Property data is messy. That is the work.
PropTech fails on data quality more than on features: listings duplicate, addresses disagree and valuations rest on thin comparables. iLeaf builds the reconciliation and modelling underneath property platforms, then adds predictive valuation, buyer matching and contract intelligence over leases and agreements.
Ask Lia about real estate / proptechWhat this includes
- Listing platforms
- Search, matching and media handling at portal scale, with deduplication that actually holds.
- Predictive valuation
- Price and yield models built on comparables, with honest confidence intervals rather than a single number.
- Rental & tenancy products
- End-to-end rental journeys — applications, agreements, payments and maintenance requests.
- Lease intelligence
- Graph-RAG over leases so break clauses, escalations and renewal dates surface before they bite.
- Data reconciliation
- Address normalisation and entity resolution across feeds that will never fully agree with each other.
- Buyer & tenant matching
- Agents that shortlist, answer questions and book viewings without a human in every exchange.
How we run it
Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.
Fix identity first
Deciding what counts as one property, one address and one owner precedes any modelling. Everything downstream depends on it.
Model with intervals
Valuations ship with confidence ranges. A single confident figure on thin comparables is a liability.
Automate the qualifying, not the closing
Agents handle enquiry, shortlist and scheduling; people handle negotiation, where relationships are made.
Watch for drift
Property models decay as markets move, so monitoring and retraining are part of the first release.
What we build it with
- TypeScript
- React
- Node.js
- Python
- PostgreSQL
- PostGIS
- Elasticsearch
- XGBoost
- Neo4j
- AWS
- Mapbox
- Redis
Questions we get asked
How accurate can automated valuation be?
It depends entirely on comparable density. In liquid urban markets with good transaction data, models are genuinely useful; in thin markets they are indicative at best. We ship confidence intervals rather than a single figure, because presenting a precise number from weak data is how these systems lose trust.
Can you work with our existing portal data feeds?
Yes, and reconciling them is usually the first real task. Feeds disagree on addresses, duplicate listings and drop fields without notice. We build normalisation and entity resolution so downstream features rest on a consistent view rather than inheriting the mess.
What can agents do in a property business?
Answer enquiries, shortlist against a buyer's criteria, book viewings and chase documents — the volume work that delays responses. Negotiation and closing stay with your people, because that is where the relationship and the margin actually are.
Let’s talk about real estate / proptech.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.
