Tool Or Toy? 5 CRE Pros Explain Their Real-Life Uses For AI

Like the rest of the business world, commercial real estate has been entranced by the prospect of generative AI since it crashed into the marketplace almost four years ago. But while major companies have made big proclamations about the tech’s potential, there has been little evidence of it making major changes for the industry.

Despite the lack of tectonic shifts, everyday CRE professionals are making artificial intelligence work for them in real, concrete ways. From automating lead generation to multiplying workload capacity to close more deals, CRE is beginning to utilize AI. 

Five representatives from different aspects of commercial real estate, from marketing to multifamily investment, told Bisnow how they started using AI, how it has become part of their everyday routine and how they see it directly impacting their efficiency and profitability. 

While all have become serious evangelists, few totally trust these tools with any jobs: They keep a human in the loop for quality assurance and fact-checking.

And one of the biggest insights from these users was that it’s key to realize that, amid the AI evangelism and hype, no program really works out of the box. Significant investment in processes, training and data collection and management is required to get a true return.

Efficient Prospecting

“Efficiencies, man, I’m a hardworking lazy person,” The Group CRE founder Taylor Avakian said of his goals for using AI at work. 

Covering all of Los Angeles County as a multifamily investment broker, Avakian began experimenting with AI about 18 months ago. He’s found that it’s been a significant time-saver and helped improve his bandwidth. 

Small tools and agents help handle simple and repetitive tasks, like creating detailed daily reports about local industry news. A custom version of the HubSpot customer relationship manager and Anthropic’s Claude combs through Avakian’s emails and calendar and reminds him to follow up with potential clients. He says he can handle a lot more listings than he did before. 

The follow-ups and efficiencies mean he’s handling 25 properties instead of five. 

“I come to a pitch and can show a potential client a custom website I’ve created for them using Lovable,” he said. “It shows the breadth and scope of what I can do.”

From a pure research perspective, Avakian uses numerous tools to research and prepare for meetings, condensing the hours he would spend going over zoning, sales data and other information in a matter of minutes. He’s done land feasibility studies using custom ChatGPT prompts that have surprised him with their level of detail. 

“A lot of the copy creation, the bullshit of marketing, has been streamlined a ton,” he said. “It’s small things, but it has a huge net effect where I can spend more of my time generating revenue and prospecting. It’s going to be game changing across the whole industry. Everything is data-related. Whoever has the most data and can use that data the best will be the most effective.” 

Automated Marketing

Working for a fast-growing startup in the co-warehousing industry, ReadySpaces Director of Marketing Nick Gardiner oversees a rapidly shifting series of locations and priorities. His job entails building a brand message while worrying about the nuts and bolts of attracting and appealing to potential tenants. 

In the two years Gardiner has been using AI tools for his work, they have rapidly evolved. What began with helping create marketing and ad copy has expanded to providing the backbone for a sophisticated data dashboard for the company’s 40 locations and helping automate a portion of the firm’s ad spend. 

Gardiner built a custom tool that uses the company’s internal dashboard, powered by AI, to manage a six-figure monthly Google Ad budget, allowing a custom AI to dynamically adjust ad spend for the firm’s different locations throughout the day. 

“I don't know if I'm ahead of the curve by any means, but I really think that it's something that, if used correctly, can help you jump ahead of some competitors,” he said. “The ones that aren't using it might not get left behind, but they're certainly going to be doing a lot more manual work than is needed.” 

Gardiner says AI can reliably save him five to seven hours per week. One of the most reliable tools is the custom internal tool for managing buildings that the company built, called Epoch, which pulls together and analyzes a number of performance factors in real time.

For instance, when a building gets below 80% occupancy and the sales close rate is also below 20%, the software sends a warning message to look into a general manager's sales performance. He can diagnose performance issues for the CEO during a phone call instead of having to run multiple reports and follow up hours later. 

