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ZestyAI today announced that Logic Underwriters has adopted ZestyAI’s Z-PROPERTY™, Z-HAIL™, and Z-WIND™ solutions to improve underwriting and rating precision across its personal and commercial property portfolio in Texas.

Texas is the most expensive severe convective storm market in the United States, with hail and damaging wind driving billions of dollars in insured losses every year.

"Texas is one of the most challenging storm markets in the U.S., and we need tools that match that reality," said Bill Motz, Director of Operations, Logic Underwriters.

"ZestyAI's detailed property insights and dedicated hail and wind models will help us continue to provide exemplary service to our clients—from more accurate risk assessments to better loss prevention guidance in increasingly volatile weather conditions."

ZestyAI’s property-specific hail and wind models predict the likelihood and severity of storm-driven claims by analyzing how local climatology interacts with detailed property characteristics—helping underwriters to distinguish meaningful differences in risk within the same rating territory. Each model is trained on validated claims data, offering transparent explanations of the key factors driving risk.

Z-PROPERTY applies AI to high-resolution aerial imagery and multi-source data to assess roof condition, structural complexity, and parcel-level features such as vegetation overhang, yard debris, and secondary structures—factors that directly influence claim frequency and severity across multiple perils.

“Logic Underwriters is exactly the kind of forward-looking partner that is redefining underwriting in high-exposure states,” said Attila Toth, Founder and CEO of ZestyAI.

"This collaboration shows how property-level intelligence can support underwriting excellence and disciplined decision-making while helping policyholders better understand and protect their properties. When insurers can identify specific risk factors like roof condition or vegetation overhang, they can provide actionable guidance that helps clients reduce their exposure and minimize losses."

ZestyAI’s severe convective storm models are approved in 30 states, spanning the nation’s highest-exposure hail and wind markets, and used by leading insurers across the country.

Wildfire risk in the United States is no longer confined to the edges of forests or traditionally high-risk zones. New analysis using ZestyAI’s property-level wildfire models shows that millions of homes classified as low or no wildfire risk under federal assessments face elevated wildfire danger when evaluated at the property level.

This analysis was recently featured in Vox, which examined how wildfire behavior is evolving — and why broad, backward-looking risk maps are increasingly misaligned with how fires spread today.

👉 Read the full article on Vox → https://www.vox.com/climate/476932/california-wildfire-los-angeles-risk-ai-housing-climate

Wildfire risk is closer — and more granular — than most maps show

Many homes damaged or destroyed in the 2025 Los Angeles wildfires were still classified as “low risk” under federal wildfire assessments. ZestyAI’s property-level analysis provides a different perspective.

By evaluating individual structures — including vegetation proximity, defensible space, building characteristics, and neighborhood-level fire dynamics — ZestyAI identified more than 3,000 properties worth approximately $2.4 billion in areas impacted by the Palisades and Eaton fires that showed elevated wildfire risk despite being classified as low or no risk under FEMA’s census-level assessments.

Across California, the classification gap is even broader. Approximately 1.2 million properties, representing roughly $940 billion in residential property value, are designated as low or no wildfire risk under federal maps, despite AI-driven property-level models indicating elevated wildfire danger.

Why census-level wildfire maps fall short

Wildfires do not spread evenly across census tracts or counties. Ember-driven ignition, structure-to-structure spread, wind conditions, and localized vegetation patterns create uneven outcomes, where one home survives and the next is destroyed.

Federal wildfire assessments are designed to provide a baseline view of community-level risk. FEMA has noted that its National Risk Index is not intended to serve as a property-specific risk assessment. When risk is evaluated at the individual property level, meaningful differences emerge that aggregated maps are not designed to capture.

What more granular wildfire risk intelligence enables

More detailed wildfire risk data can support:

• Targeted mitigation efforts at the property and neighborhood level
• More informed rebuilding and land-use decisions
• Clearer, more defensible underwriting and portfolio strategies
• Improved dialogue between insurers, regulators, and communities

A shift in how wildfire risk is understood

Wildfire risk is evolving faster than the systems built to measure it. Homes are no longer just adjacent to wildfire hazards; they increasingly influence how fires ignite, spread, and intensify, even in dense urban environments.

Property-level risk intelligence does not remove hard decisions. But without it, those decisions are made using an incomplete picture of where wildfire risk truly exists.

Read the full Vox article here.

 General Star (GenStar), a respected provider of excess and surplus specialty property and casualty insurance and a member of the Berkshire Hathaway family of companies, has selected ZestyAI to further strengthen how it underwrites hail, wind, and severe convective storm risk across its commercial property portfolio. 

The carrier will use ZestyAI’s Z-STORM™ model to gain more precise, property-level insight for multi-structure apartment and condominium risks, further supporting underwriting, pricing, and coverage decisions.

“Z-STORM gives us a more actionable view of hail risk at the individual property level,” said Matt Brown, Senior Vice President, Delegated Division at GenStar.

“In our evaluation, the model demonstrated compelling risk-splitting lift, which allows us to further differentiate risk more effectively, price with greater precision, and ultimately strengthen relationships with our customers and distribution partners.”

Z-STORM predicts the expected frequency and severity of severe convective storm losses by combining climatology with detailed property-specific characteristics. The model is designed to support more refined wind and hail peril rating, improved deductible and endorsement strategies, and earlier visibility into accumulating and emerging storm risk across a carrier’s portfolio.

By adopting Z-STORM, GenStar aims to further:

• Improve underwriting clarity by incorporating a clearer, property-level view of hail and wind risk into core underwriting decisions
• Expand policy availability by applying deductibles, endorsements, and exclusions more precisely—helping keep coverage available even in hail-prone markets
• Align pricing with risk, potentially offering more competitive premiums for favorable risks while refining pricing for higher-risk properties
• Identify emerging storm risk sooner, enabling proactive risk management and loss mitigation before exposures become potentially costlier for both GenStar and insureds

“GenStar joins a growing number of carriers using AI to modernize property underwriting,” said Attila Toth, Founder and CEO of ZestyAI.

“With Z-STORM delivering a sharper, property-level view of hail risk across complex apartment and condo portfolios, GenStar can further strengthen underwriting and pricing decisions and identify emerging exposures earlier—before they potentially turn into avoidable losses.”