Title Company DataTrace Finds AI Pros and Cons for Real Estate Records

DataTrace, a property and ownership data and title automation provider, sees artificial intelligence reshaping title workflows, but not without trusted legacy data infrastructure.

In assessing the advantages and risks of AI for title and other ownership data verification, DataTrace last week released a new white paper, “Title Search Automation: Reality, Risk, and Responsibility of AI.” The report details why insurable title requires validated data, title plants (known as indexed property records) and responsible AI deployment.

“There’s a lot of excitement right now about what we’re seeing AI do,” said Annette Cotton, senior vice president and chief data officer at Agoura Hills, Calif.-based DataTrace. “It is a game-changer, but I think our position is that it is really important to understand the source of your data. To me, that’s how AI is going to really take off and be successful — the data content that feeds it. And we are trying to share with the industry how we view the importance of high-quality data, specifically comparing our title plan content to general public record data that might be available on county websites and through other jurisdictional access.

“There is no mechanism for AI alone to deliver complete, accurate and insurable title from public records, because the record itself is not complete or verified,” Cotton added. “That’s why the future of insurable title is not AI by itself, but AI powered by structured, validated data combined with human expertise that simplifies these complex inputs into actionable information.”

Among the report’s findings is that AI outputs are only as reliable as the quality, structure and context of the data environment in which they operate. Also, public jurisdictional and court records provide an essential public index of recorded transactions, but function as a system of notice and do not validate the accuracy, completeness or legal validity of recorded documents needed for insurable decisioning. And centralized locations where title insurers and companies can access land titles can transform disparate public records into reconciled, property-centric, decision-ready datasets. That, in turn, can provide a more complete property-level analysis compared with public records alone.

In addition, title agents, real estate attorneys and title underwriters remain essential to interpreting data, resolving inconsistencies, and addressing off-record risks that impact insurability and ownership rights. The white paper also found that state-by-state regulatory frameworks introduce legal and compliance requirements beyond the reach of AI and automation solutions, and long-tail title risk often stems from common data inconsistencies repeated across millions of transactions over time — making risk systemic, not driven by edge cases.

Philip Russo can be reached at prusso@commercialobserver.com.