As a former private investment professional, I used to spend most of my days modeling investments in Excel. I reviewed dozens of investment opportunities per day across all private market sectors: private debt, infrastructure, real estate, and private equity. My nickname was the “Excel Jockey.” I was always amazed how much the due diligence focus was put on the investment model and how much trust we all put into them.
For every investment we made, we would only put 10-15% of our own equity. The rest we had to raise from banks. This is true for almost all private equity investments. For example, if we planned to invest in a $200 million wind farm, over $180 million would need to be raised by banks. And what did the banks scrutinize the most in their due diligence process? The model. It always came down to the model. Sure, there are other due diligence items to look at, and other critical documents, including teasers and investment memorandums, but most of the scrutiny came down to the valuation model to underwrite the deal.
Potential investors peppered my team with questions. How confident are you on the production numbers? What would happen to the returns if you used this type of technology? What if we restructured the debt? What if we used a separate depreciation schedule? What if we negotiated with the local municipality? What if we used a partnership flip versus an inverted lease structure? What is the impact of the terminal values if we change production by plus or minus 10%?
The answers had to be found in the models. All our private markets underwriting models were built bottom up. There is no template you can use. These are very complex investments, with 20+ tabs of structuring in a single Excel model. We would have two tabs just to structure the property tax schedule for a given investment. You would need multiple teams to give you key inputs for a given section of the model with each team providing their own Excel/CSV data files. This included finance, asset management, accounting, engineers, legal, land developers, etc. Some investments had multiple revenue streams that you would need to break out on an hourly (yes, hourly revenue) individually to stress test the model. There’s no cookie-cutter approach.
If you show me a model, I can tell you the story. Nevertheless, most private equity technology solutions neglect the model. Whether it’s an accounting solution or CRM, you are only reporting static, disconnected data. Sure you can upload a model to a document repository. But that is static as well. How do we track the hundreds of iterations across a single investment model? How do we scale this across a portfolio?
Now that I’m seeing a view from the other side—deploying private markets technology instead of being an Excel Jockey—I am always shocked by how little focus there is on the valuation model.
I am glad that this is our anchor here at Mercatus. If you can’t organize, audit, and control the models, you don’t have a data strategy. This is what ModelSync Technology delivers. That is why we have a patent on it. More and more data governance and regulations are now focusing on model data governance risk. After all, in any given private investment, the model is the story.
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