The insurance industry has been grappling for years with the skyrocketing losses caused by wildfires. As ZestyAI unveils its annual Wildfire Season Overview, we can see that insurers are in a pivotal position to navigate the ongoing threat.

Wildfire Risk Isn’t Going Anywhere

While we are currently experiencing a brief reprieve from the wildfire devastation of the last few years, the ongoing threat of wildfire remains at an all-time high.

Extreme snow and rainfall across the West in 2023 have led to wetter-than-normal conditions that have acutely reduced the risk of wildfire. However, wetter conditions lead to vegetation growth, so despite 2023 presenting lower wildfire risk, the resulting vegetation accumulation, combined with persistent drought conditions in future years, will likely result in extremely high losses in the coming years. In fact, heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Preparing for Future Wildfire Seasons

With high wildfire activity on the horizon, what steps can insurance companies take now to prepare for future wildfire seasons?

Here are three essential strategies:

1. Leverage Data for Better Understanding

Research by ZestyAI reveals that wildfires ravage 87% more land during drought years compared to non-drought years. With the western US still experiencing a megadrought that is the worst in over a millennium, it’s critical to understand the data and risks involved.

Not all homes face high risk. For the remainder, detailed property risk insights can highlight areas requiring risk mitigation. Integrate property-specific wildfire risk data into the underwriting and renewal process. This year is also an excellent opportunity to review a complete portfolio using an AI-powered wildfire risk assessment tool like Z-FIRE.

2. Educate and Empower Property Owners Through Transparency

Technology, particularly satellite/aerial imagery and artificial intelligence, can shed light on wildfire risks. Insurers can use this technology to assess the risk reduction measures that policyholders have implemented and understand how a property might withstand a wildfire.

This information is invaluable for educating homeowners and insurance agents. By knowing the specific actions that can be taken to reduce risk, such as clearing brush or using fire-resistant materials, both insurers and homeowners can be better prepared for wildfires.

3. Choose a Technology Partner Wisely

ZestyAI's Z-FIRE has set a benchmark by integrating loss data from over 1,500 wildfires and employing cutting-edge technology to derive insights on each property. By combining aerial and satellite imagery with machine learning and cloud computing, ZestyAI created Z-FIRE, a highly detailed wildfire risk assessment model.

Z-FIRE has been adopted by leading insurance carriers in every single western US state.

In 2022, Z-FIRE demonstrated remarkable performance. Its integration of data through machine learning and computer vision models has established Z-FIRE as a potent tool in wildfire risk assessment for both underwriting and rating.

Make Informed Decisions with Z-FIRE

Using Z-FIRE, insurance carriers, MGAs, and reinsurers can get access to actionable insights developed from detailed property-level risk factors. While wildfire losses may be inevitable, understanding in detail how individual properties contribute to average and tail risks is a large step forward.

The specific time and location of a wildfire is nearly impossible to predict. However, Z-FIRE can give carriers an assessment of the preconditions for that fire, and describe in detail the factors which contribute to it. Knowing, not guessing, which properties fall into a high-risk category is more important now than ever. We look forward to helping our customers through this fire season and many to come.

Z-FIRE Stands Alone in Compliance

Z-FIRE has been developed in partnership with top carriers and has been included in successful filings in California and many other western states. As regulators continue to push for additional transparency and accuracy in how insurers treat wildfire risk, AI-powered solutions provide a clear advantage because of their interpretability and sensitivity to changing conditions.

In 2023, California began requiring insurers to provide discounts based on mitigation measures, and in 2024 Oregon is poised to establish similar requirements on communications to homeowners. All of these changes create a burden on insurers, but those who can adapt to the new regulatory environment by leveraging knowledgeable partners like ZestyAI will have an advantage over competitors. AI is part of the solution, helping address climate risk and maintaining the insurability of properties across the US.

Download ZestyAI's 2023 Wildfire Season Overview 

ZestyAI has released its annual Wildfire Season Overview for 2023. This comprehensive report combines insights from recent wildfire events, prevailing drought conditions, and cutting-edge advancements in artificial intelligence to assist insurers in effectively managing wildfire risk.

