Oliver Helgemo · · Updated · 7 min read
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Last updated: November 1, 2026
We asked four AI tools five direct hiring questions in 50 U.S. real estate markets and received 1,000 answers. Specific agents or teams appeared in 88.8% of those answers. AI is already putting names in front of buyers, often before they open an agent's website.
I think that is the number worth sitting with. A buyer may get a shortlist from an AI answer, then start clicking around to decide who feels right for the job. What they find next matters.
That is where AI SEO starts. The 50-market study gives agents a clear reason to care about the pages, profiles, reviews, and local details a buyer can actually inspect.
The AI SEO statistics that matter
The numbers below come from one published study run on October 11 and 12, 2026. Why look at a study like this? Well, a direct question about an agent commonly brings back names, and the answer can look different depending on where the buyer asks. Each table row links to the study page with the result.
| Finding | Result | Source |
|---|---|---|
| Answers reviewed | 1,000 answers across 50 markets, 5 questions, and 4 tools | Study methodology |
| Answers naming an agent or team | 88.8% | Study result |
| Average full-list overlap between tool pairs | 7.31% | Study result |
| Answers citing an agent or team website | 71.6% | Study result |
| City-question combinations with the same first name from at least 2 tools | 35.2% | Study result |
The 88.8% figure means that at least one specific agent or team appeared in most of these direct hiring answers. A buyer may see a name before visiting an agent's site, which makes the information attached to that name worth taking seriously.
The average full-list overlap matters just as much. Two tools could share one name somewhere in their answers and still produce very different shortlists. I would treat a screenshot as the start of a better question: what can a buyer learn about you once they click through?
How we ran the study
We studied 50 U.S. markets: 12 large markets, 20 mid-sized markets, and 18 small markets. The group included 4 markets in the Northeast, 9 in the Midwest, 20 in the South, and 17 in the West. We chose the mix to compare a range of markets.
For each market, we asked ChatGPT, Gemini, Perplexity, and Google's AI Mode the same five questions: who is the best agent, who should sell a home, top realtors, the best listing agent, and which agent to use when buying. The design was 50 markets multiplied by 5 questions and 4 tools, producing 1,000 planned answers. Every planned answer returned.
We counted a named person separately from a named team or brokerage. For each city and question, we checked whether two tools named anyone in common. To compare full lists, we divided shared names by all unique names from both lists. This October run is one snapshot, and AI answers can change.
What the source data says about a public record
Across the study, AI named an average of 2.68 individual agents and 1.96 teams or brokerages per answer, or 4.63 named people or organizations combined. That gives a buyer several names to investigate. The practical job is to make sure the public information behind your name gives that buyer something solid to investigate.
Agent or team websites appeared in 71.6% of answers. Zillow appeared in 56.9%, FastExpert in 43.9%, other directories in 30.8%, and Homes.com in 24.0%. Those shares show which source types appeared alongside answers in this study.
Why does that matter? Well, an agent's own site is where an agent can explain local areas, the seller or buyer situations they handle, and the proof behind those claims in full sentences. Profiles, reviews, directories, and local coverage can then confirm the same public record elsewhere. Our research often shows that buyers get a clearer picture when the facts match in the places an AI answer may point them.
One page carries plenty of weight here. It can show a buyer what you know about a place, which work you take on, and how to reach you. A profile can then support the same core facts. A review can add detail about what working with you felt like. The whole picture is easier to trust when it says the same thing in plain language.
Recasi Local is a done-for-you AI search marketing service for established real estate agents and teams. We make an agent's local record easier for AI tools to understand when buyers and sellers ask who to hire.
What market size tells us
Specific names appeared in 89.2% of small-market answers, 88.3% of mid-sized-market answers, and 89.2% of large-market answers. What changed more clearly was agreement: small markets had 9.41% average list overlap, compared with 6.49% in mid-sized markets and 5.48% in large markets.
A smaller market may have more repeated local sources or a smaller pool of names. The useful move is to inspect the questions buyers ask in your own market and read the public record that meets them. That is the part you can improve with care.
The per-tool numbers make the same point. Gemini named a specific person or organization in 98.8% of its answers in this run, ChatGPT did so in 88.0%, Google's AI Mode in 85.2%, and Perplexity in 83.2%. Buyers can reach these products in different moments, so the broader picture is more useful than a single result.
What an agent can do with these statistics
Start with the things a prospective client can verify, then test the questions that reflect the work you want more of. That gives you a more practical starting point than trying to be number one in a screenshot.
- Read your own public record. Look at your site, core profiles, reviews, and local mentions as a buyer would. Confirm that your name, markets, specialties, and service details agree. If one page makes a claim that another source cannot support, fix the underlying fact before adding more copy.
- Write for the real question. A generic page about being a great agent leaves little for a buyer or an AI answer to connect to a specific need. Explain the locations and situations you actually serve, with proof a reader can inspect. Our guide on what AI looks for before naming an agent covers that public evidence in more detail.
- Check more than one question and tool. Compare a small, consistent set of relevant questions rather than celebrate one result. Keep the question wording, date, and product visible when you review a response.
- Read the sources, then the answer. The study found agent or team sites in 71.6% of answers. Source links show where a buyer may go next and where your public account needs clearer detail.
AI SEO is the ongoing work of making an agent understandable when a buyer asks a specific hiring question. The study gives a clear reason to take that work seriously: AI names people often, and a buyer needs enough real detail to decide what to do next.
Want this done for you? Recasi Local
Recasi Local is a done-for-you AI search marketing service for established real estate agents and teams. Standard includes an AI visibility audit, cleaned-up profiles and reviews, articles written for the market, quarterly visibility tracking, and a client portal.
We partner with one agent or team per market.
Frequently asked questions
What does 88.8% mean in this study?
It means that at least one specific agent or team appeared in 88.8% of the 1,000 answers we reviewed. It captures how often direct hiring questions brought back a name in this study.
What does average full-list overlap tell me?
At least two tools shared one name in 82.8% of city-and-question combinations. The average full-list figure measures how much the complete lists had in common, so it is useful for seeing how much a buyer's shortlist can move from one tool to another.
Which sources showed up most often?
Agent or team websites appeared in 71.6% of answers, followed by Zillow at 56.9% and FastExpert at 43.9%. These are the source appearances we counted in this study.
Should I track one AI tool or several?
Use several when you can. The study found different naming rates and source patterns across the four tools, so a small set gives you a fuller view of the questions buyers may ask.