Reports & Research

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How are carriers using ACV roof settlement schedules to manage roof exposure? 

Carriers use Actual Cash Value (ACV) roof settlement schedules to cap claim severity on older roofs: the schedule pays the replacement cost of a roof adjusted for age and depreciation, rather than the full cost of replacing it. ACV is one of several coverage controls carriers use to limit exposure on aging roofs, alongside percentage-based deductibles, cosmetic damage exclusions, age-based payout triggers, and anti-matching provisions.

How fast is ACV roof settlement adoption growing among homeowners carriers ?

Sixfold in a decade among the top 10 U.S. homeowners carriers, from 10% of policies in 2015 to 60% in 2025. Looking at all ~60 carriers ZestyAI reviewed in catastrophe-exposed states, 75% now use ACV roof settlement schedules.

What other roof coverage restrictions are carriers filing?

ACV schedules are only one of several controls carriers are adding. Percentage-based deductibles spread from 60% to 90% of top-10 carrier policies over the same 2015-2025 period. Cosmetic damage and anti-matching provisions — once rare, limiting payouts for damage that's visible but doesn't affect a roof's function — accelerated even faster, from 20% adoption in 2015 to 90% in 2025.

Across all ~60 carriers ZestyAI reviewed in catastrophe-exposed states, the adoption rates were: 93% use percentage-based deductibles, 78% apply age-based coverage triggers, 75% use ACV roof settlement schedules, 49% include cosmetic damage exclusions, 30% require mandatory inspections as a condition of coverage, and 17% include anti-matching language.

How are carriers stacking multiple roof coverage restrictions?

ZestyAI's analysis found that a single roof claim today routinely faces four or more stacked coverage restrictions from one loss event: a percentage deductible, an ACV settlement schedule, an age-based payout trigger, and a cosmetic damage exclusion can all apply simultaneously. Kuczynski described one common pairing: "Attaching a higher flat deductible to your policy specifically targeting wind and hail losses puts a lot more skin in the game for the insured and does help to reduce the cost when claim time does come."

Which states have the highest adoption of roof coverage restrictions?

Texas and Oklahoma lead. ZestyAI found Texas at 100% adoption of percentage deductibles among reviewed carriers, with Oklahoma at approximately 95%. ACV roof settlement schedules were most prevalent in Oklahoma (85%) and Texas (82%), while cosmetic damage exclusions appeared in 57% of Texas filings and 50% of Colorado filings. Carriers facing similar catastrophe pressure aren't converging on one product design; they're assembling the same controls in different combinations depending on geographic concentration, operating model, and risk appetite.

Do roof coverage restrictions increase policyholder complaints?

Not so far. ZestyAI cross-referenced coverage restrictions against state insurance department complaint volumes and found a negative correlation; filings with more restrictions were associated with fewer complaints, not more. "There are less complaints coming in immediately, and theoretically that is due to premium breaks," Kuczynski said. "Everybody likes to have more cash, but we're not seeing any pushback from insureds for this loss of coverage."

Why are carriers layering coverage restrictions instead of relying on rate alone?

Coverage design can target roof-specific severity in ways base rate changes cannot. Controls like percentage wind/hail deductibles and ACV schedules shift a defined share of each loss back to the insured, reducing claim costs while funding the premium relief that appears to be keeping complaint volumes down. For carriers facing rate pressure in catastrophe-exposed states, coverage architecture has become a second lever alongside rate.

What does coverage architecture mean for carrier competitive strategy?

Coverage architecture, how coverage restrictions are layered within a policy, now differentiates carriers as much as rate, because two policies can look nearly identical at bind and behave very differently at claim time, according to Kuczynski. "The difference is that one may achieve a lower premium by layering restrictions that shift more risk back to the policyholder. Consumers naturally focus on the price they pay today, while the cumulative impact of a percentage deductible, an ACV roof settlement schedule, an age-based trigger, and a cosmetic exclusion often doesn't become clear until a loss occurs. That's why a decade-long trend like ACV adoption jumping six-fold matters: the economics of a roof claim under those policies can look fundamentally different."

The broader takeaway: as coverage design increasingly shapes what a roof claim actually pays out, clearer visibility into how these terms are layered — for carriers designing products, regulators reviewing filings, and policyholders comparing coverage — is becoming as important as the rate itself.

How can carriers benchmark their roof coverage against competitors?

Approved regulatory filings show which controls competitors have filed, in which states, and in what combinations. ZestyAI’s analysis drew on more than 2,000 homeowners filings across roughly 60 carriers to quantify adoption of ACV schedules, percentage deductibles, cosmetic exclusions, and age-based triggers. ZORRO Discover gives carriers this filing intelligence on demand: competitor coverage language, adoption rates, and state-by-state comparisons, so product teams can see where their own coverage architecture sits relative to the market.

Read the full article Shrinking Roof Lifespans Create a Pricing Problem for Home Insurers → P&C Specialist, July 1, 2026, by Jennifer Ortakales Dawkins. Includes ZestyAI's regulatory filing analysis and commentary from Stephanie Kuczynski, ZestyAI's director of risk analytics.

A rate filing submitted in Wisconsin can be approved in a single day. A similar filing in California can take more than eight months. Across the United States, that gap now exceeds 250 days

For P&C insurers, approval velocity is no longer a back-office compliance metric. It is a business constraint that affects pricing agility, product availability, rate adequacy, admitted-market competitiveness, and the shift toward Excess & Surplus. 

ZestyAI analyzed 20,183 approved P&C rate filings across all 50 states and the District of Columbia for the 12 months ended May 8, 2026. The analysis covers the three highest-volume filing lines in the country: Homeowners, Personal Auto, and Commercial Property.

The analysis was conducted using ZORRO Discover, ZestyAI’s AI-powered regulatory and competitive intelligence platform for P&C insurance, which indexes more than 2 million regulatory filings and 200 million pages.

The result is the Approval Velocity 2026 report: a state-by-state benchmark of how long P&C rate filings take to get approved, where regulator objections occur most often, and what filing teams can do to reduce avoidable delay.

