How to Measure Real Estate AI Visibility

Sooner or later an agent forwards you a screenshot. They asked ChatGPT for the best realtor in town, and the answer named three teams from across the street and none of yours.

The screenshot can't tell you what you need to know: whether that's every answer or one of them, whether it happens in your other markets, and why those names.

It's worth knowing, because most sellers don't shop around. In Zillow's 2026 report on home sellers, 84% of sellers use AI, about two in three of them while selling, and 67% of sellers who used an agent contacted only one.

Zillow doesn't say how many of those sellers asked an assistant for an agent. For one who does, and then contacts one agent, the names in that answer may be the whole shortlist.

By hand, the check is one person typing one question on one laptop on one day.

After the asking, the work is extraction: turning each saved answer into rows (who was named, and with which page). An AI assistant does that now, and one person checks its rows.

I ran it on Charlotte, North Carolina, on 15 October 2026: six questions a seller or buyer would type, each asked twice in ChatGPT and twice in Google AI Mode. Every firm in the tables below is a name a real answer gave, reported as it appeared.

The log has three columns: was your firm named, which page came with the name, and is that page right about you. Each market then gets one row on a monthly scorecard, owned by the person who keeps that market's agent profiles.

You'll build a market scorecard, then see how we handle the monthly reading at Qvery, with a person approving the AI agent's drafts.

This measures the answers to hire-an-agent questions, one market at a time. It doesn't attribute leads, and it doesn't tell you why an engine picked one firm over another.

Write Six Questions a Seller Would Type, With the City in Every One

Use your own inquiries, not a list of popular searches. The first message a seller sends through your website, the line a buyer opens with on the phone, the situation your agents hear at listing appointments: an inherited house, a job move, a first purchase.

Six per market is enough to start: two seller questions, two buyer questions, two situations your firm handles well. These are Charlotte's:

• Who is the best real estate agent to sell my house in Charlotte NC?
• Which real estate brokerage should I list my home with in Charlotte?
• Best realtor in Charlotte for first-time home buyers
• I'm relocating to Charlotte for work. Which real estate agent should I use?
• Best real estate team in Charlotte for selling an inherited house
• Top real estate brokerages in Charlotte NC

Include the city. OpenAI's ChatGPT search help page says an approximate location from your IP address may affect local results.

Leave your firm's name out. A question that names your firm asks about its reputation, and it gets its own answers from its own sources. Track it in a log of its own; mixed in, it makes your named rate look better than any seller will find it.

OpenAI's Temporary Chat help page describes the Unpersonalized setting. Ask in a signed-out session or a temporary chat set to Unpersonalized; if ChatGPT doesn't search, select search from View all tools.

In Google AI Mode, go to google.com/ai signed out of Google. Google's AI Mode help page says saved search history can personalize answers.

Ask twice, every question, on each engine. In our research on ChatGPT's September answers to agent-hiring questions in US cities, each asked three times, more than half the firms in its block of local business cards turned up on only one of the three asks.

The r/realtors discussion below separates seeing a referral from knowing which questions bring your firm into answers:

Save each whole answer and its source titles and links in the Answers tab. Give it an ID made from market, engine, ask, and question number, as shown below.

When the asking is done, the tab holds 24 rows for six questions, one per question, engine and ask.

We saved Charlotte's answers through a collection service rather than by hand, asking from the US in English with search on, and keeping each answer whole with its cards, links and source titles.

Its Google AI Mode answers held two to five source links each and ChatGPT's three to eleven, so everything below about pages reads one engine at a time.

Column One: Did the Answer Name Your Firm, or One of Your Agents?

The first number on the scorecard is how many answers named you. It needs every name in every answer in one table you can sort, and building that table is the assistant's part.

We first asked for names and sources as a table, using six Charlotte answers at a time. The output couldn't be counted:

• Two tables per answer, 48 for 24 answers, and the layout changed between batches, so nothing pastes into one sheet.
• Names in one table and sources in the other: no row said which page came with which name.
• A person and their team on separate rows, so a count of names counts an agent and the agent's own firm as two firms.

The prompt below keeps one row per firm, its pages beside it, and the same columns in each batch.

You are filling a log of AI answers about real estate agents. Below are saved answers. Each one starts with its ID, the engine, the question, the answer as shown, and the list of sources the answer showed.

