Hotels - Kenya

  • Kenya
  • Kenya is projected to generate a revenue of US$291.00m in the Hotels market by 2024.
  • The revenue is expected to exhibit an annual growth rate of 6.89% (CAGR 2024-2029), resulting in a projected market volume of US$406.10m by 2029.
  • Additionally, the number of users in the Hotels market is expected to reach 4.24m users by 2029.
  • The user penetration in 2024 is estimated to be 5.1% and is expected to increase to 6.8% by 2029.
  • The average revenue per user (ARPU) is projected to be US$102.20.
  • Furthermore, in the Hotels market, 65% of the total revenue is expected to come from online sales by 2029.
  • It is worth noting that United States is projected to generate the highest revenue in global comparison, with a revenue of US$110,600m in the Hotels market by 2024.
  • Kenya's hotel industry is experiencing growth due to an increase in tourism and investment in infrastructure.

Key regions: Vietnam, Indonesia, United Kingdom, Malaysia, Saudi Arabia

 
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Analyst Opinion

Kenya's Hotels market is experiencing a notable growth trajectory, driven by various factors shaping consumer behavior and the overall economic landscape in the country.

Customer preferences:
Travelers in Kenya are increasingly seeking unique and authentic experiences, leading to a rise in demand for boutique hotels and eco-friendly accommodations. Additionally, there is a growing preference for hotels that offer wellness facilities and activities, catering to health-conscious guests.

Trends in the market:
One prominent trend in the Kenyan Hotels market is the expansion of international hotel chains into the country, attracted by the growing tourism sector and business opportunities. This influx of global players is not only increasing competition but also raising the overall standards of hospitality services in Kenya. Moreover, the rise of online booking platforms has made it easier for travelers to compare prices and book accommodations, driving hotels to enhance their online presence and marketing strategies.

Local special circumstances:
Kenya's strategic location as a gateway to East Africa has positioned it as a hub for both business and leisure travelers. The country's diverse landscapes, including wildlife reserves, coastal beaches, and vibrant cities, contribute to its appeal as a tourist destination. This unique blend of offerings presents opportunities for hotels to cater to a wide range of preferences and attract a diverse clientele.

Underlying macroeconomic factors:
The steady economic growth in Kenya, coupled with government initiatives to promote tourism and infrastructure development, has bolstered the Hotels market. Additionally, the increasing disposable income among the middle class has led to a rise in domestic tourism, further driving the demand for quality accommodations across the country. The stability of the political environment and efforts to enhance security measures have also contributed to the positive outlook for the Hotels market in Kenya.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and sales channels of hotels.

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.

Overview

  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • User Demographics
  • Global Comparison
  • Hotel Star Rating
  • Methodology
  • Key Market Indicators
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