How the Local Logic MCP Put Us in the Top 0.3% of Sources AI Engines Cite

On the neighborhood questions we monitor, we went from the 36th to the 12th most-cited source in two months, out of 4,624 domains. Here’s how we did it, and how you can too.

Summary

Challenge: Google now answers many neighborhood questions before it shows a single result, and ChatGPT, Gemini and Perplexity answer them with no results page at all. AI engines also decide whom to trust before they search. In research reported by Lily Ray, Suganthan Mohanadasan found that in 21 of 27 cases, ChatGPT’s first search already included brand names the user never mentioned. Ranking well is no longer enough. A real estate site has to be a source AI engines already consider reliable.

Solution: We published one page per specific neighborhood question under locallogic.co/ask. Every score, ranking and metric on those pages comes from Local Logic data through the Local Logic MCP. The build then uses public signals, like Wikipedia coverage and social following, to choose which parks, landmarks, restaurants and cafes to name in the text. Every page is fact-checked against our data and reviewed by a named person.

Results, on the location prompts we monitor:

• Became the 12th most-cited source in two months, out of 4,624 cited domains. That’s the top 0.3%, ahead of Trulia, Instagram and Apartment List.
• AI engines cited Local Logic 2.1 times as often in September as in August, and ChatGPT’s citation rate grew 18-fold from July to September.
• Boise went from zero citations to 7.6% of AI answers in its first month with /ask pages.

Key takeaways

• Getting cited by AI depends on authority, and authority depends on being a reliable source. You become reliable by publishing facts that come from good data and can be checked.
• Traditional SEO is no longer enough on its own. When Google shows an AI summary, people click a result about half as often.
• Brokerage and agent sites already earn 38% of AI citations on neighborhood questions, and each major portal earns less than 2%. Brokerages can win this.
• Pages built on grounded data for one specific question were cited about 90 times as often as our other pages.

Google answers before it ranks. Traditional SEO isn’t enough anymore.

Search Google for the best Atlanta neighborhoods for a short commute and a walkable weekend, and often the first thing you see is an answer, not a list of links. Gemini writes an AI Overview above the results and cites a few sources inside it. Ask ChatGPT or Perplexity the same question, and there’s no results page at all.

That changes what winning means. In a Pew Research Center study of real browsing behavior, Google users clicked a result in 8% of searches that showed an AI summary, compared with 15% of searches that didn’t. SparkToro found that 68% of US Google searches ended without a click in early 2026, and Google says AI Mode now has more than 1 billion monthly users.

A page can rank on page one and still be invisible if the AI answer above it cites someone else. In a growing share of searches, the sites a home consumer sees are the ones cited in the answer.

The good news for brokerages is that the trusted source usually isn’t a portal. On the location questions we monitor, brokerage and agent sites earn 38% of all AI citations, spread across more than 2,500 sites. Zillow, Redfin and Realtor.com each earn less than 2%.

Why AI engines cite some pages and skip others

Answer engines are built to avoid being wrong in public. When they pick sources, they favor the same signals Google’s own guidance on helpful, reliable content calls E-E-A-T: experience, expertise, authoritativeness and trust.

Most neighborhood pages on real estate sites score poorly on all four. They’re written for a city rather than a question. They describe places as “vibrant”, “charming” or “convenient”, which can’t be verified. They rarely say where their facts come from or who checked them. An engine has no reason to trust that page over another, so it cites a source it can trust.

So our thesis for /ask was simple. To get cited, you need authority. To have authority, you need to be a reliable source. And the fastest way to become a reliable source is to publish facts that come from good data and say so plainly.

How we built authority into every page

We launched /ask on July 30, 2026 in Miami, Atlanta and Chicago, with one guide per specific question in each metro. We have since expanded to eight metros: Atlanta, Boise, Boston, Chicago, Denver, Miami, Philadelphia and Washington, DC. Here is how each part of E-E-A-T shows up on the page.

Experience: local judgment, reviewed by named people

Data tells you a neighborhood has around 150 restaurants. Locals tell you which ones people actually go to. To bridge that gap, the build checks public signals: Wikipedia coverage for parks and landmarks, and social following for restaurants, bars and cafes. Those signals decide which places a page names and where it adds context. They never change a ranking or a number.

Every page is reviewed by a named person before it goes live. We use neighborhood names people actually say, like LoHi and Uptown, in the visible text and in structured data. Local testers in Atlanta said the descriptions were more accurate than they expected.

Expertise: one method, comparable everywhere

Local Logic calculates its scores the same way in every neighborhood, whether that’s Midtown Atlanta or a quiet street in Boise. That consistency lets you compare any neighborhood with any other, and it’s what makes the strongest claims on a neighborhood page possible.

Through the MCP, the build can rank all of a city’s neighborhoods on any score in a single call. In a few seconds, it can tell you that Midtown ranks third out of Atlanta’s 423 neighborhoods for nightlife. It can also show which neighborhoods clear the bar on both walkability and transit, and which ones fall short. Each /ask guide starts from that kind of cut: it takes every neighborhood in the metro, keeps the ones that meet the audience’s must-haves, and ranks what’s left.

That matters because people ask AI engines comparison questions. “Best”, “quietest” and “most walkable” can’t be answered from memory or from one neighborhood’s profile. The engine needs a source that has measured every option the same way. Every prompt in “Winning specific questions” below is a comparison question, and we think that’s no coincidence.

Every /ask page also has its own “About the data” section and links to how Local Logic data is built and how location scores are calculated. Each pick goes through a research pass whose only job is to find where our own data is wrong. Picks are compared against established lists, and any outlier has to be defensible.

Producing all this took far less effort than you’d expect. In Denver alone, the pipeline evaluated 452 neighborhoods and checked 15,717 points of interest across about 7,000 MCP and API calls, in roughly 11 hours of mostly hands-off run time.