Key Findings

Across every approved P&C rate filing in the last 12 months, 20,183 across homeowners, commercial property, and personal auto, ZestyAI found six patterns that matter for underwriting, actuarial, product, and regulatory teams: 

1. Approval times vary by more than 250 days between the fastest and slowest states. Wisconsin and South Dakota close most filings in 0–2 days; California, New York, and Maryland can take 6–8 months.
2. 44% of approved rate filings drew at least one regulator objection. Across the dataset, 8,776 filings received at least one formal objection letter before approval.
3. A single regulator objection adds a median of 38 days to approval time. In some states, the added delay exceeds 150 days. 
4. Personal lines draw materially more scrutiny than commercial lines. Homeowners filings received objections 53% of the time. Personal Auto filings received objections 51% of the time. Commercial Property filings received objections 34% of the time. 
5. Personal Auto objections are highly concentrated. GLM and rating-factor construction support is the dominant objection theme in more than 20 states, making it the most repeatable documentation opportunity in the dataset. 
6. Filing friction is one operational force accelerating the shift toward E&S. When admitted filings take months to clear, carriers face longer periods of rate inadequacy, missed effective dates, and heavier operational burden. 

Approval Times Vary by More Than 250 Days Across States

Across Homeowners, Personal Auto, and Commercial Property filings, the gap between the fastest and slowest states exceeds 250 days. 

The fastest states move quickly. Wisconsin, South Dakota, Alabama, and Arkansas close many filings in zero to two days. The slowest jurisdictions, including California, New York, Maryland, New Jersey, and South Carolina, can take six to eight months. 

This pattern appears across all three lines studied. Whether a filing moves quickly or stalls is driven more by the state than by the line of business.

For carriers, that makes state-level approval velocity one of the most important variables in filing strategy. A national approval assumption is not useful. Filing teams need state-specific expectations for effective dates, launch sequencing, objection planning, and resource allocation.

44% of Approved Filings Drew at Least One Regulator Objection

Of the 20,183 approved filings analyzed, 44% — or 8,776 filings — drew at least one formal objection letter from the state Department of Insurance before approval. Across the 12-month dataset, regulators issued more than 35,000 individual objection letters.

The cost of an objection is significant: when a filing draws one, it takes roughly four times as long to approve compared to a filing that clears without comment. The median delay an objection adds is 38 days across all lines.

The objection rate is meaningfully higher for personal lines than commercial. Homeowners filings drew objections 53% of the time. Personal Auto drew objections 51% of the time. Commercial Property, where DOIs apply lighter regulatory scrutiny to commercial pricing, drew objections 34% of the time. 

That is not simply a quality-of-filing issue. It reflects a deliberate regulatory posture: state DOIs apply heavier scrutiny to consumer-facing personal lines pricing. 

Homeowners Rate Filings: The Most Contested Line

4,297 approved filings. 53.2% objection rate. Median approval time: 36 days.

Homeowners is the most contested of the three lines studied. It is also the most heterogeneous: unlike Personal Auto and Commercial Property, where objection themes cluster around one or two dominant patterns, Homeowners themes are highly state-specific.

CAT-exposed states focus on catastrophe model documentation  

In CAT-exposed states, regulators frequently focus on catastrophe model support.

Florida, Hawaii, Maryland, Montana, and South Carolina show strong focus on hurricane-model vendor disclosure, catastrophe-model documentation, FCHLPM compliance, and ASOP 38/41 attestations.

Hawaii and South Carolina stand out. Hawaii homeowners filings drew objections 92% of the time. South Carolina homeowners filings drew objections 95% of the time.

For carriers filing in CAT-exposed states, catastrophe model documentation should not be treated as supporting material. It should be treated as a core filing asset.

Consumer-protection states focus on policyholder impact 

In consumer-protection states, regulators focus heavily on how rate changes affect individual policyholders.

Arkansas, Georgia, Kansas, Mississippi, New York, and Pennsylvania show greater scrutiny of individual-policyholder rate impacts.

New York is the clearest example. Its dominant homeowners objection theme is aggressive individual-policyholder rate capping, including strict maximum-percentage-change limits. New York’s median homeowners approval time was 220 days, the slowest homeowners approval timeline in the dataset.

For carriers, actuarial indication alone is not enough. Filing packages need to explain how rate changes affect individual policyholders, not just the overall rate level.

Actuarially rigorous states focus on model construction and reconciliation 

California, Iowa, and Nebraska show greater scrutiny of GLM construction, trend support, and indication reconciliation.

California homeowners filings received objections 100% of the time in the dataset. That reflects the granular line-by-line reconciliation required under the state’s Prior Approval Rate Application and Standard Exhibits Template.

For actuarial teams, every model choice, trend assumption, and reconciliation step needs to be regulator-ready before submission.

Fastest and slowest states for Homeowners filings

The slowest Homeowners states were New York at 220 days, California at 197 days, and New Jersey at 177 days.

The fastest Homeowners states were Wisconsin at 0 days, South Dakota at 1 day, and Alabama at 2 days.

When an objection lands in a slow state, the delay compounds. In Georgia, the median homeowners filing closed in 30 days without an objection and 187 days with one, a 157-day swing. Washington and New York each added more than 140 days when an objection was issued.

Personal Auto Rate Filings: The Highest Objection Volume

6,339 approved filings. 50.7% objection rate. Median approval time: 33 days.

Personal Auto generated more individual objection letters than any other line in the study — 12,793, compared to 12,310 for Homeowners and 10,486 for Commercial Property. The median objection in Auto adds 39 days to approval time, the largest penalty of any line. California's median of 246 days is the slowest in the entire dataset.

GLM and rating-factor support dominates Personal Auto objections

Unlike Homeowners, Personal Auto objection themes are highly concentrated.

GLM and rating-factor construction support is the dominant objection theme in more than 20 states. This reflects regulators’ sharpened focus on segmentation methodology as carriers introduce new rating constructs, telematics programs, credit-based scoring models, and by-peril rating approaches.

The practical implication is significant. Carriers introducing new rating factors can build one regulator-ready documentation package and reuse it across many states.

That package should include variable selection rationale, model form support, hold-out validation, lift analysis, multicollinearity testing, indication reconciliation, bias testing, and a clear explanation of how rating factors affect policyholder premium.

This is not just a compliance exercise. It is a speed advantage. Better model documentation can reduce back-and-forth and help carriers move faster through high-scrutiny states.

Fastest and slowest states for Personal Auto filings

The slowest Personal Auto states were California at 246 days, Maryland at 142 days, and New York at 131 days.

The fastest Personal Auto states were Wisconsin at 0 days, South Dakota at 1 day, and New Mexico at 3 days.

The with-objection versus no-objection gap is stark. The median Personal Auto filing without an objection closed in 9 days. With an objection, the median increased to 52 days, nearly six times longer.

New York had the widest swing: 71 days without an objection versus 200 days with one.

Commercial Property Rate Filings : Faster Overall, But Risky at the Slow End 

9,547 approved filings. 34.3% objection rate. Median approval time: 13 days.

