Compass has invested more than $1 billion in technology that helps the real estate company’s nearly 30,000 agents go from the first moment they contact a prospect to closing a deal, all through a single technology platform.
“Our job is to help agents grow their businesses, make more money, save time and create great experiences for their clients,” says Rory Golod, president of growth and communications at Compass.
Investments by Compass, the nation’s largest brokerage by sales volume, include “Likely to Sell,” an artificial intelligence tool that analyzes potential customers and provides recommendations on who may soon be ready to sell their home. Most recently, the company launched Compass AI, a chatbot tool that can help write property listings, marketing materials and agent profiles.
Agents may have thousands of contacts at their fingertips, Golod says, and conversation rates are often exceptionally low for traditional marketing tactics like email and social media. But Compass says that since Likely to Sell launched in the summer of 2020, nearly 8% of recommendations made through its customer relationship management (CRM) tool each month have been listed on the market within 12 months.
“We want to use AI to help an agent say, ‘If I need to contact someone, I want to reach the people who have the highest propensity to transact,’” Golod says.
Advances in generative AI could unlock $180 billion in value, according to McKinsey estimates. The sector could certainly benefit from such a shakeup, as U.S. home sales fell to their lowest level in nearly 30 years in 2023 due to high mortgage rates and low inventory that made home buying very more expensive. The industry risks major commission disruptions after the National Association of Realtors reached a settlement that could lead to homebuyers and sellers negotiating lower agent commissions. There’s also a big problem with construction, because the nation simply isn’t building enough new homes to meet demand.
But there are many complicating factors that make adopting AI in real estate particularly difficult. Experts say there are huge amounts of unorganized data, ranging from leases to contracts, investment documents to design plans. Construction operates on very thin margins. The average age of a real estate agent is higher than that of workers in most industries, and those in the industry are notoriously technology-averse. And the highly physical nature of the industry means that many technological advances are still at a relatively early stage.
“I would say that, historically, real estate has always been a bit of a laggard, in terms of using artificial intelligence,” says Alex Wolkomir, a partner at McKinsey.
Wolkomir says commercial real estate is ahead of residential real estate when it comes to AI adoption. According to him, the main challenge facing the industry is ensuring that members of its workforce – construction workers, real estate agents, designers – are adequately trained and understand the capabilities of the AI tools they are given. He is encouraged by the forward momentum in the industry’s AI journey over the past five years.
“I think about this a lot [generative] AI use cases open up new areas of great value for the real estate industry,” says Wolkomir.
Yao Morin, chief technology officer at JLL, says one of the challenges commercial real estate faces is the abundance of unstructured data, in the form of leases, contracts and invoices. “I believe that in this age of AI, the barrier of using AI will continue to lower,” Morin says. “And then you ask yourself, ‘If the use of AI isn’t a competitive advantage, then what is?’ The answer is absolutely your data.”
Last year, the company introduced JLL GPT, a generative AI model that provides insights to clients based on JLL’s proprietary market research, along with externally available market data. Morin says 20% of JLL’s 103,000-person workforce uses JLL GPT on a weekly basis because the technology allows staff to complete repetitive tasks more efficiently.
JLL is also using generative AI to better predict building maintenance needs, seek investment opportunities and implement sustainability initiatives. “If you think about classical AI, you need a higher learning curve to understand it and be able to trust the results,” Morin says. “But with generative AI, it’s much easier for us to adopt it and for people to understand the value in it.”
Startup Higharc has launched a home building automation platform that aims to transform home construction into a faster and cheaper process.
“What we do is make data available about the homes that are going to be built,” says Marc Minor, CEO of Higharc. “And when I say ‘making data available,’ I mean every part and piece of the building, where it belongs, when it is to be built, and who is responsible for that segment of the building. We check all this information automatically.”
Last month, Higharc raised $53 million in Series B funding, including from retailer Home Depot, the venture capital arm of France’s Schneider Electric, and from other construction, building products and industrial sectors manufacturing. Minor says the biggest possibilities lie in both improving how homes are built and accessing data from distributors and suppliers.
“If you build the right software layer to systematically change housing, in terms of designing homes, that gives you the ability to more easily understand ways to leverage the hardware side,” Minor says.
Prologis Ventures, founded in 2016, has invested $250 million in more than 45 startups focused on supply chain and logistics, including AI-enabled companies such as TestFit, Altana AI and Logiwa.
“People have always used intuition as a way to make real estate decisions,” says Will O’Donnell, managing partner of Prologis Ventures. “But there are mountains and mountains of data that if you could put together and analyze [it]you may have better insight to make that decision.
Prologis, for example, uses TestFit’s artificial intelligence to better evaluate the feasibility of new warehouse sites. Information about specific zoning regulations, environmental conditions, transportation around a site, and labor can be integrated to improve decision making. TestFit can also produce dozens of project renderings in as little as an hour and will make suggestions based on passed parameters.
“As a company, one of the things we spend a lot of time on is: what information is important to our customers when they make a decision?” O’Donnell asks. “What is important to them as they drive their business, and how can we enable both our employees and our customers to better understand that information?”
Augmenta, on the other hand, automates the design of electrical systems, that is, all the parts and pieces within a building that take electricity from one point and transport it to another. “The process of devising the complete and detailed plan is fraught with challenges,” says Francesco Iorio, co-founder and CEO of Augmenta.
The design process, Iorio explains, is extremely complex, because to go from sketch, to parts list, to construction, there is a long list of considerations before having an actionable plan to build. The biggest advantage of artificial intelligence, he says, is automating the pre-construction phase of electrical systems.
“Giving them the ability to design at the highest level of detail, with cost and time at the forefront from the early design stages, allows people to experiment and answer those questions that would be expensive to answer downstream,” says Iorio.