CRE Has to Move Through the Stages of Data Says Cherre Cofounder

When it comes to looking at real estate data, LD Salmanson, co-founder of real estate data firm Cherre, points to Gartner’s four stages of analytics maturity: descriptive (what happened), diagnostic (why things happened), predictive (what will happen), and prescriptive (making things happen).

The good news is there’s plenty of room for improvement. The implied bad news is that the improvement is direly needed.

“In the stock market, we never argue about where Apple was yesterday, only where it’s going tomorrow,” Salmanson says. “In the real estate industry, we’d be talking about where things were yesterday.”

Even that picture is distorted because of such silos as asset class, geography, and investment strategy. For example, according to Cherre data, the overall office vacancy rate in New York City is only 20%, despite what some are saying.

“When we used to say our buildings were full, we didn’t mean they were 100% full,” Salmanson tells GlobeSt.com. “When we said the market was good and full and saturated. In the best of times, it was in the times it was the mid or high 80s. In the worst of times, the 70s. In New York City, people are not going back to the office in droves like people would want you to think. We’re still looking at sub 30% occupancy.”

According to Salmanson, not all, but many landlords are trying to paint a rosier picture. “They’re counting the staff as occupancy—30-, 40-people teams. They’re doing everything to make it look like the city’s returning.”

Only when you know with accuracy what happened and why in the past can you begin to plan for the future.

“You could take data from some really great companies like CompStak—which will give you lease costs, what’s on the market—talk to some of the brokerage markets and see their fill rates,” says Salmanson. “We also have client data. There are outliers in the other direction as well. Some of our clients have occupancy in pre-pandemic occupancy. As you look across the city, New York has a lot of B and C buildings that no one’s going into.”

Once professionals can know what the data is, they can try to understand why things happened as they did to reach the diagnostic level.

That can give some framework but isn’t enough because strategy, which is really the art and science of knowing how to direct the business in the future, by definition often addresses new things uncovered by old practices and data.

“I’m trying to answer why do things happen,” Salmanson says. “There are some really fancy models here, but 99.9% of the time when people say they’re building fancy models, they’re building regression models.” In other words, they’re trying to predict the future, assuming it will be the same as the past.

Real estate is still in the early days of predictive, Salmanson adds, and right now it means professionals must make some educated guesses.

“Are people going to work from home?” Salmanson asks. “Are they going to work in a hub-and-spoke mile? Is the pandemic going to last? Depending on what those scenarios look like, we’ll see occupancy rates go down and stay down forever, go down a little from the high 80s, or maybe things go back to normal.” And an investor’s or operator’s view of what will happen affects the interpretation of data and plans.