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9 data points that expose hidden ride-hailing trends in NYC
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Ride-hailing has exploded in urban markets around the world. Based on publicly available data from New York City, our Research team has exposed some interesting, and somewhat unexpected, insights about the future potential for growth and profitability of ride-hailing companies and the urban neighbourhoods they serve.

Data point #1:

In New York City, app-hailed vehicles outnumber taxis by ten to one

App-based ride-hailing companies have been some of the most successful start-ups of the past decade. The most recent data indicate that there are about 135,000 vehicles and more than 180,000 drivers providing rides in New York, with app-hailed vehicles outnumbering taxis ten to one.

Number of vehicles by license type
Number of vehicles by license type

Note: For-hire vehicles (FHV) include livery and app-based ride-hailing. Green taxis are street-hail livery taxis, which are allowed to pick up passengers everywhere except Manhattan south of 96th Street on the East Side and south of 110th Street on the West Side.

Source: Taxi & Limousine Commission (TLC), Barclays Research

Data point #2:

Volume of rides nearly doubled with the introduction of ride-hailing apps

Ride-hailing’s introduction in NYC exposed huge latent demand that existing yellow and green taxis had failed to meet. The volume of rides offered by yellow and green taxis was essentially static until app-based vehicles hit the streets.

Taxi and for-hire vehicle ride volumes 2010-2018
Taxi and for-hire vehicle ride volumes 2010-2018

Source: TLC, Barclays Research

Data point #3:

Ride origins changed dramatically between 2010 and 2018

In 2010, rides mostly originated in Manhattan. But by 2018, 41% of trips were originating in Queens, Brooklyn, the Bronx and Staten Island. Given this dramatic change, it is clear that yellow and green taxis were not addressing a sizable chunk of market demand outside of Manhattan.

Ride origin 2010 - 2018

Source: TLC, Barclays Research

Data point #4:

Rides in the most under-served neighbourhoods increased nearly 45x

Ride-hailing demand is prevalent in the boroughs, excluding Manhattan. These areas are less well served by existing mass transit options, not to mention yellow and green taxis, which concentrate primarily on the lower two-thirds of Manhattan. As ride-hailing companies infiltrated new areas of NYC, they provided access to transport in neighbourhoods that lacked it.

Rides in the most under-served neighbourhoods increased nearly 45x

Source: New York City Open Data, TLC, Barclays Research

Data point #5:

Ride-hailing growth is correlated with gentrification indicators

The neighbourhoods of Brooklyn and Queens gaining the most rides are those synonymous with changing demographics, indicating that ride-hailing is connected to gentrification, though it is not clear if it facilitates or follows it.

Change in neighbourhood ethnic composition
Change in neighbourhood ethnic composition

Source: New York City Open Data, TLC, Barclays Research

Change in neighbourhood median rent
Change in neighbourhood median rent

Source: New York City Open Data, TLC, Barclays Research

Data point #6:

Regulation matters: The congestion charge reversed trip growth in most of Manhattan

New York City has been tightening regulations on taxis and for-hire vehicles (FHV) in recent years; capping new FHV licenses in Spring 2018, and introducing a “congestion zone” charge in February 2019. The charge applies to taxis and FHV trips starting or ending in Manhattan below 96th Street and has had a significant negative effect on trip volumes within the congestion zone.

Trip volumes over time
Regulation matters

Source: TLC, Barclays Research

Data point #7:

Over 80% of rides from less gentrified areas end outside the congestion zone

Despite regulation, demand for borough-zone-to-borough-zone rides has continued to grow. This is the most common use of ride hailing outside of Manhattan, again signaling that app-based rides are meeting latent demand for transportation in the boroughs.

Percentage of rides in relation to median income by destination
Percentage of rides in relation to median income by destination

Source: New York City Open Data, TLC, Barclays Research

Data point #8:

Price increases unlikely to change demand significantly

While the congestion charge did hamper demand in that zone, our analysis suggests that the demand for ride hailing is relatively inelastic, with minimal reaction to price changes. By our estimates, the price elasticity of ride hailing is between -0.4 to -0.6, versus about -1 for the typical industry. This means that for every 1% price increase, trip volumes will fall by 0.4%, yielding 0.6% in increased revenue.

Ride-hailing pricing is relatively inelastic
Ride-hailing pricing is relatively inelastic

Source: Barclays Research, Harvard University

Data point #9:

As little as a 5% price increase could lead to profitability for app-based ride-hailing companies

As little as a 5% price increase could lead to profitability for app-based ride-hailing companies

 

Estimated price increase needed for Uber to achieve profitability, accounting for reduced ride volume due to the hike

Source: Barclays Research estimates as of 12/10/19

So what impact does increased regulation of this industry have on ride-hailing operators? We believe investors in ride-hailing companies should consider the dynamics of price elasticity and incremental unmet need when assessing potential future profitability and growth.

Read the full report

Authorised clients of Barclays Investment Bank can log in to Barclays Live to read the full report.

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About the analysts

Jeff Meli is Head of Research within the Investment Bank at Barclays. Jeff joined Barclays in 2005 as Head of US Credit Strategy Research. He later became Head of Credit Research. He was most recently Co-Head of FICC Research and Co-Head of Research before being named Head of Research globally. Previously, he worked at Deutsche Bank and JP Morgan, with a focus on structured credit. Jeff has a PhD in Finance from the University of Chicago and an AB in Mathematics from Princeton.

Adam Kelleher is Head of Research Data Science at Barclays, based in New York. He is responsible for developing alternative data capabilities for research. Adam joined Barclays from BuzzFeed in May 2018. His previous work focused on large-scale machine learning for content recommendation and observational causal inference to guide data analytics.

He was recognised as one of FastCompany’s “Most Creative People” for his work on the POUND project and was an early advocate of causal inference in data science, which he now teaches at Columbia University’s Data Science Institute. He received his PhD in theoretical physics from The University of North Carolina at Chapel Hill in 2013.

Ryan Preclaw is the head of Investment Sciences, a group that creates investment insights by combining alternative data, data science, and traditional research. Previously, he was a Director in Credit Strategy, where he focused on special situations, event-driven strategies, and industries facing fundamental transitions.

Ryan has also worked as a coverage banker in Barclays' Communications and Media group. Prior to joining Barclays, Ryan worked as an economist at NERA Economic Consulting and London Economics International. Ryan received his M.B.A. from the University of Chicago in 2008, his M.A. from Western University in 2001, and his B.A. from the University of Alberta in 2000.

Ross Sandler is a Managing Director and senior research analyst for the Internet sector, which he has covered since 2004. Institutional Investor has ranked him among the top 10 Internet analysts since 2011. His team is responsible for coverage of the global consumer internet landscape, including e-commerce, digital advertising, mobile, online travel, and video games, comprising companies listed in the US and China.

Based in San Francisco, Mr. Sandler joined Barclays in 2017 after four years as a managing director in Internet and Digital Media at Deutsche Bank. Prior to Deutsche Bank, he spent eight years with RBC and four years at UBS. He earned a BS in Business Administration from the University of New Hampshire and an MBA from New York University.

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