Commercial Property is the least contested line in the analysis, with the lowest objection rate and the fastest overall median approval time. Most commercial property filings clear without ever drawing a regulator comment. When they do draw one, though, the delay is severe: the median commercial property filing closes in 7 days without an objection, and 33 days with one — nearly five times longer.

ISO and AAIS loss cost support drives many Commercial Property objections

Sixteen states share the same dominant Commercial Property objection theme: ISO/AAIS Loss Cost Multiplier adoption and support.

For carriers deviating from ISO loss costs, the filing package should include a standardized comparison exhibit that allows reviewers to verify the deviation quickly.

That exhibit should show the ISO baseline, the company loss cost multiplier, the resulting rate by class, support for deviations, and expected impact on indicated and selected rates.

This is one of the highest-value documentation opportunities in the dataset because the objection theme is both common and predictable.

Fastest and slowest states for Commercial Property filings

The slowest Commercial Property states were California at 252 days, Maryland at 210 days, and New York at 108 days.

The fastest Commercial Property states were Wisconsin at 0 days, Alabama at 1 day, and Arkansas at 1 day.

Maryland illustrates the risk at the slow end. The median Commercial Property filing there closed in 124 days without an objection and 259 days with one, a 135-day gap. New York increased from 41 days without an objection to 145 days with one.

Four Patterns That Hold Across All Three Lines

The Approval Velocity 2026 report reveals four consistent patterns across Homeowners, Personal Auto, and Commercial Property. 

State posture dominates line-of-business posture. 

Whether a filing takes days or months is determined more by where you file than what you're filing. California, New York, Maryland, and South Carolina are slow in Homeowners, Personal Auto, and Commercial Property alike. Wisconsin, South Dakota, Arkansas, and Alabama are fast across all three. Carriers building multi-state filing plans should treat state posture as the primary variable.

Personal lines draw materially more scrutiny than commercial. 

Homeowners and Personal Auto filings draw objections more than half the time. Commercial Property filings draw objections about one-third of the time.

This is not necessarily a quality-of-submission problem. It reflects a deliberate regulatory posture. State DOIs apply heavier review to consumer-facing personal lines pricing.

Objection themes vary by line

Homeowners objections are highly state-specific. CAT-exposed states focus on catastrophe model support. Consumer-protection states focus on policyholder impacts. Actuarially rigorous states focus on model construction and reconciliation.

Personal Auto objections are more standardized. GLM and rating-factor support is the dominant issue in more than 20 states.

Commercial Property objections are also concentrated. ISO/AAIS LCM adoption and support is the dominant issue in 16 states.

Each line needs a different preparation strategy.

Many objections are avoidable 

A significant share of objections are not substantive disputes over rate adequacy. Many are procedural asks that can be anticipated before submission.

Examples include Connecticut requiring the Rate Matrix and Readable Language Certification, Illinois requiring Maximum and Minimum Percentage Change values plus a rate-impact breakdown under the Rate/Rule tab, Michigan requiring the P&C Rate/Rule Filing Checklist v5 with a matching SERFF number, and Ohio requiring INS 4012 with Ohio and countrywide profit and loss data.

These issues are preventable. Filing teams that build state-specific pre-submission checklists can remove avoidable delay from the approval process.

Why Approval Velocity Matters for P&C Insurers

Approval velocity is the time between when a carrier submits a rate filing and when the state Department of Insurance approves it.

That timeline has become a strategic constraint. In fast states, carriers can respond quickly to changing loss costs, catastrophe exposure, and underwriting performance. In slow states, pricing updates can sit in review for months, leaving teams managing business against rates that may no longer reflect current risk.

For underwriters

Slow approval velocity means pricing can lag the actual risk environment.

When a rate filing takes 150, 200, or 250 days to approve, underwriters may continue writing business under rates that no longer reflect current loss costs, catastrophe exposure, inflation, or portfolio strategy.

That gap is especially important in states exposed to wildfire, hurricane, severe convective storm, non-weather water, and other fast-changing loss drivers.

Approval velocity should inform underwriting strategy. In slow states, carriers may need tighter appetite management, more disciplined renewal segmentation, and earlier filing preparation to avoid prolonged exposure to stale pricing.

For actuaries

For actuaries, the data shows where stronger support can reduce delay.

In Personal Auto, GLM and rating-factor support is the dominant objection theme in more than 20 states. In Commercial Property, ISO/AAIS LCM support is the dominant objection theme in 16 states. In Homeowners, state-specific documentation is critical, especially for catastrophe models, individual policyholder impacts, and indication reconciliation.

Actuarial teams should treat objection prevention as part of model deployment.

The best filing support does not simply show the selected rate. It explains the path from data to indication to selected rate to policyholder impact in a way the regulator can verify.

For product managers

For product managers, approval velocity changes launch planning.

A product rollout that assumes uniform regulatory timing will be wrong. Fast states may clear in days. Slow states may not clear for months.

That means product teams should plan state sequencing around approval velocity, not just market opportunity. In some cases, the best launch path may be to move quickly in fast-approval states while preparing more robust documentation for high-friction states.

Approval velocity should be part of the product roadmap, especially when launching new rating variables, by-peril rating structures, telematics programs, catastrophe model updates, or major segmentation changes.

The E&S Market Signals Hidden in The Data

There is a structural consequence that extends beyond filing operations. The harder it becomes to clear admitted rate filings — particularly in CAT-exposed, high-cost personal lines markets — the more carriers shift volume to the Excess & Surplus market, where filings are not subject to DOI rate review. The P&C E&S market has set record share in each of the past three years.

This report quantifies one of the operational pressures driving that shift. When a homeowners carrier in New York faces a 220-day median approval cycle, or when every single California filing in a given line draws at least one objection, the admitted market becomes operationally expensive in ways that compound over time: missed effective dates, rate inadequacy during the review window, and filing resources tied up in back-and-forth cycles rather than productive work.

For CEOs, chief underwriting officers, chief actuaries, and product leaders, approval velocity should be viewed as a market strategy input, not just a filing metric. 

State Benchmarks at a Glance

For quick reference, here are the median approval times for the slowest and fastest states in each line:

Homeowners

• Slowest: New York (220d), California (197d), New Jersey (177d)
• Fastest: Wisconsin (0d), South Dakota (1d), Alabama (2d)

Personal Auto

• Slowest: California (246d), Maryland (142d), New York (131d)
• Fastest: Wisconsin (0d), South Dakota (1d), New Mexico (3d)

Commercial Property

• Slowest: California (252d), Maryland (210d), New York (108d)
• Fastest: Wisconsin (0d), Alabama (1d), Arkansas (1d)

Methodology

The Approval Velocity 2026 report covers approved rate filings in all 50 states and the District of Columbia for the trailing 12 months ended May 8, 2026, across Homeowners, Personal Auto, and Commercial Property. Filings that change only forms or rules without a rate change were excluded, as were withdrawn filings. Filings with a 0% overall rate impact were included alongside filings with positive or negative changes; carriers use 0% filings to modernize pricing architecture while holding aggregate premium constant. Approval time is measured from submission date to the date the state DOI closes and approves the filing.

