I was having a discussion with a colleague the other day an he was complaining about the quality of the research available to us. He felt the research did a poor job of representing the market and didn’t represent what he was experiencing on the street. While I listened patiently, my internal dialog was screaming “BULLSHIT!”
Here’s the thing about the real estate market. It’s huge. New Jersey alone has more than 500M square feet of office space. And while it’s not the most transparent marketplace, the data is generally accurate. There are databases that track lease and sale comps, leasing activity, sales activity, vacancies and availabilities. All of this data provides accurate trend lines and a clear picture of the market.
But the market moves slowly. Deals take time. And brokers tend to focus on the things that are directly impacting their clients. This leaves brokers particularly susceptible to The Law of Small Numbers. They want to believe that the handful of deals they’re working on are indicative of the entire market. They ignore broader trends, extenuating circumstances, and the fact that generally, numbers never lie (if the sample size is big enough.)
Why is this important? Well, if you’re taking counsel from a broker that is ignoring larger trends in the market, he’s not offering you the whole picture. It’s not his fault, it’s human nature. We all love to talk our position and seek data to support it. (Economists and psychologists have studied this too. It’s call confirmation bias.)
How do you combat this tendency? Start by insisting that you see the broader market. My partner and I have a standard practice of showing our clients everything that’s available, even if we think it’s outside their geographic search area or price range. This helps provide a very clear picture of the overall market. We welcome challenges from our clients, and we challenge our clients in return. Everyone involved sees the real estate landscape. Decisions get driven by business needs and market realities rather than a short term narrative based on a small sample size.
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