One of the questions I most frequently answer about RSCM is how we value seed stage startups. Apparently, being not only willing, but eager to set equity valuations sets us apart from the vast majority of investors. It’s also the aspect of our approach that I’m most proud of intellectually. Developing the rest of our process was mostly a matter of basic data analysis and applying existing research. But the core of our valuation system rests on a real (though modest) insight.
We’ve finally accumulated enough real-world experience with our valuation approach that I feel comfortable publicly discussing it. Now, I’m not going to give out the formula. Partly, this is to preserve some semblance of unique competitive advantage. But it’s also for practical reasons:
- Our precise formula is tuned for our specific investment theses, which are based on our larger analysis of exit markets, technology dynamics, and diversification requirements.
- The current version of the formula doesn’t communicate just how adaptable the fundamental concept is (and we do in fact adjust it as we learn).
- There’s a lot of truth in the wisdom about teaching a man to fish rather than giving him a fish.
Instead, I’m going to discuss how we constructed the formula. Then you can borrow whatever aspects of our approach you think are valid (if any) and build your own version if you like.
The first part of our modest insight was to face the fact that, at the seed stage, most of the value is option value not enterprise value. Any approach based on trying to work backwards from some hypothetical future enterprise value will be either incredibly expensive or little more than a guess. But how do you measure a startup’s option value from a practical standpoint?
The second part of our modest insight was to ask, “Is there anyone who has a big stake in accurately comparing the unknown option value to some other known dollar value?” The answer was obvious once we formulated the question: the founders. If the option value of their ownership stake were dramatically less, on a risk-adjusted basis, than what they could earn working for someone else, they probably wouldn’t be doing the startup. Essentially, we used the old economist’s trick of “revealed preference“.
We knew there could be all sorts of confounding factors. But there might be a robust relationship between founders’ fair market salaries and their valuation. So we tested the hypothesis. We looked at a bunch of then current seed-stage equity deals where we knew people on the founder or investor side, or the valuation was otherwise available. We then reviewed the founders’ LinkedIn profiles or bios to estimate their salaries.
What we found is that equity valuations for our chosen segment of the market tended to range from 2x to 4x the aggregate annual salary of the founders. The outliers seemed to be ones that either (a) had an unusual amount of “traction”, (b) came out of a premier incubator, or (c) were located in the Bay Area. Once we controlled for these factors, the 2x to 4x multiple was even more consistent.
Now, the concept of a valuation multiple is pretty common. In the public markets, equity analysts and fund managers often use the price-to-earnings ratio. For later stage startups, venture capitalists and investment bankers often use the revenue multiple. Using a multiple as a rule-of-thumb allows people to:
- Compare different sectors, e.g., the P/E ratios in technology are higher than in retail.
- Compare specific companies to a benchmark, e.g., company X appears undervalued.
- Set valuations, e.g., for IPOs or acquisitions.
Obviously, 2x to 4x is a big range. The next step was to figure out what drives the variance. Here, we relied on the research nicely summarized in Sections 3.2-3.6 of Hastie and Dawes’ Rational Choice in an Uncertain World. In high-complexity, high-uncertainty environments, experts are pretty bad at making overall judgements. But they are pretty good at identifying the key variables. So if all you do is poll experts on the important variables and create a consensus checklist, you will actually outperform the experts. The explanation for this apparent paradox is that the human brain has trouble consistently combining multiple factors and ignoring irrelevant information (such as whether the investor personally likes the founders) when making abstract judgements.
So that’s what we did. We asked highly experienced angels and VCs what founder characteristics are most important at the seed stage. (We focused on the founders because we had already determined that predicting the success of ideas this early was hopeless.) The most commonly mentioned factors fell into the general categories you’d expect: entrepreneurial experience, management experience, and technical expertise. Going to a good undergraduate or graduate program were also somewhat important. Our experts further pointed out that making initial progress on the product or the business was partly a reflection on the founders’ competence as well as the viability of the idea.
We created a checklist of points in these categories and simply scaled the valuation multiple from 2x to 4x based on the number of points. Then we tested our formula against deals that were actually in progress, predicting the valuation and comparing this prediction to the actual offer. This initial version performed pretty well. We made some enhancements to take into account location, incubator attendance, and the enterprise value of progress, then tested again. This updated version performed very well. Finally, we used our formula to actually make our own investments. The acceptance rate from founders was high and other investors seemed to think we got good deals.
Is our formula perfect? Far from it. Is it even good? Truthfully, I don’t know. I don’t even know what “good” would mean in the abstract. Our formula certainly seems far more consistent and much faster than what other investors do at the seed stage. Moreover, it allows us to quickly evaluate deal flow sources to identify opportunities for systematically investing in reasonably valued startups. These characteristics certainly make it very useful.
I’m pretty confident other investors could use the same general process to develop their own formulas, applicable to the particular categories of startups they focus on—as long as these categories are ones where the startups haven’t achieved a clear product-market fit. Past that point, enterprise value becomes much more relevant and amenable to analysis, so I’m not sure the price-to-salary multiple would be as useful.
That’s a thoughtful approach, and I’m looking forward to learning how well it holds up over time!