The analysis was conducted using ZORRO Discover, ZestyAI's AI-powered competitive intelligence platform for P&C insurance, which indexes 2 million+ regulatory filings and 200 million+ pages.

For the full state-by-state data, approval time charts, and objection theme breakdowns for all 51 jurisdictions across all three lines, download the complete Approval Velocity 2026 report.

What is the 2026 wildfire outlook for insurers? 

The 2026 wildfire season is forcing carriers to reassess wildfire exposure at the property level. ZestyAI’s analysis of 2025 fire perimeters shows that structural loss, risk concentration, and emerging drought patterns are shifting the underwriting conversation beyond traditional wildfire maps. For underwriters, actuaries, and product leaders, the priority is clear: identify which properties are most likely to be reached, damaged, or destroyed before the next fire season. 

For the full property-level analysis, state risk profiles, 2025 case studies, and regulatory overview, download the complete Wildfire Season Preview 2026. 

Key Findings

1. The 2025 wildfire season burned 5.1 million acres — below the five-year average — but destroyed 18,385 structures, more than 2.5 times the average, with nearly 90% of structural loss driven by the January Los Angeles fires.
2. Properties in ZestyAI's highest wildfire risk tier were roughly 500 times more likely to fall within a wildfire perimeter in 2025 than properties in the lowest tier.
3. Across three 2025 case studies in Oregon, Utah, and North Carolina, properties inside fire perimeters were concentrated in High or Very High risk classifications at 11 to 15 times the statewide average.
4. In 2025, California accounted for 92% of all U.S. residential properties that fell inside wildfire perimeters, even though only about 11% of its properties score High Risk.
5. Colorado's HB25-1182, effective July 1, 2026, requires insurers using wildfire models to explain scores, account for mitigation, and resolve policyholder appeals within 30 days.
6. The 2026 drought outlook shows improving conditions in California but extreme and exceptional drought developing across the central Rockies and parts of the U.S. South — expanding the wildfire risk footprint beyond traditional Western exposure zones.

Why 2025 Changed the Wildfire Risk Conversation for Carriers 

The 2025 wildfire season exposed how quickly insured loss can concentrate when fire reaches dense residential areas. Nationwide, 18,385 structures were destroyed, more than 2.5 times the 2021–2025 average. Nearly 90% of that structural loss came from the January Los Angeles fires, where the Palisades and Eaton fires destroyed nearly 16,000 structures.

Those losses showed how wildfire exposure is changing for P&C carriers. Ember-driven fire can move through dense neighborhoods far from the traditional wildland-urban interface, creating severe property loss in areas that may not look extreme through conventional wildfire maps.

For underwriting, actuarial, and product teams, the implication is direct. Wildfire risk has to be evaluated at the property level, using factors such as defensible space, roof materials, vegetation, structure characteristics, surrounding fuels, topography, and local fire behavior. Two homes in the same neighborhood can carry materially different risk based on the conditions around the individual property.

Carriers preparing for the 2026 wildfire season need visibility into which properties are most likely to be reached by fire, which are most likely to be damaged or destroyed, and where risk is concentrating across the portfolio before the next major event occurs.

ZestyAI's Wildfire Season Preview 2026 draws on property-level analysis of every residential property that fell within 2025 wildfire perimeters, three regional case studies validating model performance across distinct fire environments, and an overview of the emerging regulatory landscape reshaping how insurers assess and communicate wildfire risk.

How Wildfire Exposure Varies Across U.S. Properties

ZestyAI analyzed every residential property across the U.S. using Z-FIRE, then analyzed which properties fell within 2025 wildfire perimeters to understand where exposure actually occurred. The results show a highly uneven risk landscape, with significant variation across states, regions, and individual properties.

Nationally, roughly 91% of properties score Low Risk, 6% score Medium Risk, and 3% score High Risk under ZestyAI's Z-FIRE model. But in 2025, properties in the High Risk tier were far more likely to fall within wildfire perimeters than those in the Low Risk tier — by a factor of approximately 500. 

That separation matters for carriers because wildfire exposure is not evenly distributed across a book of business. A small share of properties can represent a disproportionate share of exposure when fire activity overlaps with dense development, vulnerable property conditions, and elevated local fire potential. 

State-level exposure adds another layer of complexity. The share of High Risk properties varies significantly by state. Montana, for instance, carries a much larger proportion of High Risk properties than the national average. But high risk-tier concentration does not alone explain where losses occur — actual exposure also depends on where fires ignite, how they spread, and how many properties sit in their path.

Three states illustrate how differently the same risk tiers translate into actual exposure:

California had only about 11% of properties in the High Risk tier in 2025, yet accounted for 92% of all U.S. properties that fell inside wildfire perimeters. The scale of fire activity, combined with dense development near wildlands and major fires moving through populated neighborhoods, turned a limited High Risk segment into a dominant share of national exposure.

Texas is weighted heavily toward the lower end of the risk spectrum, with approximately 94% of properties classified Low Risk and 2% High Risk. Fire incidence within risk tiers reflects that lower baseline exposure.

Colorado shows a more distributed profile — about 70% Low Risk, 17% Medium Risk, and 12% High Risk — with fire incidence rates that track its more balanced risk distribution.

These state profiles reinforce a core finding: wildfire exposure cannot be assessed through a single national average or a fixed risk threshold. Local fire behavior, development patterns, and property density all determine how risk translates into actual loss.

Where Wildfire Risk Is Shifting in 2026 

The 2026 wildfire outlook points to a broader and more complex risk footprint for P&C carriers.

Drought conditions are improving in parts of California, but elevated dryness is developing across the central Rockies and parts of the U.S. South. That shift matters because drought can dry fuels, stress vegetation, and make fire behavior more responsive to wind, heat, and ignition sources.

For carriers, the underwriting concern is not limited to where wildfire has historically been most severe. It is where changing fuel conditions, property density, and local weather patterns are creating new pockets of exposure.

