Property insurance in Canada is getting more expensive. The Insurance Bureau of Canada reported that insured catastrophe losses topped $8 billion in 2024 — a record — driven by flooding, hail, and wildfire events concentrated in specific geographic areas. Insurers are responding by raising premiums, tightening underwriting criteria, and in some cases exiting markets entirely.
What's driving these decisions is location data — or more precisely, increasingly sophisticated models built on location data. If you're buying a property, advising a client, or building a platform that serves the Canadian real estate market, understanding how insurers think about location risk will change how you read the landscape.
The Core Principle: Location as the Primary Risk Variable
In property insurance underwriting, no variable is more important than location. Two structurally identical houses — same year of construction, same square footage, same materials — can have dramatically different risk profiles based solely on where they sit. One is in a 1-in-100-year floodplain; the other is not. One is within a wildland-urban interface zone; the other is not.
Location determines exposure to the peril. Everything else — building age, construction type, renovation history — affects how badly the building is damaged when the peril occurs, but not whether it's exposed to the peril in the first place.
This is why the shift toward richer, more granular location data is so consequential. When insurers get better at measuring location-specific risk, pricing becomes more accurate — which means that properties in high-risk locations get priced up, sometimes dramatically, and properties in low-risk locations become relatively more attractive.
The Data Layers Behind Modern Underwriting Models
Flood and water risk data
Flood risk is the dominant peril reshaping Canadian insurance pricing. Historically, Canada's flood mapping has been incomplete and inconsistently maintained — with many high-risk areas not formally designated on any public flood map. Insurers have increasingly moved to proprietary flood models that supplement government-issued maps with elevation data, drainage basin analysis, storm sewer capacity, and historical claim data.
The result is property-level flood risk scores that can distinguish between two houses on the same street — one of which sits at a slightly lower elevation and is consistently wet in major rainfall events, one of which has never flooded. Public maps can't make this distinction; proprietary models increasingly can.
Building age and construction vintage
Building age is a well-established underwriting variable because it's a reliable proxy for multiple risk factors: electrical systems (knob-and-tube wiring in pre-1950 homes is a significant fire risk), plumbing (original galvanized steel pipes corrode), roofing materials (original asphalt shingles past their service life), and seismic retrofitting status in earthquake-prone markets.
Building age data is available through municipal assessment records, but the coverage and update frequency varies significantly across Canadian municipalities. In markets where this data is stale or inaccessible, insurers rely on proxies — neighbourhood vintage as a signal for property vintage, for example.
Building permit history
Permit data is one of the most underused but highest-value inputs for property risk assessment. A comprehensive permit history for a property reveals whether major renovation work was done legally and to code — and by extension, whether unpermitted work may have introduced risk factors that aren't visible in an assessment record.
A property with a history of permitted electrical, plumbing, and roofing work is meaningfully different from a structurally similar property where the same work was done without permits. The permitted property has evidence of code compliance; the unpermitted property does not. Insurers who can access permit data at scale can use this signal in pricing models.
Environmental risk layers
Beyond flood, insurers incorporate a growing range of environmental risk signals:
- Wildfire risk: Properties within wildland-urban interface zones, or within a defined distance of forested areas with high fire weather index histories, carry elevated risk in western Canada and northern Ontario.
- Earthquake risk: Seismic hazard zones in BC, particularly Greater Vancouver and Victoria, drive up earthquake coverage costs. Insurers model soil type (soft soils amplify shaking) alongside hazard zone designation.
- Sinkhole and subsidence risk: Areas with specific geology — limestone karst, former landfill, areas with high peat content — carry elevated subsidence and foundation risk.
- Proximity to industrial facilities: Properties near chemical plants, fuel storage facilities, or other environmental hazard sites carry contamination and explosion exposure.
The Canadian Data Gap Problem
The challenge for Canadian property insurance is that the data infrastructure supporting these models is significantly less developed than in the United States, where FEMA flood maps, consistent county-level permit databases, and standardised building records have existed for decades.
| Data Type | US Availability | Canada Availability | Underwriting Impact |
|---|---|---|---|
| Flood zone maps | National FEMA coverage | Partial, inconsistent | High — primary peril |
| Building permits | Standardised, accessible | Municipal silos, varied access | Medium — construction quality signal |
| Wildfire risk | National Fire Hazard Severity Zones | Provincial patchwork | High — WUI properties |
| Property assessment records | County-level, mostly accessible | Provincial, mixed access | Medium — building vintage |
| Historical claims (industry) | CLUE database (industry-wide) | No equivalent database | High — loss history signal |
These gaps mean Canadian insurers are making pricing decisions with less granular information than their US counterparts — which introduces both systematic overpricing (charging properties for risks they don't have) and underpricing (failing to identify risk factors that aren't visible in available data). Both outcomes are suboptimal, but the underpricing problem is the one creating the current market crisis.
What This Means for Buyers and Sellers
The repricing of location-specific risk has direct implications for residential real estate transactions:
Insurance availability is a deal-breaker
In some high-risk areas, the challenge is no longer premium cost — it's availability. Properties in certain flood-prone, wildfire-adjacent, or formerly-insured-but-now-declined areas may face significant difficulty obtaining standard coverage at any price. Buyers who discover this after conditional removal are in a very difficult position.
Due diligence now requires verifying insurability — not just estimated premiums — before conditions are removed on a purchase.
Insurance cost is a carrying-cost factor
For properties that remain insurable, the cost differential between a low-risk and high-risk property of similar market value can be significant — sometimes thousands of dollars per year. For buyers calculating affordability, this carrying cost difference can change the economics of a purchase.
Location risk data is a negotiating tool
Buyers who understand a property's flood risk, environmental exposure, and permit history are better positioned to assess value and negotiate price. A property with documented unpermitted work, or with a flood risk profile that will generate high insurance costs, is worth less than a comparable property without those characteristics.
The Opportunity: Smarter Risk Data at Scale
The data gap in Canadian property insurance isn't a permanent feature of the landscape — it's a solvable problem. Municipalities are publishing more permit data. Environmental agencies are improving flood and wildfire mapping. Aggregators are building the APIs that make this data accessible at property-level resolution, at scale, via clean interfaces that integrate into underwriting and listing workflows.
For real estate platforms, the opportunity is to surface insurance risk signals in the listing and search experience — so buyers discover a property's flood risk or wildfire exposure before they fall in love with it, not after. For insurance platforms and brokerages, the opportunity is to enrich the underwriting intake flow with location data that makes pricing faster, more accurate, and less dependent on legacy models built on incomplete data.