Reports & Research

Wildfire Risk Is Rising Nationwide‍

Wildfire seasons are getting longer, more destructive, and harder to predict—and they’re no longer just a Western U.S. concern. From the Southeast to the Midwest, wildfire risk is emerging in places many insurers haven’t traditionally watched.

What the Latest Data Reveals About Wildfire Exposure

Drawing from the latest national datasets and insights from ZestyAI’s Z-FIRE™ model, this visual guide to wildfire risk in the U.S. shows:

• New wildfire hotspots: Discover where risk is rising fastest.
• Mitigation gaps: Learn how a lack of defensible space is putting thousands of homes in danger across the country.
• Top risk drivers: See how features like overhanging trees and wooden roofs are fueling destruction in high-risk areas.

Download Free Infographic

BONUS: You’ll also get access to our latest online event with IBHS and Western Fire Chiefs Association, The LA Fires in Focus: What Worked, What Didn’t, What’s Next for Insurers.

The Silent Peril Reshaping Homeowners Insurance

Non-weather water damage rarely makes headlines, but it’s quietly eroding profitability across the country.
It is now the fourth costliest peril in homeowners insurance, and claim severity has increased 80% in the last decade—a trend that’s accelerating even as frequency remains relatively flat.

Traditional risk models struggle to capture the early warning signs behind these losses, leading to mispriced policies, undetected exposure, and rising volatility for carriers.

Want the full analysis? Download the complete “Winning the Fight Against Non-Weather Water Losses” guide.

Why Loss Severity Keeps Rising

Aging homes and overlooked system failures

Many of the most expensive losses stem from aging plumbing, deteriorating materials, and slow-burn failures that often go undetected until damage is significant.

Frequency is flat—severity is not

Loss patterns suggest that while the number of events hasn’t surged, the financial impact of each event has—a signal that traditional models are not capturing the right property-level predictors.

The Property Features Most Predictive of Water Losses

The overlooked attributes that traditional models miss

Standard territory- or age-based assessments often ignore the property-specific details that meaningfully influence water loss risk, including:

• supply line material and age
• plumbing configuration
• occupancy patterns
• system maintenance and upgrades
• moisture exposure and prior loss indicators

These factors vary widely between neighboring homes—yet most models treat them as identical.

Where Traditional Underwriting Falls Short

ZIP-code and age-based proxies mask true risk

Legacy approaches rely heavily on broad territory-level assumptions that overlook structural vulnerabilities and system conditions.

Limited visibility creates mispriced policies

Without property-level insight, high-risk homes are often underpriced while lower-risk homes subsidize them—driving loss ratio volatility over time.

Get deeper insights on the drivers of water loss severity in our full guide → “Winning the Fight Against Non-Weather Water Losses”

How AI and Property-Level Data Are Changing the Landscape

AI models trained on real-world claims data can identify early signals of potential water loss by analyzing the interaction between:

• plumbing systems
• property attributes
• historical patterns
• material degradation
• repair history

This enables carriers to segment risk accurately, adjust pricing, and reduce preventable losses—long before small issues turn into major claims.

What Homeowners Actually Understand About Water Risk

Misconceptions around coverage and prevention

ZestyAI’s research shows that many policyholders:

• misunderstand what is and isn’t covered
• underestimate how much damage water can cause
• rarely take preventive actions unless prompted

This disconnect creates an opportunity for carriers to strengthen education, mitigation, and customer engagement.

Steps Carriers Can Take Today

Improve segmentation and rating accuracy

Property-level signals enable more precise risk tiers and more stable long-term portfolios.

Strengthen mitigation and reduce loss severity

Insights help identify which homes are at elevated risk and where targeted mitigation can reduce exposure.

Enhance underwriting workflows with explainable insights

Transparent, explainable AI helps underwriters understand the key drivers behind elevated risk—supporting both decision-making and regulatory review.

Get the Full Guide

Our new research paper, Winning the Fight Against Non-Weather Water Losses, breaks down the trends reshaping this growing peril—and the strategies carriers can use to get ahead of it.

Access the Guide

ZestyAI's analysis revealed that more than 12.6 million U.S. properties are at high risk of hail-related roof damage, representing $189.5 billion in potential replacement costs.

Powered by ZestyAI’s Z-HAIL™ model, the analysis underscores the growing financial threat of severe convective storms (SCS), including hail, tornadoes, and wind events. In 2024 alone, damages from SCS were estimated at $56 billion—surpassing losses from hurricanes.

Yet many insurers still rely on traditional models designed to estimate portfolio-level exposure, not property-level risk. As hail events increase in severity and frequency, these models often miss the structural and environmental conditions that drive real losses.

Kumar Dhuvur, Co-Founder and Chief Product Officer at ZestyAI said:

“Catastrophe models have helped insurers understand where storms may strike and how losses might add up at a portfolio level. But they weren’t built to assess risk at the individual property level, and they often miss the specific conditions that drive hail damage. By analyzing the interaction between structure-specific features and local storm patterns, we can distinguish risk between neighboring properties—enabling smarter underwriting, more precise pricing, and better protection for policyholders.”

Z-HAIL evaluates hail risk using a proprietary blend of climate, aerial, and property-specific data. By applying advanced machine learning to these inputs, Z-HAIL delivers highly granular predictions that reflect both the physical characteristics of a structure and the storm activity in its immediate surroundings.

Key findings from the analysis:

• 12.6 million U.S. structures flagged as high risk for hail-related roof damage
• $189.5 billion in total potential roof replacement exposure

Top five states by dollar exposure:

• Texas ($68B)
• Colorado ($16.7B)
• Illinois ($10.8B)
• North Carolina ($10.4B)
• Missouri ($9.5B)

States with the lowest dollar exposure:

• Maine ($4.7M)
• Idaho ($12.8M)
• New Hampshire ($18.5M)
• Nevada ($49.3M)
• Vermont ($64.7M)

In recent case studies, Z-HAIL has demonstrated the ability to pinpoint which properties are most susceptible to hail damage—even within the same neighborhood and exposed to the same storm. In one example from Allen, Texas, following a storm with 2.5-inch hailstones, Z-HAIL segmented risk across 483 policies, identifying no losses among properties rated “Very Low” by the model. This level of intra-territory precision gives insurers the ability to refine risk selection with confidence—even in the most hail-prone regions of the country.

Severe convective storms are becoming more frequent and costly, putting pressure on insurers to refine underwriting and risk management strategies. 

On April 2, our experts covered:

• Key drivers behind increasing severe storm losses
• What La Niña means for the 2025 season
• How AI-powered risk models improve risk segmentation
• Live Q&A – Get expert answers to your toughest questions!

Missed the live event? Stream now! 

Severe convective storms (SCS)—including tornadoes, hail, and damaging wind events—resulted in $58 billion in insured losses across the U.S in 2024.

Insurers face a dual challenge: navigating the uncertainty of storm patterns while ensuring their portfolios remain resilient enough to absorb the financial strain from clustered, high-loss events.

Research with IBHS confirms that SCS damage accumulates over time, particularly affecting rooftops after multiple exposures to intense storm activity. As housing stock deteriorates, insurers must reassess their portfolios to ensure underwriting, rating, and loss cost controls align with their risk appetite and maintain premiums that accurately reflect evolving exposure.

Get ahead of rising storm risks with expert insights that help you strengthen underwriting, risk assessment, and claims management.

Get our new report.

A staggering $2.15 trillion worth of U.S. residential property is at high risk of wildfire damage, according to a new AI-powered analysis from ZestyAI, the leader in climate and property risk analytics. The study, which assessed 126 million properties nationwide, found that 4.3 million individual homes face heightened wildfire risk—far beyond traditionally recognized high-risk areas.

Using advanced AI models trained on over 2,000 historical wildfires, ZestyAI mapped wildfire exposure at the property level, integrating satellite and aerial imagery, topography, and structure-specific characteristics. While California leads the nation with $1.16 trillion in wildfire-exposed property, other states such as Colorado ($190.5 billion), Utah ($100.3 billion), and North Carolina ($71.2 billion) also face significant risk.

Wildfire Risk is a Nationwide Challenge

While the Western U.S. has historically seen the most severe wildfire activity, ZestyAI’s findings confirm that high-risk properties exist across the country. States like North Carolina (4.6% of homes at high risk), Kentucky (2.9%), Tennessee (2.3%), and even South Dakota (11.0%) are now seeing increased wildfire exposure.

As more homes and businesses are built in fire-prone landscapes, the Wildland-Urban Interface (WUI) continues to expand. This, combined with intensifying climate conditions, is driving higher insurance costs and growing availability concerns. Today, one in eight U.S. homeowners already lacks adequate insurance coverage, and that number is expected to rise.

AI Expands Insurance Access in High-Risk Areas

Attila Toth, Founder and CEO of ZestyAI said:

"Wildfires are threatening more properties than ever before, with billions of dollars in exposure even in areas many people don’t associate with fire risk. Yet, too many homeowners are finding themselves uninsured or underinsured just as these disasters become more frequent and severe. Insurers have traditionally relied on broad, regional models that don’t account for individual property characteristics."

"That means some homeowners are denied coverage even when their true risk is much lower than their neighbors'.’"

AI-driven risk analytics are reshaping the way insurers assess wildfire exposure. By providing granular, property-specific insights, we’re helping insurers make smarter underwriting decisions—keeping coverage available in high-risk areas while ensuring that homeowners who take mitigation steps are recognized.

Last year, our models helped insurers extend coverage to 511,000 properties that had previously struggled to secure insurance due to outdated risk models. In 2025, we expect that number to reach a million, ensuring that even in high-risk areas, responsible homeowners have access to protection when disaster strikes.

ZestyAI today announced Z-SPARK™, an AI-powered model that predicts non-weather fire risk at the individual property level.

The model evaluates the factors that influence ignition and fire spread to help insurers identify the structures most likely to generate costly fire losses.

Most insurers still evaluate fire risk using neighborhood or territory level averages and limited historical loss data, even though risk can vary dramatically from one property to the next. Without property-level insight, insurers struggle to price risk accurately, increasing the likelihood of adverse selection and unexpected losses.

In 2023, $25 billion in property losses occurred from non-weather-related fire incidents from sources such as grills, appliances, heaters, and electrical faults.

Z-SPARK applies modern fire science and property-level intelligence to analyze how building materials, maintenance conditions, surrounding structures, local fire response capacity, and climate drive ignition and fire spread. 

The model predicts both the probability of ignition and potential loss severity using advanced machine learning trained and validated on millions of real fire incidents and verified insurance claims.

With these insights, insurers can:

• Price with precision — align premiums with true property-level fire risk rather than broad geographic averages
• Streamline operations — support straight-through processing for low-risk properties
• Prioritize underwriting resources — focus manual review and inspections where risk warrants closer scrutiny
• Expand confidently — write business in challenging markets with a clearer view of actual exposure
• Protect portfolio performance — proactively manage concentration risk before losses accumulate

Z-SPARK delivers 30× greater risk differentiation than traditional territory-based models.

“Non-weather fire is one of the most costly and least understood risks in property insurance,” said Kumar Dhuvur, Founder and Chief Product Officer of ZestyAI.

“Two homes on the same street can have dramatically different fire risk depending on how they were built, maintained, and what surrounds them. Yet most insurers still evaluate that risk using community-level scores. Z-SPARK reveals those hidden differences so insurers can make decisions based on the actual risk at each property.”

ZestyAI’s Z-FIRE wildfire model is widely used by insurers to assess catastrophic wildfire exposure. Z-SPARK extends that expertise to everyday building fires, applying the same property-level intelligence to the most costly source of insurance loss.

Together with models for hail, wind, severe convective storms, and water damage, Z-SPARK expands ZestyAI's suite of AI-powered capabilities across the most costly perils affecting insurance portfolios. Combined with ZORRO Discover — which applies agentic AI to help insurers research markets, prepare filings, and act on risk intelligence faster — the ZestyAI platform gives insurers a comprehensive view of property-level risk and the tools to act on it across underwriting, pricing, and portfolio management.