For every answer, write one row for each real estate agent, team, brokerage or other business that the answer puts forward as someone to hire, interview or contact. Columns separated by tabs, in this order:

1. Answer ID
2. Order: 1 for the first name in the answer, 2 for the next, and so on
3. As written: the name exactly as the answer writes it
4. Kind: agent, team, brokerage, or other (for example a cash buyer or an estate sale company)
5. Agent: the person's name, if one is named; several separated by "; "
6. Team: the team or group name, if one is named
7. Brokerage: the brokerage the answer ties the name to, if any
8. Pages: every web address linked inside or directly under this name's entry, plus any source in the answer's source list whose title is this name; several separated by "; ". Write "business listing" when the name appears as a business card (a star rating, a review count, an address or a phone number) or its only page is a Google business listing. Write "none" when nothing is shown for it.
9. Own site: "yes" if one of the pages is the named agent's, team's or brokerage's own website (an office page on its franchise's site counts; the franchise's national homepage, such as kw.com or remax.com, does not); "no" if none is; leave blank for "business listing" and "none"

Rules:
- One row per firm. If one entry names two firms, write two rows.
- The websites the answer used as sources are not names (for example Zillow, Realtor.com, Redfin, HomeLight, RealTrends, U.S. News, Reddit, Yelp, or a "best agents" list), unless the answer recommends hiring that company itself.
- If an answer names no one, write a single row: the Answer ID, then "0", then "no names".
- Copy names as written. Do not merge spellings of the same firm across answers.
- When you are not sure about a cell, write "unclear". Do not guess.
- Output only the rows. No header, no notes.

One firm means one row. An entry that names an agent, her team and her brokerage is a single row, with the three in their own columns; an entry that names two separate firms is two rows.

Paste the prompt, then six saved answers with their IDs, engines, questions, and sources. Copy the output into the Names tab; these are three of its nine columns:

Then read every row beside its answer. That part stays with a person.

On the Charlotte run we read all 124 rows the first version of this prompt returned and changed 15, one of them by splitting a two-firm row in two, so the checked log holds 125 names.

None named an extra firm, and none listed Zillow or a ranking site as a firm. Most of the changes were about pages:

Run again on the same 24 answers, it got 12 of the 15 corrected rows right on its own. That checks the new lines; it doesn't predict how the prompt does on your market.

It still missed two links inside sentences and an unlinked ranking, and misclassified Compass's own site as a franchise homepage. A person must check every row.

Add one column yourself: Firm, the single name you'll count under. Stone Realty Group appeared three ways in the Charlotte answers, under an agent's name, under its own and under its team's, and all three count as one firm.

Then count a second way, with your teams under your brokerage, because the answers often name a team rather than its brokerage. In Charlotte, Keller Williams was in 13 of the 24 answers once its teams were counted with it, and in 4 under its own name.

Your named rate in a market is the share of answers that named your firm, one of your teams or one of your agents. Here is the top of Charlotte's table, counted at the level the answers named each firm:

Column Two: Which Page or Listing Came With Each Name?

The page that came with a name is the part your team can work on, and in a city those pages are spread across many sites. Our research on agent-hiring questions found the answers citing local firms' own sites more often than the portals:

The local layer was a different set of firms in every city: the most frequent single local site appeared in 4.27% of answers that cited anything.

Your firm can also be named without its site being cited. Of the firms ChatGPT put in its business-card block in September, 10.47% had their own website among the sources of the answer that named them.

Charlotte shows the same split, read one engine at a time. All 51 of ChatGPT's names came with at least one page, and the firm's own site was only one of the kinds:

Google AI Mode's saved answers carried fewer links, so read its count on its own: 26 of its 74 names came with a page, and 16 of those 26 were the firm's Google business listing.

When AI Mode pointed somewhere for a firm in this run, it was usually that listing, which makes the listing the first page to check.

Count each roster firm's state per answer: the Names tab supplies the first three states below, the Sources column the fourth, and your roster the fifth. These are what the answer displayed, not proof of what the engine used.

Stone Realty Group's Google listing sat in the sources of three Google AI Mode answers that recommended other firms. A local team's post ranking Charlotte's top five listing agents was cited in a ChatGPT answer that named three firms, none of them its author.

Column Three: Is That Page Right About You?

The pages in column two are the ones the answers show when they describe you, and an answer can repeat a page.

In one Charlotte answer about an inherited house, ChatGPT recommended an estate-sale specialist and then told the seller to ask about her Charlotte experience, because some of her published testimonials concern New Jersey properties. The warning came from her own page.

