When someone asks ChatGPT where
to buy, you find out which listings
and developments it names
You also see which brokerages appear in the answer, along with what it would take for the answer to name you and your property business instead.
Snoika provides AI tracking for real estate marketing teams.
It measures how often a brand appears in generative AI answers and optimises the sources those models pull from.
The platform analyses buyer and tenant prompts across ChatGPT and Gemini. It includes Claude in the analysis.
It then reports share of voice and sentiment. The report also identifies the sites each model trusts.
It works on the same logic as SEO, applied to model responses instead of ranked links.
Where did the search traffic go
Your listing pages still rank, and the enquiries have thinned out anyway. A buyer who once typed "3 bed apartment Lisbon Príncipe Real" into Google now asks an assistant which neighbourhood suits a family relocating on a two-year contract, and gets back a paragraph naming two developments and one agency. If your name is missing from that paragraph, nothing in your analytics tells you it happened.
Real estate makes this worse than most sectors. Your inventory is transient, so the pages carrying your best content go stale or get delisted the moment a unit sells. Portal syndication feeds your listings elsewhere, which directs the model's credit to the aggregator. Fair housing and advertising rules also constrain what you can claim about a location or a buyer profile, and a model paraphrasing your copy has no such constraint.
Then there is the source problem. Language models lean on a small set of sites they consider trustworthy for property questions, and with 900 million weekly active users asking questions across every category, the ones covering your market decide what gets said about your stock. Guessing which those are is not an effective ai tracking approach for property marketing visibility.
How AI tracking works here
You see why enquiries dropped by examining the specific prompts that name a competing developer or brokerage in your place. Snoika runs the questions your buyers actually ask across ChatGPT
and Gemini. It runs the same questions through Claude and records who appears. It also tracks favourable and neutral mentions,
with warnings identified separately.
The mechanism is source-level. The platform identifies which domains each model drew on for that answer. You can see whether the recommendation came from a portal listing or a local news piece. Forum threads about the area are identified as well. From there
it works on your structured data and digital footprint so your own material is what the model retrieves next time.
Prompt coverage for the queries that precede a viewing request, by city and by segment
Competitor breakdown showing which rival names surface and what the model says is good about them
Trusted source mapping so you know where to place and correct information about your developments
Sentiment tracking across ai search, including how models describe your service after a bad review cycle
That combination is what turns ai search from a black box into something you can work on this quarter. The reporting is built for someone who already publishes and needs to know which pages are earning citations.
What results look like
Snoika has no published real estate customer, so the proof here comes from other sectors and you should read it that way.
1StopVAT
10x
ChatGPT visibility within 7 weeks
Vespia
220%
increase in AI brand mentions
3DPrinterOS
#1
first position in ChatGPT for industry-specific queries, 3–4 qualified inbound leads per day
Agrello
125.6%
increase in organic search sessions YoY (51,800 impressions from Snoika-created pages)
Snoika has no published real estate customer, so the proof here comes from other sectors and you should read it that way. 1StopVAT,
in tax and fintech, recorded 10x growth in ChatGPT visibility within 7 weeks. Vespia, a fintech SaaS, saw a 220% increase in AI brand mentions,
and 3DPrinterOS in 3D printing SaaS reached first position in ChatGPT for industry-specific queries alongside 3 to 4 qualified inbound leads per day. Agrello, an e-signature company, logged 51,800 impressions from Snoika-created pages and a 125.6% increase in organic search sessions year on year.
Frequently Asked Questions
Yes. Buyer prompts already name developments, so you're in scope without listings. Snoika records whether
the model names your development and which domain supplied that name. You still need durable pages
the model can retrieve, because a file that never sits on a public URL won't be cited.
The method carries across to property:
identify the prompts and find the sources
the models trust.
Then get your material into them.
Snoika measures your position in AI answers and does the structured data work
to improve it, which is the whole of ai visibility for property marketing as it stands today. Book a walkthrough and we will run your own market's prompts before
the call so you can see where you currently sit.