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For years, competitive analysis in P&C has centered on pricing. But a ZestyAI analysis of 2M+ carrier filings shows the real battleground has moved underneath the price tag: carriers are quietly redesigning the mechanics of coverage itself — deductibles, settlement triggers, exclusions, and conditional language — in ways that materially change loss outcomes long after the policy is bound. These changes accumulate across filings, are easy to miss until a loss occurs, and are reshaping competition more durably than rate moves ever did.

About this analysis. Findings are drawn from ZORRO Discover, ZestyAI's AI agent purpose-built for insurance-specific research. Zorro ingests every public P&C filing submitted over the past decade (2M+ filings, 200M+ pages), preserves table structure during processing, and grounds every response in source filings. The report focuses on homeowners insurance across high-pressure states, where coverage redesign is most advanced and most measurable.

Want the data? Download the full report → — with tier-by-tier and state-by-state breakdowns.

Why doesn't price competition tell the whole story anymore?

Pricing is still the most visible competitive lever, but it's also the easiest to benchmark and the fastest to neutralize. Coverage controls behave differently. A percentage-based wind/hail deductible, an actual cash value roof endorsement, or a cosmetic damage exclusion all change a carrier's loss exposure in ways that won't show up in a rate comparison and won't surface until a claim is filed. Two carriers can quote nearly identical premiums on the same property and end up with materially different exposure to a single hailstorm.

That's the shift. Coverage architecture is now where carriers are actually deciding how much risk they keep.

What are carriers actually redesigning?

Four levers do most of the work:

• Deductibles. Percentage-based wind/hail deductibles, often tiered by territory or roof material, that materially change the carrier’s loss participation before settlement even begins.
• Settlement triggers. Age- and material-based rules that move roof claims onto actual cash value (depreciated) settlement rather than full replacement cost.
• Exclusions. Cosmetic damage exclusions, anti-matching language, and growing carve-outs for soft metals (gutters, vents, frames) and solar panels.
• Conditional language. Mandatory inspections, geographic restrictions, and credits or surcharges that toggle coverage based on property characteristics.

Individually, each of these is a small mechanical change. Collectively, they redefine the product. Coverage controls aren't temporary responses to volatility anymore — they've become the baseline.

How are tier and state responses diverging under the same catastrophe conditions?

Carriers are adopting similar mechanisms — but assembling them very differently. Different tiers — nationals, large multi-line carriers, regional mutuals, and specialty writers — combine deductibles, settlement triggers, and exclusions in distinct configurations even when facing the same severe convective storm or wildfire exposure. State-level patterns diverge just as sharply: a market like Texas has converged on percentage deductibles and ACV settlement across nearly every filing, while a market like North Carolina is still standardizing the language for some endorsements at the rating bureau level.

This is what makes coverage architecture so durable as a competitive moat. Pricing differences can be matched in a single filing cycle. Coverage architecture takes years to assemble, defend at the regulator, and operationalize through underwriting and claims — and once it's in place, it's hard to undo.

What does this mean for 2026 competitive strategy?

The implication for product, underwriting, and competitive intelligence teams is straightforward: tracking competitor rate changes alone now produces an incomplete picture of the market. The carriers that win 2026 will be the ones watching the full coverage architecture — deductibles, settlement triggers, exclusions, conditional language, and how they're combined — and translating those patterns into their own product roadmaps. Filings have become both the strategy document and the audit trail. Pricing is increasingly just the cover sheet.

Get the full report

The New Competitive Battleground in P&C Insurance →

The full report walks through tier-by-tier and state-by-state adoption patterns, names the specific endorsements and conditional language reshaping homeowners coverage, and shows how loss outcomes diverge under the same catastrophe conditions when carriers assemble these levers differently.

Download the report

 General Star (GenStar), a respected provider of excess and surplus specialty property and casualty insurance and a member of the Berkshire Hathaway family of companies, has selected ZestyAI to further strengthen how it underwrites hail, wind, and severe convective storm risk across its commercial property portfolio. 

The carrier will use ZestyAI’s Z-STORM™ model to gain more precise, property-level insight for multi-structure apartment and condominium risks, further supporting underwriting, pricing, and coverage decisions.

