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At ZestyAI, we know that better data leads to better decisions. That’s why we’ve invested in a major upgrade to our Roof Age product, trusted by leading carriers to improve risk selection, pricing, and operational efficiency in property insurance.

Today, we’re excited to share what’s new, what’s improved, and how these advancements are already helping carriers strengthen underwriting, rating, and inspection workflows.

What’s New in Roof Age

We’ve taken a holistic approach to improving performance, accuracy, and efficiency. Here’s what you’ll find in the latest release:

Refit model with double the training data

We’ve significantly enhanced the Roof Age model, doubling the size of our training dataset to improve performance across diverse housing stock, roof types, and geographies.

This expanded dataset incorporates more confirmed roof replacement events and broader regional variation, allowing the model to generalize more effectively to different parts of the country, including historically underrepresented regions.

The model is now better able to distinguish between full roof replacements and other types of roof-related activity, such as solar panel installation, patched sections, partial replacements, or home additions.

These events may alter the roof’s appearance or condition, but don’t represent a comprehensive replacement. By learning the subtle visual and contextual cues that separate these scenarios, the model delivers more accurate predictions and reduces the risk of misclassification.

Enhanced estimation for challenging cases

In cases where no building permit is available and roof replacement can’t be clearly confirmed via aerial imagery, our improved Roof Age Estimation Model takes over. This model, now trained on double the dataset, is purpose-built for ambiguity.

It leverages not only imagery and property-level features but also regional climatology, using knowledge of local weather patterns and environmental stressors to inform its estimate.

For example, a roof in the Southeast exposed to intense sun and humidity will age differently than one in the Pacific Northwest or Upper Midwest. Incorporating these regional factors helps improve estimation accuracy, even when direct replacement signals are unavailable.

ZestyAI also establishes a minimum roof age, providing additional clarity and confidence. Using our extensive, 20-year aerial imagery catalog, we can identify the earliest visual evidence of the current roof.

If no replacement activity is detected over a known span of time, we can confidently assert that the roof is at least that old.

This minimum age is then used not just as a floor, but as a valuable input to further refine the overall roof age estimate, narrowing the prediction with greater precision than models limited to single-source or snapshot data.

This capability provides underwriters and actuaries with a powerful, high-confidence signal, particularly valuable for pricing segmentation, inspection prioritization, and risk selection strategies.

Intelligent cross-validation logic

The model doesn’t rely on a single data source. Even when a strong signal like a building permit is available, it cross-validates with high-resolution aerial imagery to detect inconsistencies, like permits that were filed but not followed through, or replacements that occurred without permits.

This layered logic helps ensure predictions are grounded in current conditions, not just administrative records. It also improves detection of fraud, data entry errors, or outdated assumptions in property records.

This logic creates a "trust but verify" framework that boosts both precision and confidence in every prediction.

To illustrate, imagine a home built 12 years ago. The model begins by anchoring to the construction year, then scans forward through our aerial imagery catalog and permit records to assess whether a roof replacement has occurred.

By grounding the analysis in the property's timeline, the model avoids misinterpreting the original roof as a newer installation and increases confidence in identifying true replacement events.

Expanded imagery catalog

We’ve enriched our aerial imagery sources to improve roof verification across geographies. The result: more accurate verification of roof replacements and improved model performance in hard-to-cover geographies.

This helps carriers score more properties with higher confidence, especially in rural or previously under-covered regions.

Confidence scores for every prediction

Every Roof Age prediction now comes with a confidence score, helping carriers make more informed decisions. High-confidence predictions can be fast-tracked for automated processing, while lower-confidence scores can trigger secondary review or inspection.

This added transparency empowers carriers to make risk-based decisions not only on the prediction itself, but on how much to rely on it.

Improved Performance Behind the Scenes

We’ve also made significant infrastructure upgrades to enhance product speed and reliability.

