Shared Mobility - Philippines

Philippines

The Shared Mobility market in the Philippines is anticipated to witness a significant upsurge in revenue, reaching a projected value of US$9,789.00m by 2024.
Moreover, the market is expected to demonstrate a Compound Annual Growth Rate (CAGR) of 2.89% between 2024-2028, ultimately resulting in a market volume of US$10,970.00m by 2028.
The largest market of this market in the Philippines is Flights, which is expected to generate a revenue of US$6,761.00m by 2024.
By 2028, the number of users in the Public Transportation market is expected to reach 72,010.00k users.
In 2024, user penetration is projected to be 64.2% and is expected to increase to 62.8% by 2028.
Additionally, the average revenue per user (ARPU) is predicted to be US$128.10.
Interestingly, a noteworthy portion of the total revenue, i.e., 75%, is expected to be generated through online sales by 2028.
Lastly, it is important to note that, in comparison to other countries, China is expected to generate the most revenue in this market, with a projected value of US$412bn by 2024.
Shared mobility services, such as ride-hailing and bike-sharing, are rapidly growing in the Philippines as urban congestion and environmental concerns continue to rise.

Key regions: United States, Saudi Arabia, Germany, Malaysia, India

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Analyst Opinion

The Shared Mobility market in Philippines has been experiencing significant growth and development in recent years.

Customer preferences: Customers in Philippines are increasingly looking for convenient and cost-effective transportation options. Shared mobility services offer them flexibility and affordability, making it an attractive choice for daily commuting and short trips.

Trends in the market: One of the key trends in the Shared Mobility market in Philippines is the rise of ride-hailing services. Companies offering ride-hailing services have gained popularity due to their ease of use and quick access to transportation. Additionally, the bike-sharing and scooter-sharing services have also seen a surge in demand, especially in urban areas where traffic congestion is a major issue.

Local special circumstances: The unique geography of Philippines, with its numerous islands and densely populated urban centers, has contributed to the growth of the Shared Mobility market. The need for efficient and reliable transportation options in congested cities has driven the adoption of shared mobility services as a practical solution for many Filipinos.

Underlying macroeconomic factors: The growing middle-class population in Philippines, coupled with increasing urbanization and smartphone penetration, has created a favorable environment for the expansion of Shared Mobility services. The rise of digital payment systems and the overall improvement in infrastructure have also played a significant role in shaping the Shared Mobility market in the country.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of car rentals, ride-hailing, taxi, car-sharing, bike-sharing, e-scooter-sharing, moped-sharing, trains, buses, public transportation, and flights.

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.