So the third column checks every page that carried one of your names against your roster. These are the fields that drift, and who fixes each:

For your own pages, paste the text and roster row into the assistant. Ask it to quote each mismatched fact and draft a correction that changes only that fact.

Have a person open outside profiles beside the roster. Zillow's terms bar automated queries that obtain information from its services; Realtor.com's terms bar automated collection without written permission.

What a portal profile says about an agent comes from your own records, so the fix is a records job inside your firm, covered in our piece on what your brokerage controls on the portal pages AI answers cite.

A person makes the fixes. Let the assistant draft the mismatches and the corrected wording. Keep these with a person: anything published under an agent's name, any reply to a review, any change to a portal profile or a Google listing, and anything that costs money.

Check that it held a month later: the same page, read again, shows no mismatch, and the answers that come with it repeat the corrected facts.

Give Each Market an Owner and One Row in the Monthly Review

So each market gets what a territory gets: a fixed question list, one number a month and one owner. The owner is whoever keeps that market's agent profiles, because column three ends on their desk anyway.

Read the scorecard at the market's monthly listings and closings meeting. Here is the arithmetic for Charlotte's most-named firm:

Read the changes with the September repeat asks in mind, and take this as an operating rule, not a measured threshold. A firm that shows on one ask out of four is weather. A firm named on both asks on an engine, two months running, is a pattern in your market.

Move the monthly work on patterns. Fix a wrong fact on a page the day you find it, however often that page shows up.

Four asks per question a month tells weather from a pattern, but slowly, over months. To see the pattern sooner, the same questions have to run more often than a person will ask them.

Have an AI Agent Build Each Market's Log Once the Month's Answers Are In

Time the first month's asking, pasting, and checking per market. The AI agent takes over the repeated reading; a person keeps the check.

The AI agent doesn't do the asking. A person does it in a clean window, or Qvery's GEO Agent does it every day. The AI agent starts once a market's answers for the month are saved, and it reads four things, all yours:

It runs once per market, the morning after that market's Answers tab is complete, and applies five rules:

1. 01

   Write the log

   One row per name, every page under it, by the log prompt's rules, into the Names tab.

2. 02

   Roll each name up to your roster

   Agent to team, team to brokerage. A name that half-matches, such as an agent who left or a misspelling, goes to a person as a question, never a guess.

3. 03

   Put every name in its state

   The five states above, so the scorecard counts the same way every month.

4. 04

   Read your own pages

   Every page of yours that came with a name, against the roster row, with a drafted correction for each mismatch.

5. 05

   List the pages it won't open

   Portal profiles, Google listings and rankings that carried your names, each with its roster row beside it, for a person to open.

It returns the Names tab, scorecard, drafted corrections, and outside pages for a person to open.

Judge it on two numbers you can compare month to month: the minutes a person spends on the check, and how many corrections are still open a week later. The month you built by hand is what you compare against.

Not all of it needs an AI agent. Counting rows by state and working out a named rate per market is a spreadsheet formula once the Firm column is filled.

Before building it, put every agent's team, office, brokerage, market, and profile links in one roster sheet, with one person responsible for it.

How Qvery Handles Your Market Scorecards

Our GEO Agent follows your brokerage into buyer and seller searches. It creates questions using your brand alongside your competitors and industry. For your selected countries, it asks Google AI Mode and ChatGPT those questions daily, keeping the answers in full along with all citations.

Its topic and country views show visibility alongside competing firms, differences in sentiment, cited sources, and share of voice. We examine those records together and correct the pages that misdescribe your brokerage.

We learn your profile keeper's monthly routine before building a custom AI agent with connections into the software your offices use. When the answers arrive, it prepares the log and proposed corrections on autopilot. Your profile keeper approves the work.

Within two weeks, your profile keeper has the first roster-check version running, plus training to operate it. Our weekly count covers hours saved preparing the scorecard and errors avoided when matching names, affiliations, and page facts.

Bring the sheet you used for your first market so we can follow the work from answer to correction.

Assign the Missing Names to Your Profile Keeper

When the first log is checked, sort the Names tab by your own agents. You'll see which of your people the answers already know, which page came with each of them, and which agents who close every month aren't in a single answer.

That last list goes to the person who keeps the profiles, with a date beside each name. Next month's log shows whether any of them is named.