• Reduced Latency: Infrastructure improvements have cut average response times to under 2.5 seconds per property, making Roof Age a real-time-ready solution for quoting and policy decisions.
• Stricter Quality Controls: We’ve added new safeguards to filter out imagery that’s blurry, outdated, or contains visual artifacts. Only high-resolution, high-confidence inputs are used to power predictions.
• Scalability: These backend improvements also allow us to handle larger portfolios with more concurrent requests. This is ideal for carriers integrating Roof Age into enterprise systems.

Easier Access for Every Workflow

Roof Age is available wherever you need it:

• In Z-VIEW: Easily visualize Roof Age predictions and supporting evidence with property-level insights directly in our web application.
• Via API: Seamlessly score entire portfolios and integrate directly into your quoting, pricing, or inspection strategies.

Ready to See the Results for Yourself?

The feedback from the market has been tremendous, and we’re just getting started. Want to see the results for yourself? We’re inviting carriers to pilot the new Roof Age model and evaluate its performance on their own book of business.

Get in touch to schedule your Roof Age pilot

ZestyAI announced that Brava Roof Tile, a leader in premium synthetic roofing solutions backed by Golden Gate Capital, has selected ZestyAI to validate the real-world performance of its roofing systems during past storms.

How Brava Roof Tile Uses ZestyAI’s Property and Roof Intelligence

Brava Roof Tile is leveraging three of ZestyAI’s proven solutions to bring greater clarity to roof performance and replacement opportunities. Roof Age synthesizes building permit data with 20+ years of high-resolution aerial imagery, applying advanced machine learning to deliver verified roof age estimates with 97% U.S. coverage.

Within Z-PROPERTY™, Digital Roof applies AI to assess roof complexity, materials, and condition, flagging vulnerabilities before they become costly failures, while Location Insights evaluates the broader parcel to surface risk factors such as vegetation overhang, yard debris, and secondary structure.

Together, these insights provide comprehensive coverage, unmatched accuracy, and fast deployment at scale, turning property-level data into actionable guidance on roof vulnerabilities and replacement opportunities.

Validating Real-World Resilience With Property-Level Data

“Brava is committed to helping homeowners protect their most valuable asset with roofs that combine durability, sustainability, and beauty,” said Matt Pronk, Chief Financial Officer of Brava Roof Tile.

“With ZestyAI, we gain a clear, data-driven view of how roofs perform in the real world and use those insights to guide families toward stronger, longer-lasting protection.”

“Brava Roof Tile is showing how ZestyAI's risk analytics can be applied to validate resilience in the real world,” said Attila Toth, Founder and CEO of ZestyAI.

“Our mission is to protect families, communities, and their financial wellbeing, and our unmatched coverage and accuracy make that possible at scale."

At ZestyAI, we’re often asked:
What Does “Property-Level” Mean? 
What Makes ZestyAI Different from Traditional Risk Models?
Are ZestyAI Models Approved for Use in Underwriting and Rating?

This post answers the most common questions we receive from underwriters, actuaries, regulators, and technology partners—using wildfire, hail, wind, water, and storm perils as examples of how we turn complex data into actionable insights.

What Does “Property-Level” Mean?

Traditional risk models often rely on ZIP codes, territories, or broad regional averages to assess hazard and vulnerability. Stochastic models may support property-specific analysis, but they typically require external data sources, which adds cost, complexity, and inconsistency.

At ZestyAI, we assess each structure based on its physical characteristics and how it interacts with the surrounding environment.

We assess:

• Parcel boundaries and building footprints
• High-resolution aerial and oblique imagery
• Topography, slope, and vegetation
• Structural details like roof shape, materials, and defensible space

By integrating climatology with the built environment, we generate contextual risk scores that capture how each property’s physical characteristics, regional climatology, and historical loss experience interact to shape real-world risk.

That drives smarter decisions in underwriting, pricing, and mitigation, without relying on assumptions or manually sourced data.

What Makes ZestyAI Different from Traditional Risk Models?

Traditional risk tools often rely on:

• Broad hazard zones that can’t distinguish risk within a ZIP code
• Infrequent model updates that fail to reflect current conditions
• Over-simplified proxies—often relying solely on factors like year built—without accounting for deeper structural nuance
• Manual inspections that are slow, inconsistent, and costly

ZestyAI takes a fundamentally different approach.

