Reports & Research

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.

Artificial intelligence is reshaping the P&C insurance industry, offering new ways to streamline underwriting, enhance risk management, and navigate evolving regulations.

But as AI adoption accelerates, insurers must ensure they’re using these technologies effectively—balancing innovation with compliance.

Our latest guide explores the most impactful AI applications in insurance, including:

• AI-powered underwriting and predictive analytics
• How regulators are shaping the future of AI in insurance
• Best practices for integrating AI while ensuring fairness and transparency

As AI-driven tools become the new standard, insurers who adapt early will gain a competitive edge.

Download our free guide to leverage these innovations while staying aligned with evolving regulations.

For years, insurers asked:

“How old is this roof?”

Now, the real question is:

“How will this roof perform?"

The way insurers assess roof risk has evolved significantly over the past two decades. What began as a simple Roof Age-based surcharge has transformed into a sophisticated approach that considers real-time condition, storm resilience, and structural complexity.

A closer look at SERFF regulatory filings traces the first recorded use of Roof Age back to 2004 when The Hartford introduced Roof Age-based pricing in Iowa.

At the time, the insurer applied a flat 10% surcharge to roofs 26 years and older—a figure that now seems outdated, as many carriers won’t insure roofs older than 15 years. 

Roof Age quickly became a key rating factor—by the 2010s, Roof Age adoption in rate filings surged, growing at an annual rate of 29%.

If you fast forward just 10 years after The Hartford’s initial filing, you’ll find a stark contrast in how roof risk was assessed. By 2014, The Hartford’s rate filing in Iowa contained 51 pages of actuarial tables, detailing various roof materials and rate adjustment factors for age.

This shift reflected a broader trend—Roof Age moved from a simple surcharge to a more nuanced risk model that accounted for material durability, wear patterns, and structural longevity.

By 2018, insurers began looking beyond Roof Age, and that’s when Roof Condition first appeared in regulatory filings.

Over the past five years, its adoption has surged 32% annually, outpacing Roof Age at its peak. Insurers also began incorporating roof complexity variables, such as pitch and facets, to further refine their risk assessment models.

These advancements provided a more nuanced view of risk, moving beyond the assumption that all old roofs posed the same level of hazard.

Now, for the first time in two decades, Roof Age is plateauing. Over the past two consecutive years, we've seen a decline in the number of filings incorporating Roof Age, bringing its usage close to 2019 levels.

This decline suggests that carriers are moving toward more sophisticated approaches, leveraging real-time condition assessments rather than relying solely on the number of years since installation. After all, a 10-year-old roof in poor condition can present a greater risk than a 20-year-old roof that has been well-maintained—and insurers are recognizing the importance of capturing these distinctions.

With severe convective storm-related insured losses reaching $58 billion in 2024, traditional risk assessment methods can no longer keep up.

A new paradigm is emerging, where advanced AI-driven risk models provide the precision and resilience needed to navigate an increasingly volatile climate. 

At ZestyAI, we’re helping insurers make this shift with models like Z-STORM, Z-HAIL, and Z-WIND, which are already filed and approved in 14 states, including Texas, Colorado, Illinois, Oklahoma, and Louisiana.

Those who embrace these innovations will gain a competitive edge—reducing loss costs, improving operational efficiency, and ultimately shaping the future of risk assessment in property insurance.

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.

Property and climate risk analytics leader ZestyAI today announced regulatory approval of its Severe Convective Storm Suite in Oklahoma, North Carolina, Louisiana, Wisconsin, and Arkansas—covering more than 12 million residential and commercial properties. 

Severe convective storms caused $58 billion in insured losses in 2024, marking the second-costliest year on record. A recent ZestyAI analysis revealed that in these five newly approved states, more than 2.1 million properties face a high risk of filing a hail claim—putting over $31 billion in potential roof replacement costs on the line. 

Unlike traditional models, ZestyAI’s AI-driven risk models predict the likelihood and severity of claims at the individual property level by analyzing the interaction of local climatology with property-specific characteristics.

