Built on real market activity, not predictions. Every record reflects observable property events captured weekly since 2014.
BrightCat provides a weekly-refreshed view of Canadian property activity — listings, sales, rentals, and commercial tracks — joined into a single property-level record. Enterprise clients use this to answer questions that fragmented MLS data or post-fact registry records can't: which households are preparing to move, where the commercial market is actually clearing, and how property values have evolved on a like-for-like basis across the country.
The data has been captured continuously since 2014. That continuity is the core moat — a point-in-time snapshot tells you the market today; twelve years of weekly snapshots tell you how every property in the dataset has behaved across multiple cycles.
BrightCat operates on licensed property data under long-term commercial contracts that have been in place for years. Licensed data is the input; a proprietary Canadian property intelligence layer — address standardization, stable property persistent property identifiers, lifecycle reconciliation, repeat-sale HPI — is the output. No continuous capture, no public-web harvest, no consumer-portal ingestion.
Where does BrightCat's data come from?
Licensed property data feeds under long-term commercial contracts. Continuous historical coverage back to 2014 is possible because the underlying data relationships have remained stable. Specific commercial arrangements behind the data supply are confidential.
Is BrightCat a data aggregator or a data builder?
A data builder. The value is in the proprietary processing layer — property identity resolution, cross-cycle reconciliation, lifecycle state machine, HPI construction — not in the raw feed.
Is BrightCat only for data scientists?
No. BrightCat is enterprise infrastructure, but clients don't need a data team to use it. Marketing and acquisition teams receive ready-to-use weekly files. Analytics and data science teams pull the same data through Snowflake or AI tools. Delivery format is matched to the client.
Can BrightCat data be used for list-based customer acquisition?
Yes. BrightCat PreMovers is specifically designed for list-based customer acquisition in telecom, insurance, banking, and direct marketing. Enterprise clients have used BrightCat pre-mover data for list-driven acquisition campaigns for more than a decade.
Property data is only useful when it behaves like a property — one record that accumulates history across listings, sales, and cycles. BrightCat's core processing work is reconciling fragmented weekly inputs into a continuous property-level view.
This solves a problem that defeats most MLS-derived data: listing identifiers change when a property comes off market and comes back, when agents switch, or when the record simply gets renumbered. That fragmentation breaks property history into disconnected pieces and makes longitudinal analysis impossible. BrightCat's reconciliation keeps property history intact across every cycle.
The practical outcome: a property listed three times over five years, sold twice, and relisted after a renovation is one record with its full observable history — not six disconnected records that look like six different properties.
Joining data across tracks at the property level is what turns raw market activity into decisions. Clients use BrightCat cross-track intelligence for:
Each use case draws from the same underlying property-level dataset. Clients aren't buying five separate products — they're buying the ability to ask any of these questions from a single source.
BrightCat's Home Price Index is built on the repeat-sales methodology: rather than comparing prices across different properties, it measures price change on the same property across two sale events. This removes the confound of property mix shifting over time — a move-up market selling more large homes doesn't falsely inflate the index.
The index draws from verified repeat-sale evidence accumulated since 2014. It updates weekly as new sold events close, with geographic and temporal segmentation available. Because construction is transparent and property-level, index values can be traced back to the underlying sales that produced them — useful for audit, model validation, and client-side price analytics.
Published data is delivered through four channels, all drawing from the same matched property records:
Every channel carries the same schema and the same update cadence. Clients can adopt the delivery that fits their stack without compromising data consistency.
Honest methodology documentation requires honest limitations. BrightCat data reflects the market as observed, not the market as idealised: