By L.D. Salmanson, CEO an Co-Founder at Cherre.

When the top commercial brokerage firms reported earnings the other week, the numbers were good. In fact, the top three firms all reported good revenue growth, and either met or beat analyst expectations. But the market flinched anyway. Investors, it seems, are no longer just reading the income statement – they’re reading the future, and what they see there is a question mark in the shape of artificial intelligence. 

This follows a general trend of late, which is the markets are struggling to price the effects of AI on core industries. The major brokerage firms recovered a lot of what the losses in the last week – mirroring the same recovery the broader sector experienced going through the same experiment. In many ways, the market is trying to price both “AI is real and is going to disrupt everything” and “AI is overhyped.” From the outside, it can feel maddening. 

The anxiety around disruption is understandable. But it may also be misdirected.

The real disruption in commercial real estate isn’t coming from some startup that’s going to out-broker the brokers. It’s coming from data – specifically, from the long-overdue reckoning with how this industry has always managed (or mismanaged) information. AI isn’t the cause of that reckoning. It’s the accelerant.

The Last Holdout

Here’s a fact that rarely gets said plainly: real estate is one of the last major asset classes that has failed to meaningfully adopt analytics. While public equities markets have been algorithmically traded for decades, and credit markets long ago built sophisticated data infrastructure, real estate has largely operated on vibes, relationships, and a back-of-the-envelope model that everyone knows is probably wrong.

Think about how investment decisions have historically been made. An analyst builds an Excel model with assumptions – cap rates, rent growth, exit multiples. The Investment Committee approves it. The deal closes. And then? Those assumptions are handed off to asset management, where they are promptly ignored during a “stabilization period” that can last years. There’s no systematic feedback loop. No accountability structure. No way to go back and test whether the model was right or wrong.

When the market goes up, everyone’s a genius. When it goes down, it’s the market’s fault. This isn’t just intellectually unsatisfying – it’s a structural failure that has cost investors billions and made the industry nearly impossible to analyze from the outside.

That is what is changing now. And that change has very little to do with the brokerage business.

Where the Data Revolution Is Actually Happening

The real data innovation in real estate today is concentrated on the asset management side. Large institutional investors – the $50 billion AUM firms, the insurance companies, the sovereign wealth funds – are finally doing something that would have seemed obvious in any other asset class: putting all their data in one place.

Internal financial data. Operational data. Third-party market data. Compliance records. They’re connecting it, cleaning it, and building the kind of unified view that lets a portfolio manager actually know what’s happening across their holdings in real time. It sounds basic. For most of this industry, it’s revolutionary.

The investment side of the house, meanwhile, is just beginning to wake up. The most sophisticated players – the ones who think more like hedge funds than traditional real estate operators – are starting to ask a different kind of question. Instead of “does this deal pencil?”, they’re asking: “out of 50,000 multifamily assets in my pipeline, which markets are showing demographic signals I can act on before anyone else?”

That’s a fundamentally different mode of investing. It’s not just faster analysis – it’s a different analytical frame entirely.

What AI Actually Changes

The honest answer to “what does AI change in real estate?” is: it changes who can do the sophisticated analysis, and how fast they can do it.

Consider a concrete example. A multifamily developer wants to build, but can’t make the economics work at current land prices. The question is: are there parcels in desirable school zones, with favorable zoning potential, owned by nonprofits or educational institutions that might be open to a joint venture – especially one that includes an affordable housing component? That search, done manually, would take months, and an army of analysts. Done computationally, against a rich, connected data set, it’s seconds.

That’s not AI replacing human judgment. That’s AI expanding the aperture of what human judgment can act on.

The same logic applies on the AI infrastructure side. When a portfolio manager can query their entire data ecosystem in natural language – asking “how did our multifamily assets in Dallas perform over the last three years?” and getting an auditable, traceable answer that accounts for how their organization actually defines “multifamily” and “Dallas” – they’re not replacing an analyst. They’re giving every analyst on their team the equivalent of a full data engineering team working behind them.

The best analogy might be what happened with Excel. Goldman Sachs was hiring the smartest analysts before Excel. They hired a lot more of them after Excel. Smart people doing smart things with better tools produce more value – they don’t disappear.

The Brokerage Question

So should shareholders of big brokerage firms be scared? The honest answer is: it depends on the firm, and it depends on the time horizon.

In the near term – the next year or two – the big brokerages are doing what rational incumbents do: investing in the tools that make their people more productive. The core brokerage business is a relationship business. At the top of the market, every major firm has talented people with strong networks. What differentiates them isn’t proprietary data – it’s trust, timing, and deal judgment. Better analytics tools can sharpen the edges there, but they’re unlikely to reshape the fundamental competitive dynamics.

But zoom out five to ten years, and the picture gets more interesting. Information barriers in real estate are coming down. Deal flow is becoming more marketplace-like. The friction that has always protected broker relationships – the fact that buyers and sellers couldn’t easily find each other without intermediaries – is being systematically reduced by technology. Real estate assets, as heterogeneous as they are, are actually not that different from other complex financial instruments when you have enough data. You can compare buildings the same way you compare companies. In fact, two office buildings often have far more in common than two stocks.

If real estate starts to look more like a liquid, traded market – even partially – the role of the traditional broker changes. Not disappears, but changes. The firms investing now in data infrastructure and AI tooling are positioning themselves for that future. The ones waiting to see what happens are not.

The Real Stakes

The market’s anxiety about AI and commercial real estate is, in a way, asking the wrong question. The question isn’t whether AI startups are going to out-compete brokerage firms on commissions. It’s whether the industry’s data infrastructure – long neglected, full of gaps, inconsistently defined – is finally going to be brought into the modern era.

The answer is yes. That process is already well underway at the largest firms. The gap between those firms and the ones still managing their portfolios with disconnected spreadsheets and tribal knowledge is widening, not narrowing. AI accelerates that divergence.

For investors watching earnings calls and wondering what it all means: the firms that are treating data infrastructure as a core strategic investment are the ones worth watching. The firms that are not – regardless of how good their brokers are – are going to find that the world is eating their lunch.

• Tagged as:
• AI
• AIinCRE
• commercial brokerage