Kathy Kwak has spent her career at Proper Title in Illinois untangling property records that AI would choke on. She described one active transaction to Bisnow this spring: a university campus where the ownership history is so fragmented that the attorneys involved cannot determine who owns the alleys and parking lots, let alone reconstruct a clean chain of title. Records stretch back decades. Some are handwritten. Some list plot numbers instead of addresses.
"If you don't have the experience, you have no idea what you're reviewing," Kwak said. "Unless you understand what a chain of title means, unless you understand what a lien means or can decipher a handwritten document from the 1800s, you need an expert."
She is describing the exact records that AI title search tools are now being trained to read.
The Speed Promise
In April 2026, First American Title introduced AgentNet Assist: Title Intelligence, an AI capability that analyzes title search packages, extracts key information, and flags potential breaks in the chain of title. First American is the largest title insurer in the country, with $7.5 billion in total revenue in 2025 and 23.1% market share. When they say AI is the future of title search, the industry listens.
On paper, the pitch is compelling. Paul Bandiera, First American's senior vice president of agent technology, told BusinessWire that the tool reduces processing time by "as much as 30 minutes per file." Title search packages routinely run to hundreds of pages of deeds, liens, legal descriptions, and supporting documentation. Shaving 30 minutes off each one, across millions of transactions, sounds like the kind of efficiency gain that should make everyone happy.
Except the tool is reading county records. And county records are a disaster.
3,600 Filing Systems, Zero Standards
The United States has no national property registry. Ownership records are maintained by more than 3,600 individual county recording offices, according to Gregg Lester, co-CEO of Balcony, a firm building modern data infrastructure for local land records. Each county sets its own filing conventions, indexing systems, and digitization standards. Some counties have fully digital searchable databases. Others store records as scanned images with no text layer. A meaningful number still maintain handwritten ledgers in physical courthouses that have not been comprehensively indexed since the records were created.
DataTrace, the nation's largest provider of property ownership data, put out a white paper in April 2026 that said the quiet part out loud: "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." Annette Cotton, DataTrace's chief data officer, was blunt about what public county records actually are. They function as a "system of notice," meaning they tell you a document was filed. They do not validate whether the document is accurate, whether it was filed in the correct county, whether it references the correct parcel, or whether subsequent documents that should have been filed actually were.
DataTrace's white paper modeled what happens when AI operates on imperfect data at scale. A 1% variance in data accuracy, applied across roughly 5 million existing home sales per year in the U.S., could produce up to 50,000 instances of inaccurate title. Those errors do not detonate immediately. They surface over five to ten years, during refinancing, resale, or litigation, when the new buyer's lender orders a fresh title search and finds a lien that the first search missed because the county never indexed it properly.
The Workforce That Catches What the Records Miss
Title examiners, abstractors, and searchers are the people who have historically filled the gap between what county records say and what is actually true about a property. Their job is not just reading documents. It is recognizing that a street name was spelled differently in 1987 than it is today, or that a deed was recorded in the wrong county because the property sits on a county line, or that a satisfaction of mortgage was never filed even though the mortgage was paid off, or that a lien released by one party was re-recorded by another party six months later in a different index.
There are fewer of them every year. According to the Bureau of Labor Statistics, 96,000 title examiners and abstractors in 2025, down 11% from 107,000 in 2021. Pat Stone, the founder of Williston Financial Group, described the recruitment problem with a candor that most industry executives avoid: "The fact is we are an obscure little industry that doesn't attract a lot of people, and then you have an aging population."
Both trends are moving in the wrong direction. Title insurance premiums totaled $18.5 billion in 2025, up 13.8% from 2024, according to the American Land Title Association. Claims paid: $667 million. Meanwhile, real estate fraud losses reported to the FBI's Internet Crime Complaint Center jumped to $275.1 million in 2025, up from $173 million in 2024. That is a 59% increase in one year. AI is being used by criminals to create more convincing fraudulent documents, deepen wire fraud schemes, and fabricate identities that pass automated verification. Fewer examiners. More fraud. Rising volume. The title industry's workforce is shrinking at the exact moment that both volume and fraud complexity are rising.
What AI Actually Does Well (and What It Cannot)
The honest answer, which both First American and DataTrace have essentially acknowledged, is that AI excels at reading structured, validated data quickly. First American's Steve Vincini, president of the Agency Division, described AgentNet Assist as "fueled by First American Title's underwriting excellence, proprietary data assets developed over decades, and unmatched domain expertise." He is describing a title plant, not raw county records. Title plants are proprietary databases where the big title insurers have spent decades normalizing, cross-referencing, and validating county records into structured, searchable, property-centric datasets.
AI applied to a well-maintained title plant is a genuinely useful tool. It can surface patterns in deed chains faster than a human examiner, flag common recording errors, and identify missing documents that match known templates. That is real value.