The 2025 season already showed signs of this expansion. Major wildfire events occurred in Arizona and Oklahoma, and the Black Cove Fire in North Carolina demonstrated how quickly wildfire can become relevant in markets where it has not traditionally been treated as a primary underwriting peril. That fire was driven by dry conditions, wind, and heavy storm-damaged fuels left by Hurricane Helene.

The 2026 outlook reinforces the need for carriers to reassess wildfire exposure beyond legacy high-risk geographies. Underwriting, actuarial, and product teams should evaluate where wildfire risk is rising across the portfolio, which properties are most exposed, and whether existing pricing, eligibility, and renewal strategies reflect the current risk environment.

A static view of wildfire territory is no longer enough. Carriers need a property-level view of exposure that can adapt as drought conditions, vegetation, development patterns, and fire activity shift across regions.

How Z-FIRE Performed Across 2025 Wildfire Events 

ZestyAI evaluated Z-FIRE performance across three 2025 fires representing distinct regional settings and fire environments: a Pacific Northwest WUI fire, a Mountain West fire in rugged lower-density terrain, and a Southeast fire in a region where wildfire has not historically been a primary underwriting concern. 

Across all three events, properties inside fire perimeters were concentrated in High or Very High classifications at 11 to 15 times the statewide average. That consistency matters for carriers validating wildfire models across different geographies, fire environments, and portfolio segments. 

Flat Fire, Oregon (August 2025)

The Flat Fire was a Central Oregon wildland-urban interface fire that grew to more than 23,000 acres in under a week. Of the 107 Z-FIRE-scored properties that fell inside the perimeter, 95% had been classified as High or Very High before the fire. None were Low or Very Low.

Statewide, roughly 8 in 100 Oregon properties carry a High or Very High wildfire risk score. Inside the Flat Fire perimeter, more than 95 in 100 did — approximately 11 times the statewide average. For carriers with Central Oregon exposure, the implication is direct: the fire did not reach a random cross-section of properties. It reached a concentrated group that had already been identified as materially more exposed.

Monroe Canyon Fire, Utah (July–September 2025)

The Monroe Canyon Fire burned through rugged, lower-density terrain in south-central Utah, growing to more than 73,000 acres over 54 days. Every scored property inside the perimeter had been classified as High or Very High before the fire. 80% were Very High; 20% were High. None were Medium, Low, or Very Low.

Statewide, only 9% of Utah properties are classified as High or Very High. Inside the Monroe Canyon perimeter, that share was 100%. The Very High concentration was even sharper: only 2% of Utah properties fall in the Very High classification statewide, compared with 80% inside the perimeter.

Monroe Canyon illustrates a specific underwriting challenge: in lower-density Mountain West terrain, the affected population may be small — cabins, second homes, rural recreational properties — but the properties that fall inside a large fire perimeter are not random. They are the ones already scored highest. Geographic and ZIP-code views can show where exposure is located; property-level scores show which specific properties carry the most risk.

Black Cove Fire, North Carolina (March 2025)

The Black Cove Fire started in Polk County, North Carolina after a downed power line ignited dry vegetation in the Green River Gorge. Hurricane Helene had deposited heavy storm-damaged fuels in the area six months earlier. Drought and wind helped the fire become one of the most active early-season events in the eastern United States in 2025.

Inside the Black Cove perimeter, 84% of properties had been classified as High or Very High before the fire arrived — 64% High and 19% Very High. None were Low or Very Low. Statewide, only 5% of North Carolina properties are classified as High or Very High. Inside the perimeter, that share was 84%, more than 15 times the statewide average.

Black Cove is the most important case study for carriers reconsidering their exposure in non-traditional wildfire markets. The fire occurred in the Southeast, in a state where wildfire has not historically been treated as a primary underwriting peril. The signal was there before the fire. Carriers that treat wildfire as a Western-only concern are operating without visibility into a growing share of their exposure.

How Wildfire Modeling Regulations Are Changing in 2026 

Wildfire regulation is moving from model permission to model accountability. Carriers increasingly need to explain how wildfire scores are used, recognize mitigation, support policyholder appeals, and document model governance. 

Across the West, four states are pursuing that standard through different paths.

California has tied wildfire model adoption to coverage obligations through its Sustainable Insurance Strategy. Carriers using approved wildfire catastrophe models must write a proportional share of homes in high-risk areas relative to their statewide market presence. A carrier with 10% of the California market must write at least 8.5% of homes in high-risk zones. Meeting that obligation requires property-level risk assessment — not just catastrophe model outputs — to distinguish individual homes worth writing from those that are not.

Colorado's HB25-1182, enacted in 2025 and effective July 1, 2026, requires insurers that use wildfire risk models to explain how models are used, account for property-specific and community-level mitigation, provide annual notices about wildfire risk scores and available discounts, and create an appeal process for disputed scores. Appeals must be acknowledged within 10 days and resolved within 30 days. The law moves wildfire modeling from a permissioning question to an accountability standard.

Washington's Senate Bill 5928 would require insurers to disclose wildfire risk scores when they are used in coverage or pricing decisions, explain the factors driving the score, and provide plain-language steps homeowners can take to improve it. Wildfire scores can no longer operate as a black box.

Oregon repealed its statewide wildfire hazard map in 2025 after public pushback against classifications that were too broad to reflect property-specific characteristics. The state is now moving toward a more granular, property-level approach. For insurers, the Oregon experience illustrates the risk of relying on broad geographic classifications: when scores cannot distinguish vulnerable homes from better-protected ones, or when policyholders cannot understand what drives their rating, the regulatory and consumer response can be swift.

Beyond state-level rules, the NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, adopted in December 2023, establishes national governance expectations for AI-based underwriting tools. As more states adopt the bulletin's guidance, wildfire models will increasingly be evaluated on how they are governed, tested, explained, and monitored — not only on predictive performance.

What Carriers Should Prioritize for Wildfire Underwriting in 2026 

Wildfire underwriting in 2026 requires more than identifying high-risk geographies. Carriers need a sharper operating model for how wildfire risk is segmented, monitored, validated, and explained across the policy lifecycle. 

1. Segment wildfire risk at the property level

Broad geographic proxies can miss the variation that matters most for underwriting and pricing. ZIP codes, static wildfire maps, and community-level classifications may show where exposure exists, but they cannot reliably distinguish the individual properties most likely to be reached, damaged, or destroyed.

Property-level risk segmentation gives underwriting, actuarial, and product teams a more precise view of exposure. It can support new business eligibility, renewal decisions, pricing refinement, mitigation targeting, portfolio concentration management, and reinsurance planning.

The 2025 wildfire season reinforced the need for that precision. Properties inside fire perimeters were not a random cross-section of the market. They were disproportionately concentrated in higher-risk classifications before the fires occurred.

