How AI Chooses Its Sources in the Real Estate Development Industry (Q2 2026)

Highlights

• The dataset: This study is an analysis of over 180,000 AI source citations using prompts specific to the real estate development industry. It analyzes AI responses from four AI models run in 11 countries over the course of Q2 2026.
• Yelp fell from #1 out of the top 25: Google.com climbed to #2 in the same window.
• 21% of Grok's citations are from user-generated domains: Yelp, Reddit, and social profiles, 2x the share of any other model.
• 68% local-domain sourcing in Dutch: The most localized market.
• 91% of top sources differ between any two models: Any two of the four share only about 9% of their top-20 cited domains.
• 72% of Microsoft Copilot's citations are from commercial domains: Developer directories and first-party project sites, making Microsoft Copilot the most commercial model.

Which sources should you target to get cited as a real estate development brand?

Each model leans on its own distinctive set of top sources.

How have AI source rankings changed over time in the real estate development industry?

We split the ~110-day window at its midpoint (early May 2026) and compared each top domain's within-half rank across the two halves. We read movement as relative rank shifts within each half, not absolute declines.

The second-half mix leaned relatively more on Google's own surfaces, reference sites, and first-party developer domains, and less on third-party review and directory aggregators. Yelp, f6s.com, and clutch.co all vanished from the top 25; Reddit climbed to #1, Wikipedia entered the top 25, and first-party developer sites like blog.neivor.com and marignan-immobilier.com rose.

What type of content do AI models cite for real estate development?

Commercial content, developer sites, listing platforms, service directories, is the backbone of every model's citations, but the dependence varies sharply. We classified every cited domain into content categories.

The headline difference is Grok. One in five of its citations points to user-generated content, Yelp reviews, Reddit threads, Instagram and Facebook profiles, versus fewer than one in ten for every other model. Microsoft Copilot sits at the opposite pole: it is the most commercial model, sending nearly three of every four citations to developer and directory sites and almost none to reviews. ChatGPT leans hardest on reference and editorial content, Wikipedia and newspapers, at a combined ~17%, more than any other model. For most models a placement in a business journal moves the needle far less than a strong developer profile on a directory.

Do AI models cite local-language content for real estate development?

Using country-code top-level domains as a proxy for local-language sourcing, 47% of citations in non-English prompts sit on a local-country domain, an almost even split with global, mostly English-language .com sources. English prompts are excluded, as English content overwhelmingly lives on generic domains.

The Dutch, Italian, and German markets are heavily localized, two of three Dutch citations stay on .nl domains. Spanish is by far the most globalized: only one in three citations carries a local country code, partly because Spanish-language content spans three markets (Argentina, Spain, Mexico) and many regional .com properties this measure cannot credit. English-anchored content remains a viable path into every non-English market.

Which AI model relies most on local sources for real estate development?

Localization behavior splits by model as cleanly as content type does. ChatGPT and Microsoft Copilot rely on local sources most consistently, while Grok defaults to global .com properties even in non-English markets.

ChatGPT is the most reliably local where it has volume, 81% local in both German and Dutch. Grok is the most globalized, sitting lowest in nearly every market (as low as 30% local in Spanish). Google AI Overview returned no French or Dutch citations and only 8 German citations in this window, far too thin to read as a stable rate, so those cells are omitted. A German developer chasing ChatGPT visibility needs strong .de content, while the same brand chasing Grok benefits more from a strong global .com presence.

Should real estate development companies optimize for each AI model separately?

Almost no overlap, so emphatically yes. We compared the 20 most-cited domains for each model and measured how much every pair shares. The average across all six model pairs is just 9.4%, the typical pair agrees on fewer than two of every twenty top sources.

No pair clears a quarter, and not a single domain appears across all four top-20 lists. geoln.com, a global new-development listing portal, and goodfirms.co, a B2B service-provider directory, are the only domains cited widely enough to appear in three of the four models' top-20 lists. A developer visible on one model can be absent from roughly 91% of the sources another model cites.

How many sources does each AI model cite per answer?

