Vacation Rentals - Indonesia

Indonesia

Highlights

Indonesia's Vacation Rentals market is projected to generate a revenue of US$605.80m by 2024.
The revenue is expected to grow at an annual rate of 8.16% between 2024 and 2029, which will result in a market volume of US$896.50m by 2029.
The number of users in this market is anticipated to reach 16.45m users by 2029.
In 2024, the user penetration is 4.5%, and it is expected to increase to 5.7% by 2029.
The average revenue per user (ARPU) is projected to be US$47.94.
By 2029, 79% of the total revenue in the Vacation Rentals market will be generated through online sales.
In global comparison, United States is expected to generate the highest revenue of US$20bn in 2024.
Indonesia's Vacation Rentals market is experiencing a rise in popularity due to its unique cultural experiences and stunning natural landscapes.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Indonesia is experiencing significant growth and development.

Customer preferences: Travelers in Indonesia are increasingly seeking unique and authentic experiences, leading to a growing demand for vacation rentals over traditional accommodation options. Customers prefer the flexibility, privacy, and local charm that vacation rentals offer, allowing them to immerse themselves in the culture and lifestyle of the destination.

Trends in the market: One prominent trend in the Indonesian vacation rentals market is the rise of eco-friendly and sustainable properties. Travelers are becoming more environmentally conscious, driving the demand for eco-friendly accommodation options. Additionally, the increasing popularity of remote work is fueling the trend of longer stays in vacation rentals, as individuals look to combine work and leisure in inspiring locations.

Local special circumstances: Indonesia's diverse landscape and rich cultural heritage make it a prime destination for vacation rentals. From beachfront villas in Bali to traditional joglo houses in Yogyakarta, the country offers a wide range of unique accommodation options that cater to different preferences and budgets. The warm hospitality of the Indonesian people also contributes to the popularity of vacation rentals, creating a welcoming and memorable experience for guests.

Underlying macroeconomic factors: The growing middle class in Indonesia, coupled with increasing disposable income, is driving domestic tourism and fueling the demand for vacation rentals. Additionally, government initiatives to promote tourism and improve infrastructure are making it easier for travelers to explore different regions of the country, further boosting the vacation rentals market. The competitive pricing of vacation rentals compared to hotels is also a key factor attracting budget-conscious travelers looking for affordable yet quality accommodation options.

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.