2. Monitor mitigation and property conditions continuously

Wildfire risk changes between policy cycles. Defensible space can be cleared or allowed to regrow. Roof materials can be replaced or deteriorate. Vegetation conditions shift with drought, storms, land use, and seasonal growth.

For carriers, mitigation recognition cannot be a one-time underwriting event. It requires current data on the property and its surroundings, along with the ability to explain how mitigation affects eligibility, pricing, or renewal decisions.

This is becoming both an underwriting priority and a regulatory expectation. As states move toward greater transparency around wildfire scores, carriers need defensible ways to identify mitigation, reflect it in risk decisions, and communicate the impact to policyholders.

3. Validate wildfire models across multiple fire environments

Wildfire exposure is no longer confined to one familiar pattern. Carriers may face risk in dense California neighborhoods, Western wildland-urban interface zones, rugged Mountain West terrain, rural recreational areas, and emerging non-traditional markets in the Southeast.

Model validation should reflect that diversity. A wildfire model that performs well in one region or fire environment may not provide the same signal across an entire book of business.

Carriers should require evidence that wildfire models separate risk across the geographies and property types represented in their portfolios. That means evaluating performance against observed fire outcomes, not only relying on theoretical risk assumptions or broad hazard classifications.

4. Prepare for explainability, governance, and appeals 

Wildfire modeling is moving into a more accountable phase. Carriers increasingly need to explain how scores are used, identify the factors driving risk, recognize property-level and community-level mitigation, and support policyholder appeals.

This changes what underwriting and actuarial teams need from wildfire models. Predictive performance still matters, but it is not enough on its own. Carriers also need transparency, documentation, governance, and operational workflows that can stand up to regulatory and policyholder scrutiny.

For 2026, the strongest wildfire strategies will combine accurate risk segmentation with explainable decision-making. Carriers that can see risk clearly, act on it consistently, and explain it credibly will be better positioned for underwriting discipline, regulatory readiness, and profitable growth.

Frequently Asked Questions

What is the 2026 wildfire outlook for P&C insurers?

The 2026 wildfire outlook points to a broader and more complex risk environment for P&C insurers. Drought conditions are improving in parts of California, while elevated dryness is developing across the central Rockies and parts of the U.S. South. For carriers, the key issue is not only where fires may occur, but which properties are most likely to be reached, damaged, or destroyed when wildfire moves into populated areas.

Why do insurers need property-level wildfire risk models?

Insurers need property-level wildfire risk models because wildfire exposure can vary sharply from one home to the next, even within the same neighborhood. Defensible space, roof materials, vegetation, topography, structure characteristics, surrounding fuels, and local fire behavior can all affect whether a property is reached, damaged, or destroyed. ZIP codes, static maps, and broad geographic classifications cannot capture that variation with enough precision for underwriting, pricing, renewal, and portfolio management.

How many structures were destroyed in the 2025 wildfire season?

18,385 structures were destroyed in the 2025 wildfire season — more than 2.5 times the 2021–2025 average. Nearly 90% of that structural loss came from the January 2025 Los Angeles fires (Palisades and Eaton), which together destroyed nearly 16,000 structures and generated an estimated $40 billion in insured losses. Total acres burned in 2025 came in below the five-year average, making the divergence between acreage and structural loss the defining characteristic of the season.

How can carriers use wildfire risk scores in underwriting and pricing?

Carriers can use wildfire risk scores to segment new business, evaluate renewals, refine pricing, identify mitigation opportunities, manage portfolio concentration, and support reinsurance planning. The most useful wildfire risk scores are property-specific, explainable, and validated against observed fire outcomes, so underwriting and actuarial teams can understand both the risk level and the drivers behind it.

How much does wildfire risk vary by property?

Wildfire risk varies significantly at the property level. In ZestyAI’s analysis of 2025 wildfire perimeters, properties in the highest wildfire risk tier were roughly 500 times more likely to fall within a wildfire perimeter than properties in the lowest tier. Across case studies in Oregon, Utah, and North Carolina, properties inside fire perimeters were concentrated in High or Very High risk classifications at 11 to 15 times the statewide average.

Is wildfire risk growing outside the Western United States?

Yes. Wildfire remains a major concern in the West, but 2025 showed that wildfire exposure is also relevant in non-traditional markets. The Black Cove Fire in North Carolina occurred in a state where wildfire has not historically been treated as a primary underwriting peril. Inside the Black Cove perimeter, 84% of properties had been classified as High or Very High Risk before the fire, more than 15 times the statewide average.

Which states face elevated wildfire exposure?

Wildfire exposure depends on both the share of properties in higher-risk tiers and the location of actual fire activity. California accounted for 92% of all U.S. residential properties that fell inside wildfire perimeters in 2025, even though only about 11% of its properties score High Risk. Colorado has a more distributed profile, with approximately 12% High Risk and 17% Medium Risk. Montana carries a higher share of High Risk properties relative to the national average.

What does Colorado HB25-1182 require from insurers?

Colorado HB25-1182, effective July 1, 2026, applies to insurers that use wildfire risk models, catastrophe models, or scoring methods to assign property risk. The law requires insurers to explain how models are used, account for property-specific and community-level mitigation, provide annual notices about wildfire risk scores and mitigation discounts, and establish an appeal process for disputed scores or classifications. Appeals must be acknowledged within 10 days and resolved within 30 days.

How are wildfire modeling regulations changing for insurers?

Wildfire regulation is moving toward greater transparency, mitigation recognition, model governance, and consumer explainability. Insurers increasingly need to show how wildfire scores are used in underwriting and pricing, which factors influence those scores, how mitigation is reflected, and how policyholders can dispute or improve their classification. This shifts wildfire modeling from a purely analytical capability to an operational and regulatory requirement.

What is Z-FIRE?

Z-FIRE is ZestyAI’s property-level wildfire risk model. It produces scores designed to estimate both the likelihood that a property will fall within a wildfire perimeter and the likelihood that the property will be damaged or destroyed if exposed. The model incorporates property and environmental factors such as defensible space, roof materials, structure characteristics, vegetation, topography, and surrounding fuels.

How did Z-FIRE perform in 2025 wildfire events?

ZestyAI evaluated Z-FIRE across three 2025 wildfire events in Oregon, Utah, and North Carolina. In each case, properties inside the fire perimeter were heavily concentrated in High or Very High risk classifications before the fire occurred. Across the three case studies, that concentration was 11 to 15 times the statewide average, showing strong risk separation across different fire environments.

How many structures were destroyed in the 2025 wildfire season?

