Vacation Rentals - Argentina

Argentina

Highlights

Argentina's Vacation Rentals market is projected to generate revenue of US$2.31bn by 2024.
The market is expected to show an annual growth rate (CAGR 2024-2029) of 17.35%, resulting in an estimated market volume of US$5.13bn by 2029.
By 2029, the number of users in the Vacation Rentals market of Argentina is expected to reach 15.09m users.
The user penetration is projected to decrease from 27.2% in 2024 to 31.8% by 2029.
The average revenue per user (ARPU) is expected to be US$183.80.
Moreover, 82% of the total revenue in this market in Argentina will be generated through online sales by 2029.
In terms of global comparison, United States is projected to generate the most revenue (US$20bn in 2024) in the Vacation Rentals market.
Argentina's Vacation Rentals market is experiencing a rise in demand due to its beautiful coastal towns and affordable prices.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Vacation Rentals market in Argentina has been experiencing significant growth and development in recent years.

Customer preferences: Travelers in Argentina are increasingly seeking unique and authentic experiences, opting for vacation rentals over traditional hotels. This shift in preferences can be attributed to the desire for more personalized and immersive stays, as well as the flexibility and cost-effectiveness that vacation rentals offer.

Trends in the market: One prominent trend in the Vacation Rentals market in Argentina is the rise of digital platforms and online booking services, making it easier for both hosts and guests to connect and transact. Additionally, there has been a notable increase in the number of property owners converting their homes into vacation rentals to capitalize on the growing demand.

Local special circumstances: Argentina's diverse landscapes and rich cultural heritage make it a popular destination for both domestic and international travelers. This has led to a steady increase in tourism and subsequently, a growing demand for vacation rental accommodations across the country. Additionally, the economic fluctuations in Argentina have made vacation rentals an attractive option for budget-conscious travelers looking for affordable yet comfortable lodging options.

Underlying macroeconomic factors: The economic conditions in Argentina, including inflation and currency devaluation, have influenced the Vacation Rentals market in various ways. As the local currency depreciates, international travelers find Argentina to be a more affordable destination, driving up the demand for vacation rentals. Moreover, the rise of the gig economy has encouraged more individuals to become hosts and offer their properties as vacation rentals, supplementing their income in the face of economic uncertainty.

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.