Download ZestyAI's 2023 Wildfire Season Overview

Here are some key findings from the report:

A Chance To Prepare While Wildfire Fuels Accumulate

Despite a brief respite from recent wildfire devastation, the current threat remains high. Over the past decade, wildfire risk has notably increased, particularly in California. However, the occurrence of extreme snow and rainfall in the West during 2023 has temporarily reduced the risk due to wetter conditions.

It's important to note that vegetation accumulation and ongoing droughts will likely lead to substantial losses in the coming years. California remains highly susceptible to losses and significant vegetation growth. This temporary relief in 2023 creates an ideal opportunity for insurers to review the risk technologies they have in place and embrace innovative solutions to prevent future losses.

No Role for Drought in Underwriting

Drought is indicative of fire intensity, but not losses. Although drought is an important factor in seasonal wildfire risk, the presence of drought shouldn't drive underwriting. Instead, insurers should look at property-specific solutions that consider wildfire risk over the lifetime of a policy.

Research has shown that this year's heavy rainfall may be a leading indicator for severe wildfire years to come. A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

Using Advanced Models to Adapt to Changing Risks & Regulations

AI-powered risk models play a key role in mitigation. Insurers who write business in wildfire states have found increasing value in AI-powered wildfire risk models as they offer actionable risk insights, adapt quickly to changing climate risks, and comply with all regulations.

Over the last year, several western states have begun to implement new regulations for insurers in response to the changing risk environment. Discounts and transparency for mitigation efforts and property-specific decisions may become an industry standard as they have in California and Oregon.

What This Means for Insurers

In evaluating wildfire risk, many analyses tend to focus on the number of fires and the size of the area they burn. However, what really matters to insurance companies and property owners is the loss of structures and what can be done to mitigate those losses.

For example, those providing insurance in California might be surprised to learn that despite smaller losses in 2022 compared to 2021, the total national count of acres burned and fires ignited in 2022 actually exceeded that of 2021. This mismatch between yearly wildfire activity and the number of structures lost suggests that wildfire losses are not simply dictated by wildfire activity.

The most significant factor is not how many fires start, or how far they spread, but the potential resilience of every structure and what the communities and homeowners have done to prepare for wildfire exposure. Research from ZestyAI and IBHS shows that for a more precise understanding of potential losses, insurers need to zoom in on individual properties. They should consider a structure’s location, building materials, surrounding vegetation, and efforts taken by the surrounding community to prepare for wildfires.

Modern wildfire risk tools like ZestyAI's Z-FIRE do just that. They analyze individual property features and measure the impact of those features on the probability of loss. They also factor in nearby vegetation, community preparations, local infrastructure, and the lay of the land. This property-centric approach doesn’t try to predict exactly what a wildfire will do. Instead, it gives valuable information on how and why properties might be damaged by wildfires.

These models don't just offer a simple risk score, but also help explain what makes a particular property vulnerable and what steps can be taken to protect it.

 
Find out more, including how Z-FIRE performed in 2022, in this year’s Wildfire Season Overview.

Download ZestyAI's 2023 Wildfire Season Overview

San Francisco, CA, April 19, 2023 – Today ZestyAI, the leading provider of climate and property risk analytics solutions powered by artificial intelligence (AI), and the Insurance Institute for Business & Home Safety (IBHS) released new research examining catastrophic losses from severe convective storms, particularly hail. The study focuses on hail-driven losses in property and casualty insurance.  

Hail losses are a persistent problem for property insurers’ risk management efforts. Historically, carriers have focused on intense events to predict hail risk, with supporting data confined to storms with hailstones larger than one or two inches. The study Small Hail, Big Problems, New Approach shows high concentrations of small hail are more important than previously thought, pointing to an opportunity to broaden data sets to account for the cumulative effect all hailstorms have on a roof’s susceptibility to damage over time, leading to a claim. 

This new research shows all hail needs to be accounted for when modeling and ultimately understanding losses. Using data from all hail events, not just those with hail that meet the severe criteria of one inch or greater, allows carriers to consider valuable data on smaller hailstone impacts. Additionally, insurers can integrate climate and materials science to better understand hail frequency and severity. Research suggests using this new approach could perform as much as 58 times more accurately than looking at events with large and very large maximum hail sizes alone, allowing carriers to more effectively assess hail risk, achieve more profitable underwriting and open up ratings to previously avoided areas.  