The 2025 wildfire season destroyed 18,385 structures nationwide, more than 2.5 times the 2021–2025 average. Nearly 90% of that structural loss came from the January Los Angeles fires, where the Palisades and Eaton fires destroyed nearly 16,000 structures.

What should carriers do to prepare for wildfire risk in 2026?

Carriers should reassess wildfire exposure at the property level, monitor mitigation and property conditions continuously, validate wildfire models across multiple fire environments, and prepare for greater explainability, governance, and appeals requirements. The goal is to understand which properties carry the highest wildfire exposure before the next major event occurs, then use that insight to improve underwriting, pricing, renewal, and portfolio decisions.

For the full property-level analysis, state risk profiles, case study data, and regulatory overview, download the complete Wildfire Season Preview 2026.

Wildfire dominates the headlines, but the everyday fire — the cooking accident, the electrical short, the dryer that overheats — drives more than 20% of all U.S. property claims and 22% of every dollar paid out in homeowners insurance. A new ZestyAI on-demand session examines why everyday fire (also called non-weather fire) has become the least understood and most expensive peril in homeowners portfolios — and introduces Z-SPARK™, ZestyAI's new property-level model for predicting fire risk before ignition.

About this session. Everyday Fire Risk: The Hidden Severity Problem in Homeowners is an on-demand webinar covering why non-weather fire severity has risen 43% in four years, what ignition signals traditional underwriting misses, and how Z-SPARK turns property-level data into actionable claim frequency and severity scores. Presented by Abdul Mohammed (Product Marketing) and Alex Kallos (Director of Product) at ZestyAI.

Prefer to watch instead? Access the full on-demand session → — includes a live Z-SPARK demo and Q&A.

Why is everyday fire the largest hidden risk in homeowners insurance?

Three facts that don't usually appear together:

• Non-weather fire generated $25 billion in insured losses in 2024, or 22% of every dollar paid out in homeowners insurance that year.
• 26 cents of every premium dollar in a representative sample of carriers goes to non-weather fire — making it the single largest base-rate component, bigger than hail and wind, and roughly four times the size of hurricane.
• Over the past five years, wildfire destroyed roughly 35,000 structures. Non-weather fire produced 1.7 million incidents — a 50x volume difference.

Everyday fire isn't hiding because it's small. It's hiding because it's been the least systematically measured peril in the industry — assessed primarily through community-level fire protection (the nearest fire station, the response time) rather than at the individual property.

Why has fire claim severity jumped 43% in four years?

ZestyAI's analysis shows average non-weather fire claim severity climbed from $120K in 2020 to $173K in 2024 — a 43% increase, while claim frequency stayed essentially flat at 0.15–0.16%. This is a severity problem, not a frequency problem. Three drivers explain it:

• The escape window collapsed. UL Fire Safety Research Institute data shows the time between smoke alarm activation and untenable conditions has shrunk from 17 minutes (40 years ago) to 3 minutes today — driven by synthetic furnishings, open floor plans, and lightweight construction. Meanwhile, fire department response averages 7 minutes against a 4-minute national standard. Many fires reach the whole house before help arrives.
• Rebuild costs spiked. Building material prices are up 40% over five years; construction wages up 20%. Add stricter code compliance and longer rebuild timelines and a moderate claim five years ago is a major loss today.
• Smaller fires hide. A single fire claim can raise a homeowner's premium by 29%; a second by 60%. Some homeowners absorb the cost rather than file. Others file and switch carriers. Either way, the carrier writing the next policy inherits a property whose early-warning history is invisible — and the next claim is usually larger.

Once ignition happens, the loss is mostly set. The only meaningful place to intervene is before the fire starts.

What ignition signals do traditional fire risk models miss?

Four that show up repeatedly in ZestyAI's analysis:

• The vacancy next door. A vacant building within 10 meters of a property increases fire risk by 25%.
• The five-foot ignition zone. The first five feet around a structure is the most critical area for ignition. Combustibles in that zone can be the difference between containment and total loss.
• Deferred maintenance. A ZestyAI homeowner survey (500+ respondents) found that roughly half don't regularly clear combustible debris around their property.
• Battery-only smoke detectors. 65% of fatal fire incidents involve battery-only alarms rather than hardwired systems.

All four are predictive. None is consistently captured in current underwriting.

How does Z-SPARK change everyday fire risk assessment?

Z-SPARK is ZestyAI's property-level non-weather fire model, built on millions of fire incidents and hundreds of thousands of verified claims from national carriers. For every property it returns two 1-to-10 scores — Claim Frequency and Claim Severity — along with the top risk drivers behind each score, surfaced automatically so underwriters, customers, and regulators all see the why behind the number.

The segmentation power matters. Across Z-SPARK's hundred discrete risk segments, the highest-risk tier shows roughly 30x the measurable risk of the lowest tier. Two homes next door to each other in the same neighborhood can produce wildly different scores based on debris accumulation, vegetation density, and maintenance state — a level of discrimination that territory-based assessment can't see.

Z-SPARK sits alongside ZestyAI's wildfire model (Z-FIRE), which has received more than 200 regulatory approvals across 41 states. The non-weather fire model builds on the same foundation but turns the lens to the peril carriers were never able to assess at the property level.

What can carriers do with property-level fire intelligence?

Four moves become available once everyday fire risk is visible at the property: identify the highest-loss properties already sitting on the books, prevent new ones from entering as misclassified low-risk policies, segment to fast-track clean risks while setting guardrails on the rest, and allocate inspection resources to the properties that actually need them. Expansion into historically high-risk geographies also becomes tractable, because the risk is no longer a black box at the territory level.

The everyday fire risk hiding in a homeowners book doesn't have to stay hidden. Once it becomes visible at the property level, the entire economics of the peril change.

Watch the full session on demand

Everyday Fire Risk: The Hidden Severity Problem in Homeowners →

Featuring Abdul Mohammed (Product Marketing) and Alex Kallos (Director of Product) at ZestyAI, the session walks through the severity drivers behind the 43% jump in average claim size, the four ignition signals traditional models miss, a live Z-SPARK demo, and Q&A on residential vs. commercial coverage, model explainability, claims-data bias, and how to combine frequency and severity scores in pricing and inspection triage.

Watch the on-demand session — or request a Z-SPARK walkthrough to see how property-level non-weather fire scores apply to your own book.

