This is the foundation of the study. The property-level classifications produced here feed directly into Q2’s portfolio loss estimation, Q3’s correlated season risk, and Q4’s honest boundary analysis. Every downstream finding depends on what this simulation produces.
Zone-Based Classification Doesn't Differentiate
Most insurers classify wildfire risk using zone-based systems like CAL FIRE's Fire Hazard Severity Zones (FHSZ). Every property in the WUI gets the same classification — typically HIGH — regardless of terrain, vegetation, wind exposure, or defensible space. The premium is set by the zone, not the property.
A more detailed fire behavior model could differentiate risk within the zone. But does it change the classification enough to matter? If 95% of properties stay in the same tier, the added complexity isn't worth the cost. If most properties move, insurers are mispricing risk at scale.
We tested this by running 316 WUI properties through four progressively detailed models and tracking how many properties changed risk tiers at each step.
Four Levels of Classification
Distance to Wildland
Binary: inside the WUI or not. Uses CAL FIRE FHSZ boundaries. Every property near wildland vegetation gets the same classification.
Terrain + Fuel + Wind
Feature-based scoring using slope, fuel type, wind speed, and aspect. Adds an EXTREME tier for properties with compounding risk factors.
Fire Behavior Model
Physics-based fire spread model (Rothermel + wind vectors). Catches directional effects: properties upwind of fuel loads are safer than those downwind.
200-Draw Uncertainty
Samples over wind direction, speed, fuel moisture, and ignition location. Classification based on the probability distribution of outcomes, not a single run.
Each level builds on the previous. L1 adds features to L0's zone. L2 adds physics to L1's features. L3 adds uncertainty to L2's deterministic answer.
How Classification Shifts Across Fidelity
The biggest shift happens between L0 and L1: the screening model alone reclassifies 80% of properties by adding terrain and fuel features. The deterministic simulation (L2) reclassifies another 31% by catching directional wind effects. The Monte Carlo (L3) reclassifies 13% more — smaller, but these are the properties where uncertainty in wind and moisture conditions changes the answer.
Reclassification = property changes risk tier relative to the previous fidelity level. Total 91% is L0 to L3.
What If You Stop Earlier?
| If You Stop At | Properties Differentiated | EAL Estimate | Time to Compute | What You Miss |
|---|---|---|---|---|
| L0 (zone) | 0% (all HIGH) | $54M | <1 second | Everything — no differentiation |
| L1 (screening) | 80% reclassified | $118M | Seconds | Directional fire effects, weather uncertainty |
| L2 (deterministic) | 91% reclassified | $127M | ~10 seconds | Tail risk from weather variability |
| L3 (Monte Carlo) | 91% reclassified | $127M | ~12 minutes | Nothing — this is the full answer |
How Well Does Each Level Detect Actual Fire?
We validated each fidelity level against NIFC fire perimeter data for the Kincade Fire. Sensitivity measures how many properties that actually burned were classified HIGH or EXTREME. Specificity measures how many properties that didn't burn were correctly classified LOW or MEDIUM.
| Level | Sensitivity | Specificity | Interpretation |
|---|---|---|---|
| L0 — Zone | 1.00 | 0.04 | Catches everything, flags everything |
| L1 — Screening | 1.00 | 0.04 | Same detection, same over-classification |
| L2 — Deterministic | 1.00 | 0.07 | Slightly better specificity |
| L3 — Monte Carlo | 1.00 | 0.06 | Perfect detection, conservative |
All four levels achieve perfect sensitivity — no property that actually burned was classified LOW. But specificity is uniformly poor (4–7%), meaning the models over-classify nearly every property as high risk. This is expected in the WUI: these properties are all near wildland fuels. The value of higher fidelity isn't in catching fires the zone model misses — it's in differentiating risk within the high-risk zone.
Everything Is High Risk in the WUI
At Kincade, 96% of properties are classified HIGH under zone-based pricing. Under Monte Carlo, the distribution shifts — but it shifts from HIGH to EXTREME, not from LOW to HIGH. In a WUI area, almost nothing is safe. The model’s value isn’t finding safe properties — it’s differentiating between “high risk” and “catastrophic risk.” That distinction is where the premium differential lives: the difference between EXTREME (86% of properties, 5% annual loss rate) and HIGH (9%, 2% loss rate) is a 2.5× premium multiplier. For an insurer, that’s the decision that matters.
This finding generalizes: in any WUI area, zone-based classification will always lump most properties together. The value of higher-fidelity modeling is within-zone differentiation, not safe-vs-unsafe classification.
Reclassification Across Different Fires
The reclassification effect is not specific to Kincade. Camp Fire — a faster, more destructive fire in different terrain — shows an even higher reclassification rate of 88%. The more extreme the fire environment, the more the zone-based model fails to differentiate.
When Everything Burns
Camp Fire destroyed Paradise, California in November 2018. With 85% of properties inside the fire perimeter, almost every property burned. The zone-based model says they're all HIGH risk — and it's not wrong. But even when nearly everything burns, the Monte Carlo still differentiates.
87% classified EXTREME, 12% HIGH, 1% LOW. Even in the worst-case fire, properties are not equally at risk. The ones classified HIGH instead of EXTREME had slightly better terrain position, more defensible space, or less exposure to the dominant wind direction. That 12% matters for pricing — they should pay less than the 87%.
Camp Fire's 88% reclassification rate confirms the pattern: zone-based classification consistently misprices WUI properties. The properties at the extreme end of the risk spectrum subsidize those with moderate exposure, and everyone pays the same premium.