Real Estate · Lead ScoringReal Estate Lead Scoring & Buyer Matching
AI that ranks every enquiry by how likely it is to convert, and matches buyers and renters to the listings that genuinely fit — so agents call the right people first.
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TRUSTED BY TEAMS
Real estate lead scoring ranks enquiries by how likely each one is to become a deal. Buyer matching goes further than search filters, learning what a buyer actually wants from what they open, save and ignore.
Sodio builds AI for real estate leads into the CRMs, portals and apps brokerages already use — so the most serious buyers, sellers and renters reach an agent first, and see the listings most likely to suit them.
/// IS IT WORTH BUILDING YET?
When Lead Scoring Is Worth Building
A score only matters if it changes what someone does. For a small team with a handful of leads a week, the agent already knows who is serious.
Worth building when
- Leads arrive faster than agents can call them all
- You record which leads became deals
- Routing can act on the score
- You run a portal or app with behaviour to learn from
Not yet, when
- Your team can personally follow up every enquiry
- Outcomes have never been recorded anywhere
- Leads are shared out in rotation whatever the score
- Most leads arrive by phone with little history
/// WHAT IT COVERS
What Lead Scoring and Buyer Matching Cover
Lead scoring
Lead qualification
Speed-to-lead routing
Behaviour signals
Buyer matching
New-buyer matching
Seller and landlord signals
Match explanations
Agent feedback
Reporting
/// WHAT THE MODEL LEARNS FROM
What the Model Learns From
///WHAT BUYERS SAY, AND WHAT THEY DO
What Buyers Say, and What
They Do
A buyer asks for three bedrooms near a good school, then saves two-bedroom houses with large gardens. Filters only know what they said. Matching learns what they do, and shows them what they are actually looking for — sometimes before they have realised it themselves.
For a new buyer with no history, matching starts from what they said and from buyers with similar needs, then shifts to their own behaviour as they browse.
Matching must never steer buyers by protected characteristics. Models are built without them or any neighbourhood demographics, and tested for anything that could stand in for them.
/// FAQ
Frequently Asked Questions
It is ranking enquiries by how likely each one is to turn into a transaction, so agents spend their time on the people most ready to buy, sell or rent. A score is built from where the lead came from, what they have told you, how they have behaved on your site or app, and what happened to similar leads in the past.
Real estate lead qualification usually means an agent asking the same questions on every call — budget, timeline, financing, reason for moving. The system captures what the lead has already said or done, summarises it, and highlights what is still unknown, so the first call starts further along.
This is the cold-start problem, and it is the hard part of any matching system. With no history, matches come from what the buyer tells you and from what buyers with similar stated needs went on to choose. As they browse, save and ignore listings, their own behaviour takes over. We have built cold-start matching as the core problem on an interest-based matching platform where no history existed at all.
It can if it is built carelessly, which is why it is not. Steering buyers towards or away from areas based on protected characteristics is illegal under the US Fair Housing Act, and equality law in the UK sets similar limits. Models are built without protected characteristics or neighbourhood demographics, and tested for patterns that could act as a proxy for them.
By comparing conversion across score bands. If high-scoring leads convert far more often than low-scoring ones, the model is doing its job. Before launch, we test it on a year of your past enquiries and outcomes; after launch, agent feedback and actual results keep it honest.
/// RELATED SERVICES
Related Industries and Services
Routes where scope overlaps with real estate software.
/// GET STARTED
Start With Last Year's Leads
Share a year of enquiries and which of them became deals. We will assess, free, how well a model could have ranked them — and what it would change for your agents.