Our models:

• Use gradient boosted machines that capture complex interactions between property features and environmental conditions
• Are trained on millions of actual insurance claims, not simulations, ensuring outputs reflect real-world loss experience
• Leverage both imagery (e.g., high-resolution aerial and oblique photos) and non-imagery sources (e.g., permits, climatology, topography)
• Continuously incorporate new data to reflect changing exposures
• Provide parcel-level risk scores with full transparency and regulator-ready documentation

With 97%+ U.S. property coverage, ZestyAI delivers national models with localized precision, helping carriers segment risk, price accurately, and respond to today’s evolving climate risks.

Are ZestyAI Models Approved for Use in Underwriting and Rating?

Yes. ZestyAI’s models, including Z-FIRE™, Z-HAIL™, Z-WIND™, Z-WATER™, and Z-STORM™, have been approved for use in underwriting and rating across the U.S.

Our regulatory approach is grounded in a few key principles:

• Transparency: We provide clear, regulator-ready documentation, including model methodology, variable selection rationale, and statistical validation.
• Collaboration: We work directly with carriers and state regulators throughout the filing process, from pre-submission briefings to objections.
• Responsible Innovation: Our models are trained on real-world claims, regularly updated with new data, and built with fairness and explainability in mind.

We support filings with:

• Detailed methodology and input documentation
• Variable importance rankings and validation studies
• Pre-built regulatory summaries to streamline the review
• Ongoing support throughout the regulatory lifecycle

ZestyAI has a track record of success navigating regulatory review. We help carriers adopt cutting-edge risk models with confidence and compliance.

Roof Age vs. Roof Condition: What’s the Difference?

ZestyAI’s models distinguish between roof age and roof condition, treating them as complementary signals that together provide a more accurate picture of roof-related risk, especially for hail and wind.

Roof Age is validated using a combination of building permit data and aerial imagery, analyzed through multiple proprietary methods simultaneously. We assign confidence scores to each roof age and apply minimum roof age rules to avoid false positives, ensuring the data is robust, even in jurisdictions with limited permitting records.

Roof Condition is assessed through computer vision models applied to high-resolution aerial imagery. These models detect visual signs of degradation, such as discoloration, wear, patching, and debris, that may not correlate with official replacement dates.

Why does this matter?

Because many insurers rely solely on reported roof age, which is often missing, outdated, or self-reported.

Our approach captures:

• Properties with older roofs that are still in good shape (and may be lower risk)
• Properties with newer roofs already showing signs of wear (and may be higher risk)
• Up-to-date roof vulnerability that static datasets can’t match

Together, roof age and condition power smarter decisions in underwriting, pricing, and mitigation—grounded in observable reality, not assumptions.

Modeling Approach: Why Gradient-Boosted Machines (GBMs)?

ZestyAI's risk models use gradient boosted machines (GBMs), a machine learning technique that delivers powerful predictive performance while remaining transparent and regulator-ready.

We use GBMs because they:

• Achieve high predictive accuracy by combining many simple decision trees into an ensemble that learns from its own errors over time, ideal for capturing complex insurance risk signals.
• Model non-linear interactions between variables, such as how roof complexity, condition, and regional climatology jointly influence risk, something traditional models or GLMs often miss.
• Enable transparency and explainability, with tools like feature importance rankings, partial dependence plots, and SHAP values that help underwriters, actuaries, and regulators understand what’s driving risk scores.
• Support a wide range of input types, from imagery-derived features to structured data like permits, topography, and property characteristics, all in one unified framework.

The result: a modeling approach that delivers real-world impact, supporting smarter underwriting, better pricing, and confident regulatory adoption.

How Z-FIRE Evaluates Wildfire Risk

Z-FIRE is ZestyAI’s structure-level wildfire risk model, built to capture both traditional wildland fire exposure and the growing threat of urban conflagration. Unlike traditional hazard maps that apply uniform risk zones across ZIP codes or counties, Z-FIRE delivers granular, property-specific risk scores for every structure in the U.S.

