Artificial intelligence is attracting substantial investment across real estate, with businesses exploring applications in valuation, underwriting, portfolio management, and property operations. According to the Center for Real Estate Technology & Innovation (CRETI), global proptech investment reached $16.7 billion in 2025, reflecting renewed capital deployment across the sector. JLL’s survey found that 88% of investors, owners, and landlords had begun piloting AI, while 92% of corporate real estate occupiers were running AI pilots. Yet only 5% of occupiers reported achieving all their program goals. These findings suggest that translating investment and experimentation into operational results may depend partly on the quality of the information feeding these systems.
That consideration brings property data into sharper focus. JLL’s Occupancy Benchmark Report identifies improving space data accuracy as a leading corporate real estate priority, reflecting growing recognition that AI-enabled planning depends on reliable underlying information. Buildings contain layers of detail involving dimensions, construction materials, occupancy, and physical condition, often recorded through different systems. Commercial properties can present additional complexities because specialized facilities, such as hospitals, warehouses, and laboratories, have distinct spatial and structural requirements. When records are incomplete or inconsistent, AI-generated analysis may require extensive human review before decision-makers can confidently apply it.
The origins of these data challenges often extend back decades, as legacy property information was traditionally maintained across separate paper files, lease documents, and appraisal records. As digital systems were adopted, these older records were frequently transferred into departmental spreadsheets and databases. Over time, portfolios have often accumulated disparate formats that can prove difficult to reconcile across regions. Because digitization has progressed unevenly in some areas, the core challenge typically involves making historical records sufficiently consistent to support modern analytical tools.
Terminology adds another layer of complexity. The same property characteristic can be recorded differently across local assessment offices, multiple listing services, and private databases. Boston Consulting Group’s Fourth Annual Global AI at Work Survey found that 41% of regular AI users reported increased cognitive load. Its findings offer relevant context for property professionals who may spend considerable time reviewing machine-generated outputs and reconciling inconsistent source records. For real estate, the implications can be particularly tangible when terms such as “gross area,” “living area,” and “finished basement” carry different definitions across datasets.
Todd Rissel, co-founder and CEO of e2Value, a web-based property valuation technology provider, argues that these inconsistencies deserve greater attention as AI enters more valuation and risk-management workflows. “AI is dependent on the quality and structure of the data it receives. If you want meaningful insight, you need meaningful data architecture supporting it,” he explains.
His perspective points to a foundational question for the industry: How can advanced systems interpret property information reliably when the underlying records may describe similar characteristics according to different conventions? Standardized terminology and consistent data structures may help create the conditions for AI to deliver more useful analysis beyond initial experimentation.
The implications extend directly to property buyers and the professionals advising them. “Two homes can both say 3,000 square feet on paper and feel totally different in real life,” Rissel explains. “One might have airy, high-ceiling rooms, while the other counts cramped attic spaces or half-finished basements toward that total. Because measurement standards vary so much across different public records, comparing homes isn’t always apples-to-apples. That creates confusion for everyone involved, from buyers to lenders.” As Rissel’s observations suggest, a numerical value becomes more informative when the definition and context behind it are also understood.
Addressing this challenge calls for coordination among the organizations that collect, maintain, and distribute property information. Rissel notes that local tax jurisdictions, real estate associations, multiple listing services, and data providers each contribute to the records used throughout the property lifecycle. Their definitions and reporting practices may require greater alignment to make information more transferable between systems.
“I believe efforts to improve data quality are already underway across commercial and residential real estate, although progress can depend on cooperation among stakeholders with different responsibilities and established processes,” Rissel remarks. He adds that the expansion of AI adds urgency to this work because automated analysis can extend the reach of existing information, including inconsistencies embedded within it.
The potential benefits also reach industries that depend on reliable property records. Banks and mortgage companies use property information in lending and appraisal workflows, while insurers rely on structural characteristics and rebuilding-cost estimates when assessing coverage and risk. Government-backed lending programs and other financial institutions may also benefit from more consistent property information.
Within this ecosystem, e2Value provides web-based valuation tools for residential, commercial, and farm properties, including estimates of replacement costs and support for insurance-to-value assessments. Its work illustrates how specialized valuation systems can contribute to a broader data environment in which physical building characteristics and economic factors inform financial decisions.
The wider opportunity for real estate lies in developing information that can travel more consistently between the systems and professionals responsible for evaluating property. Shared definitions, better documentation, and ongoing data maintenance may help make AI-assisted analysis more useful for valuation, planning, lending, and risk assessment. Achieving that progress will likely require sustained collaboration among public agencies, private organizations, and technology providers.
As investment in real estate AI continues, the quality of its underlying information may become an increasingly important consideration for executives and property stakeholders. The sector’s ability to translate technological experimentation into practical value could depend partly on how effectively it organizes decades of property knowledge into reliable, interpretable data. In that environment, data standards may become an essential part of the infrastructure supporting the next generation of real estate decision-making.
Disclaimer: This article is provided for general informational and educational purposes only and does not constitute investment, financial, real estate, appraisal, lending, insurance, legal, tax, technology, or other professional advice. Information regarding artificial intelligence, proptech investment, property data, valuation, underwriting, lending, insurance, and related technologies is provided for general context and should not be relied upon as a substitute for independent analysis or professional advice. Property measurements, classifications, valuations, replacement-cost estimates, and other real estate data may vary among sources and jurisdictions and may contain inaccuracies, inconsistencies, or omissions. AI-generated or AI-assisted analysis is dependent on the quality and context of the underlying data and should be independently reviewed before being used for material business, financial, lending, insurance, investment, or property decisions. Market statistics and other information cited in this article are based on the referenced sources and may change over time. Information about e2Value and its products, services, capabilities, and statements is based on information provided or published by the company and may change over time. Readers should consult qualified real estate, appraisal, financial, legal, lending, insurance, or technology professionals regarding decisions specific to their circumstances. No particular valuation, investment return, financing outcome, insurance result, cost savings, or business outcome is guaranteed.
