How Do Insurers Use Property-Level Risk Data in Pricing and Underwriting?

Two neighboring homes can go through the same hailstorm and come away with very different losses. Carriers are increasingly designing around that difference because it shows up directly in results: growth in areas once considered too high-risk (without stepping outside risk appetite or underwriting guidelines), less adverse selection, and sharper segmentation for more accurate underwriting and rating. Regulators have embraced the approach, too: property-level risk models are already being accepted in rate filings across the country. That's the shift detailed in an August 12, 2026 P&C Specialist article by Carl Winfield, with commentary from ZestyAI co-founder and chief product officer Kumar Dhuvur on why property-level risk differentiation is becoming central to how carriers price, underwrite, and grow.

We've tracked this same shift in our own filings research, most recently in The New Competitive Battleground in P&C Insurance.

Why are carriers designing products around property-level risk data?

Because accurate pricing and underwriting depend on seeing risk at the individual property, not the territory average — and that precision is what lets carriers select risks within appetite, avoid adverse selection, and grow with confidence in areas once deemed too high-risk. Demand for more granular wind and hail risk assessment is growing, particularly among Midwest carriers,as reinsurers raise the attachment point for severe convective storm coverage and regulators in storm-prone states audit broad ZIP-code-based rate increases instead of approving them by default. "Severe convective storm losses have exceeded $50 billion annually for three consecutive years while reinsurance absorbs less of the loss," said Kumar Dhuvur, ZestyAI's chief product officer. "That makes it essential to demonstrate to regulators why one property represents a materially different risk from another nearby property."

Which states are pushing back hardest on ZIP-code-based rate increases?

Minnesota, Oklahoma and Iowa have actively challenged blanket ZIP-code surcharges. In those three Midwest states, where homeowners rates have surged by double digits over the last several years, regulators have pushed back on carriers relying on ZIP-code-level pricing rather than approving it outright. Illinois went further this month: Gov. JB Pritzker signed legislation ending 50 years of insurers being able to set rates without prior state approval. Across storm corridors, the pattern is consistent — regulators want evidence that a rate reflects the risk of the specific properties it applies to, not just the region. Regulatory acceptance is also what makes property-level analytics usable inside a filing rather than only in internal analysis: Z-HAIL, Z-WIND and Z-STORM, ZestyAI's severe convective storm suite, have been accepted in 32 states, and ZestyAI's risk models have secured more than 200 regulatory approvals nationwide — including Illinois.

What variables go into a property-level risk score?

Roof geometry, material and condition, accumulated hail exposure, and localized climatology — evaluated together for how they interact during a severe storm. Carriers are building mathematical formulas around these inputs to produce a score for each individual property, which they submit as supporting evidence in rate filings. The more precise scores also look beyond a home's standalone characteristics to how a structure interacts with its immediate surroundings — tree density or structural geometry can amplify or reduce wind and hail damage during the same storm event, even between two nearby homes.

Are property-level risk scores used at quote time, or only after a policy is bound?

Increasingly at quote time, before the policy is ever written. Carriers once ordered aerial photos weeks after binding a policy to spot-check a roof; now that same aerial and property data can be scored during the initial quote, pricing roof hazards before a policy is ever written rather than catching issues after the fact.

Are percentage deductibles and ACV roof schedules rising?

Both have grown sharply over the same decade property-level data has become more available. A ZestyAI audit of more than 2,000 filings between 2015 and 2025 found that adoption of actual-cash-value roof settlement schedules surged from 10% to 60% among the top 10 homeowners carriers, while mandatory percentage wind and hail deductibles grew from 60% to 90% over the same period. Coverage architecture is doing on the claims side what property-level data is doing on the rate side: giving carriers a more granular way to match terms to actual risk. The important shift is not simply toward more restrictions — it is toward making those decisions more closely reflect the underlying risk, so carriers do not end up penalizing good risks while retaining the properties that are actually driving losses.

How does parcel-level pricing change the relationship between carriers, agents and policyholders?

It moves the pricing conversation from territory-level generalities to the risk factors of one specific address. That actuarial precision changes the dynamic industry-wide: an agent can no longer explain a quote by pointing to a rating territory alone, because two nearby policyholders can now be priced very differently based on their own property's characteristics rather than the neighborhood they share. It also changes what a policyholder can do about their rate. ZestyAI's severe convective storm models are mitigation-aware: verified improvements such as a roof replacement, an upgrade to more impact-resistant roofing material, or corrected property data can be reflected directly in the risk score. That gives carriers a clearer way to show not only why two nearby properties are priced differently, but how specific actions can reduce that risk.

Is territory-based rating still viable alongside property-level scoring?

Risk varies more within a single territory than a territory-level rating factor can capture. Roof shape and geometry, features like vents and skylights, material and age all affect a property's susceptibility to storm damage, Dhuvur said. "Two neighboring homes can experience the same hail event and still have very different loss outcomes," he said. Modeling the interaction between the structure and localized climatology is what produces real separation between good and bad risks within the same territory — segmentation a territory factor alone cannot see. For carriers, that means competitve and internal benchmarking has to move to the same parcel-level granularity rather than staying anchored to territory averages.

How can carriers benchmark their own rate filings against competitors?

By reviewing the risk models and coverage language competitors have already gotten approved in the same states, rather than starting from a blanket rate ask. As property-level scoring becomes table stakes in catastrophe-exposed states, the carriers with the most defensible filings are the ones who can show regulators exactly how a property's risk score was derived — and how it compares to similar filings already accepted in that state. ZORRO Discover gives filing and product teams that reference point: competitor risk models, coverage language and adoption rates cited in approved filings, so a new filing can be built on evidence regulators have already accepted.

Read the full article: Insurers Turn to Property-Level Risk Models to Justify Rate Increases → P&C Specialist, August 12, 2026, by Carl Winfield. Includes commentary from ZestyAI co-founder and chief product officer Kumar Dhuvur.