Valuation of a start-up: how deep do we choose to see into the future?

here’s a dark side to it. Valuing a start-up company seems all but a linear exercise. Standard academic models fail to provide a solid and unequivocal ground. Many of the valuation pillars are simply untraceable in the perilous realm (mostly valleys, hills and roundabouts) of start-ups. In other words, relative or intrinsic valuation models struggle before non-existent revenues, staggering expenses, negative earnings and a modest survival rate.

How do we crack this? Let’s get down to business.

I sat with Professor Damodaran once. It was during a corporate training in a NYC hotel somewhere in Midtown Manhattan. His honest and rigorous approach was an essential part of my education on the subject and helped me navigate beautifully even the most challenging aspects. Damodaran’s response to Uber valuation last year shed new light on the infinite possibilities of start-up valuation and the potential shortcomings of an overly optimistic view of their potential. Likewise, his “Valuing Young, Start-up and Growth Companies: Estimation Issues and Valuation Challengesoffers a comprehensive guide for all those wishing to contend with this difficult task. However, he concludes:

While these approaches require us to estimate inputs that are often difficult to nail down, they are still useful insofar as they force us to confront the sources of uncertainty, learn more about them and make our best estimates.

So? Can we all agree on a number? NO.

His detailed approach helps us confront uncertainty, but does not help eliminate or reduce it.

Ultimately, it all depends on assumptions. No value is univocal, even more so in the volatile and uncharted start-up territory. The investor takes a view (an informed view) on virtually every variable: market size, market share, strategic evolution, growth, cost of capital, return on capital, probability of failure.

However fascinating the role of a clairvoyant, I suggest we limit our numerical estimates to a few variables only (revenues and probability of failure, for instance) and focus, instead, on qualitative aspects to formulate a strong and compelling investment thesis (Martin Mignot and Jean de La Rochebrochard wrote two of my favorite pieces on the subject). The greater the number of uncertain variables we choose to challenge, the higher the probability to fail in our estimation and ultimately deviate from a fair value. I will report below a common valuation framework. Albeit un-detailed, this framework provides a good starting point for anyone wishing to attempt a ballpark valuation of a start-up business.


A small software start-up has developed a superior anti-virus platform and is currently looking for Series A funding. An interesting mix of developers, Stanford drop-outs and business school graduates approach various VCs for some 30 million dollar.

Before we write a 30-million-dollar check, I suggest we run some numbers. Quickly.

  • Find market comparables
  • Calculate an industry multiple (EV/Sales, P/E, EV/EBIT, etc.)
  • Estimate revenues (earnings are most likely negative for quite some time)
  • Define your Target Rate of Return (this would normally factor in your perceived probability of failure of the company)

Let’s see what a 30-million-dollar check can buy.

Assumptions

  • The company has no debt (Enterprise Value = Equity Value)
  • Based on your forecast model, the company will achieve $300 million in revenues in the third year

Results

  • The software company is worth $202 million today

Following Damodaran’s approach, based on a detailed estimation of all variables, we can potentially land two very distant results: the same software company is worth $ 111.54 million with an intrinsic valuation, $338.93 million with a relative valuation.

Does our $202 million look bad after all? I don’t think so.

By and large, regardless of the framework we choose, start-up valuation remains a tricky business. As the company grows defining historical performance and strategic identity, late investors can rely on a broader set of data to produce a more accurate estimate of the company’s future (possibly realizing a smaller return on their investment).

Seed and early stage financing is fundamentally a statement of vision. It consists of a brave dive into the challenges of our future. Crunching whimsical forecasts in search of the perfect estimate (which is imperfect by definition) we might misconstrue more fundamental questions, to only realize later that the difference is basically a matter of rounding.

While we question the blind Tiresias in search of the fortunate number, we risk missing opportunities that lie right before our eyes. Until we realize it’s too late.