“As we’ve learned more about hailstorms, we've discovered storms that produce large concentrations of small hail are more common than we thought, and despite causing less individual damage than a single large hailstone, small hail, especially in high concentrations, is likely a meaningful contributor to the loss we see each year from hail,” said Dr. Ian Giammanco, managing director of standards and data analytics at IBHS. “Experiments also show large concentrations of smaller hailstones cause degradation to the asphalt shingles, specifically dislodging large amounts of granules. Once enough granules are lost, the underlying asphalt material can become more susceptible to aging and weathering. Repeated exposure to these types of hailstorms can shorten the life of an asphalt shingle roof and increase the damage caused by large hailstones in the next storm.” 

“Hail losses are a persistent problem for property insurers’ risk management efforts,” said Attila Toth, founder and CEO of ZestyAI. “Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

“Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

Hail risk can be especially costly to insurers because, unlike other catastrophic perils like hurricanes and wildfires, it can be difficult to identify the storm that caused a hail claim. As a result, insurance carriers could be forced to raise overall premiums or introduce high deductibles to compensate for the added costs.

As climate and materials science have developed, more data has become available providing  improved hail risk evaluation options that can lead to better decisions at earlier stages of the policy life cycle. Other benefits could include more profitable underwriting, a greater ability to rate previously-avoided areas and significantly reduced loss ratios.

For the complete ZestyAI and IBHS research paper visit this page.
 

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information. 

About the Insurance Institute for Business & Home Safety (IBHS)

The IBHS mission is to conduct objective, scientific research to identify and promote effective actions that strengthen homes, businesses and communities against natural disasters and other causes of loss. Learn more about IBHS at ibhs.org. 

### 

For more information, contact:

Linsey Flannery

Director of Communications, ZestyAI 

linsey@zesty.ai 

416-939-9773 

Mary Anne Byrd

Communications Director, IBHS

mbyrd@ibhs.org

803-669-4216 

Reinsurance rates are spiking to an all-time high. Fitch estimated a 20-60% rate increase for cedants in the overall property reinsurance market at the January 1st renewals.¹ Terms and conditions are also tightening - many reinsurers are limiting their cedants to much higher attachment points², or exiting CAT-exposed lines altogether

The main drivers for uptick in reinsurance rates

Our research has found three drivers underpinning the trend:

1. Devastating CAT losses, particularly from secondary perils

59% of all CAT losses come from secondary perils³, and those losses have caused major shifts in the reinsurance landscape. Howden estimates that global property CAT reinsurance rates were up 37% at the January renewals⁴.

2. A new urgency to improve return on capital

“When the cost of capital is equal to the rate of return, something has to change.” - Aditya Dutt, CEO of Aeolus Capital Management⁵. The reinsurance industry has underperformed since 2017, with an average return on equity of just under 5%⁶. Poor underwriting performance was a key driver, with an industry average 101% combined ratio over the same period⁷. Reinsurers are poised to use the tightening market as a chance to improve performance, with Fitch forecasting a 4pp underwriting margin expansion for reinsurers in 2023⁸. Unfortunately for primary insurers, Goldman Sachs predicts that the same tightening market will create significant volatility for cedants⁹.

3. Value erosion in reinsurer investment portfolios

Macroeconomic factors are driving significant unrealized investment losses for reinsurers, particularly on fixed income portfolios due to rising interest rates. Aon estimates that these investment portfolio losses drove a 17% decline in global reinsurance capital across the first 9 months of 2022, with some players reporting equity value losses as high as 40-50% over that period¹⁰. Reinsurers will look to shore up these losses with better underwriting performance, which likely means tougher rates for primary carriers.

How property insurers can improve their odds with AI-powered predictive climate and property risk platforms

These factors mean that primary insurers can expect challenging reinsurance negotiations at the June 1st renewal deadline, particularly on property lines. However, new AI-powered predictive climate and property risk platforms can improve the odds for property insurers in three areas:

1. Rapid improvements in risk mitigation

Implementation-free portfolio reviews can quickly drive major loss ratio improvements.

