Blog post by Collin West
Over here in the Kauffman Fellows Research Center we have a deep well of data that we have struggled to find time-efficient ways to share with startups and VCs.
So, as a beta, we are going to blog a few short posts and keep the analysis light.
This one is pretty interesting. And we all know it’s true:
VCs are *generally* not very sophisticated when pricing a new round.
This is reasonable given the dynamics involved in pricing a company – competition from other VCs, termsheet levers designed to adjust the effective post-money in order to achieve a certain price, fewer comparables than public markets… the list goes on.
But, I think the below is pretty fun to dig into. We built a chart displaying a sample of post-money counts from our global dataset which includes over 240,000 rounds. These rounds closed between the years 2008-2018. The result was far from a smooth and efficient curve – there are distinct spikes at most $100M intervals along the X-axis.
Us humans sure have a gravitational pull toward nice round numbers!
In fact, this is probably an example of Round Number Bias which could hurt a VC’s returns over time. For example, we do not see many companies valued at a $900M Post-Money. But we do have many $800M and $1B post-money counts. Perhaps the fabled Unicorn $1B mark has too much of a gravitational pull to ignore. So, if a VC were to round up from $900M to $1B in valuation without adding in new protections to achieve a nice round number their returns would certainly be hurt.
To extend this analysis a bit, we also split the data up by Geography, Sector, and Time and the results did not vary by much. However, one comparison stuck out to us that left us scratching our heads.
When comparing a similarly sized sample of Information Technology and Healthcare companies we found what looks like a stronger pull toward round numbers for IT companies than Healthcare companies.
I would love to hear comments from readers about this:
- Do you believe Healthcare has more efficient pricing mechanisms than Information Technology?
- Is this just a lack of high valuations in the space? What may be happening here?
Comment and share! Happy to answer questions.
Thank you,
Collin West and the Kauffman Fellows Team.
You can also engage with me on Twitter or Linkedin.
Great article!
Yes but sometimes the round number bias has some anchoring value in a negotiation. So it may not always indicate a VC is overpaying – it may also indicate when an entrepreneur is underselling because it’s tough to move from $300m pre to $302m pre.
That makes sense to me as well. Round number bias “Could” hurt returns as it has been show to do in other industries. The anchoring value in negotiation is interesting as well – stay tuned for my next post about the average % of a company sold per round. VERY anchored toward a particular %.
Loved it! 🙂
Many more “dreamer” businesses in IT, harder to estimate, like MagicLeap etc. Healthcare is more about specific implementation, with more specific market research. That’s my guess
Makes sense to me Alex. Much harder to estimate the potential impact of a “dreamer” business. Thanks for commenting.
-Collin
Interesting data Colin, thank you for sharing. My guess would be two factors – first the people, and second the market. On the people front, generally speaking, you see more scientific backgrounds in Healthcare focused VC’s, vs more entrepreneurial and finance backgrounds at generalist (tech) firms, something about the different backgrounds of those GP’s may lead to a higher occurrence of round number bias amongst tech VC funds. Second, while looking at the healthcare market, and in particular pharma, we have seen a high number of sub $1B IPO’s and small-cap companies. These companies have tended to access public markets more frequently compared to their IT peers, which helps to create more valuation data that flows back down the funding path, leading to more data to price earlier rounds.
https://techcrunch.com/2016/03/26/what-life-science-investors-got-right-in-this-most-recent-boom/
Great thoughts Riley – thank you for sharing. Solid hypothesis!