The model includes two levels of scoring:

1. Level One: Exposure Risk – Evaluates how likely a structure is to fall within a future wildfire perimeter, based on vegetation, slope, elevation, proximity to the wildland-urban interface (WUI), historical burn patterns, and regional climatology.
2. Level Two: Structure Vulnerability – Assesses how likely that structure is to be damaged if a wildfire occurs nearby. This score factors in structural characteristics like building materials, defensible space, and surrounding fuels, extracted from aerial imagery using computer vision.

Together, these scores provide a more complete view of wildfire risk: not just where fires may happen, but how individual structures are likely to perform.

Z-FIRE also captures non-traditional wildfire scenarios, including embers and wind-driven fires that jump the WUI and ignite dense suburban and urban neighborhoods. This makes the model particularly valuable for identifying concentration risk, urban conflagration, and managing PML across books of business.

Z-FIRE is validated on millions of insurance claims and performs reliably across all geographies—from the forests of California and the grasslands of Texas to emerging risk zones in Colorado, Oregon, and the Eastern U.S.

How Z-HAIL Accounts for Roof Vulnerability

Z-HAIL is a property-specific hail risk model designed to assess not just the likelihood of hail, but how damaging it will be to a specific structure.

Unlike traditional models that rely on historical hail frequency alone, Z-HAIL captures the interaction between local climatology and structural resilience by analyzing:

• Hail climatology: storm frequency, hailstone size, and intensity at a hyperlocal level
• Roof geometry and materials: pitch, complexity, covering type, and other features that influence how hail impacts a roof
• Property-specific vulnerability factors: including building height, exposure, and roof condition (derived from imagery and computer vision)

By modeling how hail behaves in a given location and how a specific roof is likely to perform under those conditions, Z-HAIL delivers precise risk segmentation at the parcel level. 

Carriers using Z-HAIL have seen significant improvements in underwriting performance. In an independent third-party review, Z-HAIL demonstrated a 20× lift in loss ratio segmentation between high- and low-risk properties—enabling more accurate pricing, better risk selection, and actionable mitigation strategies.

How Z-WIND Analyzes Wind-Driven Damage

Z-WIND is a property-specific model that evaluates vulnerability to both straight-line winds and tornadic activity by analyzing how wind climatology interacts with structure-level characteristics.

The model captures:

• Roof geometry: including shape, pitch, and surface area, which influence uplift forces
• Building elevation: to assess exposure to wind at various heights
• Local terrain and land cover: which impact wind speed, turbulence, and exposure to flying debris
• Historical wind climatology: including storm frequency and intensity
• Real-world claims data: to ensure outputs reflect actual loss performance

Z-WIND generates property-level frequency and severity scores, helping insurers move beyond broad wind zones to more precisely identify risk at the structure level. By understanding how specific buildings respond to local wind conditions, Z-WIND enables more accurate pricing, underwriting, and mitigation strategies across both inland and coastal regions.

How Z-WATER Tackles Non-Weather Water Losses

Z-WATER is an AI-powered model that predicts the frequency and severity of non-weather water and freeze claims at the property level, covering every structure in the contiguous U.S. 

While many traditional models depend heavily on basic indicators such as “year built,” Z-WATER combines those inputs with a broader set of property, climate, and infrastructure features to capture the interaction between three core dimensions of risk:

• Construction & Architecture: Property-specific features that influence vulnerability and claim severity, such as number of bathrooms, number of stories, presence of a pool, and overhanging vegetation (a signal for potential tree root intrusion).
• Climatology: Environmental stressors like temperature swings and freeze/thaw cycles that contribute to pipe bursts and system strain.
• Local Infrastructure & Hydrology: How local plumbing systems and electrical grids perform when real-world cold snaps or heat waves exceed what regional codes anticipated, exposing systemic weak points that lead to burst pipes and interior water damage.

These variables are derived from aerial imagery, tax assessment data, and regional climate and infrastructure datasets, all processed through ZestyAI’s proprietary AI framework.

