Reports & Research

New research from ZestyAI reveals that 62% of U.S. homeowners are deferring critical home maintenance, adding up to $317 billion in potential claims exposure for insurers.

These findings come as Severe Convective Storms (SCS) caused an estimated $58 billion in insured losses in 2024, surpassing hurricane-related losses and marking the second-costliest SCS year on record.

Tornadoes, hail, and wind events now account for over 60% of all U.S. catastrophe claims, and research from the Insurance Institute for Business & Home Safety (IBHS) shows that roof damage accounts for up to 90% of residential catastrophe losses.

Key Findings from ZestyAI’s Homeowner Survey

According to ZestyAI’s nationally representative survey, 62% of homeowners have delayed essential repairs due to budget constraints, representing nearly 59 million U.S. homes with unaddressed vulnerabilities. Forty percent said they would rely on an insurance claim to cover major repairs like roof replacement, adding up to an estimated $317 billion in potential exposure for carriers.

Alarmingly, 63% of homeowners who weren’t living in their home at the time of the last roof replacement don’t know how old their roof is, making it even harder to detect aging systems before they fail. Meanwhile, 12% admitted they would delay repairs indefinitely, further increasing their risk of property damage.

Severe Convective Storms: The Growing Catastrophe Risk

This blind spot compounds known risks: prior ZestyAI analysis has identified over 12.6 million U.S. properties at high risk for hail-related roof damage, representing $189.5 billion in potential roof replacement costs.

“Deferred maintenance has long been a known risk factor, but today the stakes are higher than ever,” said Kumar Dhuvur, Co-Founder and Chief Product Officer of ZestyAI. "With claim severity rising and storm losses compounding, insurers need more than hazard maps to navigate this landscape."

"Property-level insights allow carriers to proactively address known vulnerabilities, improve underwriting precision, and work with homeowners to reduce losses before they happen.”

ZestyAI’s findings support a growing push toward data-driven, preventative underwriting strategies, especially as carriers face rising claim severity and pressure to improve combined ratios across storm-prone states.

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

Watch the Full Webinar → LA Fires in Focus: What Insurers Need to Know

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Meet the Experts

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn:

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Watch now.

Featured Speakers:

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

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.