2. Turn the tables of CAT risk screening in your favor

Improving data quality can lead to more favorable stochastic model portfolio screens, particularly with insight about the roof.

3. Enter the room as a leader in cutting-edge risk practices

Showing the same commitment to new technologies as industry leaders can help cedants build a better case.

Conclusion

With the right mitigation action and a cutting edge view of portfolio risk, cedants can navigate the upcoming 6/1 renewal successfully.

Learn more about how an AI-powered predictive climate and property risk platform can help you.

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Sources

1 & 8 - Fitch, Reinsurers’ Underwriting Margins to Expand by 4pp in 2023

2 & 3 - Gallagher Re, Gallagher Re Natural Catastrophe Report 2022

4 - Howden, Howden’s renewal report at 1.1.2023: The Great Realignment

5 - AM Best, Reinsurance: Roundtable Discussion on Renewals and What 2023 May Hold

6, 7 & 10 - AON, Reinsurance Market Dynamics

9 -  Reinsurance News, Hard market to increase volatility for primary insurers: Goldman Sachs

San Francisco, CA, September 20, 2022 – ZestyAI, the leading provider of property risk analytics solutions powered by Artificial Intelligence (AI), has developed a 180-day playbook to support insurance carriers as they work to meet the Mitigation in Rating Plans and Wildfire Risk Models regulation expected to be adopted by the California Department of Insurance (CDI) before year-end. The playbook reflects the company’s unique ability as the only comprehensive solution in the marketplace to help insurers meet or exceed every single requirement in the new regulation — meeting 100 percent compliance inside the tight 180-day window.

On September 7, 2022, Insurance Commissioner Ricardo Lara announced he had submitted the department’s insurance rating regulation recognizing wildfire and safety mitigation efforts made by homeowners and businesses, to the California Office of Administrative Law for final approval. This first-of-its-kind regulation will require all insurers in California to refile their existing rating plans on an aggressive 180-day timeline. 

“Eight of the ten most destructive wildfires in California’s history have occurred in the last five years,” said Attila Toth, Founder and CEO of ZestyAI. “While the new wildfire regulations will have a significant impact on California’s insurance industry, adapting to this peril is key to having a sustainable insurance ecosystem in California. As the leader in property-specific wildfire risk assessment, we have offered input at each step of this process. We are here to support admitted carriers with a turnkey solution complying with every single requirement as they navigate this process and work to meet the new regulations.”

The new wildfire safety regulation requires insurance companies to consider the structure of a home, its surroundings, and community-level mitigation. Insurers with concerns about the regulation can reach out to ZestyAI to get a complete explanation of how the regulations will impact them. This includes access to the 180-day playbook, which breaks down the regulatory compliance process into an orderly roadmap that addresses all three major challenges that insurers will face:

• Operational — The process of rapidly integrating new data sources, educating the public on how wildfire mitigation affects insurance policies, and a framework for a compliant appeals process.
• Rating — How to weight property-specific characteristics, including those with and without historical loss data, in rating plans as well as guidance on mitigation credits.
• Filing — Carriers who use a rating plan reliant on traditional wildfire models without property-specific information will need to overhaul their rating framework. Relying on multiple approved rate filings, ZestyAI has developed a comprehensive filing toolkit that can support carriers at every facet of the filing process.

ZestyAI’s Z-FIRE™ model has quickly become the leader in property-specific wildfire risk assessment. Using AI algorithms trained on more than 1,500 wildfire events across 20 years of historical loss data, Z-FIRE™ provides a level of detail that is of essential value to both the insurer and the homeowner.

The model was the first AI model ever approved as part of a rate filing by the CDI and the second wildfire risk model. It has been widely adopted across the Western U.S., where its use has been approved for both underwriting and rating. During 2021's APCIA Western Region Conference, CDI representatives expressed that the agency’s familiarity with Z-FIRE™  means in future filings the focus will be limited to the carrier's specific use of the model, not the details of the model itself, potentially greatly expediting the reviews of carriers using the Z-FIRE™ model.

