Digital Transformation of Real Estate Hits Roadblock at AI Data

Deep data is property's private data library to AI and the construction industry's promise of achieving quantifiable efficiency in build and operations.

SARATOGA, CA, UNITED STATES, August 13, 2025 /EINPresswire.com/ -- SLACi addresses the great white elephant confronting PropTech and AI in a new white paper titled “AI Data in Real Estate”. Reliable and accessible property data is the foundation on which AI will operate. Industrywide, property data is not limited to components listed in the building information model, O&M manual or balance sheet.

Only a small fraction of the real estate industry uses advanced digital technology. Most applications hit a brick wall in usability due to bottlenecks exchanging data between build, operations and diverse service providers across industry sectors.

Many challenges industry faces today result from different sectors focusing on different pieces of property data. Transitioning real estate into digital technologies requires a global view of how users in each industry sector ingest and contribute to the property and its data.

Property related tasks involve users with expertise in diverse areas. Although some Sector-based technologies have achieved technical how-to, they have yet to embrace the diverse ways users collaborate in build, property operation, maintenance, upkeep and improvements.

The next generation of solutions must integrate the contributions of diverse stakeholders across a broad range of data-driven user technologies.

What is property data, who owns its fragments, and how do data owners manage accessibility to friendly AI and user technologies? The authors of “AI Data in Real Estate” explore the answers as they relate to five different industry sectors.

What is Property Data?
The definition of property data usually depends on who you ask. Professionals in tenant-facing segments may lead with leasable space and maintenance or market rates, availability and tenants. Service providers in build and maintenance tend to highlight technical data related to construction and design specifications, buildings, systems and equipment.

The first two rely on obscure public and market data whereas the most reliable technical details are often confined to a private building information model (BIM) or Operations and Maintenance (O&M) manual.

Property details include both market data and information models that list buildings, grounds, parking and landscaping. Each assets data library represents an isolated dataset for a specific property in distinct file formats for various use cases.

Viewed from a higher level, the individual components and materials that comprise property data encompass the universe of data published by industry producers. The entire list of a single supplier’s products, their profiles, performance and technical specifications would comprise one of the thousands of data libraries required by the real estate industry. AI of service providers will drill into details in these libraries to deliver improved user efficiency.

Don’t undervalue the data libraries assembled by producers. This data is not only critical to build, it is also required to perform maintenance and improvements in operations for the duration of a products serviceable life. In fact, when contractors perform services, producer and property data containers become intertwined.

Property Data Exchange
In the absence of centrally stored and digitally accessible data, PropTech solutions combine storage and application features under a single roof. Because the PropTech landscape is relatively young, the utility of property data beyond the application space of an independent solution provider is unclear.

Independent property owners historically used Construction Operations Building information exchange (COBie) office templates to organize information on property assets. In 2023, the National Institute of Building Science (NIBS) updated its official COBie (standard to include a digital format (.json).

Architecture, Engineering and Construction (AEC) technologies like CAD typically support a range of digital data standards although proprietary formats face challenges between product versions. Today, all practical solutions are capable of providing property owners and developers a full set of digital deliverables in multiple formats. Existing property has the advantage of exporting Office files to BIM formats.

However, neither the latest in PropTech or COBie practices address the “exchange” component of building information exchange. SLACi teams defined methods and techniques to exchange data in CaPSCi (construction project supply chain infrastructure) published by its BidForms division.

CaPSCi dynamically creates a secure, on-demand technological infrastructure to authenticate exchange of information between users, industry data libraries, and technologies. These infrastructures establish connectivity autonomously and dissolve upon completion of user performance requirements.

Data Origins
Property data originates in all phases from concept to design and build. Deep data emerges naturally from material and components associated with property improvements. Data production continues in operations through technologies, routine services and basic maintenance activities.

Currently, most data required in operations remains static because it is not consistently formatted, stored or accessible via the cloud to users and their technologies. As a result, service providers who simply access digital records for systems such as or HVAC or surveillance usually need to manually download data from a local PC to be sent elsewhere for analysis.

AEC started digitizing build technology nearly two decades ago. Architects have been producing digital deliverables for years. However, most property lacks the centralized data containers to collect and store data digitally. Owners are capable of receiving build data only in bulk (for example as ZIP files).

Technology for Property Data Owners
Building information model (BIM) objectives improve efficiency in storage, processing and deployment of data on the property, its buildings, systems and equipment. SLACi achieves this by consistently applying open standards and define data structures.

SLACi develops digital identity solutions and data APIs for the physical property, management firms and users. Its BidForms division builds solutions for AEC industry data, contractors and service providers. Additional members of the CaPSCi blockchain consortium independently support the six other industry sectors.

Additional Information
“AI Data in Real Estate” is the first in a series that explore the data AI requires and the importance of recognizing the value deep property data offers build, operation, external interests and supply chains.

Other topics in this series include:
• Hidden Value in Property Data
• Introduction to AI Data Libraries in Real Estate
• Storage Options for Property Data
• How Digital Data Helps NNN Owners
• How Independent Data Effects Property Valuations

Kelly Langford
BidForms Organization
+1 408-992-5202
kelly.langford@bidforms.org
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