Authoritativeness: data the industry already relies on

The facts on /ask come from the same data that powers neighborhood insights on more than 12,000 real estate websites and reaches 50 million home consumers every month. That data includes more than 100 billion data points from over 3,000 datasets, covering 250 million addresses across the US and Canada. It combines open data, proprietary models and partners such as ATTOM, HomeJunction and Transit App.

A page grounded in that data carries the data’s authority. A page written from an agent’s impressions can’t.

Trust: every number comes from data you can check

Each page is built end to end on the Local Logic MCP, which connects an AI model directly to Local Logic’s data. The model doesn’t describe a neighborhood from memory. It pulls scores, points of interest and profiles and writes from those.

The difference shows up on the page. A generic guide calls Midtown Atlanta “walkable, with great restaurants and easy transit.” The /ask page names the MARTA stations that serve it, scores its dining at 9 out of 10 and its nightlife at 9.4, and counts the restaurants and bars along the same stretch of Peachtree. An engine can check every one of those details.

We also measured accuracy directly. In our AI hallucination benchmark, a low-cost model grounded on the MCP scored 97.6% accuracy on neighborhood questions. A frontier model answering from memory scored 96.9%, at about seven times the cost per answer.

How we measured AI citations

We track 150 home-search prompts across 11 AI engines, including ChatGPT, Perplexity and Microsoft Copilot. The prompts cover 3 audiences across 8 metros. In September alone, that produced 17,997 AI responses and 70,489 citations across 4,624 cited domains. We cross-check against Google Search Console (AI Overviews and AI Mode impressions) and Bing Webmaster Tools’ AI performance report.

We chose the prompts ourselves, so every rank and rate below applies to the location prompts we monitor. Any brokerage can do the same. Start with the markets you want to win in, list the questions your buyers and sellers actually ask about those markets, and track who gets cited for them every month.

Results

From #36 to #12 in two months

Before /ask launched, Local Logic was the 36th most-cited source on our monitored prompts. After one month, we were 24th. After two months, we were 12th out of 4,624 cited domains, ahead of Trulia, Instagram and Apartment List.

Two metros, two starting points

Boise: from zero to cited in one month. In August, Local Logic had no pages about Boise at all, neither /ask guides nor blog posts, and no AI answers about Boise neighborhoods cited us. In September, its first month with /ask pages, 7.6% did.

Denver: /ask pages beat blog posts. Denver started from a different place. A few of our blog posts about Denver neighborhoods were already earning citations in 7.2% of AI answers in August. After we published the Denver /ask guides, that rose to 10.7% in September, a 49% increase. The biggest gains came on the questions the guides were written to answer. Citations on young-professional prompts nearly doubled, from 11% to 21%. On family prompts, they more than doubled, from 6% to 14%.

Together, the two metros show that publishing grounded, question-specific pages gets a site cited from scratch, and gets it cited more often than general blog content does.

Every major engine picked it up

A change to ChatGPT’s search behavior in August also contributed to its growth. Microsoft Copilot and Bing were already citing Local Logic for business questions about location data, and August was the first month they cited our /ask pages on home-search questions. In September, Bing’s AI citations included our data pages and the hallucination benchmark. When Philadelphia and Washington, DC launched, Google impressions on the existing /ask pages went up too.

Winning specific questions

We are now the #1 cited source on 6 of our top 10 prompts, up from 1.

Across all our monitored prompts, pages built on grounded data for one specific question were cited about 90 times as often as our other pages.

People follow the citations

Citations are the main result, and people are starting to click through. Visitors referred by AI engines grew 24% month over month. Google clicks to /ask pages grew 88%, on about four times as many total Google search impressions. Reddit ads pointing to /ask earned a 5.86% click-through rate, at least 7.3 times a typical Reddit ad, and those visitors engaged with the page 1.5 times as often as other visitors.

What we learned, so you don’t have to

Two months in, a few lessons shape how we’d build the next metro. They’re also the details that separate a page an engine cites from one it skips.

• Match how locals talk. Official neighborhood boundaries don’t always match the names people use. We now put the local names in the visible text and structured data from day one.
• Weigh what buyers weigh. Testers wanted affordability to count for more in the picks. The MCP includes typical home prices and market statistics, so the next round builds that in.
• Count citations first, then clicks. Clicks grow from a small base. In an answer-first world, citations are the leading indicator of who a home consumer sees, and they’re the right thing to measure first.
• Scope your claims. Our numbers apply to the prompts we chose and a short window. The rise coincided with launch, and Boise and Denver are our clearest evidence that publishing grounded pages made the difference. Being precise about this is part of being a source worth citing.

What you get with the Local Logic MCP

/ask runs on the same MCP our customers use to build their own AI-ready neighborhood content, assistants and search experiences. With it, your team gets:

• A verified location layer your AI model can query directly, instead of describing neighborhoods from memory.
• 18 proprietary location scores, points of interest, neighborhood profiles, schools, demographics and market statistics across the US and Canada.
• Scores calculated the same way everywhere, so your pages can rank and compare every neighborhood in a market.
• Higher accuracy at lower cost, as our grounding case study shows.
• Data built for compliance, with no crime data and no protected-class fields. Pair it with a Fair Housing review before you publish.
• Production speed that makes one page per question realistic for every market you serve.

Every brokerage, franchise and portal already has neighborhood pages. The question is whether an AI engine trusts them enough to cite them. Book a demo, and we’ll show you how the MCP grounds neighborhood content, how we measure citation rate, and what a proof of concept on your markets would look like.

Further reading: AI is confidently wrong about neighborhoods. We measured it, then fixed it. · How grounding reduces hallucinations at a lower cost · RISMedia: Local Logic launches MCP server