ZestyAI’s Z-FIRE™ considers features such as topography and historical climate data in combination with factors extracted from high-resolution imagery of the property itself and its surroundings, including homeowner and community mitigation efforts, to provide both neighborhood and property-specific risk scores. 

A significant advantage to insurance carriers is that they can use these data elements to communicate with homeowners on what specific actions can be taken to lower their property’s risk, such as upgrading building materials and cutting down surrounding dry brush or overhanging vegetation. The impact of mitigation efforts can be significant. A joint study by the Insurance Institute for Business & Home Safety (IBHS) and ZestyAI, which studied over 71,100 wildfire-exposed properties, found that property owners who clear vegetation from the perimeter of their home or building can nearly double their structure's likelihood of surviving a wildfire.

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in the United States. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information.

Download Report

Today, ZestyAI released its 2022 Wildfire Season Overview. Each year, ZestyAI prepares a comprehensive overview to help guide insurers based on recent wildfire events, persistent drought conditions, and advancements in artificial intelligence for managing wildfire risk.

If it seems like wildfires are burning at all times of the year, it's not just you. Very destructive events, like last December's Marshall Fire, are occurring in months not typically associated with high wildfire danger. Those who study wildfires, including ZestyAI, have begun to start thinking in wildfire "years" instead of wildfire "seasons'. Strong wildfire years, with 10+ million acres burned, have quickly become the new normal. The last 10 years have been the worst on record for property and casualty (P&C) insurers when it comes to wildfire. 8 of the top 20 fires in California history, and more than half of the acreage burned by them, occurred in just the years 2020 and 2021.

What can insurers do to prepare themselves for persistent wildfires?

• Understand the Data: Instead of sticking with decades-old approaches, assess wildfire risk at the property level.
• Continue to Bring Transparency and Education to Homeowners: Insights from AI-based wildfire risk models may be passed on to homeowners and agents, enabling a much better understanding of wildfire risk.
• Find the Right Technology Partner: Aerial and satellite imagery, machine learning, and infinitely scalable cloud computing resources were combined to build the most granular wildfire risk assessment model (Z-FIRE™). Using Z-FIRE™, ZestyAI can accurately estimate an individual property’s wildfire risk, plus highlight the key property-level factors that contribute to that risk.

Click here to download ZestyAI's 2022 Wildfire Season Overview.

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building in North America, encompassing 200B property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit https://zesty.ai for more information.

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

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.

Insurance availability has become a constraint on the housing market.

That’s the central argument Ross Martin, VP of Risk Analytics at ZestyAI, makes in ATTOM’s newly released Q4 2025 Housing News Report—and it’s one that will increasingly shape affordability, underwriting, and buyer behavior heading into 2026.

Housing discussions still focus on mortgage rates and inventory. But in many markets—especially catastrophe-exposed ones—insurance is becoming a gate in the transaction. If a property can’t get insured, or coverage is uncertain, deals stall. And when insurance costs spike unexpectedly, affordability breaks even when the mortgage penciled out.

Ross’s point isn’t simply that insurance is getting more expensive. It’s that availability and predictability now matter as much as price—and the market would function better with clearer, property-level risk signals.

Today, homes in similar locations can carry meaningfully different risk based on factors like roof condition and materials, defensible space and vegetation management, yard and debris conditions, and documented improvements captured in permits or listing data. When those distinctions aren’t consistently reflected in underwriting or pricing, mitigation efforts go unrewarded—and higher-risk properties don’t get early, property-specific signals to improve.

For insurers, this lack of granularity creates real portfolio risk. When individual properties aren’t differentiated clearly enough, volatility increases, adverse selection becomes harder to avoid, and long-term participation in catastrophe-exposed markets becomes less sustainable. Property-level, mitigation-aware models help address this by improving segmentation and enabling insurers to stay in market with more confidence.

Recent advances in property-specific data and modeling now make this differentiation possible at scale. Insurers can assess dozens of attributes—including roof age and materials, defensible space, vegetation conditions, building permits, occupancy type, and hazard-specific science—to build a clearer view of a structure’s vulnerability. Just as importantly, these models can recognize mitigation actions—like roof replacements, defensible space creation, and debris removal—and incorporate them more consistently into underwriting and pricing.

