When S&P 500 companies are ranked by R&D spending as a percentage of revenue, real estate sits at the bottom alongside utilities and basic energy, effectively at zero. The pharmaceutical industry invests roughly 19% of revenue in research and development. Software and ICT services companies invest around 14%. Even industries not traditionally associated with heavy technology development, consumer goods, industrials, and financial services, are committing meaningful percentages of revenue to innovation. Real estate is not, and has not been for as long as the data has been tracked.

That gap has always been explainable, if not entirely defensible. The industry’s capital goes into physical assets, not intellectual ones. Innovation in real estate has historically meant better buildings, not better software. And the companies that needed software to run those buildings bought it from vendors rather than building it themselves, which kept R&D spending off the balance sheet without obviously hurting performance. What is changing is that AI is making that gap harder to explain and harder to ignore simultaneously. The boards and investors asking real estate companies what they are doing about AI are not asking an abstract question. They are asking whether the organization has the technological foundation to stay competitive as the tools available to the industry change faster than they ever have.

The economics behind the gap are real and worth understanding before judging them. “Real estate is a huge asset class but it doesn’t create that much revenue compared to other industries,” said Chase Garbarino, CEO of HqO. “When you think about core competency, it’s probably not designing software.” A large REIT managing billions in assets may generate a relatively modest management fee revenue against which technology development spending has to be justified. The margin structure of property management, brokerage, and investment management doesn’t naturally produce the kind of surplus capital that funds internal R&D at scale.

The industry has historically addressed that constraint by buying software rather than building it, a rational response to the economic reality but one that cedes control of technology strategy to vendors whose incentives don’t always align with their customers. A vendor building a leasing platform for thousands of customers is optimizing for the average use case, not for the specific competitive advantages that any individual operator might want to develop. That tradeoff has been acceptable for most of real estate’s history. It is becoming less acceptable as AI creates the possibility of meaningful differentiation between organizations that figure out how to use it well and those that don’t.

“Real estate companies will have to wrap their heads around how much they are willing to spend and how it will help them become more resilient,” Garbarino said. The pressure to have that answer is intensifying. Deloitte’s commercial real estate outlook survey found that 81% of CRE leaders identified data and technology as the area where they are most likely to focus spending. That number reflects a genuine shift in how seriously the industry is taking the question. The gap between that stated intention and actual R&D investment is where the industry’s real challenge lives, and closing it requires more than a budget line item. It requires a strategic framework for what the investment is supposed to accomplish.

The AI era is complicating the buy-versus-build equation in ways the industry is only beginning to work through. As more real estate companies have explored building internal tools and custom AI applications, they have encountered a dynamic that experienced technology organizations understand well but that real estate is learning for the first time. Software is not a one-time investment. It is an ongoing operational commitment. “Real estate needs to understand that code is a liability not an asset,” Garbarino said. “The more you have, the more you need to spend to maintain it.”

AI has lowered the cost of writing code considerably, which has made it tempting for real estate organizations to build custom tools more readily than they would have before. A property management company that once needed a full development team to build an internal reporting tool can now produce a working version with a fraction of the effort. That feels like progress, and in some ways it is. But lower initial development costs don’t change the maintenance equation. Every line of code eventually needs to be updated, debugged, integrated with changing external systems, and revisited when the underlying AI models it depends on are themselves updated. Organizations that build aggressively without accounting for that ongoing cost often find themselves holding technical debt that consumes the budget they thought they were saving.

There is also a skills question that the industry has not fully reckoned with. Building and maintaining software requires people who understand how to do it, and those people are expensive, in high demand, and not typically drawn to industries that have historically treated technology as a support function rather than a core capability. Real estate companies that decide to build internal technology face not just the cost of development but the organizational challenge of attracting and retaining the talent required to do it responsibly.

None of that argues against technology investment. It argues for more clarity about what that investment should actually look like in a real estate context. The answer for most organizations is not building more software. It is developing the organizational capability to evaluate, select, configure, and get maximum value from the tools that vendors are building. That is a different kind of investment, one that prioritizes people, process, and judgment over code. It means training teams to understand what AI can and cannot reliably do. It means building the data infrastructure that makes AI outputs trustworthy. It means developing the internal expertise to ask the right questions of vendors and hold them accountable for what they promise rather than simply signing contracts and hoping for the best.

“The overwhelming majority of real estate companies are getting questions from their boards and investors,” Garbarino said. “They need to show that they are at least working on being innovative.” Working on being innovative and building software are not the same thing, and the distinction matters more than it might seem. A real estate company that invests in training its teams to use AI tools effectively, that builds the data governance structures required to make those tools reliable, and that develops the judgment to evaluate which vendor claims are credible and which are not, is doing something genuinely valuable. It may not show up as an R&D line item. It may not produce a proprietary platform to demonstrate to investors. But it builds the organizational capability that separates the companies that will get real returns from AI from those that will spend the next decade wondering why the technology never delivered what they were promised. The gap between those two outcomes will not be determined by how much code was written. It will be determined by how clearly organizations understood what they were actually trying to build and why.

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