Insurers exposed to billions in potential losses from hail-related roof damage: ZestyAI

ZestyAI, a company specialising in property and climate risk analytics, has disclosed that over 12.6 million US properties are at high risk of hail-related roof damage, costing about $189.5 billion in potential replacement costs.

The information was obtained through ZestyAI’s Z-HAIL model, and highlights the growing financial threat of severe convective storms (SCS), including hail, tornadoes, and wind events.

In 2024, damages from SCS were estimated at $56 billion, surpassing losses from hurricanes. Currently, traditional risk models are designed to estimate portfolio-level exposure, not property-level risk. Since hail events increase in severity and frequency, the models often miss the structural and environmental conditions that drive real losses, warns ZestyAI.

The company explained, “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.”

According to this model’s findings, the top five states by dollar exposure are Texas with $68 billion; Colorado at $16.7 billion; Illinois at $10.8 billion; North Carolina at $10.4 billion; and then Missouri at $9.5 billion.

The model has also revealed the states with the lowest dollar exposure, with Maine at $4.7 million, Idaho at $12.8 million, New Hampshire at $18.5 million, Nevada at $49.3 million, and Vermont at $64.7 million.

Kumar Dhuvur, Co-Founder and Chief Product Officer at ZestyAI, commented, “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.”

Additionally, while running case studies, Z-HAIL has successfully been able to pinpoint properties most susceptible to hail damage, even within the same neighborhood and exposed to the same storm.

In March, the model was approved for use in five states, which has currently expanded to 14 states, with additional approvals pending.

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