Citation breadth is where the models diverge most starkly. Grok cites 29.2 sources per answer on average, about 4.3 times ChatGPT's 6.8. Google AI Overview (9.4) and Microsoft Copilot (8.0) sit close to ChatGPT.

A developer has roughly four times as many chances to be cited inside a single Grok answer as inside a ChatGPT answer, but those Grok slots are diluted across far more competing sources, so each individual citation carries less weight.

Context

This analysis draws from Temso's AI visibility monitoring platform, which tracks how brands appear in AI model responses across ChatGPT, Microsoft Copilot, Grok, and Google AI Overview. The dataset covers real-estate-development prompts, questions a buyer, investor, or partner might ask about the best or leading real estate development companies, across 11 country markets and six prompt languages (English, Spanish, German, Italian, Dutch, French) between 10 March and 29 June 2026.

It measures what AI models cite, not why they pick a source or the quality of the underlying page. These findings reflect citation behavior for real-estate-development prompts in the monitored markets and may not generalize to all developers, prompt types, or other verticals.

Methodology

How we measured this

We tracked four AI models responding to real-estate-development prompts across 11 markets and six languages over roughly 110 days. Each response was parsed to extract its source citations: the URLs, domains, and metadata referenced. Domain categories (commercial, user-generated, editorial, reference, institutional) were assigned from a global domain registry. Cross-model overlap was measured on each model pair's top-20 most-cited domains. Temporal analysis split the observation period at its midpoint and compared domain rankings in each half.

The analysis covers over 180,000 cited sources from 17,344 AI responses. Sample sizes exceeded recommended thresholds for every reported finding. Localization is measured by a country-code-domain proxy (the source records carry no detected content language), so those figures are directional and exclude English-language prompts, which sit overwhelmingly on generic domains. Model coverage varied across the observation window, so temporal movements are reported as relative ranks within each half.

Frequently asked questions

Do different AI models cite the same sources for real estate development?

No, barely at all. Any two models share only about 9% of their top-20 cited domains on average, so roughly 91% of one model's top sources are missing from another's. No single domain appears in all four models' top-20 lists.

Which AI model cites the most sources per answer?

Grok, by a wide margin, about 56 sources per answer versus roughly 7 for ChatGPT, a 7.5x gap. Google AI Overview (9.3) and Microsoft Copilot (7.8) sit close to ChatGPT.

What type of content do AI models cite for real estate development?

Overwhelmingly commercial content, developer sites, listing platforms, and B2B directories, at 54–72% of citations depending on the model. Editorial coverage is minor (4–12%). The exception is Grok, where about a fifth of citations are user-generated content like Yelp, Reddit, and social profiles.

Does Grok really rely on reviews and social media?

Yes, dramatically more than its peers. 20.9% of Grok's citations are user-generated content, roughly 2.6 times the share of any other model. For Grok visibility, online reviews and social profiles are a first-order signal, not an afterthought.

Do AI models cite local-language content outside English markets?

About half the time. Across non-English prompts, 47% of citations sit on a local-country domain. But it ranges from 68% in the Dutch market down to 33% in Spanish, and English-language .com content remains a path into every market.

Which model is best for reaching non-English real estate development audiences?

ChatGPT and Microsoft Copilot rely on local sources most consistently, ChatGPT reaches 81% local in both German and Dutch. Grok is the most likely to default to global English sources in non-English markets, as low as 30% local in Spanish.

Did the most-cited sources change over the period?

Yes. Review and directory sites like Yelp, f6s, and Clutch fell out of the top ranks while Google's own properties, Reddit, and first-party developer sites rose. We report relative rank shifts rather than absolute counts.

What should a real estate development company do with this?

Treat AI visibility as four separate channels, not one. Maintain a strong commercial presence (your own site plus key directories like geoln.com and GoodFirms) for all models; cultivate reviews and social profiles specifically for Grok; ensure a solid Wikipedia and press footprint for ChatGPT; and invest in local-domain content for non-English markets, especially for ChatGPT and Microsoft Copilot.