Across the P&C industry, delayed rate approvals are costing an estimated $72.8 million per day in foregone premium — and the largest single category of objection causing those delays is procedural, not substantive. A P&C Specialist article published May 13, 2026 by Jennifer Ortakales Dawkins covers a ZestyAI analysis of more than 2 million P&C rate and form filings on SERFF, with commentary from ZestyAI's senior director of regulatory and government affairs Bryan Rehor on what's actually driving objection cycles and how filing teams can systematically reduce them.

Read the full article in P&C Specialist →

How much does P&C rate filing delay actually cost?

About $72.8 million a day across all lines, per the ZestyAI analysis. That figure is the aggregate cost of delayed approvals translating into lost premium — premium the rate change would have produced if it had taken effect when intended rather than weeks or months later. The cost compounds two ways: directly, in foregone premium for the period of delay, and indirectly, in continued exposure to the loss patterns the rate change was meant to address.

It also lands unevenly. California took a median of 267 days to approve a rate filing in 2025; Maryland took 206. At the other end of the spectrum, use-and-file states like Wisconsin clear filings in a median of three days, and Wyoming doesn't require rate filing at all. Most of the daily-cost burden concentrates in the slow-approval states.

What causes most rate filing objections?

According to the ZestyAI analysis, 74% of carriers receive objections on at least half of their filings. The most common reasons are procedural rather than substantive: submission gaps, missing or inconsistent supporting exhibits, unclear rationale for the rate change, and incomplete responses to regulator inquiries.

"Submission gaps are a very common and avoidable cause of delay," Rehor told P&C Specialist. The deeper challenge is that each state has its own filing requirements, and the volume of state-specific procedural rules makes it easy to miss something even when the substantive content of the filing is sound.

Which states have the highest rate filing objection rates?

For homeowners filings, the top objection rates are concentrated in the Northeast and the largest markets: New Jersey (87.7%), Massachusetts (87%), New York (84.5%), California (83.1%), and Texas (80.9%). For auto, California (87%) and Massachusetts (85.2%) lead, followed by New Jersey, Texas, Kansas, and Michigan.

Texas has the highest absolute number of objections in both lines, but its overall objection rate is moderated by very high filing volume — more than double the second-place state in either line. The most common reasons for objections in Texas were underwriting errors in home filings and missing or incorrect values in auto filings.

Why does the second objection round matter so much?

Because delay compounds, not stacks linearly. ZestyAI's data shows that after the first objection, each additional challenge adds approximately two months to the filing process as review clocks reset and the scope of scrutiny expands. That's why preventing the first objection is worth more than resolving it efficiently. Filing teams that systematically reduce procedural gaps before submission collapse the timeline far more than teams that respond well to objections after they arrive.

What separates fast-moving filing teams from slow ones?

Less about regulatory environment than execution. The P&C Specialist article includes practical guidance from state insurance departments — a Pennsylvania regulator's reminder that carriers should actually use the department's checklist before submitting, and a Washington regulator's note that subjective language in rate manuals ("above average," "better") will get flagged because regulators require any two people reading the manual to arrive at the same premium for the same risk.

The unifying point: filing teams that internalize the procedural patterns regulators care about — checklists, supporting exhibits, specific language, complete responses — systematically reduce both objection volume and the number of objection rounds. That's the operational gap behind the $72.8M-a-day cost. It's mostly addressable.

Read the full article

The Industry's $73M-a-Day Problem: Rate Filing Delays →

P&C Specialist, May 13, 2026, by Jennifer Ortakales Dawkins. Includes state-by-state objection data from ZestyAI's analysis, additional commentary from regulators in Pennsylvania and Washington and actuarial consultants at Perr&Knight, and detailed examples of what drives objection cycles in the slowest-approval states.

AI-driven property models in U.S. home insurance are shifting from experimental tools to a baseline capability for underwriting — and the economics of consecutive record catastrophe years are accelerating the move. P&C Specialist reporter Vrushank Nayak detailed the shift in a May 2026 analysis, Ignore AI Property Models at Your Own Risk?, featuring perspectives from ZestyAI co-founder and chief product officer Kumar Dhuvur and senior director of regulatory & government affairs Bryan Rehor.

Read the full article in P&C Specialist →

What's actually driving the shift?

Per Dhuvur, the pressure is economic, not technological. The U.S. property insurance market absorbed roughly $89B in insured natural catastrophe losses in 2025 and $108B in 2024 per Swiss Re Institute — what Dhuvur described as "the most expensive operating environment in the history of U.S. property insurance," with no visible path back to historic loss levels. In that environment, the territory-level averages and patchy property-level inputs underwriters have historically relied on aren't fine enough to differentiate risk anymore.

What changed on the supply side is just as important. Aerial and satellite imagery, combined with computer vision, now generate consistent structure-level signals at portfolio scale. Roof condition, structural characteristics, visible damage, and prior exposure can be evaluated across an entire book in the same way they'd be assessed on a single inspected property. That capability didn't exist seven or eight years ago.

How widespread is AI property model adoption today?

Dhuvur estimates roughly half of U.S. carriers now use AI property models in some form, with underwriting the most common use case. Pricing adoption is slower, in part because of regulatory complexity. The P&C Specialist analysis of state rate filings identifies major national carriers — Nationwide, Liberty Mutual, Allstate, and State Farm — among those citing third-party AI property models in homeowners filings, alongside specialty and regional carriers using a wider mix of providers.

Why don't rate filings always reflect actual AI model usage?

This is where Bryan Rehor's regulatory perspective in the article matters most. Filing citation counts can understate real adoption because filing rules differ state to state. Some states require carriers to resubmit their full rating manuals with each filing; others require only the portions that have changed. In change-only states, carriers may continue using an AI model year over year without re-citing it in every filing. Colorado, for example, doesn't require the AI model to be mentioned in every rate filing, but does require carriers to document and govern any model in use, because regulators can audit at any time.

The implication: market-wide AI property model adoption is harder to read from filings alone than the raw citation counts suggest.

What's the cost of moving slowly?

Dhuvur framed the strategic risk as a compounding adverse-selection problem. When some carriers price properties at their true individual risk and others continue pricing on territory averages, the precise pricers systematically capture the better risks and the territory pricers absorb the worse ones. Over time, that gap compounds into loss ratios, retention, and growth — and the carriers most exposed are the ones whose competitors have already moved.

As AI property models shift from differentiator to default, the question isn't whether to adopt. It's how to govern adoption credibly enough to satisfy regulators while still capturing the underwriting gains.

Read the full article

Ignore AI Property Models at Your Own Risk? →

P&C Specialist, May 4, 2026, by Vrushank Nayak. Includes additional commentary from Patrick Schmid at the Insurance Information Institute and other experts, plus detailed analysis of which carriers and states are citing AI property models in current rate filings.