“Z-STORM gives us a more actionable view of hail risk at the individual property level,” said Matt Brown, Senior Vice President, Delegated Division at GenStar.

“In our evaluation, the model demonstrated compelling risk-splitting lift, which allows us to further differentiate risk more effectively, price with greater precision, and ultimately strengthen relationships with our customers and distribution partners.”

Z-STORM predicts the expected frequency and severity of severe convective storm losses by combining climatology with detailed property-specific characteristics. The model is designed to support more refined wind and hail peril rating, improved deductible and endorsement strategies, and earlier visibility into accumulating and emerging storm risk across a carrier’s portfolio.

By adopting Z-STORM, GenStar aims to further:

• Improve underwriting clarity by incorporating a clearer, property-level view of hail and wind risk into core underwriting decisions
• Expand policy availability by applying deductibles, endorsements, and exclusions more precisely—helping keep coverage available even in hail-prone markets
• Align pricing with risk, potentially offering more competitive premiums for favorable risks while refining pricing for higher-risk properties
• Identify emerging storm risk sooner, enabling proactive risk management and loss mitigation before exposures become potentially costlier for both GenStar and insureds

“GenStar joins a growing number of carriers using AI to modernize property underwriting,” said Attila Toth, Founder and CEO of ZestyAI.

“With Z-STORM delivering a sharper, property-level view of hail risk across complex apartment and condo portfolios, GenStar can further strengthen underwriting and pricing decisions and identify emerging exposures earlier—before they potentially turn into avoidable losses.”

For property insurers, roof age is more than just a data field — it’s a critical underwriting decision point that directly impacts pricing, risk selection, and loss costs. But what happens when that data is wrong two-thirds of the time?

A large U.S. carrier with over $500 million in direct written premium recently found out. Relying on self-reported and agent-estimated roof ages, they were systematically underpricing risky properties while overpricing safer ones. The result: adverse selection, elevated loss ratios, and underwriting decisions built on shaky foundations.

The Scale of the Problem: Two-Thirds of Roof Age Data Is Wrong

ZestyAI’s research shows that 67% of self-reported roof ages are inaccurate:

• 43% underestimate roof age — meaning roofs are older and riskier than reported
• 24% overestimate roof age — leading to overpricing or turning away good business

This isn’t just a pricing issue. Analysis also found that 78% of carriers in key U.S. regions use age-based triggers for ACV roof endorsements, with some starting as early as 8 years old. When roof age is wrong, policies can be misclassified across underwriting, eligibility, and coverage terms — creating compounding risk across the insurance lifecycle.

From Estimates to Evidence: How ZestyAI Verifies Roof Age

To replace guesswork with ground truth, the carrier deployed ZestyAI Roof Age, which analyzes building permits, more than 20 years of aerial imagery, and regional climatology using advanced machine learning. Each assessment is paired with a transparent confidence score.

Unlike traditional approaches that rely on policyholder memory or limited inspections, ZestyAI Roof Age:

• Anchors assessments in the property timeline to prevent false positives
• Cross-validates imagery with permits and climatological patterns
• Provides confidence scores to distinguish high-certainty predictions from cases requiring inspection
• Delivers explainable, auditable results that underwriters and actuaries can trust

The difference was immediate.

Real-World Examples from the Carrier’s Portfolio

In one Denver property, the agent reported an 8-year-old roof. ZestyAI identified it as 10 years old, confirmed by aerial imagery showing the replacement event.

In a Baltimore case, what was reported as a 5-year-old roof was actually 21 years old — verified through imagery and permitting history.

These weren’t edge cases. They reflected a systemic pattern across the portfolio.

The Impact: A 1.71% Improvement in Combined Ratio

By integrating verified roof age into underwriting and pricing workflows, the carrier achieved a 1.71% reduction in combined ratio. The improvement came from three measurable levers:

• Loss Cost Controls (-1.08%)
  Accurate age enabled appropriate use of deductibles and ACV endorsements, lowering claims severity.
• Better Risk Selection (-0.38%)
  More precise pricing attracted lower-risk properties while deterring higher-risk ones.
• Inspection Optimization (-0.25%)
  Confidence scores guided inspections to properties that truly needed them, reducing wasted expense.

