Commercial Using AI to ‘Enhance Transaction Experience’
Cushman & Wakefield says artificial intelligence (AI) will influence how fast a commercial property gets to market, converging “data, people, processes and technology.”
NEW YORK – Soon, artificial intelligence (AI) will be a vital part of buying and selling property, at least for those who are clients of Cushman & Wakefield. The commercial real estate giant announced that it will soon embed AI “across its commercial real estate transaction lifecycle” to improve the experience of employees and better the results for clients, according to a recent press release.
The new integration will increase access and how fast a property will be put on the market, according to the release, which will help brokers and researchers support client needs and decisions. Salumeh Companieh, the chief information officer of Cushman & Wakefield, said the use of AI will support convergence of “data, people, processes and technology.”
This is not the first time that Cushman & Wakefield has experimented in AI use. In 2018, the company started using it, according to the release, and calls its current move toward AI usage, “AI+.”
In a statement to Finance & Commerce, Mike Ohmes, the managing principal for Cushman & Wakefield’s Twin Cities office, said the firm has partnered with groups like Microsoft and MIT AI Labs to ensure the “cutting-edge services” shape the future of commercial real estate.
“Timely data and operational efficiency in real estate is essential to creating organizational and client success, whether here in the Twin Cities or around the world,” he said.
Manjeet Rege, director of the Center of Applied Artificial Intelligence at the University of St. Thomas, said AI has many applications in the field of commercial real estate. It can serve to interpret data and make predictive modeling for clients based on their specific needs for a property or to better match up a client with prospective property.
He said AI can generate property plans for a developer too. These designs for a property may take a long time for a human, but for AI, it can generate plans in a matter of minutes.
Alok Gupta, senior associate dean at the University of Minnesota’s Carlson School of Management, also pointed to AI applications that are “deep learning models,” which can look at how a consumer responds to or interacts with a photograph of a home and can extract what draws people to a property. He said this is done with short-term rentals.
“[Airbnb] would do analysis on lots of images and find out what really attracts people,” he said. “So which rooms would get booked faster in similar locations? Everything else being equal, does some type of picture attract people more? Is it a bedroom? Is it lighting in a bedroom? Is it some kind of furniture?”
There are shortcomings to how data can be aggregated using AI. Rege said the results from AI are only as good as the data it learned from. He said if a developer based in, say, California wanted to get into the Minnesota real estate market and build a house, an AI that normally designs California homes may not be knowledgeable to the preferences and needs of Minnesota residents.
Companies will still need human validation for the results presented by AI. Otherwise, an AI application may start giving results it thinks are correct but are not.
This poses a challenge, Gupta said, because AI needs recorrecting from the people whose jobs it may take.
“If you look 10 years down the road, maybe we won’t have as many experienced real estate agents,” he said. “Who will train these systems as the needs of the design sensibilities and the needs of humans change over time? If we don’t have the experienced people who can guide these systems, it’s not clear how it will work.”
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