It’s Still Early Innings for AI, Crypto in Commercial Real Estate: Forum

Commercial Observer’s quarterly AI and Innovation Forum made its return Wednesday morning, with some of the top leaders in tech, cryptocurrency and commercial real estate discussing how they are moving with the times.

Speakers and panelists in the morning event delivered insights on how they are deploying investments like bitcoin to get the most out of assets, as well as where companies should be starting in their adoption of machine learning, whether that’s starting small or going big.

Grant Cardone, CEO of Cardone Capital, and CO finance reporter Brian Pascus, the event’s emcee, kicked off the online event with a keynote session called “Breaking the Deal Wide Open: Tokenization and the New Rules of Real Estate.” Cardone spoke about the relationship between cryptocurrency and commercial real estate.

What he said he has developed is a fractional ownership in real estate that has offered an alternative investment vehicle to real estate investment trusts, which he said have been less profitable for investors than they have ever been over the last 15 years.

“I was parking cash in real estate, and when I was introduced to Bitcoin, I asked myself, ‘How can these things work together? How would a digital asset and a physical asset work together?’” Cardone said. “One of them has cash flow, one of them doesn’t; one is illiquid, one is extremely liquid. One’s very stable and one’s volatile, very volatile.
If I could put these together and allow my investors to take a very stable return, 10 or 12 or 15 percent annualized on a multifamily real estate portfolio, and combine that with an asset that’s very volatile, I actually could get better returns.”

The upside of Cardone’s bitcoin hybrid model is that, when the two stabilize one another, and even if the cryptocurrency is down in price, the real estate will remain at a consistent valuation. Plus, bitcoin doesn’t get bedbugs, Cardone said.

The first (and last) panel of the day, called “AI and the Future of Real Estate: How Owners, Operators & Capital Partners Are Driving Transformation,” was moderated by Sam Chandan of the Chen Institute for Global Real Estate at New York University’s Stern School of Business, with insights from Yaakov Zar of AI data firm Lev, Colin Joynt of office landlord BXP, and Jack Harnett of Brookfield Asset Management.

Zar said he believes AI adoption needs to be more specialized for cost-saving and time-saving efforts, especially for companies that are often getting licensed for basic software like Claude for every employee. That can be done as the technology evolves through firms with a specific objective, he said.

“How do you bring these systems into the enterprise level in a way where you’re actually not handing the work to a person who’s using AI and they’re not running it in a silo, but it’s more of a member of the team that’s doing work for you?” Zar said. “This happens a lot in document review and extraction, insurance review and extraction, underwriting-type tasks, et cetera. It’s not saying, ‘We’re gonna do the entire scope of this set of tasks with AI,’ but it’s figuring out how we can have AI assigned to a step in the process, just like a human employee that you have.”

Harnett said Brookfield has been forced to filter through a lot of noise in terms of implementing AI, sorting speculative uses from real solutions and building the models and framework necessary to get real work done, especially when deciding what’s important for assets depending on the region.

“I think everybody understands what’s possible, but to actually get something that is compounding and learns over time, that’s the difficulty that people are facing,” Harnett said. 

“We’ve always talked about having one source of truth, but the truth is, for a big company like Brookfield, we do still have silo processes,” Harnett added. “The AI moment is actually now making it imperative that we have one source of truth to actually feed the right context into AI and have governed data that feeds into those systems. So if anything, I’d say it just incentivizes what we’ve always said that we wanted to do, rather than a different view across regions.”

Like the other panelists said, adopting AI by beginning with smaller tasks is the most logical starting point, according to Joynt. And it can lead to the development of a larger framework for companies that don’t have fully fledged processes.

“My advice to anybody who’s going to start this is don’t try to — I hate this phrase — boil the ocean,” Joynt said. “Start with a process or a task, and then run it all the way through. Start with, ‘What are we trying to solve? Do we have a data issue or do we have a security issue?’ Tackle all of those things, because realistically what you’re doing is you’re building a framework. Once you have the framework, then you can literally start adding more in your efficiencies of proof.”

Mark Hallum can be reached at mhallum@commercialobserver.com.