The AI platform Real Estate uses to get recommended on
Buyers and sellers ask a model about a neighbourhood long before they call an agent, and the portals are answering. LBOX measures what the engines say about your brokerage, shows who they name instead, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Real Estate different
Not general marketing advice. These are the constraints your category actually operates under.
The portals own the answer, and your listing is on their page.
Ask about homes in an area and the engines return Zillow, Redfin and Realtor.com. Your own listing is there, described in their words, on their domain, with their brand attached to the transaction you sourced.
Hyperlocal is the entire business and models resolve it badly.
Who is the best agent in a specific neighbourhood is the question that matters most and the one engines answer worst, falling back on review counts and syndicated profiles that have nothing to do with who actually sells there.
Market questions are answered by national outlets.
Is now a good time to buy here. Are prices falling. Those conversations used to begin a relationship with a local expert. They are now answered with national averages that may be the opposite of what is happening on your streets.
Agents move and the record does not.
Profiles, past sales and team pages scatter across portals and old brokerage sites. A model assembling a picture of your agent is reading several versions of their career, some of them belonging to a firm they left years ago.
Listings beget listings, so being the named agent compounds
Real estate has a flywheel most categories lack. A seller choosing an agent asks who sells in this neighbourhood, and the honest answer cites recent sales. Win the listing and you generate the very evidence that wins the next one, which is why a small number of agents end up owning an area and why that position is so durable once established. What has changed is where the question gets asked. It used to be neighbours and signboards, both of which reflected local reality. Now it is a model reading whatever is written down, so the flywheel turns on documentation rather than reputation, and an agent who genuinely dominates an area can be invisible in the answer that decides the next listing.
What you get back
The console, measuring a company in Real Estate against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See which brokerages the engines name
Visibility and position on the unbranded who-should-I-list-with questions your buyers and sellers ask, measured per engine.
- Ranked against the brokerages the engines actually named
- See where a model reads your pages without naming you
You against the field, engine by engine
unbranded questionsTurn what the answers get wrong into work that ships
Findings arrive as a worklist sorted by who can act: what LBOX writes, what needs your decision, what needs your agents.
- Scored against your neighbourhoods and price bands, not a generic SEO checklist
- Nothing publishes without your sign off
The worklist
Where you disappear as the seller gets closer to choosing an agent
Presence falls away as intent rises. The who-should-I-list-with questions are the ones that convert, and the ones the portals win.
- Visibility split by where the question sits in the journey
- Names the stage that costs you the listing
Visibility by buying stage
where you show up in the journeyThe sources the engines trust about real estate
The portals decide what the engines repeat about your market, and your own listings are described in their words on their domains.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
624 citationsThe program for Real Estate
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your brokerage today, through the engines own APIs
- The questions buyers and sellers actually ask, mapped by neighbourhood, price band and property type
- Where a portal is answering in your place
- Whether the engines describe your agents, coverage areas and recent sales correctly
- Which sources the engines cite for your areas, and which of those you control
- Neighbourhood, agent and market-report pages built so the engines can read and quote them
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.