Beyond loss ratios, better roof age data improved portfolio transparency, supported expansion into previously restricted markets, and strengthened actuarial and underwriting decision-making.

What’s Next: Expanding the Foundation of Property Intelligence

After proving the value of accurate roof age, the carrier is now building on that foundation. They are incorporating additional property attributes — including roof complexity, roof quality, and parcel-level features — through ZestyAI’s Z-PROPERTY™ platform.

By standardizing and elevating property data quality at scale, the carrier expects to unlock similar gains across quoting, underwriting, renewals, and even reinsurance discussions.

The takeaway is clear: in an industry built on precision, even a single data point — when made accurate — can deliver outsized impact.

Read the full Roof Age Accuracy case study to see how verified roof age drives measurable underwriting and pricing gains → From Self-Reported to Verified: Roof Age Accuracy That Pays Off

Across personal auto, workers' compensation, commercial auto, and homeowners, P&C carriers are rewriting their products faster than at any point in the past decade — and the clearest signal isn't market commentary, it's the filings themselves. ZestyAI used ZORRO Discover™ to review more than 2 million SERFF filings, with a deep dive into 1,700+ homeowners filings across 58 carriers and $51B in premium in five severe convective storm states. The findings show carriers redesigning coverage faster than most teams can track, regulators tightening expectations alongside, and — counterintuitively — smaller carriers leading adoption of the most material changes.

About this analysis. Insights are drawn from Zorro Discover, an AI agent purpose-built for insurance-specific research. Zorro ingests every public P&C filing submitted over the past decade (2M+ filings, 200M+ pages), preserves table structure during processing, and grounds every response in source filings. The homeowners deep dive covered Texas, Oklahoma, Colorado, Ohio, and North Carolina from 2023 to 2025. Presented by Stephanie Kuczynski, Director of Risk Analytics at ZestyAI (formerly Progressive, American Integrity, and The Hartford).

Prefer to watch instead? Access the full on-demand session → — full state-by-state data, tier-by-tier breakdowns, and live Q&A.

What's changing in personal auto filings?

2025 personal auto filings show a clear divergence. Most filings remain mechanical, but a meaningful subset is layering telematics signals, vehicle feature factors, and combined rating structures into a single filing rather than tweaking one variable at a time. COVID is still in the data — many carriers continue to exclude 2020 entirely or reset older years to a post-pandemic baseline before trending forward. Meanwhile, regulators are raising the proof bar: model factors increasingly need direct statistical support to be approved.

The 2026 advantage will go to carriers that can move fast and defend each change clearly.

How is the loss cost multiplier reshaping workers' comp?

In workers' compensation, the loss cost multiplier (LCM) is no longer a tuning knob — it's become the primary economic and structural control surface, subject to continuous regulatory oversight. Carriers are building risk segmentation directly into LCM structure rather than layering it on through schedule credits, and competitive pressure now shows up as surgical, state-level LCM moves rather than broad national adjustments.

What does the rideshare picture look like in commercial auto?

The market is splitting. Some commercial auto programs decline rideshare exposure outright; others continue to write it but rate it through negotiated, individually set prices that diverge sharply between states. A handful of filings have also begun referencing autonomous and driver-assist use at the rule level — early intent signals, even where pricing isn't yet defined.

What did 1,700+ homeowners filings reveal about severe convective storm?

This is where the redesign is most visible. The most striking finding: smaller Tier 4 carriers — regional mutuals, farm bureaus, and specialty writers — are leading adoption of percentage deductibles, ACV roof endorsements, and cosmetic damage exclusions, while Tier 1 nationals tend to wait and follow. Tier 4 carriers can't spread severe convective storm exposure across geographies or perils, so they have to manage it surgically.

