Car-sharing - Kenya

  • Kenya
  • Kenya is projected to see revenue in the Car-sharing market reach US$5.91m in 2024.
  • The revenue is expected to demonstrate an annual growth rate (CAGR 2024-2029) of 8.34%, leading to a projected market volume of US$8.82m by 2029.
  • By 2029, the number of users in the Car-sharing market in Kenya is forecasted to reach 177.80k users.
  • The user penetration is projected to be 0.2% in 2024 and 0.3% by 2029 in Kenya.
  • The average revenue per user (ARPU) in Kenya is expected to be US$51.64.
  • By 2029, 100% of the total revenue in the Car-sharing market in Kenya will be generated through online sales.
  • When compared globally, the United States is anticipated to generate the most revenue (US$2,986m in 2024) in the Car-sharing market.
  • Kenya's Car-sharing market is witnessing a surge in demand driven by urbanization and a growing preference for convenient transportation options.

Key regions: Europe, Germany, India, United States, Malaysia

 
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Analyst Opinion

The Car-sharing market in Kenya has been experiencing significant growth and development in recent years.

Customer preferences:
Customers in Kenya are increasingly looking for more affordable and convenient transportation options. The rise of urbanization and traffic congestion in major cities like Nairobi has led to a growing demand for alternative modes of transportation. Car-sharing services provide a flexible and cost-effective solution for individuals looking to avoid the hassles of owning a car in a congested city.

Trends in the market:
One of the key trends in the Car-sharing market in Kenya is the adoption of technology. Companies offering car-sharing services are leveraging mobile apps and digital platforms to streamline the booking process, improve user experience, and optimize fleet management. This technological advancement has made it easier for customers to access and use car-sharing services, contributing to the market's growth.

Local special circumstances:
In Kenya, the car-sharing market is also influenced by the growing awareness of environmental sustainability. With increasing concerns about pollution and carbon emissions, more Kenyan consumers are seeking eco-friendly transportation options. Car-sharing services, which promote carpooling and reduce the overall number of vehicles on the road, align with the country's sustainability goals. This emphasis on environmental consciousness is driving the adoption of car-sharing services among environmentally conscious consumers in Kenya.

Underlying macroeconomic factors:
The development of the Car-sharing market in Kenya is also supported by favorable macroeconomic factors. The country's growing middle class, rising disposable incomes, and increasing urbanization are creating a conducive environment for the expansion of shared mobility services. As more Kenyans move to urban centers and seek convenient transportation options, the demand for car-sharing services is expected to continue growing. Additionally, the government's efforts to improve transportation infrastructure and reduce traffic congestion are further fueling the growth of the car-sharing market in Kenya.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of car-sharing services.

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, third-party studies and reports, federal statistical offices, industry associations, and price data. 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 demographic data, GDP, consumer spending, internet penetration, and device usage. 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, the S-curve function and exponential trend smoothing methods are applied.

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
  • Global Comparison
  • Methodology
  • Key Market Indicators
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