When mitigation is visible and rewarded:

• Homeowners and investors gain more control over premiums
• Insurers can maintain more stable portfolios, even in high-risk regions
• Housing markets get clearer signals—making insurance availability and long-term cost less of a guessing game for buyers and lenders

Regulators are paying attention as well. In several states, regulators are examining how property-level data, mitigation, and modern risk modeling approaches can be incorporated more consistently into rate structures, with transparency as a common objective.

The takeaway is straightforward: insurance is shifting from a background cost to an active constraint—and clearer, property-level risk signals are key to easing that constraint. As 2026 approaches, the ability to differentiate risk at the individual property level will play a growing role in restoring predictability, supporting availability, and shaping housing market outcomes.

Read the full article, “The Insurance Shift Reshaping the 2026 Property Market,” in ATTOM’s Q4 2025 Housing News Report.

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By Abdul Mohammed, Director of Product Marketing, ZestyAI

Every year, carriers and filers submit hundreds of thousands of rate, rule, and form transactions to state insurance departments (DOIs), many through SERFF. In 2023 alone, SERFF processed 517,571 transactions (NAIC SERFF, reported 2025). These filings are the DNA of the insurance market: the definitive record of how competitors set rates, where they plan to expand, and where they get approval for new ideas or pull back.

Even though much of this information is publicly available in many jurisdictions, with different rules for access and confidentiality, most carriers still miss out on the competitive signals hiding in plain sight. 

Key takeaways

• Regulatory filings are a strategic dataset, not just compliance paperwork. They reveal competitor intent and market shifts.
• “Public” does not mean “easy to analyze.” Filings are often massive, cross-referenced, and inconsistently structured, making manual review at scale impossible.
• The winning approach is a filing intelligence stack. Success requires filing data tracked across amendments and effective dates, structured parsing, deterministic calculation, and precise citations.

How Filings Got So Complicated

From 1945 to The Modern Filing Ecosystem

The story begins with the McCarran-Ferguson Act of 1945, which gave states the power to regulate insurance rather than the federal government. As a result, requirements differ by state, making rates, rules, and forms complicated across jurisdictions. 

In 1998, the National Association of Insurance Commissioners (NAIC) introduced SERFF (System for Electronic Rate and Form Filings), developed in collaboration with regulators and industry to digitize rate and form submissions. While it successfully moved filings online, the underlying complexity of the content remained.

Since then, the volume and complexity of filings have exploded. Our analysis of SERFF filing packages shows that the largest homeowners’ rate filing now exceeds 300,000 pages, including attachments, exhibits, and correspondence. In our data, the biggest package grew from 38,102 pages in 2009 to 346,064 pages in 2024.

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Why Public Filings Remain Inaccessible for Analysis

A public filing is not always easy to access or understand. Carriers know that regulators, consumers, and competitors will review their filings, so the resulting documents are often dense, cross-referenced, and hard to piece together. If you have ever tried to reverse engineer a competitor rate change, you have probably faced these challenges:

The Trade Secret Exception 

Some carriers request confidential treatment for specific elements of a filing that may qualify as trade secrets under state rules, such as granular territorial data or specific model inputs. When those sections are redacted or withheld, you lose visibility into important details even though the overall filing is public, and the level of protection varies by jurisdiction.

"When a Product Filing contains Trade Secret information, the Product Filer may identify those portions of the Product Filing, including correspondence with the Compact Office, that contain Trade Secret and seek to protect their disclosure." 

Interstate Insurance Product Regulation Commission, FIN 2024-1

The Reference Maze 

Sometimes, instead of putting all rate information in one document, a filing will reference several other filings across multiple years. To fully understand the change, an analyst has to track down and reconcile multiple historical filings, creating a confusing trail of "breadcrumbs"  that is almost impossible to follow manually.

The PDF Image Trap 

Many carriers submit rate tables as scanned images in PDFs instead of machine-readable text. While human eyes can read these tables, most data tools and basic OCR software treat them as pictures, so the information cannot be easily searched, filtered, or analyzed at scale.