ZestyAI today announced that American European Insurance Group (AEIG) has selected its suite of regulatory-approved AI models to enhance underwriting, pricing, and exposure management across its multi-state property portfolio.

Through the ZestyAI platform, AEIG has adopted models for non-weather water (Z-WATER™), hail (Z-HAIL™), and wind (Z-WIND™), along with Z-PROPERTY™ to deliver property-level risk insight.

ZestyAI’s models are built, trained, and validated on carrier-contributed loss data and analyze the interaction between property characteristics, environmental conditions, and peril behavior to predict claim frequency and severity at the individual property level. Carriers apply these insights at new business and renewal to sharpen risk selection, improve pricing alignment, and manage portfolio concentration. ZestyAI’s models have secured regulatory approvals that enable transparent and defensible use in underwriting and pricing.

“As we operate across states with varied exposure profiles, we need consistent, property-level insight into what drives losses across wind, hail, and non-weather water,” said Steve Hartman, President and CEO of AEIG.

“ZestyAI gives us the precision to align underwriting and pricing with expected loss performance, support our agent partners with greater transparency, and maintain disciplined growth. Corporate experience alone is no longer sufficient to intelligently compete in a crowded marketplace, and the ability to leverage both traditional and non-traditional data in best-in-class account underwriting and pricing is a requirement for sustained profitability.”

“Regional insurers like AEIG are balancing expansion with the need to stay highly disciplined in how they evaluate property risk,” said Attila Toth, Founder and CEO of ZestyAI.

“By grounding underwriting and pricing decisions in verified, property-level intelligence, AEIG can expand across diverse exposures with confidence while protecting portfolio performance.”

ZestyAI has been named one of the Everest Group’s Top 50™ Property & Casualty (P&C) Insurance Technology Providers for 2026, marking the second consecutive year the company has been recognized for helping shape the future of insurance technology. 

Each year, Everest Group evaluates hundreds of providers across the global insurance technology landscape. The Top 50 ranking highlights providers demonstrating strong market momentum, broad industry impact, and continued investment in advanced technology.

Over the past year, ZestyAI has significantly expanded its platform and product capabilities to help insurers understand and manage property risk with greater precision. This includes the launch of ZORRO, ZestyAI’s agentic AI platform that enables insurers to research markets, analyze regulatory filings, and act on risk intelligence faster.

Everest Group also recognized ZestyAI within the Property and Location Intelligence category—solutions that help insurers evaluate structural, environmental, and catastrophe risk at the individual property level to improve underwriting, pricing accuracy, and operational efficiency.

ZestyAI’s AI-powered models help insurers assess exposure across the most costly perils affecting property portfolios, including wildfire, hail, wind, severe convective storms, and non-weather water damage. Together, these capabilities give insurers a more complete view of risk and the tools to act on it across underwriting, pricing, and portfolio management.

As AI moves from experimentation to enterprise deployment across the insurance industry, ZestyAI continues to invest in technologies that help insurers operate with greater precision, transparency, and confidence.

ZestyAI today announced that Harford Mutual Insurance Group (HMIG) has selected the ZestyAI Platform to enhance property-level exposure insight across its commercial portfolio.

Harford Mutual Insurance is grounding its book in verified, structure-level intelligence — strengthening underwriting discipline and improving visibility into the property characteristics that directly influence loss volatility and portfolio stability.

ZestyAI’s Roof Age delivers verified roof age by cross-validating building permit records with over 20 years of aerial imagery, to detect roof replacement events and assign confidence scores across 97% of U.S. properties. Z-PROPERTY™ further evaluates roof complexity, materials, and condition, and surrounding risk factors that materially impact underwriting outcomes and exposure management.

“With ZestyAI, we have verified property-level intelligence that changes how we evaluate and manage risk,” said Wayne Gearhart, Senior Vice President and COO of Harford Mutual Insurance Group.

