Inside PropIntel: Building a Real Estate Knowledge Layer
Real estate decisions depend on signals scattered across listings, permits, spreadsheets, valuation notes and internal memos. For governments, banks and developers, this fragmentation creates blind spots just where clarity is most needed – around risk, opportunity and program performance.
PropIntel is our attempt to turn that fragmentation into a real estate knowledge layer: a geospatially aware, transaction-aware view of land and housing activity that can support better programs, pipelines and products.
PropIntel is not another dashboard. It is the connective tissue that lets cities, lenders and operators see the same housing reality from different angles.
Why a knowledge layer, not just analytics
Traditional analytics tools assume the hard work has already been done: clean tables, consistent identifiers and stable schemas. In real estate, that assumption fails quickly. The work is 80% stitching and only 20% visualising.
PropIntel focuses on three simple questions:
- What do we know about this place – this parcel, corridor or neighbourhood?
- What has actually happened here – transactions, permits, projects, disputes?
- Who needs to act on that information – a planner, banker, developer or regulator?
How PropIntel sees the world
At the core is a simple design principle: anchor everything to geography and legal reality. That means:
- Parcels and administrative units as first-class objects with geometry and identifiers.
- Data from SlasProp – listings, allocations, registrations – attached to those objects.
- Signals from SlasPay – payments, settlements, flows – linked to projects and buyers.
- External layers – planning zones, transport, points of interest, socio-economic data – overlaid on top.
Once this scaffold is in place, PropIntel organises questions into several lenses:
- Supply lens: where is new stock emerging, and of what type?
- Demand lens: who is buying, at which price points and with what financing?
- Risk lens: where are arrears, disputes or stalled projects clustering?
- Program lens: how are specific housing or land programs performing over time?
Powered by the Slas stack
PropIntel is intentionally built on top of the broader Slas stack:
- SlasProp contributes parcel, project and transaction structure.
- SlasPay provides payment and settlement signals tied to those assets.
- External datasets add planning, socio-economic and market context.
This means that every new pilot or deployment enriches the knowledge layer without exposing sensitive underlying data. The focus is on patterns and performance, not individual files.
Examples of how partners can use PropIntel
A few practical use cases we design PropIntel for:
- Housing agencies exploring which corridors to prioritise for new supply or infrastructure.
- Banks and DFIs assessing portfolio concentration, early-warning signals and program impact by location.
- Cities tracking how formalisation or titling programs are changing tenure patterns on the ground.
- Developers and operators evaluating where demand is building up quietly before it shows in raw prices.
Where we are headed next
Our roadmap for PropIntel includes deeper GIS tooling, richer program-evaluation modules and cleaner ways for partners to plug in their own datasets without losing control or compliance.
Underneath all of this, the objective stays simple: create a real estate knowledge layer that is trustworthy enough for regulators, useful enough for operators and flexible enough to adapt across markets.
PropIntel is where SlasProp, SlasPay and our geospatial foundations come together as a single lens. It is how we help partners move from anecdote-driven decisions to evidence-backed strategy.
