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For property insurers, roof age is more than just a data field — it’s a critical underwriting decision point that directly impacts pricing, risk selection, and loss costs. But what happens when that data is wrong two-thirds of the time?

A large U.S. carrier with over $500 million in direct written premium recently found out. Relying on self-reported and agent-estimated roof ages, they were systematically underpricing risky properties while overpricing safer ones. The result: adverse selection, elevated loss ratios, and underwriting decisions built on shaky foundations.

The Scale of the Problem: Two-Thirds of Roof Age Data Is Wrong

ZestyAI’s research shows that 67% of self-reported roof ages are inaccurate:

• 43% underestimate roof age — meaning roofs are older and riskier than reported
• 24% overestimate roof age — leading to overpricing or turning away good business

This isn’t just a pricing issue. Analysis also found that 78% of carriers in key U.S. regions use age-based triggers for ACV roof endorsements, with some starting as early as 8 years old. When roof age is wrong, policies can be misclassified across underwriting, eligibility, and coverage terms — creating compounding risk across the insurance lifecycle.

From Estimates to Evidence: How ZestyAI Verifies Roof Age

To replace guesswork with ground truth, the carrier deployed ZestyAI Roof Age, which analyzes building permits, more than 20 years of aerial imagery, and regional climatology using advanced machine learning. Each assessment is paired with a transparent confidence score.

Unlike traditional approaches that rely on policyholder memory or limited inspections, ZestyAI Roof Age:

• Anchors assessments in the property timeline to prevent false positives
• Cross-validates imagery with permits and climatological patterns
• Provides confidence scores to distinguish high-certainty predictions from cases requiring inspection
• Delivers explainable, auditable results that underwriters and actuaries can trust

The difference was immediate.

Real-World Examples from the Carrier’s Portfolio

In one Denver property, the agent reported an 8-year-old roof. ZestyAI identified it as 10 years old, confirmed by aerial imagery showing the replacement event.

In a Baltimore case, what was reported as a 5-year-old roof was actually 21 years old — verified through imagery and permitting history.

These weren’t edge cases. They reflected a systemic pattern across the portfolio.

The Impact: A 1.71% Improvement in Combined Ratio

By integrating verified roof age into underwriting and pricing workflows, the carrier achieved a 1.71% reduction in combined ratio. The improvement came from three measurable levers:

• Loss Cost Controls (-1.08%)
  Accurate age enabled appropriate use of deductibles and ACV endorsements, lowering claims severity.
• Better Risk Selection (-0.38%)
  More precise pricing attracted lower-risk properties while deterring higher-risk ones.
• Inspection Optimization (-0.25%)
  Confidence scores guided inspections to properties that truly needed them, reducing wasted expense.

Beyond loss ratios, better roof age data improved portfolio transparency, supported expansion into previously restricted markets, and strengthened actuarial and underwriting decision-making.

What’s Next: Expanding the Foundation of Property Intelligence

After proving the value of accurate roof age, the carrier is now building on that foundation. They are incorporating additional property attributes — including roof complexity, roof quality, and parcel-level features — through ZestyAI’s Z-PROPERTY™ platform.

By standardizing and elevating property data quality at scale, the carrier expects to unlock similar gains across quoting, underwriting, renewals, and even reinsurance discussions.

The takeaway is clear: in an industry built on precision, even a single data point — when made accurate — can deliver outsized impact.

Read the full Roof Age Accuracy case study to see how verified roof age drives measurable underwriting and pricing gains → From Self-Reported to Verified: Roof Age Accuracy That Pays Off

ZestyAI today announced that Augusta Mutual has selected ZestyAI’s Roof Age and Z-PROPERTY™ to enhance underwriting and rating accuracy, target inspections more effectively, and support sustainable growth across Virginia.

Based in Staunton, Virginia, Augusta Mutual is a single-state carrier serving Virginia since 1870 with a longstanding reputation for personalized service and local expertise. By upgrading from traditional imagery and inspection approaches to ZestyAI’s computer vision and machine learning technology, the insurer gains broader, more consistent property coverage and a more comprehensive, AI-driven view of property risk—unlocking property-level insights such as verified roof age, roof condition, vegetation overhang, and debris accumulation that directly influence claim frequency and severity.

“ZestyAI’s solutions bring a new level of precision to our underwriting process,” said Gretchen H. Collins, Vice President of Underwriting at Augusta Mutual.

“We moved from legacy property risk tools to gain broader, verified property coverage, helping us make faster, more consistent, and more confident decisions for our policyholders across Virginia.”

ZestyAI’s Roof Age delivers verified roof age by cross-validating building permit records with over 20 years of aerial imagery, detecting roof replacement events and assigning confidence scores across 97% of U.S. properties. Z-PROPERTY™ further enhances this insight by assessing roof complexity, materials, and condition, along with other parcel-level attributes that influence loss potential.

ZestyAI works closely with regulators to ensure transparency, validation, and continuous monitoring of its AI-driven models. Its portfolio of risk models has secured nearly 100 approvals from regulators nationwide, giving insurers confidence they can be deployed immediately with the accuracy and transparency regulators demand.