Package Holidays - Pakistan

Pakistan

Highlights

It is projected that the Package Holidays market in Pakistan will witness a substantial growth in revenue over the years.
By 2024, the revenue is expected to reach US$1.67bn, and it is estimated that the market volume will reach US$2.49bn by 2029, with an annual growth rate (CAGR 2024-2029) of 8.41%.
This growth will be accompanied by a significant increase in the number of users which is expected to be 9.05m users by 2029.
Furthermore, the user penetration rate is expected to increase from 2.6% in 2024 to 3.4% by 2029.
The average revenue per user (ARPU) is predicted to be US$259.70 which is expected to contribute significantly to the total revenue.
Additionally, the Package Holidays market in Pakistan is expected to generate 64% of its total revenue through online sales by 2029.
It is noteworthy that China is the country that is projected to generate the most revenue in the Package Holidays market globally, with an estimated revenue of US$49bn in 2024.
Despite economic challenges, package holidays to Pakistan are gaining popularity among adventure-seekers and cultural enthusiasts.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Package Holidays market in Pakistan is experiencing a significant growth trajectory in recent years.

Customer preferences: Customers in Pakistan are increasingly opting for package holidays due to the convenience and cost-effectiveness they offer. With busy lifestyles, travelers appreciate the all-inclusive nature of package holidays, which saves them time and effort in planning and booking individual components of their trip.

Trends in the market: One notable trend in the Package Holidays market in Pakistan is the rise of domestic tourism. As more Pakistanis explore their own country, there is a growing demand for package holidays that showcase the diverse cultural and natural attractions within Pakistan. Additionally, the increasing popularity of adventure tourism and experiential travel is driving the demand for specialized package holidays catering to these preferences.

Local special circumstances: Pakistan's unique geographical features, including stunning mountain ranges, historical sites, and vibrant cities, make it an attractive destination for both domestic and international tourists. The country's rich cultural heritage and warm hospitality also contribute to the appeal of package holidays in Pakistan. Moreover, the improving security situation in certain regions has boosted confidence among travelers, leading to a surge in tourism activities.

Underlying macroeconomic factors: The growing middle class in Pakistan, coupled with rising disposable incomes, has made travel more affordable for a larger segment of the population. This increase in purchasing power has fueled the demand for leisure travel, including package holidays. Furthermore, government initiatives to promote tourism and improve infrastructure are creating a more conducive environment for the tourism industry to thrive in Pakistan.

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.