Built, tested, and validated on an extensive claims database, the models provide a granular, transparent understanding of risk—delivering the top risk factors for each property, and equipping insurers with the accuracy needed to improve underwriting, optimize pricing, and reduce preventable losses.

“Severe convective storms now cost insurers more than hurricanes, yet traditional underwriting tools don’t provide the precision needed to keep pace with rising losses,” said Bryan Rehor, Director of Regulatory Affairs at ZestyAI.

“These approvals reinforce the insurance industry’s shift toward data-driven, property-level risk assessment."

ZestyAI’s SCS models have now been thoroughly vetted and approved by regulators across 14 states—covering more than 44 million properties across the Midwest, Great Plains, and South.

ZestyAI announced today that Lemonade, the digital insurance company powered by AI and social impact, has adopted the ZestyAI platform to further optimize underwriting for key catastrophe perils in the U.S., building on the company’s existing technology and underwriting operations.

ZestyAI’s predictive analytics platform leverages advanced AI models to analyze the interplay of climatology, geography, and the unique characteristics of each structure and roof, enabling precise and transparent property risk assessments.

By leveraging unique risk insights, Lemonade can make smarter catastrophe risk mitigation decisions. Additionally, ZestyAI’s proactive regulatory approach, with approvals in key states, simplifies compliance and enables Lemonade to implement these models faster.

“Since our launch, we've always been committed to using technology to create smarter, more accessible insurance products,” said Ori Hanani, Senior Vice President of Insurance at Lemonade.

“In leveraging ZestyAI’s advanced risk models, we're able to further support homeowners in securing comprehensive coverage for their most valuable assets, while also continuing to strengthen our underwriting capabilities as we continue to grow." 

Attila Toth, Founder and CEO of ZestyAI, said:

Lemonade is a natural partner for ZestyAI.

“Their innovative approach to insurance and customer-centricity aligns perfectly with our commitment to provide actionable insights that drive smarter risk decisions.”

This partnership reflects a shared vision for addressing increasing climate risks and sets a new standard for resilience, efficiency, and innovation in the insurance industry. 

ZestyAI today announced a partnership with the Colorado FAIR Plan to expand insurance access for homeowners facing coverage challenges.

The partnership leverages ZestyAI’s AI-driven risk models—Z-FIRE™, Z-HAIL™, and Z-WIND™—to deliver property-specific risk assessments for wildfire, hail, and wind. These insights will support risk-based pricing and help the Colorado FAIR Plan guide homeowners on mitigation strategies.

“Our mission is to ensure every Coloradan has access to insurance that reflects their property’s actual risk, not outdated assumptions,” said Kelly Campbell, Executive Director of the Colorado FAIR Plan.

“ZestyAI’s models will help us bring greater fairness and resilience to the market while equipping homeowners with practical mitigation guidance.”

Over the next year, Colorado FAIR Plan expects to provide coverage to nearly 30,000 families previously classified as high-risk under traditional models.

By incorporating granular risk data, the plan can better align premiums with actual risk while offering homeowners actionable steps to protect their properties.

Those who invest in mitigation may also transition back to the standard insurance market over time.

Colorado regulators have prioritized risk-based pricing and transparency to stabilize the insurance market. Colorado Insurance Commissioner Michael Conway has led efforts to integrate mitigation into coverage decisions, aligning with the FAIR Plan’s adoption of ZestyAI’s AI-driven insights.

“This partnership ensures risk assessments reflect real property conditions—not just broad classifications—so homeowners can access both coverage and meaningful mitigation guidance,” said Bryan Rehor, Director of Regulatory Affairs at ZestyAI.

“Through AI-powered insights, we’re helping homeowners secure risk-aligned coverage options.”

ZestyAI’s risk platform integrates aerial imagery, historical building permits, geospatial data, and structural attributes to provide precise, property-level risk insights.

Insurers using ZestyAI’s models can assess key risk factors—including vegetation proximity, roof condition, and building materials—to inform underwriting, pricing, and mitigation recommendations to policyholders.

The collaboration builds on ZestyAI’s success with the California FAIR Plan, which expanded coverage for hundreds of thousands of homeowners in 2024.