Overview
The growing impact of a changing climate—wildfires, floods, and severe storms—has exposed a critical flaw in property insurance. Traditional insurers relied on historical loss data to price risk, but as disasters became more frequent and severe, historical data became insufficient. Instead, the industry swiftly shifted to location-based risk models. These models predict the likelihood of disasters, like wildfire, reaching specific points on the surface of the earth, often using satellite data on vegetation and terrain mapping.
The industry’s preference for these models is understandable: location-based risk analysis is far easier than creating models for an individual home. They generalize risk across large areas, “lump” physics into reduced-complexity models, which generate relatively quick insights. But importantly, part of this “lumping” includes ignoring how individual properties are affected by the fire.
The unfortunate, but predictable consequence of this approach is that the industry has, until now, conflated location risk with property risk, treating all structures in high-risk zones as equally vulnerable. A fire-resistant brick home may be priced similarly to a home wood siding, if they’re in the same neighborhood. Homeowners who invest in mitigation measures often see no premium relief and are sometimes dropped by insurers altogether. The result: homeowners are disincentivized from investing in mitigation, with serious consequences.
Why Creating Physics-Based Modeling is Essential
The lack of proper mitigation incentivization goes hand-in-hand with the lack of high-accuracy, house-specific modeling. Only risk models on a structural level can enable insurers and stakeholders to quantitatively assess, and help mitigate, risk for an individual home.
As mentioned, there is a reason why these have not been prioritized: developing a property-level model is not trivial – wildfire behavior is complex, governed by interactions between terrain, vegetation, weather, built structures, and how they relate to each other in 3D space. However, we are not without precedent for implementing complex numerical models to understand airflows, weather, materials, and heat.
Our process mirrors the rigor of aerospace engineering, where wind tunnels and simulations ensure aircraft and spacecraft function within safe limits. We apply similar methods to simulate fire, embers, wind, and how they interact with the structure, in a future, hypothetical wildfire. Meanwhile, digital twins—virtual replicas of real-world properties—enable continuous simulation under varying conditions. Ultimately, we operate at the intersection of trends in 3D modeling, compute scaling, and remote sensing proliferation.
Impact on Insurance and Community Resilience
This modeling approach allows us to scale detailed risk assessments, delivering precise, actionable insights for individual homeowners while maintaining portfolio-wide risk evaluations. It also gives a granular incentivization for how choices—roofing material, defensible space, or window placement—affect survivability in wildfire scenarios.
Accurately assessing wildfire risk requires more than historical data and regional trends—it demands a physics-based understanding of fire dynamics combined with scalable technology. This approach stabilizes insurance availability, lowers premiums for safer properties, and encourages community-wide resilience. Ultimately, this fusion of science and technology is critical to building a sustainable insurance model in the face of worsening wildfire threats.
