Resources

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.

ZestyAI helped carriers and insurers of last resort extend coverage to over 511,000 properties previously deemed uninsurable in 2024. This year, ZestyAI aims to double its impact, helping to bring coverage options to over a million families and businesses, ensuring that those in catastrophe-prone regions have access to sustainable, risk-aligned insurance.

A 2024 Deloitte survey found that nearly a quarter of homeowners in high-risk states are struggling to find coverage, while over half cite affordability as a growing concern—underscoring the industry’s need for granular insights that support underwriting and pricing decisions aligned with true property-level risk.

“For too long, insurers have had to make high-stakes decisions with incomplete information,” said Attila Toth, Founder and CEO of ZestyAI. “Advanced AI models are changing that. With granular, property-specific risk insights, insurers can close protection gaps and build a more resilient market.”

Traditional risk assessment methods rely on territory- or ZIP code-level evaluations, overlooking the property-level characteristics that drive risk. This approach leads to adverse selection, inaccurate pricing, and widespread market withdrawal.

ZestyAI replaces this approach with transparent, AI-powered models that integrate climatology, geospatial data, historical losses and structural attributes to deliver precise views of wildfire, hail, and wind risk, among other perils.

The results:

• Clear mitigation guidance to help policyholders take action.
• Risk-aligned premiums that support responsible market expansion.
• Improved underwriting precision through AI-driven risk scores and near-complete U.S. coverage.
• Optimized loss cost controls via more effective deductible, Actual Cash Value (ACV), and coverage strategies.
• A supportive experience for carriers, families, and businesses—enabling faster decisions, better communication, and greater confidence in coverage options.
• Streamlined inspections that lower expenses and improve efficiency.

ZestyAI collaborates closely with regulators to ensure transparency, validation, and model oversight. Its wildfire model, Z-FIRE, is approved across all Western states, while its severe convective storm models have gained broad acceptance from Texas to Colorado and throughout the Midwest and Great Plains.

As regulators support the use of advanced models, they are also paving the way for smarter risk-based pricing and proactive mitigation—revitalizing insurance’s core mission: protecting the livelihood of home and business owners and their communities.

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! 

ZestyAI has partnered with EarthDaily Analytics (EarthDaily), a global provider of Earth Observation analytics and data.

Through this partnership, ZestyAI’s advanced models—including Z-FIRE™, Z-HAIL™, Z-WIND™, and Z-STORM™—will be available through EarthDaily’s Ascend platform, delivering geospatial data, risk modeling, and post-event insights to insurers.

With the insurance industry facing escalating challenges from climate-driven catastrophes and increasing pressure to accurately price risk, ZestyAI’s models provide granular, property-level risk data. These models analyze factors like vegetation density, construction materials, and historical weather patterns to offer insights beyond traditional methods.

“At EarthDaily, we’re committed to delivering cutting-edge property insights to customers navigating today’s climate risks,” said Rachel Olney, VP of Insurance at EarthDaily.

With advanced AI models covering wildfire, hail, wind, and property data, ZestyAI is an ideal partner to support our mission.

"By including their advanced analytics in our Ascend platform, we’re excited to empower clients to take proactive steps in managing and mitigating risk with confidence."

ZestyAI’s solutions achieve nearly 100% hit rates, offering actionable insights that insurers and businesses can trust. By bolstering the data available in the Ascend platform with property-level insights, the partnership provides decision-makers with a new level of clarity to mitigate risks, improve underwriting, and allocate resources more effectively.

The collaboration exemplifies the growing importance of innovative technology in the insurance and property management sectors, especially as global climate risks continue to evolve.

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.