package holidays market. As consumers have more spending power, they are more inclined to invest in travel experiences, including package holidays that offer value for money and a high level of convenience. Additionally, government initiatives to promote tourism and infrastructure development have further boosted the attractiveness of Brunei Darussalam as a holiday destination, supporting the growth of the package holidays market.

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.