“The platform improves our visibility into exposure across the portfolio and supports disciplined, resilient growth in our commercial book.”

“Harford Mutual Insurance has a long history of doing what’s right for its agents and policyholders, and that starts with understanding risk accurately at the property level,” said Attila Toth, Founder and CEO of ZestyAI.

“By grounding decisions in verified property-level intelligence, its team is strengthening underwriting discipline, enhancing portfolio resilience, and setting a strong example for how regional carriers can leverage trusted AI to navigate today’s risk environment.”

ZestyAI works closely with regulators to ensure transparency, validation, and continuous monitoring of its AI-driven models. Its portfolio of risk models has secured more than 200 approvals from regulators nationwide, including in markets served by Harford Mutual Insurance across the Mid-Atlantic and South.

Two insurers operating in hurricane-exposed coastal markets — Southern Oak Insurance Company and Lilypad-Centauri Insurance — are using the ZestyAI platform to bring greater structure-level insight to underwriting and portfolio oversight

As catastrophe volatility intensifies in states like Florida, insurers are increasingly turning to property-level intelligence — including roof condition, parcel characteristics, and structural features — to better understand exposure and manage accumulation risk.

Southern Oak Insurance Company expanded its partnership with ZestyAI following nine months of portfolio impact using Z-PROPERTY™ to analyze structural condition across its Florida homeowners portfolio.

Lilypad-Centauri Insurance adopted Roof Age and Z-PROPERTY™ to improve visibility into roof replacement history, parcel characteristics, and structural exposure across its coastal homeowners and dwelling fire portfolios.

Click here to view the Southern Oak press release.
Click here to view the Lilypad-Centauri press release.

Industry Coverage

Read more from leading publications covering the P&C insurance industry:

PropertyCasualty360 — Who’s Using What in P&C Insurance

Insurance Edge — ZestyAI Wins Over More US Insurers

Insurance Innovation Reporter — Coastal Insurers Adopt ZestyAI Property-Level Intelligence for CAT Risk

Fintech Global — ZestyAI Grows Insurer Partnerships Amid Coastal Volatility

Insurtech Analyst — Coastal Insurers Expand ZestyAI Partnerships for Property Risk

ZestyAI today announced that Horace Mann, the largest national insurer focusing on educators’ financial needs, has selected ZestyAI to support the modernization of its property underwriting operations.

Horace Mann will deploy Z-PROPERTY™, ZestyAI’s property intelligence platform that applies computer vision and machine learning across aerial imagery, building permit records, tax assessment data, and other verified sources to generate insights across more than 70 structural and parcel attributes for 150 million residential and commercial properties nationwide. 

These property-level insights provide clearer visibility into condition and exposure, enabling underwriting teams to better differentiate risk across new business and renewals, streamline workflows, and reduce reliance on traditional inspection-heavy processes.

As part of its underwriting evolution, Horace Mann will leverage ZestyAI’s broader portfolio of property intelligence and peril-specific risk models—including Roof Age, Z-HAIL™, Z-WIND™, Z-STORM™, and Z-WATER™—to evaluate how advanced property intelligence can inform underwriting and rating decisions across weather- and non-weather risks over time.

“This partnership is about rethinking how underwriting decisions are made in a modern property market,” said Vanessa Jackson, SVP, Head of P&C at Horace Mann.

“By bringing scalable, property-level intelligence into our underwriting process, we’re positioning our teams to make faster, more informed decisions today, and to continuously adapt as risk and expectations evolve.”

“Horace Mann is taking a thoughtful, measured approach to underwriting modernization,” said Attila Toth, Founder and CEO of ZestyAI. “By adopting property-level intelligence, they are enhancing underwriting decisions today while building a strong foundation for continued operational improvement.”

"We are proud to support Horace Mann in their mission to provide educators with peace of mind, allowing them to focus on what matters most — shaping the next generation."

ZestyAI supported Horace Mann through a rigorous evaluation process, demonstrating how accurate, complete property-level insight delivered at scale can materially improve enterprise data quality, supporting more reliable underwriting and analytics across the portfolio.