Predicting a Startup Valuation with Data Science

The following is a condensed and slightly modified version of a Radicle working paper on the startup economy in which we explore post-money valuations by venture capital stage classifications. We find that valuations have interesting distributional properties and then go on to describe a classical statistical model for estimating an undisclosed valuation with considerable ease. With that said, we would suggest reading the entirety of this article before using the model. This is not magic and the details matter. With that said, grab some coffee and get comfortable––we’re going deep.

Introduction

It’s often difficult to comprehend the significance of numbers thrown around in the startup economy. If a company raises a $550M Series F at a valuation of $4 billion [3] — how big is that really? How does that compare to other Series F rounds? Is that round approximately average when compared to historical financing events, or is it an anomaly?

At Radicle, a disruption research company, we use data science to better understand the entrepreneurial ecosystem. In our quest to remove opacity from the startup economy, we conducted an empirical study to better understand the nature of post-money valuations. While it’s popularly accepted that seed rounds tend to be at valuations somewhere in the $2m to the $10m valuation range [18], there isn’t much data to back this up, nor is it clear what valuations really look like at subsequent financing stages. Looking back at historical events…