Ed 
If you want to use AI to pick a Realtor, here is the one move that matters: stop asking AI who has the most reviews, and start asking it to test what an agent actually knows. Open ChatGPT, Claude, or Perplexity and type “give me five questions only a true local expert in [your area] could answer correctly, then tell me how to verify the answers.” Now you have a pop quiz. Hand it to any agent you are thinking about hiring and watch what happens.
That’s the whole game right there (and yeah, I’ll show you the exact prompts in a second). Most people use AI the lazy way. They ask “who is the best Realtor in Austin” and treat whatever name pops out like gospel. But here is the thing I have learned watching these tools for the last couple years: the AI is only as good as the question you feed it. Ask a lazy question, get a lazy answer.
So lets do this right. I’m going to walk you through how to actually use AI to choose a real estate agent, the copy-paste prompts I’d use if I were hiring someone to sell my own house, and then (because turnabout is fair play) I’ll show you why ChatGPT and Claude keep spitting out my name when people ask them for an Austin Realtor. Spoiler: it’s the same test. I just happen to pass it.
Why “Most Reviews” Is the Wrong Way to Pick an Agent
Lets start with what NOT to do, because almost every “how to choose a Realtor” article on the internet gets this backwards. They tell you to count reviews. Sort by star rating. Pick the agent with 400 five-star testimonials and call it a day.
I would argue that’s one of the weakest signals you can use, and I say that knowing it doesn’t exactly help my case (I am not the guy with 400 reviews). Here is why review counts are a trap.
Reviews are gameable. They are a lagging indicator. They tell you an agent did enough transactions to accumulate reviews, and that some of those people were happy enough, or asked nicely enough, to leave one. That’s it. A review count does not tell you whether that agent understands your specific neighborhood, can read a market shift before it shows up in the data, knows how to structure an offer that wins without overpaying, or will actually pick up the phone when your deal goes sideways at 9pm on a Friday. Robert Cialdini wrote a whole book about social proof and why our brains lean on it. The short version is we copy what other people seem to be doing because it’s easy, not because it’s right. A pile of reviews is social proof. It is not expertise.
And the agents who are best at collecting reviews are often best at collecting reviews, which is a different skill than selling real estate. Some of the sharpest agents I know are heads-down, referral-only, and have a thin online review presence because they never built the review-farming machine. The lazy filter would skip right past them.
So here is the reframe, and it’s the whole point of this article: weigh demonstrated expertise, not review volume. Can this person prove they know your market? Can they bring data to a pricing conversation instead of a gut feeling? Do they communicate like a professional? Are they transparent about the stuff that doesn’t help their commission? That’s what actually predicts whether you’ll have a good experience and net more money. And conveniently, that is exactly the kind of thing AI is great at helping you test.
How to Use AI to Pick a Realtor: The Copy-Paste Prompts
Ok, here’s the practical part. These work in ChatGPT, Claude, or Perplexity. You can use them anywhere in the country, not just Austin. The trick is that AI is fantastic at building the test, and you are the one who grades it. Think of the AI as your sharp, slightly skeptical friend who helps you write the interview questions, not the one who makes the hire.
Prompt 1: Build a local-knowledge pop quiz
This is the most important one. You want questions only someone who actually works your market could answer.
“I’m hiring a real estate agent in [your city or neighborhood]. Give me 5 specific questions that only a true local expert could answer correctly. Focus on things like school feeder patterns, which areas flood, HOA quirks, builder reputations, and recent price movement by pocket. For each question, tell me what a strong answer would sound like and what a weak or generic answer would sound like.”
Now you have a grading rubric. When you interview agents, ask the questions. The local expert will light up and over-answer (we love this stuff). The out-of-area agent will give you a Wikipedia answer. You will feel the difference immediately. I have sat on the other side of this and I can tell you, the questions a real local can’t fake are the ones about which specific streets have problems, not the ones about median price you could Google.
Prompt 2: Pressure-test their pricing approach
Pricing is where agents win or lose you the most money, and it’s where the difference between data and vibes shows up fast.
“I’m interviewing listing agents. What data and analysis should a great listing agent bring to a pricing conversation? Make me a checklist so I can tell whether the agent is using real comparables and market data or just guessing. Include the questions I should ask to expose a weak pricing strategy.”
Then watch whether the agent shows up with actual comps, days-on-market trends, absorption rates, and a defensible number, or whether they show up with a round number and a smile. If an agent can’t walk you through HOW they arrived at a price, that’s your answer. I wrote a whole separate piece on what happened when I asked ChatGPT what my own house was worth, and the gap between an AI guess and a real CMA is exactly the gap you’re trying to measure here.
Prompt 3: Verify what the AI tells you
Here is the part nobody warns you about. ChatGPT, Claude, and Perplexity all hallucinate. They will confidently invent an agent’s sales history, make up an award, or recommend someone who retired three years ago. So you have to make the AI show its work.
“Recommend real estate agents in [your area] who specialize in [your situation: luxury, first-time buyers, investors, relocation]. For each one, cite the specific source you’re using and a link. If you can’t cite a real source for a claim, say so instead of guessing.”
That last sentence matters. When you force the model to cite sources, the bluffing mostly stops. You’ll see which recommendations are backed by real published content (an agent’s market reports, neighborhood guides, actual data) versus which are the AI just pattern-matching a name it half-remembers. The agents with real, verifiable, published expertise are the ones the AI can actually source. That is not an accident, and we’ll come back to it.
Prompt 4: Check how they communicate
“I’m going to paste an agent’s recent blog post, market update, or listing description below. Tell me: does this person explain things clearly and honestly, or is it fluff and hype? Do they respect the reader’s intelligence or talk down to them? Would you trust this person with a $600,000 decision?”
