Vacation Rentals - Iran

Iran

Highlights

By 2024, it is projected that Iran's Vacation Rentals market will generate a revenue of US$666.20m.
The revenue is expected to display an annual growth rate (CAGR 2024-2029) of 19.02%, resulting in a projected market volume of US$1.59bn by 2029.
In the same year, the number of users in the Vacation Rentals market is anticipated to reach 16.29m users.
Moreover, the user penetration rate is expected to increase from 11.1% in 2024 to 17.6% by 2029.
The average revenue per user (ARPU) is expected to be US$66.91.
In Iran, around 67% of the total revenue generated by the Vacation Rentals market is projected to be generated through online sales by 2029.
It is worth noting that in comparison to other countries, the highest revenue is expected to be generated by United States, with a projected revenue of US$20bn in 2024.
Despite political turmoil, vacation rental demand in Iran has been steadily increasing due to the country's rich cultural heritage and natural beauty.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Iran is experiencing a surge in popularity, with an increasing number of travelers opting for alternative accommodation options over traditional hotels.

Customer preferences: Travelers in Iran are showing a growing preference for vacation rentals due to the flexibility, privacy, and authenticity they offer. Many tourists are seeking unique and immersive experiences, and vacation rentals provide them with the opportunity to stay in local neighborhoods and interact with residents, enhancing their overall travel experience.

Trends in the market: One of the notable trends in the Vacation Rentals market in Iran is the rise of online platforms connecting property owners with travelers. These platforms offer a wide range of options, from budget-friendly apartments to luxury villas, catering to different preferences and budgets. Additionally, the increasing popularity of short-term rentals among domestic travelers is driving the growth of the market.

Local special circumstances: Iran's unique cultural heritage and diverse landscapes make it an attractive destination for travelers seeking authentic experiences. The country's rich history, vibrant bazaars, and stunning architecture draw tourists from around the world. Vacation rentals in Iran often reflect the local culture and architecture, providing guests with a truly immersive stay.

Underlying macroeconomic factors: The Vacation Rentals market in Iran is also influenced by macroeconomic factors such as government regulations, currency exchange rates, and political stability. Economic reforms and improvements in infrastructure are making Iran more accessible to international tourists, boosting the demand for vacation rentals. Additionally, the growth of the sharing economy and digital platforms is facilitating the expansion of the market.

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.