Prologue: “How did you arrive at that valuation?” That is the commonest question we are asked – by investors and start-ups alike. I’d love to say “like conclusions, it jumped on us!” (but obviously I don’t :). We resort to mumbo jumbo.
The raw, stark, blunt, miserable truth is that there exists no known, accepted and undisputed method to arrive at valuation. Undoubtedly, there are a plethora of tools and methodologies that are available but THE answer remains elusive, like Sir Winston Churchill said “a riddle, wrapped in a mystery, inside an enigma.”
Uber, it was last reported to be valued at around $68 bn give or take, makes for a good vehicle to toss it around. Back in the summer of 2014, Bill Gurley authored “How to Miss by a Mile: An alternative look at the Uber’s potential market size” in his blog (http://bit.ly/1qqc5dk), taking issue with Aswath Damodaran (the acclaimed NYU Stern Professor) and his view that Uber was worth only $5.9 bn (http://bit.ly/1q5qgrm). At that time Uber raised $1.2 bn at a valuation of $17 bn! So what could possibly explain the difference between the estimate of a wise professor (he has authored multiple books on business valuation, voted by most as a valuation guru) and an acclaimed venture capitalist?
I would assume that the arithmetic difference between the two is a function of how each one is matching the immediate with the perspective. If one argues that the opportunity space is that of the global taxi/cab service market, then it would lead to a certain assessment, if one thinks that the disruptive potential of Uber will redefine mobility and in fact become an alternative to personal car ownership, then the outcome can be absolutely startling.
If we drag ourselves further down to the deep end of logic, it would present a whole new world, one where every current assumption can be turned on its head. Demand aggregation for car purchases (Walmartizing car purchasing), car financing, car fuel & maintenance, autonomous cars, flying cars!…Okay, the last one’s a little bit of masala, but the possibilities are infinite. Estimating the value of disruptive start-ups based on existing markets and use cases is prone to significant prediction errors. As Aaron Levie (co-founder of cloud company Box) puts it, it’s akin to sizing the car industry off how many horses there were in 1910.
Here’s the Economist to our rescue (http://econ.st/2mop07Q). The column Schumpeter estimates that over 80% of Uber’s market worth is attributed to fortune pots to be mined in the distant future. For Tesla, it’s greater still, at over 90%. Sure, there is a high risk of these start-ups dying during their gold rush but it is also equally true that investors are willing to look beyond the current and wager on the realm of possibilities. Which of these will succeed and which ones will bite the dust? Which of these models will establish a gold standard, become a verb (some have already), be flattered by being copied (cut paste in today’s parlance)? Will those that perish ever be resurrected? What will endure and what will be a mere fashion of the season? These are all unknowns (albeit the known unknowns), but we can certainly bet that the cue lies in asking if anything new does indeed serve something new. As is attributed to Henry Ford, solving for existing demands would have only resulted in faster horses.
That $68 bn number is therefore not the question. I ask instead, if it’s the answer. The more relevant question to ask is why is it that only newbies are given the benefit of this magnanimity? Reliance disrupted the Indian cellular telephony market in the early aughts – mobile phones no longer were an aspirational luxury but a basic tool to communicate, and made affordable for the common person. Yet I doubt if Reliance enjoyed similar multiples. I can think of several large, well known, big brand, established companies that have changed the paradigm. Yet their wands seem to lack similar magic. I must admit, I don’t have an answer to this dichotomy.
Epilogue: “So how do you value a start-up?” I don’t know. But hey! No one else does either.