Due Diligence

Uncommon Capital Group founder Ben Harris’ desk at his Chicago-based firm has two monitors. One is where he does his everyday work and communications, while the other is always open to ChatGPT. Uncommon has become a big believer in artificial intelligence.

“In some ways, it’s more than a team member,” Harris said. 

But considering Uncommon’s line of business, which is sourcing deals for high-worth private equity investors and family offices, Harris can’t afford hype. 

“I like the description of AI as an intern with a Ph.D, JD, MBA,” he said. “The reason I like calling it an intern is that you need to teach it and coach. Once you coach it, it can perform at a very high level.” 

One of the firm's most-used tools, the Uncommon Fund Screener, evaluates deal proposals. It’s not a final word, but more of a first pass. Harris and his colleagues created their own internal scoring system, the Uncommon Seven, and fed it previous deal data to create this system. Now, they can drop a PDF into the tool and get a detailed analysis of the proposal in minutes. 

Uncommon makes it a point to describe this tech to clients as a means of leveraging their own expertise and giving them more time to source deals. Harris calculated that between looking at a deal and calling an investment manager to follow up, it takes him an hour per offer. Since he typically gets pitched by 400 investment managers a year, this tool saves significant time.

The key to making this work, he said, was leveraging the team’s own expertise and judgement and training AI not to have an opinion or make a guess, but to simply run through the same evaluation method he would. The more expensive alternative would be hiring a handful of professionals to get trained and up to speed to evaluate these offers. 

“It’s a closed system that actually only digests what we give it,” he said.

Quicker Communication

For Renee McIntyre, vice president of marketing for Scully Co., working with AI has been much more conversational and low-hassle than most people make it out to be. 

You simply have to ask. McIntyre’s programs have been fed a stream of internal communications and are connected to a knowledge bank that outlines company philosophies and communication approaches. But she’s found that the ability to quickly craft informed messages has provided a significant benefit. 

It just takes a little more work at the outset. McIntrye typically uses voice mode when prompting and writing with AI, to get a less filtered response and result. There’s also constant coaching and fact-checking.

The firm can pull reports with varying data points about any record in its system and use those reports to look for patterns and insights that inform strategy and actions. Personally identifiable information is never included.

McIntyre trusts AI with a lot of key communications and identifying patterns in data. But it’s not ready for truly crucial tasks like crisis communications.  

“The other place I think people are getting tripped up is trying to do too much at once — thinking right off the bat, ‘We're going to restructure our department and build out functionality and be able to have these huge gains,’” she said. “For someone starting out, pick one thing that is most suited for AI, and once you’ve successfully done it, try another.”

Pitching Partner

When Aspire Commercial Real Estate Head of Operations Topher Stephenson started at the Houston multifamily brokerage, he created an automated AI tool that collected the region’s multifamily deals and created a news digest. 

It was so popular with his colleagues, Stephenson now shares it with the entire team. It’s one of many examples of how he’s using AI to alter the processes at Aspire to remove friction and help brokers focus on deals. 

Many of the everyday tasks of a broker are touched by this new technology. Premade templates for pitch decks make it easier and quicker to assemble a professional proposal. AI searches scour local business media to see what companies are growing, expanding or landed new funding, creating a list of potential tenants to pitch. 

When he started two years ago, Stephenson spent his first week interviewing the entire staff on how processes get done and fed the transcripts through a large-language model. The resulting analysis has been used to create a better process management system that speeds up the creation of things like pitches and proposals and accelerates sign-offs. 

The entire project management system and internal email system are constantly being analyzed by AI and a custom generative pretrained transformer, or GPT, program to look for places to create automations, speed up work and answer internal questions.

“For the most part, everyone here has their own quick tricks with the technology,” Stephenson said. “Mostly AI is allowing us to spend more time doing the things AI can’t.”

Stephenson doesn’t have a specific indicator to prove AI has impacted the bottom line for Aspire, but he does think it’s given the company a leg up in competing for deals because it cuts out a lot of busywork.  

“We’re not a giant company, we’re a boutique,” he said. “But we’re able to show up with these big firms.”