The financial stakes are real. A single cosmetic damage exclusion can remove 15–25% of hail claim dollars from a carrier's books, and ACV settlement can shift 30–60% of roof replacement cost back to the insured. State-level patterns diverge as much as the carrier strategies do — Texas is the most restrictive market across nearly every endorsement, while North Carolina has barely adopted cosmetic damage exclusions at all because its rating bureau is still standardizing the language.

Regulators in states like Colorado and Texas now require actuarial parity: proof that premium credits given for these endorsements match the loss reductions they're claimed to produce.

What it means for 2026 product strategy

Across every line, the same pattern emerges: better data is producing faster product moves, and regulators are responding with tighter expectations for how that complexity is supported and explained. The carriers that win 2026 will be the ones that can see competitor filing moves in days rather than weeks, defend each change with traceable evidence, and translate state-specific patterns into targeted product decisions. Filings have become both the strategy document and the audit trail.

Watch the full session on demand

The Hidden Redesign of P&C Insurance: What 2 Million Filings Reveal About 2026 Product Strategy →

The on-demand session goes deeper on the homeowners study with full tier and state breakdowns, plus live Q&A on anti-matching, solar panel exposure, ACV labor depreciation, lendability under Fannie/Freddie guidelines, and more.

Access the on-demand recording — or request a trial of Zorro Discover to run your own filings analysis on any line, state, or carrier set.

ZestyAI today announced that Augusta Mutual has selected ZestyAI’s Roof Age and Z-PROPERTY™ to enhance underwriting and rating accuracy, target inspections more effectively, and support sustainable growth across Virginia.

Based in Staunton, Virginia, Augusta Mutual is a single-state carrier serving Virginia since 1870 with a longstanding reputation for personalized service and local expertise. By upgrading from traditional imagery and inspection approaches to ZestyAI’s computer vision and machine learning technology, the insurer gains broader, more consistent property coverage and a more comprehensive, AI-driven view of property risk—unlocking property-level insights such as verified roof age, roof condition, vegetation overhang, and debris accumulation that directly influence claim frequency and severity.

“ZestyAI’s solutions bring a new level of precision to our underwriting process,” said Gretchen H. Collins, Vice President of Underwriting at Augusta Mutual.

“We moved from legacy property risk tools to gain broader, verified property coverage, helping us make faster, more consistent, and more confident decisions for our policyholders across Virginia.”

ZestyAI’s Roof Age delivers verified roof age by cross-validating building permit records with over 20 years of aerial imagery, detecting roof replacement events and assigning confidence scores across 97% of U.S. properties. Z-PROPERTY™ further enhances this insight by assessing roof complexity, materials, and condition, along with other parcel-level attributes that influence loss potential.

ZestyAI works closely with regulators to ensure transparency, validation, and continuous monitoring of its AI-driven models. Its portfolio of risk models has secured nearly 100 approvals from regulators nationwide, giving insurers confidence they can be deployed immediately with the accuracy and transparency regulators demand.

By Attila Toth, Founder & CEO of ZestyAI

The U.S. P&C industry enters 2026 with stronger balance sheets, renewed underwriting profitability, and a sense that the hardest part of the cycle may be behind it. But beneath the surface, the risk environment is moving in the opposite direction. Climate-driven loss volatility, localized catastrophe patterns, and structural property vulnerabilities are accelerating — even as markets begin to soften.

The result is a widening gap between carriers chasing growth and those wiring discipline deeper into how risk is selected, priced, and managed. Here are three dynamics that will define P&C performance in 2026.

1 — A Softer Market Meets a Hard Climate Reality

The industry enters 2026 from a position of renewed financial strength: the last couple of years produced the best U.S. P&C underwriting results in more than a decade, with combined ratios improving into the mid‑90s and a clear swing back to underwriting profit. 

Capital has rebuilt, competition is intensifying in many property segments, and some markets are now seeing flat or slightly negative renewals, encouraging carriers to cautiously re‑enter territories that were pulled back during the hard market. 

The risk environment, however, has not softened; insured catastrophe losses have exceeded USD 100 billion for multiple consecutive years, and recent nat‑cat studies now describe annual insured losses approaching USD 150 billion as the emerging “new normal,” driven disproportionately by severe convective storms, wildfire, localized flooding, and non‑weather water losses rather than a single headline hurricane season.