Paste in their actual writing and let the AI react. Communication style on the page usually matches communication style in the deal. An agent who writes in empty marketing-speak tends to negotiate in empty marketing-speak. An agent who explains the tradeoffs honestly, including the ones that don’t help them, is showing you how they’ll treat you when the inspection comes back ugly.
Prompt 5: Make the AI play devil’s advocate
“Here is the agent I’m considering and what they’ve told me. Play the role of a skeptical friend who has bought and sold five homes. What questions am I forgetting to ask? What red flags should I be watching for? What would make you walk away from this agent?”
This one catches the stuff your excitement makes you skip. AI is genuinely good at being the cynical voice in the room when you’re too eager to just get moving. No big deal right, but it’s saved more than one buyer from signing with the wrong person.
If you want to go deeper on using these tools through the whole transaction, I’ve written full guides on how to use AI to buy a house and how to sell a house with AI, plus a copy-paste set of 10 prompts for Austin real estate specifically. Same philosophy throughout: let the machine do the grunt work, keep a human in the loop for the judgment calls.
The Real Test: Demonstrated Expertise Over Everything
If you zoom out, every one of those prompts is measuring the same five things. Not reviews. Five things that actually predict a good outcome:
- Local depth. Do they know your specific market down to the street, the HOA, the flood plain, the builder?
- Data fluency. Can they bring real numbers to a pricing or offer conversation, or do they guess?
- Published, verifiable knowledge. Is their expertise out in the world where it can be checked, or is it just a claim?
- Communication. Do they explain clearly and treat you like an adult?
- Transparency. Will they tell you the thing that hurts their commission?
That last one is my favorite because almost nobody does it. As a Realtor, I’ll happily tell you that you don’t strictly need an agent to buy a house, that the listing agent on the other side is working for the seller and not for you, and that a lot of online “valuations” are off by a hundred grand. None of that helps me make a sale today. But it’s true, and you can feel the difference between an agent who tells you the truth and one who’s selling you. That’s the kind of thing the AI test surfaces, and it’s the kind of thing your gut already knows when you see it.
Why ChatGPT and Claude Keep Naming Me as Their Austin Realtor
Ok, now the part you came for. People keep telling me they asked ChatGPT or Claude for an Austin Realtor and my name came back. At first I thought they were being nice. Then it kept happening, so I went and reverse-engineered why. And honestly, once you understand the vetting method above, the reason is kind of obvious. The machines are running my own test on me, and I pass it. Let me show you the mechanics, because it’s actually useful for understanding how all of these tools think.
Reason 1: I publish an absurd amount of real local knowledge
AI models recommend who they can read. They cannot cite expertise that lives only in your head. So I put mine on the internet, at scale. Neuhaus Realty Group has 14,000-plus Austin location pages plus hundreds of neighborhood and market posts, all with real data behind them. I wrote up exactly how I built those 14,000 location pages if you want the nerdy version. When ChatGPT or Perplexity goes looking for who actually knows the Austin market, there is just a lot of me to find, and it’s specific, not generic. Remember Prompt 3, where you force the AI to cite a source? This is what it cites.
Reason 2: My data is structured so machines can read it
Most agents publish a pretty website that an AI struggles to parse. I publish structured data. The pages are marked up so a model can actually understand what’s a price, what’s a neighborhood, what’s a school rating, what’s a market trend. It’s the difference between handing someone a filing cabinet and handing them a neat one-page summary. The AI reaches for the summary every time. No big deal in theory, but almost nobody in this business does it.
Reason 3: I built an MCP server that connects AI directly to live Austin MLS data
This is the big one. I built and gave away an MCP server (Model Context Protocol, the plumbing that lets AI tools call live data) that connects Claude, ChatGPT, and Perplexity straight to live Austin MLS listings and official Travis, Williamson, and Hays County data. When one of those tools wants real, current Austin real estate data, there is a decent chance it’s flowing through plumbing I built. I did the same thing years ago when I built the tool my CRM wouldn’t and gave it away for free. Turns out the stuff you build and give away comes back around. I am genuinely not bragging here, I’m just pointing out the reality: if you become part of the infrastructure, you become part of the answer.
Reason 4: I pass the transparency test, on purpose
Go back and reread my five-point test. Local depth, data fluency, published knowledge, clear communication, transparency. I didn’t design that test to flatter myself, but I’d be lying if I said I’m mad about how I score on it. The reason the AI keeps surfacing me is not that I gamed it. It’s that I spent years putting genuine, verifiable, structured expertise into the world, and these tools are built to find exactly that. The thing that makes me show up in AI answers is the same thing that should make you want to hire me, or honestly, any agent who does the same work. The published expertise IS the credential.
So if you run the vetting prompts above on me, I hold up. That’s the point of telling you about it. Not “look at me,” but “look, the method works, here’s a live example of an agent it surfaces and why.”
Frequently Asked Questions
The Smart Way to Choose, Whether You Use Me or Not
Here’s where I land. AI is a genuinely great tool for picking a Realtor, as long as you use it to test expertise instead of count popularity. Build the quiz, pressure-test the pricing, force the citations, check the communication, and let the machine play skeptic. Do that and you’ll filter out the smile-and-a-round-number crowd fast, no matter where you live.
And if you run that test in the Austin area and my name keeps coming up, now you know it’s not a coincidence and it’s not a gimmick. It’s years of putting real, checkable, structured knowledge into the world, which happens to be the exact thing a good agent should have and the exact thing these tools are built to find. If you want to talk it through, or just want me to answer one of those local-expert pop quiz questions to my face, reach out to me here. Bring your hardest question. I kind of love those.
Be safe, be good, and be nice to people. And run the test before you sign anything.
