Uber Valuation

Preliminary Models, Working Draft

In his analysis on Uber’s valuation, Aswath Damodaran develops a TAM model which defines Uber’s total addressable market as the yellow cab industry. The two primary levers affecting Uber’s valuation include the following:

  1. Potential Market Size
  2. Target Market

Aswath’s Model (also recreated here):

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A primary limiting factor of his model is such that Uber’s potential market does not only include the taxi industry.

I take a population based approach to developing a valuation framework for Uber. First, I logged each city which Uber has a presence (from their website). Then, I recorded the population for each of the 225 cities Uber has a presence. The total potential market for Uber to access (so far) is 650MM people. Based on their financials leaked about a year ago, Uber has about 400K active users at a time. This represents less than a tenth of a percentage market penetration (or, 0.068%).

Uber Leaked Financials + Preliminary Analysis

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I derived key drivers from Uber’s leaked financials that are fundamental aspects of the new model I propose which include

  • Completed rides per active client
  • Revenue per ride

Preliminary Valuation Model – Revenue Build

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Key Considerations:

  • I assume that Uber will be able to reach .712% market penetration by 2024; which implies 7.4mm active clients of a 1bn person addressable market (growing from 400K active users of a 650mm addressable market 1 year ago)
  • Completed Requests per Active Client – users will continue to access the app with greater frequency as ease of access improves (shorter wait time due to greater supply). Additionally, 96 requests per active client per year is based on the assumption that a user requests 2 rides per week (annualized). This value will need to be modified to consider 1. users that share rides with friends 2. A straight line 52x multiplier is not sufficient to explain usage trends
  • Revenue per Ride – drivers will begin to earn greater revenue per ride due to increased operational efficiency (less stop time with greater usage)