Ed 
I can decide whether an Austin listing is worth my client’s time in about 5 minutes, and most of that is me typing four or five plain-English questions into Claude. Price history, days on market, the real comps, the school zoning, the obvious red flags. Five minutes, real MLS data, before anybody loses a Saturday driving across town to a house that was never going to work. Sounds fast right. It is, and the reason it works is not the AI being clever, it’s that the AI is wired into the live Austin MLS instead of guessing.
I’m Ed Neuhaus. I’m a broker here, I’ve been selling Austin homes for 19 years, and I run a real estate connector that lets Claude pull from the same Austin MLS my team uses every day. Not Zillow. Not a Zestimate. The actual closed-sale data. (If you want the nuts and bolts of how that connection works, I walked through it in how to search Austin homes with Claude using real MLS data.) So when I say “vet a listing in 5 minutes,” I’m not talking about asking a chatbot for its opinion. I’m talking about running real numbers fast.
Let me walk you through exactly what I do, in the order I do it. Steal the whole workflow if you want. None of it is that hard.
First Question: What Has This House Actually Done?
A listing photo is a sales pitch. The price history is the truth. So the very first thing I ask Claude is the boring stuff that tells me the most.
“Pull the full listing and price history for 1234 Example Lane, Austin, TX 78704. When did it first list, at what price, any price drops and when, current days on market, and has it gone pending and come back?”
Thirty seconds later I know the story. And the story is usually one of three.
Story one, fresh on market, priced sensibly, no drops. Fine. We keep going. Story two, listed 60 days ago, already dropped twice, still sitting. That’s the market talking, and what it’s saying is the house was priced on hope. Story three, and this is the one I watch for, it went pending, then came back to active. That means a deal fell apart. Maybe financing, maybe the buyer got cold feet, or maybe (and this is the one that matters) the inspection found something and the buyer ran. I don’t know which yet, but now I know to ask.
The reason I lead with this and not the pretty stuff is simple. Days on market and price drops are the cheapest red flags you’ll ever find. They cost you one question. A house that’s been re-listed under a couple of different MLS numbers to reset its days-on-market counter is trying to look fresh, and the MLS history sees right through it. You just have to ask.
Second Question: Is the Price Anywhere Near Real?
Now the comps. This is where the live MLS data earns its keep, and it’s the single biggest difference between vetting a house with real data and vetting it with a robot’s guess. I covered the deep version of this in how to get real Austin comps from Claude in 30 seconds, but here’s the version I run when I’m vetting one specific listing.
“Pull closed comps for this property. Same neighborhood, within half a mile, last 90 days, within 15% of its square footage, same bed count. Show close price, original list price, days on market, and seller concessions. Then tell me where this listing’s asking price falls against those closings.”
What I’m looking for is the gap between what this seller is asking and what real buyers actually paid down the street last month. Not the asking prices of other listings, those are aspirational too. The closed prices. The keyword is always closed.
If the comps come back and the house is priced right at the median close per square foot, good, the seller is being honest. If it’s asking 12% above every closing in the last 90 days, the seller is either fishing or got bad advice, and now my client and I get to decide whether it’s worth writing an offer that’s going to feel like a slap to that seller. Sometimes it is. Sometimes the right move is to wait three weeks for the price drop that the price history tells me is probably coming.
This is the part generic AI gets dangerously wrong. Ask ChatGPT with no MLS connection for comps on an Austin house and it will hand you confident, plausible, made-up numbers. I wrote a whole piece on why AI doesn’t know what just sold in your neighborhood because this exact mistake costs people real money. A guess that’s high by 8% on a $700,000 house is a $56,000 guess. The connector is the difference between asking the cab driver and asking a tourist.
Third Question: Where Are the Kids Going to School?
In Austin this is not a side note, it’s frequently the whole decision. Two houses on the same road can sit in two different elementary zones, and one of those zones can be worth six figures of resale value. So I ask.
