Toronto's real estate market ranks among Canada's most-searched local markets. TRREB (Toronto's regional real estate board) tracks hundreds of thousands of GTA transactions a year, and buyers research hard before they contact an agent. Increasingly that research starts in AI chat. Someone asks Perplexity which Toronto neighbourhoods suit first-time buyers, or asks ChatGPT to weigh a Danforth semi against a North York high-rise. The agents whose websites answer those questions clearly are the ones that get recommended.
Most Toronto real estate websites were built to show IDX listings (the MLS listing feed) and little else. They rank for the agent's name and not much beyond it. In 2025 that costs you, because the buyers arriving through AI search already know they're weighing Roncesvalles against Bloor West Village or trying to make sense of OREA agreements. They are further along and closer to moving.
What AI Search Needs from a Real Estate Site
AI answers are assembled from structured, citable content. For a Toronto agent or team, that means clear pages answering the questions buyers actually type:
- "What is the average condo price in King West?"
- "What does a buyer agent do under Ontario REBBA rules?"
- "Is Liberty Village a good investment in 2025?"
Pages like these show local knowledge, which is what AI systems cite. A listing feed answers none of those questions.
We add JSON-LD schema (RealEstateAgent, LocalBusiness, FAQPage) to every site we build, plus an llms.txt file and an AI-bot allowlist in robots.txt so GPTBot and PerplexityBot can crawl and cite your content. Templated platforms skip all of it, whether that's Wix, Squarespace, or the IDX-bundled site your brokerage hands you.
The RECO Reality: Why Most Agent Sites Underperform
RECO (the Real Estate Council of Ontario) sets specific rules for how agents present themselves online: how teams are named, how listings are attributed, how advertising is labelled. Templated sites often break these rules, and fixing them usually means a rebuild. We build compliant from the start, with proper team-page structure, compliant listing disclaimers, and accurate brokerage attribution. That spares you the awkward conversation with your managing broker later.
Neighbourhood Pages That Rank
The real estate pages that earn the most traffic in Toronto work at the neighbourhood level rather than the city level. A page that explains what it's like to buy in Leslieville (walkable, semi-detached inventory, close to the DVP), The Beaches (detached premiums, school catchments, Queen East retail), or North York (condo towers against older bungalow stock near the TTC) will rank for those queries in both Google and AI search. We write them from how those neighbourhoods actually trade, not from a template with the name swapped out.
Neighbourhood pages come with the Story ($5,000) package and up. The real estate build page has the full breakdown.
What the Build Includes
- Custom design, not a broker template
- IDX integration where you need it, or clean listing presentation
- AI search baseline (Answer Engine Optimization): JSON-LD schema, llms.txt, AI-bot allowlist, Lighthouse 90+
- 2 to 5 neighbourhood content pages written to Toronto specifics
- RECO-compliant structure throughout
- Hosted on cite-met.com, on fast Canadian-resident servers
Builds run from $2,500 (Site) to $9,000 (Engine) for teams that want full neighbourhood authority coverage. The ongoing Local retainer ($300/mo) keeps your Google Business Profile, content, and citations current as the market shifts.
The Pricing Rule We Hold Ourselves To
Every project we quote comes in below what you currently pay for SEO. If you're on a $500/month retainer that isn't producing leads, we'll show you how a build plus a Care plan ($60/mo) buys more visibility for less. We operate that way on purpose. Get the free 24-page audit and we'll show you exactly what your current site does and doesn't do in AI search.