Z-WATER helps insurers:

• Set fair and adequate rates based on true exposure
• Target high-risk homes for mitigation (e.g., water sensors or shutoff valves)
• Streamline operations by automating low-risk decisions and focusing resources where they matter most

What Is Z-STORM?

Z-STORM is a predictive, property-specific model built for carriers that rate hail and wind as a combined peril. It provides structure-level risk scores across the contiguous U.S., enabling more accurate pricing, underwriting, and mitigation decisions.

Unlike traditional territory-based approaches, Z-STORM models how storm climatology interacts with the built environment—capturing the real-world conditions that drive loss at the individual property level.

The model incorporates:

• Storm climatology: frequency and severity of wind and hail events at a hyperlocal scale
• Structural features: roof shape, material, pitch, and condition—key factors in a structure’s vulnerability
• Environmental context: including open terrain and nearby vegetation, which can amplify damage

Z-STORM predicts both:

1. Claim frequency: the likelihood a property will experience a storm-related claim
2. Claim severity: the expected loss as a percentage of Coverage A, providing a more precise view of financial exposure

This dual prediction enables carriers to:

• Accurately rate combined wind and hail risk at the property level
• Target mitigation strategies (e.g., roof improvements that reduce exposure to both hazards)
• Improve risk segmentation and pricing

Z-STORM offers a single, AI-powered solution for capturing the true complexity of convective storm risk—from climate data to construction detail to expected loss outcome.

ZestyAI announced today a new partnership with California Casualty, a trusted auto and home insurance provider serving educators, police officers, firefighters, and healthcare workers for over 110 years.

The collaboration will enhance California Casualty’s wildfire underwriting and pricing capabilities while reinforcing its long-term commitment to serving California’s community heroes and supporting the California Department of Insurance’s Sustainable Insurance Strategy (SIS).

Enhancing Accuracy, Supporting Affordability

California Casualty will deploy ZestyAI’s Z-FIRE™ model and Wildfire Mitigation Pre-Fill to better align premiums with property-specific wildfire risk and recognize homeowners’ efforts to mitigate that risk.

These advanced models analyze each property’s vulnerability based on factors such as topography, vegetation, building materials, defensible space, and the characteristics of the built environment.

Unlike traditional models that stop at the wildland-urban interface, Z-FIRE accounts for the dynamics of urban conflagration, where fire spreads rapidly from structure to structure in densely built neighborhoods. Wildfire Mitigation Pre-Fill complements this by automatically surfacing mitigation features at scale, bringing greater accuracy and transparency to underwriting.

Todd Brickel, Senior Vice President, Chief Risk & Product Officer at California Casualty, said:

“As wildfire threats intensify, our responsibility is to ensure that educators, peace officers, firefighters, and healthcare professionals continue to have access to reliable and affordable coverage."

"Partnering with ZestyAI equips us with data-driven insights needed to price risk accurately, reward mitigation, and sustain our role as a long-term solution in California’s insurance market.”

Commitment to California

California Casualty has long stood with California’s community heroes, protecting their homes in both city neighborhoods and wildfire-exposed areas. Even as other insurers have reduced their footprint, California Casualty continues to expand access to coverage in support of Commissioner Ricardo Lara’s Sustainable Insurance Strategy.

Through its investment in advanced wildfire analytics, the company is ensuring that affordability and availability can coexist in California’s evolving insurance landscape.

The strength of Z-FIRE’s analytics was reaffirmed during the 2025 Los Angeles wildfires, when the Palisades and Eaton fires escalated into fast-moving urban conflagrations.

The model’s performance reinforced how advanced analytics can anticipate where fire risk is greatest and help insurers strengthen preparedness and resilience well before events occur.

These insights enable California Casualty to maintain confidence in providing coverage for community heroes throughout California’s most challenging environments.

Attila Toth, CEO of ZestyAI, said:

“We are proud to partner with California Casualty, a company that has served community heroes for more than a century.”