“What schools is this address zoned for, elementary, middle, and high? Which district?”
I want to flag something honestly here, because overclaiming is how you lose trust. School attendance zones change. Districts redraw boundaries, and the MLS school field is entered by the listing agent, who is human and sometimes wrong. So I treat the school answer as a strong starting point, not gospel. If a family is buying specifically for a Lakeway or Eanes zoning, I confirm the boundary against the district’s own map before anybody falls in love. The AI gets me most of the way there in 10 seconds. The last stretch is me doing my job. That’s the deal with all of this, the tool does the fast part, I do the part that protects you.
Fourth Question: What’s the Neighborhood Actually Like Right Now?
This is the texture question. A single listing tells you about one house. The pocket around it tells you whether you’re buying into a street that’s appreciating or one that’s quietly stalling.
“For this zip code and this subdivision, what’s the median close price, median days on market, and list-to-close ratio over the last 90 days versus the 90 days before that? Is it speeding up or slowing down?”
A list-to-close ratio sitting at 98% with falling days on market means buyers are showing up and paying close to ask. A ratio at 94% with days on market climbing means sellers are negotiating, which is good news if you’re my buyer. I can run this for Bee Cave, a single zip in 78704, or one subdivision, and the answer takes about as long as it took you to read this paragraph. If you want to see the side-by-side version of this across whole towns, I did exactly that in comparing Westlake, Lakeway, and Bee Cave with Claude.
Fifth Question: What Doesn’t Add Up?
The last thing I do is the most human, and it’s the one I can’t fully outsource. I ask Claude to lay everything I just learned next to each other and tell me what looks off.
“Summarize everything: price vs comps, days on market, price drops, the pending-and-back history, and the neighborhood trend. Then list anything that seems inconsistent or worth a second look.”
Sometimes nothing’s off and we go see it. But sometimes the pieces don’t fit, and that’s the whole point of doing this before we drive. A house priced 10% over comps, that came back from pending after 30 days, in a neighborhood where days on market is climbing? That’s not a house, that’s a question. The question is “what did the last buyer find out that we haven’t yet.” Maybe it’s foundation. Maybe it’s a neighbor. Maybe it’s nothing and the buyer just got divorced. But now we walk in knowing to ask, instead of walking in starry-eyed.
Daniel Kahneman’s whole thing is that we decide with the first story we’re told and then defend it. A pretty listing photo is a story. Five minutes of real data, before the showing, is how you keep that photo from doing your thinking for you. That’s really all this workflow is. A way to think clearly before the emotional part starts.
Why This Beats the Old Way
For 19 years the honest answer to “is this house worth seeing” was “let me pull it up, give me a day.” I’d log into the MLS, the interface from 2008, click eight times, build a comp report, eyeball the history, and get back to you tomorrow. By tomorrow the good ones were under contract.
The connector didn’t make me smarter. It made me faster at the exact thing I was already doing. The judgment is the same judgment I’ve had for two decades. The data is the same MLS data I’ve always used. What changed is that pulling it went from a day to five minutes, which means I can vet a stack of listings in the time it used to take to vet one, and my clients only spend their Saturdays on houses that survived the screen. (For why I run this on Claude specifically and not ChatGPT, that’s a separate post worth reading.)
I’m not going to oversell it. The AI doesn’t tour the house, doesn’t smell the crawl space, doesn’t notice that the “updated kitchen” is updated everywhere except where it matters. That’s still me. But for the question “should we even bother,” it’s the best five minutes in the business.
Frequently Asked Questions
Want This Run on a Real Listing?
If you’re eyeing a specific Austin house and you want it vetted before you spend a weekend on it, that’s honestly my favorite thing to do. Send me the address and I’ll run the real numbers, comps, history, the works, and tell you straight whether it’s worth your time. No pitch attached. Reach out through the contact page and lets take a look together.
Get an Austin Listing Vetted: Contact Neuhaus Realty Group | (512) 366-3270