The Objection and Response Trail

Often, the most valuable intelligence isn't in the initial filing, but in the "Objection and Response" exchanges between the carrier and the state regulator. These discussions can reveal rationale, supporting evidence, and the boundaries regulators will accept. However, this material is often spread across multiple attachments and correspondence, making it easy to miss critical insights without a structured way to collect and review them.

Non-Standard Nomenclature

There is no universal dictionary for insurance variables. One carrier might call the roof age factor “rf_yr_mod”, while another uses “const_age_rel”. Without a way to normalize these labels, mapping equivalent factors across carriers becomes manual work, making benchmarking slow, error-prone, and difficult to repeat.

Why General Purpose LLMs Often Struggle Here

We are in the age of Generative AI, so the natural question is: "Why not just upload these PDFs into a tool like ChatGPT or Gemini and ask questions?"

You can upload filing PDFs to ChatGPT or Gemini, and you may get a helpful summary. But insurance filing work is not “writing assistance.” It is a precision workflow in which small mistakes lead to incorrect conclusions. General-purpose LLMs are built to generate plausible text from the input you give them, not to reliably preserve filing structure, track versions, run exact calculations, and produce audit-ready citations. 

The following are the top reasons why general LLMs often struggle in the inusrance domain:

1. The Math Problem

Insurance filings require exact math and exact linkage across tables, factors, relativities, and formulas. LLMs are probabilistic; they predict likely answers rather than performing exact calculations. If you ask an LLM to calculate a 3.5% rate increase over three years using a specific table, it may give a confident answer that is still wrong. In insurance, even a 0.01% mistake can mean millions in lost premium. As actuarial researchers noted in a 2024 paper from Cambridge University Press, “while LLMs can explain concepts, they often provide inaccurate or incorrect mathematical facts, sometimes in subtle ways.”

2. Structure Blindness

LLMs are mostly trained on regular text, such as books and articles. They are not skilled at understanding tables, following footnotes, or applying formulas consistently across multi-part exhibits, especially when documents are scanned or formatted inconsistently. A standard LLM treats a table as plain text and often fails to understand how the cells are logically and mathematically connected.

3. Context Window Overload

State filings are often longer than what standard LLMs can handle. If you give a model a 2,000-page document, it may lose track of what appeared earlier in the filing and still try to answer questions as if it remembered everything. This can cause the AI to make up numbers (hallucinate) to fill in missing information. 

What a Modern Filing Intelligence Approach Looks Like

The industry does not need a better chatbot. It requires a filing intelligence stack that combines structured data, deterministic computation, and auditable reasoning.

Below are the key steps in building this intelligence stack:

Build a clean, versioned filing archive 

Ingest filings continuously and preserve them with consistent metadata such as state, carrier, line, status, effective date, and relationships to related submissions. This creates a single, reliable system of record for all filing history.

Parse filings into insurance native components

Break filings into rates, rules, forms, exhibits, objections, and responses. Store rating tables, factors, and hierarchies as structured data instead of plain text, so they can be queried and reused.

Pair language with deterministic calculation

Use deterministic engines for calculations and rate reconstruction, then use language models to explain the results, clarify their meaning, and support structured analysis. The math engine produces the numbers; the LLM explains what they mean.

Make everything traceable

Every conclusion should link back to the exact filing section it came from. This traceability is what turns AI output into something regulators, actuaries, and executives can trust and defend.

The Future Belongs to the Agile

The number of filings and their sizes keep growing. As climate risk reshapes markets, rate reviews and underwriting changes will happen more often. The carriers who succeed will be those who treat regulatory filings as a source of strategic insight rather than a compliance burden. 

Those who don’t will pay the price. Without visibility into how competitors are changing rates, rules, and eligibility in near-real time, carriers slip out of sync with the market, underwriting yesterday’s risk at today’s loss costs. That gap is where adverse selection takes hold.

Having access to filings is not enough to gain an edge. The real advantage comes from understanding them quickly and accurately, in a way that can be repeated across teams and product lines. ​​To do this well, you need systems built for the job, not just a general-purpose model. This is where a purpose-built filing intelligence stack changes what is possible.