As more and more commercial real estate brokers adopt AI to increase efficiency, engage more effectively with clients, and improve decision-making, a number of SIORs are now incorporating predictive analytics to help them identify future trends and predict market conditions to help their clients achieve better outcomes.
The terms "predictive analytics" and "AI" are sometimes used interchangeably in commercial real estate, so it’s important to note that while AI encompasses a broad range of technologies, predictive analytics does not necessarily require the use of AI. However, most models now leverage AI’s more sophisticated analysis of large datasets and machine learning to analyze historical and current data to make forecasts.
“Predictive analytics and AI can help me analyze tenant needs, behavior, and historical data, and then predict future outcomes,” says Kim Ford, SIOR, CEO of tenant rep firm Rise Pittsburgh. “And I think it also helps justify or build the case for that client as to what they should do. It's not just me making market recommendations. It’s evidence-based.”
Use Cases For Predictive Analytics
Ford says one area where the application has been useful is in optimizing site selection for the firm’s clients. Using AI models, they evaluate factors such as commute time, competition, foot traffic, and labor market trends to identify the best strategic locations for their clients. Predictive analytics can also help to determine the ideal term of a lease. For instance, if based on the data, market trends indicate that a spike in demand will drive up rates in the coming years, it may be advisable to secure a longer-term lease. “We might not be able to see right now that this particular market has this potential opportunity, but using modeling through AI could—and a lot better than us,” says Ford. “Right now, knowing what we know using our own market data and conversations we have with other people in the industry, we're making predictions, but I think in the future predictive analytics and AI are going to make a huge difference.”
Bridget Richards, SIOR, principal and broker at BRAND Real Estate in Las Vegas —which specializes in owner-occupied medical office and industrial properties —recently began working on a ground-up med spa project. Using what she describes as a “relationship-first lens” for predictive analytics, Richards started the process by doing a deep research dive into the client and their niche industry using ChatGPT. “That way, when I meet with them, I understand the challenges that they're facing, and the growth and the demand that’s encouraging them to want to buy a building with me,” says Richards. “It allows me to step into that first meeting, being very well-versed in their business, so I can build a rapport with them.”
AI helps me ask better questions, frame smarter strategies, and show up as the most prepared person in the room—so my clients don’t just feel seen, they feel understood.
After the project was underway, Richards was able to upload the architectural drawings (a 30-megabyte file) into ChatGPT to produce a marketing brochure, then put the icing on the cake by asking ChatGPT to create a list of likely candidates for acquiring the building (surgeons, cosmetic dentists, etc.). It not only produced the names of specific practitioners, but also their contact information. “So I was able to get 20 people who were the most likely buyers for my project because I prompted ChatGPT and was able to get the report in about seven minutes. AI helps me ask better questions, frame smarter strategies, and show up as the most prepared person in the room—so my clients don’t just feel seen, they feel understood.”
Tobias Schultheiß, SIOR, managing partner at German commercial real estate firm Blackbird Real Estate GmbH, says his firm is using AI models to simulate different leasing strategies, targeting optimal tenant mixes based on historical absorption, space typologies, and fit-out preferences, which helps shape marketing tactics and timeline planning. “Based on historical data and macroeconomic developments, we use AI models to forecast leasing trends and regional demand indicators to understand where markets sit within the cycle,” says Schultheiß. “This helps guide strategic decisions on whether to enter, hold, or exit a location or asset type.”
Tim Tran, SIOR, founder and president of The Ivy Group, says that one of the ways his firm uses predictive analytics and AI is for leasing and sales prospecting. He uses AI to create a readable abstract from a 28-page lease and also incorporates data from CoStar to determine when the lease was signed and the length of the lease term. “If we know this tenant has a lease that's expiring in 12 months, that tells us we should be reaching out to that tenant—and landlord for that matter—to find out if the tenant is moving or expanding or contracting, because they may need our help,” says Tran. “And now the landlord has a vacant space, so they may need our help as well.”
The Ivy Group uses a customized large language model (LLM), so for sales prospecting, Tran can input data obtained from a title company and then establish criteria to help identify potential off-market sellers. For instance, the model can be instructed to produce a list of industrial owners who have held assets for 30 years or more. “We can then ask the owner, ‘If the tax benefits of owning the property are about to expire, would you consider selling?’”
Tran also sees additional benefits on the horizon as AI and predictive analytics models continue to evolve. “If you can combine the data that you get from the title company, and let's say you also have data on when loans are maturing, or maybe there's a partnership dispute that's been filed, and the partner in the dispute needs to sell the building…Can you imagine what you could do with that kind of data?” he asks.
When used with intention, they help us lead with both insight and heart. Don’t wait for a roadmap. Just start. The only way to find what works for you is to try.
The Caveat: Garbage In, Garbage Out
Despite the enormous potential of AI and predictive analytics, numerous challenges inherent to all AI applications remain, including data accuracy, adoption barriers, and regulatory concerns.
In Deloitte’s 2025 Commercial Real Estate Outlook, the report notes that real estate data has historically not been standardized, and data fragmentation is a common issue. AI results rely on the data underpinning the results being accurate and complete, and bad data could be detrimental to both AI-generated content and the business decisions that are based on it.
“In many ways, predictive analytics isn't quite there yet using AI, because you have to have enough good information to make it valuable,” says Ford, who serves as the chair of SIOR’s Innovation and Technology Committee. “I think it's really important that you're using a software application that allows you to see the source right online, because as they say, ‘garbage in, garbage out,’ so we double-check everything and would never (make predictions) without backing it up.”
Tran, who also comes from a tech background, agrees. “A lot of this is experimental, trial and error, and things are constantly changing, so that's the challenge in this market.”
The Learning Curve
As with all new technologies, there is a learning curve associated with AI and predictive analytics. Despite his tech background and nearly 25 years in commercial real estate, Tran took a course specifically designed for AI as it relates to CRE. “If I want to continue in this business for the next 20 or 30 years, I’ve got to innovate and I’ve got to keep up, or I’m going to be left behind,” he says.
For members wanting to learn more about AI and predictive analytics, SIOR is there to help. The SIOR Innovation and Technology Committee hosts periodic webinars on the practical implementation of AI, which can be found on the events calendar. In addition, Richards has taught numerous classes via Zoom for SIOR chapters “from Florida to Saskatchewan” on how to utilize ChatGPT, which serves as a building block for implementing predictive analytics. She was also a featured panelist on the topic at the SIOR Spring Event in Las Vegas. A link to that session can be found here, and a “cheat sheet” on how ChatGPT prompts can be found here.
“These tools aren’t just about efficiency,” says Richards. “When used with intention, they help us lead with both insight and heart. Don’t wait for a roadmap. Just start. The only way to find what works for you is to try. You might be surprised by how much clarity and confidence you gain from simply sharing the burden and getting a new perspective.”
This article was sponsored by the SIOR Foundation - Promoting and sponsoring initiatives that educate, enhance, and expand the commercial real estate community. The SIOR Foundation is a 501(c)(3) not-forprofit organization. All contributions are tax deductible to the extent of the law.
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