In Part 1, Part 2 and Part 3 of the "Why We Started CompStak Series" I spent a lot of time talking about the commercial real estate technology businesses we DIDN'T start. Now, it's finally time to talk about the one we DID start and why.
The meeting followed it's normal routine. We would go around in a circle, and each broker would recite the terms of a deal they just heard about (a lease comp). The biggest deals, for the highest prices per square foot would surely be shared, and after everyone around the room spoke about the deals they heard about that week, we would leave. As relatively junior broker in the industry, I was intrigued by 50,000 square foot deals of $175 per sf at 9 West 57th Street or 767 Fifth Avenue, but like most brokers, I wasn't doing too many deals with multi-million dollar commissions and these comps were not particularly relevant to my day to day business.
I realized, that these two hour meetings every other week were essentially a complete and utter waste of time. The information being shared was, and IS VITAL to the work of any commercial real estate professional, but only in context. I saw that brokers were comfortable sharing lease comps with their colleagues both inside and outside of their firms as long as they received other comps in return, but brokers were sharing comps in the least effective way possible - via phone, email, and market meetings.And then it hit me . . .
What if I could provide a random comp to a centralized database, and receive exactly the comp I need in return?
So, I started talking to people - brokers, appraisers, researchers, investors, asset managers, bankers, landlords, etc, and they all seemed to get it. I learned that I wasn't the only one struggling with a complete lack of transparency in commercial leasing transactions. Other brokers wanted this data to canvass for tenants, better negotiate deals, and understand market trends. Appraisers wanted the data to support the valuation of buildings and to establish "Fair Market Value" in rent disputes. Investors wanted the data to make pro-forma revenue projections for potential building acquisitions. Asset managers wanted the data to value their portfolio of real estate assets and determine when to buy or sell. Bankers wanted the data to underwrite mortgages and sell off distressed debt. Landlords wanted to benchmark their buildings against the competition.