Riders, trips, and their frequency grew strongly during the third quarter, providing support for our network effect-sourced narrow moat rating for Uber. Plus, improvement in take rates accommodated solid growth in gross bookings. The network effect moat source is also allowing Uber to more easily control costs which led to further improvement in adjusted EBITDA losses. All of this resulted in the company posting third-quarter results above the top- and bottom-line S&P Capital IQ consensus expectations. Uber expects to generate full-year adjusted EBITDA in 2021 as the rides segment expanded its adjusted EBITDA margin for the second consecutive quarter. Management also guided for sequential revenue growth acceleration in the fourth quarter. With continuing improvement in take rates, we upped our 2019 revenue projection. However, we still expect losses in Uber Eats, along with more aggressive investments in ATG, to delay Uber's first full-year adjusted EBITDA until 2022, one year later than the firm's goal.
While third-quarter numbers surprised to the upside, the stock is down 5% in after-hours, which we think may be due to the expected Nov. 6 IPO lockup expiration which could push the stock down much further. We continue to believe that investment in narrow-moat Uber requires patience. We are maintaining our $58 fair value estimate and continue to view the stock which is trading at a 0.51 price/fair value estimate as attractive.
Founded in 2009 and headquartered in San Francisco, Uber Technologies has become the largest on-demand ride-sharing provider in the world (outside of China). It has matched riders with drivers completing trips over billions of miles and, at the end of 2018, Uber had 91 million users who used the firm's ride-sharing or food delivery services at least once a month. In light of Uber's network effect between riders and drivers, as well as its accumulation of valuable user data, we believe the firm warrants a narrow moat rating.
Uber helps people get from point A to point B by taking ride requests and matching them with drivers available in the area. The firm refers to this as personal mobility, which also includes micromobility, or shorter-distance transportation via electronic bikes and scooters. Uber generates gross booking revenue from this service, which is equivalent to the total amount that riders pay. From that, Uber takes the remaining after the driver takes his or her share. Ride-sharing gross booking grew 32% in 2018, while revenue increased 33% with a slightly higher average take rate, although we estimate the take rate will decline in the long-run.
We believe Uber has 30% global market share and will be the leader in our estimated $411 billion total addressable ride-sharing market (excluding China) by 2023. The firm faces stiff competition from players such as Lyft (mainly in the U.S.) and Didi, a business in which Uber has a 20% holding after the sale of its operations in China to Didi in 2016. While Uber no longer operates in China, it does compete with Didi in other regions around the world. The firm also has a stake (27.5%) in Grab, another former competitor in the Southeast Asian market. Globally, the market remains fragmented, and Uber competes with many local ride-sharing platforms and taxis.
Uber Eats, the firm's food delivery service, will be one of the main revenue growth drivers for the firm as it will benefit from cross-selling to its large ride-sharing user base. Further utilization of Uber's overall on-demand platform can also help the firm progress toward profitability, in our view.
In our view, Uber Technologies' core business, the ride-sharing platform, benefits from network effects and valuable intangible assets in the form of user data. We think these sustainable competitive advantages will help Uber to become profitable and generate excess returns on invested capital. For this reason, we assign Uber a narrow moat rating.
Uber's network effects benefit drivers and riders, creating a continuous virtuous cycle. Drivers and riders make up the supply and demand in ride-sharing, respectively. As a first mover in this market, where requests for rides from anywhere could be made in real time via a simple-to-use mobile app, Uber began to attract riders mainly via word-of-mouth. Growth in demand and further word-of-mouth marketing attracted more drivers, or, in other words, increased the supply of Uber vehicles. In turn, as the number of drivers increased, the timeliness and reliability of the service improved, which drove the number of users or riders higher, which in turn attracted more drivers, all of which indicates a network effect. Uber was able to accelerate this network effect by focusing on smaller areas, such as San Francisco, before expanding into more cities. A comparable tech leader that profits from network effects is Facebook, which started at Harvard before expanding to all colleges and then opened up globally.
We find evidence of Uber's network effect in New York City, where the number of daily rides provided by Uber in NYC grew at an average of 37% per year from 2015 to 2017. In addition, more drivers headed to provide service for Uber. As reported by the NYC Taxi and Limousine Commission, the city's count of yellow cabs declined to 13,587 in 2016 (from 13,635 in 2014, which was an increase from 13,437 in 2013). We think the decline accelerated in 2017 and will continue during the next three to five years, while we see Uber cars in New York City increasing. A New York Times article published in early 2017 said there were more than 46,000 Uber-enabled vehicles in New York City in 2016, which increased to over 50,000 the year after, according to the New York City's Taxi & Limousine Commission (TLC).
We must note that growth in demand is driven not only by more users, but also likely by more rides or trips per user. Globally, Uber's monthly average platform consumers, or MAPC, and number of trips support this and our network effect assumption. During the last three years, total MPACs and trips, along with trip per MPAC have been growing.
