June 21, 2020
10 minute read
Colin James Belyea
Founder of Building PropTech
COO and Co-Founder of Cherrehttps://cherre.com/
I was born and raised in Jerusalem, Israel and have worked in Israel, London and Boston. I came to New York with a healthy sense of awe thinking about, “What could I do that hasn’t been done before?” I was luckily in a position to start yet another business with my cofounder L.D. Salmanson, with whom I have co-founded several companies over the years. It’s been a pleasure and an honor to work with him over the years, so it was a no-brainer to decide to work together again.
L.D. and I both had prior experience in real estate investment and we both had a sense that something was missing in the industry. As an asset class, real estate is bigger than the stock market and yet, there is nothing like a Bloomberg terminal or other tools that would technologically put it on par with the stock market. The total addressable market in real estate confirms there is a real need to look closer at what opportunities there are to better understand, analyze, and manage this gargantuan asset class. L.D. and I felt optimistic that we could build new tools that would enable the industry to catch up to the 21st century.
The multi-trillion-dollar real estate industry is one of the oldest, and largest industries that has yet to fully undergo a digital transformation. As a result, real estate stakeholders such as investors, underwriters, and other intermediaries, have a wealth of experience and are extremely smart and accomplished players, but their decisions are predominantly based on uncodified experience and limited supporting data. Furthermore, the data that is available to them is segmented, partial, siloed, expensive, and fundamentally incompatible from both a conceptual and data structure perspective. This means that even seemingly simple questions around asset value and risk are still very complex and hard to answer.
To help provide real estate stakeholders with a solution to leverage their data and make better, more informed decisions, Cherre has built a platform that seamlessly connects disparate real estate data and makes it accessible to an entire organization. By connecting their various datasets, Cherre clients can conduct analysis that considers more data points and uses more sophisticated algorithms. This approach yields deeper insights with consistent quality for their entire organization.
Cherre provides flexible delivery methods so customers can either access their connected data via a powerful GraphQL API, or incorporate that data into Business Intelligence tools, visualization applications, or modeling solutions. Or, customers can leverage Cherre’s purpose-built real estate analytics stack to explore and analyze their connected data.
The hardest part was finding an “in” and figuring out where to start. We knew we wanted to connect data and we wanted to build a foundation for connecting other sources. In order to demonstrate that we knew what we were doing, we started working with New York Public data. New York is bountiful when it comes to public data, which is a blessing because there is so much data to work with, and a curse because the data is convoluted and complicated. There are a lot of NYC specific edge cases that one has to understand.
Once we had a foundation layer, we had something visual to demonstrate. People really respond to images much more than to tables, which made selling the dream a lot easier. I learned that when you try to sell a vision, you can’t just rely on the client’s imagination of what could be. Before a client buys in, we must really show them what the future looks like. This isn’t because people can’t dream for themselves, but rather because there has been talk of how technology can change the industry for a long time, yet nothing has really been delivered. “Seeing is believing” and we’ve had to make people believe that we’re worthy of their time.
For the longest time, the industry has transacted large amounts of money based on hunches. The more “advanced” CRE players usually have had one system for internal data and another for some third party data sources. Data vendors see one another as competitors, and don’t cooperate with one another. That’s slowly changing.
For Cherre, the hardest part was to get the industry to view us as insiders. We had to prove ourselves. We had to stay humble and listen to clients, because this is a huge industry and there’s so much to learn, even if you have years of experience and knowledge.
We remained optimistic that we could indeed change something so we had to learn to speak the language of our clients. Take, for example, an insurance company. To say “you will be able to better match your premium to the underlying risk” is much more specific, and therefore clearer than to say “you will have better data to make decisions”. The next step was, of course, to demonstrate our solution because clients know that words are cheap and it’s not the first time that they’ve been promised a pie in the sky.
With regards to the long tail of data vendors, we had to show them that we come in peace and that we’re not a competitive data provider. We explained that, “data wants to be connected” which is to say, that data becomes more valuable when it is connected to that of other vendors’ data. Connected data gives a clearer picture of the truth on the ground.
Once both clients and data vendors started to the promised land of “a single source of truth,” that’s when the magic started happening and we gained momentum. One still needs to spin the flywheel, so to speak, but it became easier.
Some of the things I’ve learned while building Cherre includes:
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