The Consumer Reports Uber and Lyft data analysis consists of two files: uber.pdf and lyft.pdf
— All 30 of the virtual routes we tested (15 Uber-only routes and 15 Lyft-only routes) across the country had at least two price clusters separated by at least 5%. Most of the routes we tested had many more. Across the routes we tested, the median of the difference between the lowest and highest price groupings was 42.4%. (We calculated the percentage change between the lowest and highest price groups and then determined the median for all 30 routes.)
— Both apps also regularly entice customers to book rides by offering supposed discounts off what appeared to be inflated original prices, a practice that experts say not only is deceptive and manipulative but also may violate several states’ consumer-protection laws. We found that 12.4 percent of all discounts advertised on both platforms fell into this category. We believe these discounts to be fake—what experts and regulators call false reference pricing or fictitious discounts.
— Uber and Lyft take between 43% and 49.5% of each fare, a percentage that has been growing in recent years as drivers’ shares have fallen. CR conducted an in-person test in Portland, Oregon, with volunteer riders and drivers from Drivers Union Oregon. Those results are not included because they contain personally-identifiable information for both types of volunteers.
Uber and Lyft deny that they engage in any fictitious pricing, attributing our findings to real-time marketplace conditions.
They also challenged our methodology and conclusions and stated that they do not personalize base fares for individual consumers or engage in behavioral or surveillance pricing. CR is not disputing this; rather, it is questioning whether the price differences observed are based only on market forces.
CR’s testing was conducted in March and April 2026 and consisted of both “virtual” testing, in which volunteers checked the prices of select routes across 17 states, and in-person tests in which volunteer riders purchased the same rides at the same time in Portland, Oregon. The CR tests examined advertised offers and promotions for both Uber and Lyft before rides were ordered and paid for.
We designed the tests to determine if time was a factor in the prices our volunteers saw. Because dynamic pricing can shift from one moment to the next, we had our volunteers price the same routes at almost the same time—generally within six minutes of one another and, in many cases, within the same minute. This is how we define volunteers pricing the “same ride.”
Our virtual tests were conducted in the following 17 U.S. states: Alabama, Arizona, California, Colorado, Georgia, Florida, Idaho, Illinois, Kansas, Louisiana, Minnesota, Missouri, New York, Tennessee, Texas, Virginia, and Washington state.
For our virtual tests, volunteers were guided by CR staff to look at quoted Uber fares on 30 predetermined routes across the U.S., representing a mixture of short, medium, and long trips; urban, rural, cross-city, cross-county, and cross-state; and other trip types. To attempt to control for time, we had our volunteers open, review, and screenshot their offer screens at roughly the same time, and then submit those screenshots to us, along with a demographic and usage intake form. We then evaluated all screenshots programmatically, with a manual check, for accuracy to confirm original or base fares, discounts or promotions, final fares, route accuracy, and timestamps, and excluded all examples of user error. We then evaluated the findings internally and shared them with experts externally, for analysis and interpretation.
For each virtual test, there were between 33 and 65 volunteers for each of the 30 routes. There were three separate virtual test sessions, each with 10 distinct routes, on multiple days and at different times. While CR ran statistical regression analysis and evaluated preliminary findings with respect to device type, UI, promotion language, and other variables, we ultimately decided to only focus on findings with the highest degree of confidence given the relatively small sample size: differential pricing and price grouping; fictitious or false reference pricing; and platform take rates.
We evaluated different ride types but for the purposes of this report are showing only UberX and Lyft Standard prices, and excluding all Uber XL, Priority, Wait and Save, and other ride categories.
For our in-person field tests, drivers and riders were matched in the same physical location, typically less than a foot apart from each other.
We observed multiple forms of rider-facing messaging in the app interfaces. Our evaluation of potential fictitious or false reference pricing relied on the relationship between “original” or crossed out fares and final fares ultimately presented to riders. Uber has argued that fares accompanied by “historical comparison” banners, such as “Fares lower than usual” are not meant to suggest that a discount is being offered; we nevertheless counted those fares as discounts because experts supported our conclusion that consumers were likely to view them as discounted fares.
To calculate the average amount each company takes from each trip, we conducted a first-of-its-kind test in which volunteers requested rides and were subsequently matched with a driver from a select pool of workers affiliated with the Drivers’ Union in Portland, where municipal laws impose minimum pay levels and fees. We then compared the receipts from both riders and drivers to see how much people were paying and how much drivers were receiving from fares.
