Relationships in CRE Are Important and So Are Science and Math

Miami University in Ohio recently opened its 92,000 square foot McVey Data Science Center. Not a climbing wall, new food court, or administrative building, but “as a place to foster transdisciplinary research, a forum for industry partners to connect, and a venue for academic instruction, student activities, and informal conversation.”

Data makes the commercial world go around and companies in all industries need to make use of it. While often discussed in the context of the technologies that control it, the information is what is ultimately of use, whether done with computers, adding machines, abaci, or on papyrus or stone tablets.

With all the talk of artificial intelligence and the need for data in commercial real estate, there is a risky unspoken assumption that vendors will do what a company needs. They will collect data, host it, analyze it, process it with “AI” (whatever that exactly means), and deliver what you need. Unfortunately, this is ultimately a risky approach that companies should avoid.

Although the ubiquitous description of the foundation of CRE is “location, location, location,” it’s likely ore commonly seen among professionals as “relationship, relationship, relationship.” People are the ones buying, selling, and leasing. Aside from a superimposed AI chatbox, they’re the ones who talk, chat, email, and otherwise interact.

Technology may not be everything in real estate, but it isn’t nothing either. Directly and indirectly, data sets are repositories of people’s decisions and habits. They allow companies to document individual interactions, make broader observations of how people act in general, monitor performance of physical systems, and much more.

It’s easy to see how fundamental this is to a business. But too many companies hand it all over to vendors that host in a cloud, process through various types of software, deliver alerts, but perform actions that are probably unclear. What are these AI algorithms? What do they actually do? How do they reach conclusions of make suggestions? Are the results and advice realistic? How are you supposed to check.

Not to beat on AI because these questions have always hovered over technologies, questioning whether the speedier routes to what a business was trying to do was trustworthy. AI has thrown in some wrinkles. If you have an accounting program and there are people in-house who understand accounting principles, you can check whether things are moving as they should. AI can bring things to a point of abstraction — like performing portfolio analysis — where even its creators aren’t absolutely sure what the software does in its decision processes.

You likely don’t have people going through specialty courses in that data science building. Even without such extremes, there is much CRE firms can do, like knowing where their data across multiple programs and how it’s pulled together. You should always have guaranteed access, even if you decide to stop using a given software platform. If you need specific information, know what program and system has it. Don’t give up total control to something that you may not control in return.