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Have startup valuations fallen enough to feel sane again?
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Welcome to the new week.
While recording Equity this morning, I noted with surprise an interesting bit of data that got me wondering about the current premium that venture investors are paying for startup shares, and if we can see anything new compared to 2022 data, especially given that complaints about YC startup prices are once again news.
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What follows is a somewhat numerate consolidation of several pieces that The Exchange put out last week. Read on if you have a minute and a fresh coffee, and thank you as always for your patience:
Today's startup prices
One way to think about startup valuations is to bucket them by their venture stage, using median results to create a mental market map. Carta recently bucketed median pre-money valuations for Series A, B and C startups into $40 million, $90 million, and $173 million buckets, respectively, to pick a recent example.
Each figure is down from recent highs: $49.4 million for Series A in Q1 2022; $161 million for Series B in Q1 2022; and a staggering $416 million for Series C rounds in Q2 2021.
Those valuations, however, are imperfect. First, Carta doesn't have insight into every deal out there, as it can only see the ones that its startup customers raise, so there's some very understandable data completeness issues in its very useful dataset.
Second, just looking at the sticker price isn't enough. We need more data than just the trailing median price range to really understand how startup valuations are earned.
How prices come to be
Why do we need all that data? We also want to know what the startups in those Series buckets have under the hood: the why behind the what. That's why Nine Point's SaaS napkins, which provide time-bounded data on the inputs that yield the valuations, have become a staple for us data nerds who track the startup fundraising game. And the historical record of SaaS napkins -- basically all the data concerning software startup fundraising that can fit on a small piece of paper -- lets us compare and contrast norms across time. It's helpful.