Our methodology and findings were reviewed by several subject-matter experts, who offered feedback, some of which was incorporated into this report. Those experts are:
— M. Keith Chen, Professor of Behavioral Economics, University of California, Los Angeles, Anderson School of Management
— Veena Dubal, Professor of Law, University of California, Irvine, School of Law
— Alan Mislove, Professor of Computer Science and Senior Associate Dean for Academic Affairs, Northeastern University
— Len Sherman, Executive in Residence and Adjunct Professor, Columbia Business School
— Laura Smith, Legal Director, Truth in Advertising
— Mark Tremblay, Assistant Professor of Economics, University of Nevada, Las Vegas, Lee Business School
— Katie Wells, Senior Fellow, AI Now Institute
— Christo Wilson, Professor of Computer Science and Associate Dean of Undergraduate Programs, Northeastern University
Our analysis did not control for certain marketplace variables, such as driver supply, differences in estimated arrival times, routing differences, traffic changes, rider location precision, or network latency, because those factors were outside the scope of the rider-facing data we collected. The analyses were designed to evaluate observed rider-facing pricing patterns and platform economics across comparable routes and closely aligned booking windows, rather than to model all internal marketplace variables that may influence platform pricing in real time.
Our volunteer sample was not representative of the U.S. population, and we collected demographic, device, and Uber usage data through intake forms for each volunteer. No volunteers participated in more than one test session, and nearly all participants participated in multiple route tests.
Volunteers were guided by CR staffers through a multistep process of entering specific pickup and drop-off addresses. Instances of user error were accounted for and excluded.
Lyft challenged CR’s findings, citing an “observer effect,” meaning that by having dozens of people checking prices for the same route at the same time, CR may have artificially inflated demand for that ride and influenced the final prices our volunteers saw. Uber said that because its ride prices change “nearly every second,” it was “impossible” for us to ensure that trip requests happened at exactly the same time.
“In an open, dynamic marketplace like ours, with nearly 1.7 million mobility and delivery trips per hour, a trip is defined just as much by when it is requested and what’s happening nearby as where it is going,” Uber said in a statement to CR.
Lyft said that a wide variety of factors—rider demand, the supply of available drivers, location, time, estimated trip time and distance, weather, promotional offers, and traffic patterns, among them—all play a part in both original and final prices.
“Price differences reflect real marketplace dynamics,” Lyft’s Sid Patil, executive vice president of the company’s marketplace division, said in a statement. ”At any given moment, more drivers may be available in a specific area, different demand levels, or different promotional activity. All in all, our marketplace ebbs and flows, depending on locations, times, events, weather, and other factors.”
Uber and Lyft said the only truly personalized pricing on their platforms is through their promotional offers, such as new-rider discounts and “re-engagement offers,” which they use to entice back customers who haven’t used the app in a while. Neither company responded to CR’s requests for a complete list of all the factors they use to personalize promotional offerings.
Both Uber and Lyft also denied offering their customers fictitious discounts. Lyft attributed our findings on these discounts to the fact that “prices change constantly based on real-time marketplace conditions.” Uber called our testing “fundamentally flawed” because, in its view, you can’t establish a true baseline price on its platform.
As noted above, Uber also took issue with our fake discount analysis. We counted fares as purportedly discounted when what appeared to be an original price had a strikethrough and a lower price was displayed. Uber said in a statement that when these prices are accompanied by labels such as "Fares lower than usual," they are not meant to suggest a discount but instead a "historical" or “informational” comparison.
Uber and Lyft said the percentage of each fare they take is much lower than what CR calculated. They put their U.S. “take rates” at “around 20%” and “significantly lower than 30%,” respectively. But neither includes what Uber and Lyft spend on drivers’ commercial auto insurance in those calculations; experts we consulted argue that insurance should be counted as a cost of doing business under standard accounting practices.
Correction: On June 25, 2026, Uber wrote to CR saying that it used the anonymized test data CR provided to re-identify our volunteers and their quoted prices and found three specific data-collection user errors among the hundreds of Uber prices recorded in screenshots by our 174 volunteers. After investigating those cases, we are amending the article as follows:
— We are changing a data visualization that referenced a volunteer named Camille, who incorrectly typed the wrong address for the Florida #2 route. (Our screenshot showed she entered the correct location name; Uber said its data shows she entered a different location with the same name.) We also removed this volunteer’s quoted price from our calculations, decreasing the highest fare on that route from $94.96 to $89.05.
— One volunteer mistakenly set the Uber app to collect prices for Uber Reserve trips, which are generally more expensive than standard on-demand Uber trips. We removed from our dataset the five UberX route prices collected by this volunteer, recalculated the percentage spread between the highest and lowest price groups for each route and the median across all 30 routes, and are updating the data visualizations accordingly. The median difference between the highest and lowest price groups across all 30 virtual routes for both Uber and Lyft changed from 49.8 percent to 42.4 percent as a result. These changes also resulted in the percentage of fictitious discounts we observed increasing from 10.8 percent to 12.4 percent.
— One volunteer typed the wrong address for the Idaho-to-Washington route. We removed this volunteer’s quoted UberX price from our calculations, which did not change that route’s percentage spread between the highest and lowest price groups.
— We also clarified that rides on any given route were generally booked by our volunteers within a few minutes of each other, specifically within six minutes of each other.
None of the corrections change our overall findings or conclusions that both Uber and Lyft use AI to charge consumers different prices.