In 2026, carriers will not move in lockstep. Some will quietly relax property underwriting and broaden appetite to chase top‑line volume in what feels like a more forgiving market, even as U.S. SCS losses alone have entered a period where annual insured losses now consistently exceed USD 40 billion, while others will double down on discipline by wiring property‑level climate and vulnerability metrics into day‑to‑day decisions. 

Early in the year, the visible story may favor the volume‑chasers as premium growth accelerates, but by late 2026 the more revealing story will be in loss ratios, with hail‑, SCS‑, wildfire‑adjacent, and water‑heavy portfolios that were loosely underwritten posting the most uncomfortable deterioration.

2 — Hyperlocal Exposure Management Becomes a Core Profit Lever (and Reinsurers Will Expect It)

Even with some rate relief on better risks, carriers face a structural problem going into 2026: loss volatility is increasingly driven by frequent, highly local events and structural property issues rather than a single major catastrophe. 

A two‑block hail cluster, an ember‑exposed hillside parcel at the wildland–urban interface, or aging roofs can generate thousands of mid‑sized claims that erode margin even when headline cat activity looks “average.” 

When property‑level secondary modifiers are missing or stale, catastrophe models and capital providers default to conservative assumptions, inflating modeled losses, uncertainty loads, and reinsurance costs; reinsurers are responding by demanding clearer visibility into roofs, vegetation, defensible space, elevation, and mitigation before offering the most favorable terms.

In this environment, hyperlocal exposure management is becoming a core profit lever rather than a niche analytics exercise. Leading carriers are using verified parcel‑level attributes to identify frequency‑prone parcels inside ZIP codes that look stable in aggregate, to counter overly conservative model assumptions with auditable evidence, and to walk into reinsurance renewals with property‑level documentation rather than broad averages. 

They are steering appetite, pricing, inspections, and mitigation strategies on a near‑real‑time basis instead of waiting for annual rate cycles, effectively trading unmanaged volatility for intentional, data‑driven control. The net result is that 2026 will reward carriers that can prove property‑level truth to reinsurers, regulators, and their own underwriting teams, replacing assumptions with evidence and episodic adjustments with continuous portfolio management.

3 — Agentic AI Becomes Insurance’s Next Operating System

2026 is shaping up as the year agentic AI shifts from experimental to essential in P&C, as carriers discover that the binding constraint is no longer access to data but the speed, consistency, and defensibility of decisions across underwriting, filings, compliance, and product change. Risk conditions are moving materially faster than traditional annual guideline refreshes can accommodate, supervisors and rating agencies are sharpening expectations around explainability and consistency, and decades of underwriting and regulatory expertise are retiring faster than they can be replaced.

Across the market, early adopters are already using agent‑like systems to flag likely regulatory objections before filings go in, compress filing and approval timelines from months to weeks, and synthesize competitive and regulatory intelligence with strong safeguards and human‑in‑the‑loop review. These systems are also starting to refresh underwriting and pricing playbooks using live property‑risk signals instead of static territorial assumptions, closing the loop between climate data, filings, and front‑line decisions. For many carriers, 2026 will be remembered as the year AI stopped being primarily predictive and became operational infrastructure — software that can understand intent, reason through complex rules, coordinate multi‑step workflows, and take auditable action alongside human teams.

How Leading Carriers Are Responding

The most forward-positioned carriers entering 2026 are already using parcel-level intelligence to refine appetite, pricing, inspections, and mitigation in high-hazard and water-exposed regions, treating hyperlocal data as a core underwriting input rather than an afterthought.

They are refreshing eligibility criteria and underwriting guidelines based on property-specific hazard, vulnerability, and mitigation features, and preparing regulator-ready and reinsurer-ready documentation on defensible space, roof condition, and other secondary modifiers.

They are steering portfolios continuously, adjusting aggregates, concentrations, and mitigation incentives throughout the year instead of relying solely on renewal season to reset course. Together, these behaviors signal a broader shift away from episodic, once-a-year recalibration toward continuous, property-level risk management supported by AI-enabled operating systems.