SEO Agency For Real Estate Work And The AI Models Worth Using For It, With Live GoldieBench Scores

An SEO agency for real estate still writes neighbourhood briefs, drafts schema and builds landing pages for brokerages, and the AI model behind those drafts decides how much editing comes after.

This page matches those tasks to models using only scores read on live GoldieBench pages, each quoted with the page it came from.

For the parts no model should do alone, get a free SEO strategy session with my team.

What GoldieBench measures, and what it leaves out

The GoldieBench models page describes the method: the same fixed prompts, one shot each, scored 0–10 by Julian.

The tasks page lists 50 one-shot tasks, and three of them sit in the Page category: AIPB Promo, Landing and Web-OS Desktop.

Landing is the closest match to real estate SEO work, because it asks for a complete modern marketing page.

There is no GoldieBench task for Business Profile audits, review policy, listing canonicals or local outreach.

So read the scores as a guide to page-building quality, not a verdict on the whole job.

The real estate SEO tasks a model actually touches

Landing scores: the best proxy for brokerage pages

The GoldieBench Landing task page reports 26 models tested, 21 of 26 scored, an average of 8.11/10, and Fugu Ultra named as winner.

A 9.0 on Landing means a polished page from one prompt, which is a good start for a new-build development page or an agent's personal landing page.

It says nothing about whether the facts on that page are right, and for a brokerage the facts are the whole point.

Overall averages: a signal for longer drafting work

Neighbourhood pages and market updates are longer, mixed tasks, so the cross-task average is a fairer guide than one task.

These averages come from the models page:

The averages lean on games and visual builds, so treat them as a broad capability signal rather than a writing score.

The leaderboard is the place to check before you pick, because new models join the bench often.

How to pair models with each real estate task

• Development or agent landing pages: start with a model that scored 9.0 on Landing, then replace every placeholder with real details.
• Neighbourhood drafts: use a high-average model, but feed it only the agent's own notes and forbid invented figures.
• Schema: any capable model can draft RealEstateAgent JSON-LD; a person then checks it matches the visible page, as Google's AI features guidance asks.
• Review replies: let a model suggest short, personal replies, and have a person post them.

A worked example: one neighbourhood page, model plus agent

Here is how a small brokerage could split a single area page between a model and a person.

1. The agent records a ten-minute voice note walking through the neighbourhood: housing types, who buys there, the commute, the shops.
2. A high-average model, such as Claude Opus 5 at 8.27 on the models page, turns the transcript into a structured draft with headings and an FAQ.
3. The model lists every claim it could not verify from the transcript, and the agent answers or deletes each one.
4. A Landing-strong model, such as GPT-5.6 Sol at 8.7 on the Landing page, suggests the page layout and a clear enquiry section.
5. A person adds the agent's own photos, checks the listing link works, and publishes.
6. The page goes into the internal links from the area hub and the agent's profile page.

The model saves the blank-page time; the agent supplies the knowledge that makes the page worth ranking.

Where no model score helps: the policy layer

The riskiest real estate SEO work is following rules, and no benchmark tests that.

Google's Business Profile guidelines class agents as individual practitioners, with naming rules for shared and branded offices and no extra profiles per speciality.

The Maps content policy bans rewards for reviews, asking only happy clients, and staff review quotas.

And Google's spam policies treat cloned town pages as doorways and mass-produced pages as scaled content abuse, however they are made.

A model that writes a beautiful page can still write one that breaks all three.

What AI search means for the content you draft

Google's 2026 guide to generative AI features says that, for Google Search, optimising for AI is still SEO, and you can skip llms.txt files and chunking.

ChatGPT needs one extra check: OpenAI's crawler docs recommend allowing OAI-SearchBot if you want to appear in its search answers.

Google also says AI Overviews and AI Mode traffic is reported inside Search Console's normal "Web" search type, so you measure model-drafted pages the same way as any other page.

So the model's job is good pages; crawler access and policy are human checks.

If you want to learn AI SEO yourself, including model choice, the AI Profit Boardroom covers it step by step.

The human and agency part of the job

Google's page on hiring an SEO warns that no one can guarantee a #1 ranking, and the same goes for an AI answer.

What an agency adds is judgement: which neighbourhoods deserve pages, which profiles conflict, and which local sites will genuinely link.

Going by figures Julian publishes himself, the agency has run over a thousand campaigns for 240-plus clients with a perfect Upwork Job Success Score, and it calls its link building "manual outreach, no PBNs".

For a wider view of hiring, see our guide to hiring an SEO agency.

Models can draft the work, but a strategy still needs a human call. Book a free SEO strategy session to talk it through.

Also On Our Network

• Agent Operatorsthe nine-step operator process from agent profiles to listing canonicals
• AI Income Deskwhether hiring is worth it for brokerages, solo agents and lettings firms
• AI Tool Verdictfive ways to get real estate SEO done, scored out of 10
• agentos.guidefive copy-paste agent prompts that do the real estate groundwork first

Pick a Landing-strong model for drafts, keep people on facts and policy, and Book a free SEO strategy session with an SEO agency for real estate for the rest.