Commercial real estate investors allocate up to a third of their IT budgets to making data usable, a recurring expense that reduces returns before artificial intelligence or other technologies can add value.
Nonprofit OSCRE International reports that 20% to 30% of the industry’s annual IT spending goes toward integrating systems and information. The group describes this as an “integration tax”—funds spent connecting data rather than analyzing it.
The cost of fragmented data
OSCRE CEO and executive director Richard Reyes stated the expense is avoidable but remains widespread. “Many organizations want to free up capital for AI,” he said. “They need to find ways to reduce waste in integration work.”
Without consistent, interoperable data, AI systems fail to meet expectations. “Everyone is purchasing AI tools,” Reyes noted. “Few have data that AI can actually process.” The issue extends beyond the cost of AI itself—it includes the additional spending required to make incompatible data sets work together.
The underlying problem is inconsistency. Even when teams use identical terms, their definitions often vary. A term like “inventory cost” might refer to an average price for one group, first-in-first-out pricing for another, and first-in-last-out for a third. These differences force IT teams to spend time aligning definitions, mapping fields, and verifying reports.
Some of the largest commercial real estate firms have begun standardizing their data internally. Reyes said 35 major companies now use OSCRE’s Industry Data Model (IDM), a framework that provides a shared language for real estate data. By adopting common definitions, these firms reduce the effort needed when information moves between systems or portfolios.
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Investors see benefits in clearer reporting, more accurate benchmarking, and better control over asset performance. Less money is spent on reconciling data, leaving more for analysis.
Standardized data allows firms to shift resources from maintenance to performance. The shift moves spending from integration to insight. Smaller firms must decide whether to follow industry leaders or continue paying the tax.
A smarter way to structure data
OSCRE is expanding the IDM’s reach beyond large firms. The organization is adding “smart layers” to the model—tools that define terms and clarify context. For instance, a “pump” isn’t just equipment; it’s located in a “room,” which is part of a “building,” which belongs to a portfolio.
“We’re using AI to interpret the information,” Reyes explained. These layers function as translators, reading data structure and meaning before converting it across platforms. The aim is to lower the cost and effort of making systems compatible.
Wider adoption could reduce the integration tax. Less IT spending would go toward making data usable, freeing up funds for AI, analytics, and other initiatives that improve investment performance. Currently, the industry remains trapped in a cycle of paying for compatibility instead of results.
Reyes stated, “This is an annual expense the entire industry bears, and it doesn’t have to be.” The challenge isn’t only technical—it’s also cultural. Firms must choose whether to accept integration as an unavoidable cost or invest in standards that could transform their operations.
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