Vacation Rentals - Pakistan

Pakistan

Highlights

By 2024, Pakistan's Vacation Rentals market is projected to witness a revenue of US$532.10m.
Moreover, it is expected to show an annual growth rate (CAGR 2024-2029) of 7.26%, which will result in a market volume of US$755.30m by 2029.
In the same period, the number of users for this market is estimated to reach 22.17m users.
In 2024, user penetration will be 5.8%, which is expected to decline slightly to 8.2% by 2029.
The average revenue per user (ARPU) is projected to be US$37.37.
Additionally, it is expected that 66% of the total revenue generated by the Vacation Rentals market in Pakistan will be through online sales by 2029.
It is noteworthy that in a global comparison, United States is anticipated to generate the highest revenue of US$20bn in 2024.
Pakistan's Vacation Rentals market is seeing growth due to an increase in domestic tourism and a rise in popularity of vacation homes.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Over the past few years, the Vacation Rentals market in Pakistan has been experiencing a significant growth trajectory, with various factors contributing to its development.

Customer preferences: Customers in Pakistan are increasingly drawn to vacation rentals due to the personalized and unique experiences they offer compared to traditional hotel stays. The flexibility in accommodation options, ranging from cozy apartments to luxurious villas, caters to diverse preferences of travelers seeking a homely environment during their stay.

Trends in the market: One prominent trend in the Vacation Rentals market in Pakistan is the rise of online booking platforms and mobile applications, making it easier for travelers to discover and book rental properties. This shift towards digital platforms has not only expanded the reach of vacation rental providers but has also enhanced the overall booking experience for customers.

Local special circumstances: In Pakistan, the growing trend of vacation rentals is also influenced by the rise in domestic tourism. With an increasing number of Pakistanis exploring their own country for leisure and travel, the demand for vacation rental properties has surged. Additionally, the unique cultural heritage and scenic landscapes of Pakistan have positioned the country as an attractive destination for both domestic and international travelers seeking authentic experiences.

Underlying macroeconomic factors: The economic growth and stability in Pakistan have played a significant role in boosting the Vacation Rentals market. As disposable incomes rise and the middle-class expands, more individuals have the financial capacity to travel and explore different regions within the country. This economic prosperity has not only increased the demand for vacation rentals but has also encouraged investments in the tourism and hospitality sector, further fueling the market's growth.

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.