Plus, increasing supply is based on more drivers and further capacity utilization of each driver and the vehicle. Network effects, by definition, not only allow new network participants to benefit, but also existing ones. Thus, the riders on the platform benefit as more drivers are added, and existing drivers benefit from more riders, making the driver's utilization even more efficient. A figure that we believe supports this and demonstrates the increase in vehicle capacity utilization is growth in average number of rides dispatched per unique Uber vehicle, which has been increasing gradually from 2015 through 2018.
While Uber has benefited nicely from network effects in recent years, we don't believe it benefits from customer switching costs. In our view, the ride-sharing industry lacks barriers to entry or exit for customers and drivers. Both customers and drivers can easily switch to Lyft, while customers have other transportation options like taxis and public transit. In general, just as firms with network effects benefit from the positive flywheel effect when a network expands, they also run the risk of a negative flywheel if customers, drivers, or both start to depart, especially if the network lacks meaningful switching costs. Networks in tech are strong when faring well but can unwind quickly (think MySpace).
In early 2017, Uber faced much criticism for appearing to support an immigration order signed by President Donald Trump and attempting to profit from protests related to that event in New York City, both of which led to the #deleteUber campaign launched on Twitter and eventually tainted the Uber brand and reputation. Then CEO Kalanick was appointed to Trump's economic advisory council, to which some employees at Uber objected. The lack of exit barriers and switching costs for riders and drivers was on display during this period, as other ride-sharing providers, such as Lyft, made headway in New York City and experienced faster growth in trips as riders easily downloaded apps for other services.
While we may have witnessed a slight pause in Uber's network effect in 2017, we think the firm's return to faster growth in trips serviced supports our assumption that the platform still benefits from the moat source characteristic. After February 2017, growth in Uber trips reaccelerated and hit the triple-digit rate again in April. At the same time, Lyft's growth slowed a bit. In addition, Uber continues to dominate the New York City ride-sharing market, with a 2018 average daily trips run rate more than 5 times that of Lyft, based on data provided by the TLC. In 2017, Uber received 4 times more ride requests than Lyft. However, there remains a risk that what currently appears to be a strong network effect for Uber could reverse.
In our view, Uber's ride-sharing network effect can also help the firm tap into other markets and generate additional revenue streams. An example is the meal takeout and delivery market, in which Uber has gained traction with its Uber Eats service. According to data from Second Measure, as stated by Recode, Uber Eats has grabbed share from Grubhub and has more than a fifth of the U.S. market. In Uber's S-1, the firm mentioned a lower take rate, which implies that Uber Eats is willing to compete on price in the short term.
Regarding the network effect, the same can be said about Uber extending its reach into the bike-sharing and freight brokerage markets. Perhaps a good comparison for adjacent network effects, where one strong network enables a firm to expand elsewhere, is Microsoft: Its dominant network around Windows allowed it to leverage its strength into the productivity software market with Microsoft Office. Similarly, Uber's strength in ride-sharing might be leverageable elsewhere.
As Uber benefits from its network effect, we think it gains access to valuable intangible assets in the form of user data, which we suspect helps the firm improve its services and increase its vehicles' capacity utilization. In turn, Uber's service may become more effective as it further monetizes its riders via real-time supply and demand-driven pricing. Uber may also use this extensive data and knowledge to tap into other markets.
Uber gathers data about riders and drivers. As the firm compiles data from the rider app about the locations users request rides to and from and at what times of day, Uber can get a clearer picture of its users' tendencies. When combined with the user-generated driver ratings, we think such information helps Uber improve the timeliness of matching riders with drivers. Such overall enhancement in service could help the firm strengthen its network effect by increasing users and ride requests per user, which helps Uber gather additional data, possibly further increasing the overall value of the data. Simply put, data can also be considered an indirect network effect moat source. Google's search engine is an example of the benefits from indirect network effects associated with data, as more searches lead to better algorithms, better results, and in turn, more searches. Similarly, more rides with Uber may lead to better algorithms and supply/demand balance, thus reducing wait times for riders and idle capacity for drivers, leading to more ride requests.
Our fair value estimate for Uber is $58 per share. Our fair value estimate represents enterprise value/net sales multiples of 8, 6, and 5 in 2019, 2020, and 2021, respectively. On an EV/gross billings basis, our valuation represents net revenue multiples of 1.7, 1.3, and 1.1. We project that Uber's net revenue over the next five years could grow at a 21% CAGR, in line with gross bookings growth rate we assume for Uber's $730 billion total addressable market.
We expect strong net revenue growth for Uber at a 19% 10-year CAGR through 2028, resulting in net revenue of $63 billion (representing $309 billion gross revenue or bookings), up from $11.3 billion (equivalent to $50 billion gross revenue) in 2018.