“Our AI-driven models provide the transparency, accuracy, and property-level detail needed for insurers to remain confident in challenging markets, rewarding mitigation efforts and supporting regulatory goals for long-term stability.”

Built and validated on more than 2,000 historical wildfire events and two decades of claims data, Z-FIRE has been widely adopted by insurers across the West and recognized by regulators for use in both underwriting and rating.

ZestyAI, the leading provider of AI-powered property and climate risk analytics, today announced that Universal North America Insurance Company, a property insurer, part of the One Alliance Group of companies, has adopted ZestyAI’s Roof Age solution to bring greater accuracy and confidence to property risk assessment across its portfolio.

Why Accurate Roof Age Data Matters

Roof-related claims are among the costliest in property insurance. Yet insurers have long struggled with inconsistent or incomplete roof age data. ZestyAI’s analysis shows that nearly one in three roofs are at least five years older than recorded in policy data, creating blind spots that drive higher losses and mispriced policies. 

How ZestyAI’s Roof Age Model Works

ZestyAI’s Roof Age solution closes this gap by synthesizing building permit data with two decades of high-resolution aerial imagery, applying advanced machine learning to deliver verified roof age estimates with 97% U.S. coverage.

Strengthening Portfolio Performance

"Accurate roof data is foundational for managing one of the costliest drivers of property insurance losses,” said Miguel Barrales, President of Universal North America Insurance Company. “ZestyAI’s Roof Age solution provides the reliability we need to make more confident risk decisions and strengthen portfolio performance.”

"For years, insurers have had to make critical decisions without reliable roof data, and the cost has been enormous,” said Attila Toth, Founder and CEO of ZestyAI. “Universal North America Insurance Company’s adoption shows what’s possible when carriers embrace trusted, property-level insights to strengthen their portfolios and the market as a whole.”

ZestyAI, the leading provider of AI-powered property and climate risk analytics, today announced that its non-weather water risk model, Z-WATER™, has received approval in Wisconsin for use in underwriting and rating.

Why Non-Weather Water Losses Are Rising

Non-weather water is one of the costliest and fastest-growing perils in homeowners insurance, now ranking as the fourth costliest peril overall, with claim severity up 80% over the past decade—surpassing hurricanes. These losses stem from everyday risks like burst pipes, appliance failures, and plumbing leaks. With average claim costs now exceeding $13,000, their financial impact rivals catastrophe events.

How the Z-WATER Model Works

Z-WATER is built, tested, and validated with real insurer loss data, ensuring accuracy and regulatory credibility. The model uses computer vision to analyze aerial imagery alongside tax assessor data, permit records, climatology science, and infrastructure insights to assess key property-level risk factors. By modeling how these variables interact, Z-WATER predicts both the frequency and severity of non-weather water claims with up to 18x greater accuracy than traditional models.

What This Approval Enables for Insurers

With this approval, insurers in Wisconsin can begin using Z-WATER to:

• Set more accurate, property-specific rates
• Align coverage with actual home vulnerabilities
• Optimize inspections and mitigation strategies, such as the adoption of water sensors
• Reduce cross-subsidization and improve portfolio performance

Regulatory Confidence in Explainable AI

“Non-weather water is one of the most frequent and expensive sources of loss for insurers, and it behaves differently than other perils,” said Bryan Rehor, Director of Regulatory Strategy at ZestyAI.

“Z-WATER captures the property-level features that truly drive risk—such as plumbing systems, home design, and even vegetation patterns, giving insurers a much clearer picture of where losses are likely to occur.

"This approval demonstrates that regulators recognize the value of AI models that are explainable, data-driven, and validated against real claims," he added.

Part of a Growing Nationwide Regulatory Track Record

This approval adds to ZestyAI’s growing regulatory momentum. Across five perils, including wildfire, hail, wind, storm, and now non-weather water, ZestyAI has secured more than 70 approvals coast-to-coast.

In addition to these peril models, ZestyAI’s Z-PROPERTY™ solution has also earned nationwide approvals, giving insurers trusted roof and parcel-level insights with the same regulatory credibility.