As hail damage continues across the U.S., new research from ZestyAI and IBHS works to make hail losses more predictable

Hail losses are a persistent problem for property insurers' risk management efforts. Historically, carriers have focused on intense events to predict hail risk, with supporting data confined to storms with hailstones larger than one or two inches. The study Small Hail, Big Problems, New Approach shows high concentrations of small hail are more important than previously thought, pointing to an opportunity to broaden data sets to account for the cumulative effect all hailstorms have on a roof's susceptibility to damage over time, leading to a claim.

This new research shows all hail needs to be accounted for when modeling and ultimately understanding losses. Using data from all hail events, not just those with hail that meet the severe criteria of one inch or greater, allows carriers to consider valuable data on smaller hailstone impacts. Additionally, insurers can integrate climate and materials science to better understand hail frequency and severity. Research suggests using this new approach could perform as much as 58 times more accurately than looking at events with large and very large maximum hail sizes alone, allowing carriers to more effectively assess hail risk, achieve more profitable underwriting and open up ratings to previously avoided areas. 

"As we've learned more about hailstorms, we've discovered storms that produce large concentrations of small hail are more common than we thought, and despite causing less individual damage than a single large hailstone, small hail, especially in high concentrations, is likely a meaningful contributor to the loss we see each year from hail," said Dr. Ian Giammanco, managing director of standards and data analytics at IBHS. "Experiments also show large concentrations of smaller hailstones cause degradation to the asphalt shingles, specifically dislodging large amounts of granules. Once enough granules are lost, the underlying asphalt material can become more susceptible to aging and weathering. Repeated exposure to these types of hailstorms can shorten the life of an asphalt shingle roof and increase the damage caused by large hailstones in the next storm."

"Hail losses are a persistent problem for property insurers' risk management efforts," said Attila Toth, founder and CEO of ZestyAI. "Three of the nation's five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem." 

Hail risk can be especially costly to insurers because, unlike other catastrophic perils like hurricanes and wildfires, it can be difficult to identify the storm that caused a hail claim. As a result, insurance carriers could be forced to raise overall premiums or introduce high deductibles to compensate for the added costs.

As climate and materials science have developed, more data has become available providing improved hail risk evaluation options that can lead to better decisions at earlier stages of the policy life cycle. Other benefits could include more profitable underwriting, a greater ability to rate previously-avoided areas and significantly reduced loss ratios.

For the complete ZestyAI and IBHS research paper visit: bit.ly/41lKRfO

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property's value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information.

The IBHS mission is to conduct objective, scientific research to identify and promote effective actions that strengthen homes, businesses and communities against natural disasters and other causes of loss. Learn more about IBHS at ibhs.org.

SOURCE ZestyAI