SaaS Revenue Multiples, Interest Rates, and Modeling in R

SaaS Revenue Multiples, Interest Rates, and Modeling in R

SaaS Revenue Multiples, Interest Rates, and Modeling in R. 6 Simple Steps to Prove that Interest Rates don't Fully Explain SaaS Valuation.

Every year it seems like software companies become more valuable and more central to equity markets. Just this week, the prototypical Software-as-a-Service (SaaS) company, Salesforce, was added to the Dow Jones Industrial average. It’s now the third largest component of the index. Since the Covid crisis, the accretion of value to these companies has even accelerated.

Looking at Salesforce data, it’s clear that a large part of its growing valuation comes from increasing revenue. Salesforce and other SAAS companies just keep making more money.

Still, that isn’t the only factor. Look at the company’s Price-to-Sales ratio, or stock ‘multiple’. Over the last 5 years it hovered around 8 before spiking to almost 12 this year. This change has many of us wondering, have SAAS valuations gotten ahead of themselves?

To answer that question, brilliant venture capitalist Chamath Palihapitiya has an answer — low interest rates.

This got me wondering, how well can we model tech company valuations using just interest rates? If we average all SAaS companies together, can we ignore things like revenue growth, margins, customer acquisition, lifetime value, retention, and other metrics that comprise the classic SaaS model?

I attempted to answer that question using some basic modeling techniques in R. Code and data for the project are all available here. The exercise provided a few excellent lessons in the pitfalls of both simple models and the assumptions we make about software company valuation.

stock-market data-science business tech saas

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