We expect net revenue to grow faster than portions of Uber's cost of revenue, including hosting, transaction processing, and insurance costs, which will result in gross margin expansion. We also project that Uber will benefit from operating leverage in the years ahead. With the network effect economic moat source, we think Uber might be able to increase revenue at a faster pace than selling, general, and administrative costs, especially in the sales and marketing lines, while also spending relatively less on operations and support costs. However, we anticipate that R&D will remain elevated as Uber is likely to invest in new ventures, resulting in only slight declines in R&D as a percentage of net revenue. Within our 10-year discounted cash flow model, we assume the firm will begin generating operating income in 2024 and we expect operating margin expansion over 20% by 2028.
We assign Uber a very high fair value estimate uncertainty rating. First, Uber faces intense competition in the U.S. from Lyft which has gained market share. In addition, it remains possible that Lyft out-innovates Uber in order to emerge as a winner-take-all (or most) ride sharing provider. Plus, there are certain concerns about whether Uber's network effect can remain an economic moat source if the firm is forced to incur additional costs imposed through regulations at the municipal, state, and/or federal levels. For example, Uber may be forced to conduct more thorough background checks on all driver applicants, such as adding costlier fingerprinting to the driver application process everywhere in the U.S., although the firm already conducts an annual background check on all its drivers.
Other regulatory concerns include whether Uber will have to pay a minimum amount to each driver per trip. While the firm may have to concede and implement such policies, it will also likely take an overall higher percentage from the gross revenue generated per ride, as its price is likely to remain competitive with Lyft's. We note that both dominating firms, Lyft and Uber, are likely to demand higher take rates.
Last, there also lies the risk of larger technology companies such as Alphabet's Waymo, or car companies such as General Motors' Cruise, more aggressively pursuing the growing ride-sharing market.
We view Uber's stewardship of shareholder capital as Standard. While Uber's legal issues under former CEO Travis Kalanick (who will own 6.7% of Uber shares after the IPO) gave Uber a tainted reputation (involving everything from data breaches swept under the rug to a culture of sexual misconduct and accusations of not addressing internal racial discrimination issues) we believe Uber will see better days under Dara Khosrowshahi, the firm's current CEO. Khosrowshahi also has a seat on Uber's board of directors.
Since joining Uber in September 2017, Khosrowshahi has launched what appear to have been successful campaigns to repair the firm's image. In addition, he completed an investment deal with Softbank, settled legal disputes with Waymo, and most recently agreed to purchase the leading ridesharing service provider in the Middle East, Careem. Prior to Uber, Khosrowshahi was the CEO of Expedia for nearly 12 years. Khosrowshahi continues to serve on the board of directors of Expedia. Prior to Expedia, he was also the CFO of IAC/InterActiveCorp. Khosrowshahi brought Nelson Chai onboard in September 2018 as Uber's CFO. Chai's experience includes having served as the CEO of The Warranty Group, President and Chairman of CIT Bank NA, and CFO of NYSE Euronext and before that the NYSE Group.
From a strategic standpoint, we think the firm has taken the right steps to remain a participant in nearly every ride-sharing market in the world. While it operates in regions such as the Americas and most of Europe, it faced difficulties competing with local players in other areas including Russia, China, and Southeast Asia. The firm turned and sold those operations to the market leaders and now it has material ownership in Russia's Yandex Taxi (38%), China's Didi, and Grab in Southeast Asia (23%). For this reason, we foresee Uber benefiting from overall global growth in ride-sharing directly and indirectly. While the deal will not close until early 2020, Uber also increased its presence in the Middle East and Africa by acquiring Careem.
We note that Uber has also taken steps to become the one-stop-shop for transportation as it has added micromobility options such as bike sharing (via the acquisition of Jump in 2018) to its app, which we think may further strengthen the firm's network effect moat source.
While the firm battled and settled with Alphabet's Waymo regarding autonomous vehicle technology, we agree with its continuing investment in self-driving technology within the firm's advanced technology group, or ATG. While we expect further progression toward fully autonomous vehicles may help increase Uber's take rate, it will probably keep drivers even in autonomous vehicles over the long run for safety reasons. ATG has received investments from various parties, including Toyota, which bodes well for both as the two may more quickly and smoothly commercialize autonomous vehicle technology and cars. Similar to Lyft, we believe Uber's platform will become attractive to autonomous vehicle makers as it provides additional opportunities for technology and/or vehicle monetization.
On the food delivery front, Uber Eats is gaining market share, especially in the U.S. battling firms like Grubhub and DoorDash, as it may be benefiting from cross-selling to its large ride-sharing user base. In addition, Uber has continued to aggressively invest in and price its service to attract more restaurants and expand the food delivery business. Such strategy could lead to establishing another network effect moat sourced business within the firm.
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