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.