The 2023 wildfire season burned more Canadian forest than any year on record — by a margin that made international news for most of the summer. Subsequent seasons have kept the pattern uncomfortably visible: smoke events reaching cities thousands of kilometres from the fires, evacuation orders touching communities that never considered themselves at risk, and the destruction of structures in places like Lytton and Jasper that made the stakes concrete.
For property data, this has changed the question. Wildfire used to be treated as a regional, seasonal news story. It's now an address-level underwriting and disclosure topic — and the data infrastructure around it is consolidating quickly.
The Wildland-Urban Interface Is the Unit of Analysis
Fire risk to property isn't really about forests; it's about the boundary where development meets vegetation — the wildland-urban interface, or WUI. Researchers and fire agencies map it explicitly, and a meaningful share of Canadian communities sit partly inside it, particularly across British Columbia, Alberta, and the boreal fringe of several other provinces.
What makes WUI mapping useful for property products is that it's inherently address-level. Two homes in the same municipality — even the same neighbourhood — can carry very different exposure depending on their position relative to vegetation, slope, and access routes. That's the granularity buyers and underwriters are starting to expect.
What the Data Landscape Looks Like
Several distinct data families feed a wildfire risk picture, and they come from different places:
- Historical fire perimeters. Federal and provincial agencies publish decades of burn perimeter data — the empirical record of where fire has actually reached.
- Hazard and fuel mapping. Vegetation type, slope, and climate inputs combine into forward-looking hazard ratings, maintained by agencies and refined by researchers.
- Community risk assessments. Many municipalities and provinces run FireSmart-style assessments identifying at-risk communities and mitigation status.
- Live situational data. Active fire locations, danger ratings, and smoke forecasts — operational rather than analytical, but increasingly surfaced in consumer products during fire season.
Like everything in Canadian open data, these are published by different bodies at different resolutions with different update habits. The integration work — normalizing them into something you can query by address — is exactly the kind of unglamorous plumbing that determines whether a platform can actually ship a risk feature.
Insurance Is Moving First
As with flood, the insurance industry is ahead of the consumer platforms. Insurers already price wildfire exposure, and in the highest-risk areas that pricing has begun to shape what coverage is available at all — which in turn affects mortgageability, which affects value. The sequence should be familiar from the flood story: risk shows up in premiums first, then in lending, then in buyer behaviour, and eventually in disclosure norms.
For platforms serving insurers, lenders, or brokerages, the implication is that wildfire context is becoming part of the standard due-diligence bundle rather than a specialty add-on. The teams asking us for flood data increasingly ask for fire in the same breath.
Presenting Risk Without Panic
A closing note on product design. Risk data is easy to present badly — a red badge on a listing helps no one and overstates what hazard models can say about a single address in a single year. The better pattern, which we've seen mature in flood-aware markets, pairs exposure with context: the hazard class, the community's mitigation status, and what has actually occurred nearby historically. Framed that way, wildfire data does what property data should do — it informs a decision rather than making one.