AI applied to raw county records, which is what smaller title agencies and startups without access to title plant infrastructure are increasingly attempting, is a different proposition entirely. Data is inconsistent between counties, often incomplete within counties, and occasionally wrong in ways that only a human with local knowledge would recognize. Ashkán Zandieh, managing director of the Center for Real Estate Technology & Innovation, summarized the structural problem to Bisnow: "Title insurance exists because of fragmented, inconsistent historical records and the need to transfer legal risk. That doesn't disappear with better technology."
The County Digitization Problem Nobody Talks About
In 2022, a ransomware attack on Suffolk County, New York, shut down the county clerk's office for weeks and froze real estate transactions across the county. It was a dramatic illustration of a mundane reality: county recording offices are critical infrastructure running on infrastructure that ranges from modern to antique, and there is no federal standard or funding mechanism requiring them to digitize, secure, or standardize their records.
Balcony's Lester described what he sees as a perfect storm. AI and machine learning have made it cheaper to digitize and analyze written documents at scale. Counties are simultaneously facing rising deed fraud and cybersecurity threats that make digitization both more feasible and more urgent. But no one is coordinating it. Each county digitizes at its own pace, using its own vendor, its own format, its own quality standard. What you get is a patchwork where AI tools trained on Maricopa County's clean digital records may perform beautifully, while the same tool applied to records from a rural county that scanned its deed books in 2003 at 150 DPI with no optical character recognition produces results that are worse than useless, because they are confidently wrong.
Nobody has published a comprehensive survey of how many U.S. counties have fully digitized, text-searchable, cross-indexed property records. Title professionals cite informally a range of 40% to 60%, which means that somewhere between 1,440 and 2,160 county recording offices are feeding AI tools data that ranges from "scanned image with no text layer" to "handwritten ledger that has not been updated since the Reagan administration."
What This Means If You Are Buying a Home
Your title search is almost certainly already touching AI in some form. ALTA's CEO Chris Morton has said that "as fraud schemes become more sophisticated and transactions grow more complex, the expertise of title and settlement professionals has never been more important." That statement is doing real work: the industry's own trade association is signaling that AI is a tool for professionals, not a replacement for them.
Three questions worth asking your title company before closing. First: does your title search rely on a proprietary title plant, or does it pull directly from county records? It materially affects the reliability of the search. A title plant means someone validated the data before the AI read it. Raw county records mean the AI is the first set of eyes on data that may be incomplete, misindexed, or simply absent.
Second: what happens when the AI flags an issue? Is there a human examiner reviewing the flag, or is the flag itself the final output? First American has explicitly stated that "final title determinations remain with the title professional." Not every company using AI tools has made that commitment.
Third: has the AI been tested on the specific county where your property is located? Performance on Maricopa County records tells you nothing about performance on records from a county that indexes deeds by grantor name only and has not re-indexed since 1994.
The Strongest Case Against This Article
Title plants work. Five firms control 95% of the market and have spent decades building proprietary data infrastructure that solves most of the problems described here. First American's $7.5 billion in revenue funds the kind of data normalization that a county clerk's office running on a state-allocated budget cannot. When AI is applied to title plant data, the result is genuinely faster, possibly more consistent, and potentially more thorough than a human examiner working under time pressure on a Friday afternoon before a Monday closing. DataTrace's 50,000-inaccurate-titles figure is a hypothetical projection of what could happen, not a measurement of what has happened. And the $667 million in actual claims paid in 2025, against $18.5 billion in premiums, represents a raw loss ratio of 3.6%, which is spectacularly low and suggests that the current system, whatever its flaws, catches the vast majority of defects before they become claims.
That is a fair point, and it is probably true for transactions processed through major title plants. Risk lives at the margins, in the transactions that touch raw county records, in the counties that have not digitized, in the startups applying AI without the proprietary data assets that make First American's version work. A 3.6% loss ratio looks excellent until you remember that title defects can take a decade to surface, and the AI-processed titles being written today have not yet been tested by that timeline.
Limitations of This Analysis
First American's "30 minutes per file" claim is based on early internal usage and has not been independently audited. DataTrace's 1% variance figure is illustrative and hypothetical, not derived from a measured error rate. No comprehensive, publicly available survey of county recording office digitization rates exists. That 40%-60% estimate cited here is informal and unverified. BLS employment data captures a broad occupational category that may include workers not directly involved in title examination. FBI IC3 fraud data is complaint-based and almost certainly undercounts actual losses. Title insurance claims data reflects only defects that resulted in paid claims, not the universe of defects that were caught during the examination process, and a full accounting of near-miss defects has never been published by any title insurer.
Catherine Chen covers building codes, zoning, and regulatory policy for AI Home Building. She finds title insurance oddly fascinating for an industry with a 3.6% loss ratio, which either means it works perfectly or means nobody has looked hard enough at the denominator.