Roof Age Accuracy for Insurance Underwriting

This document is a case study that outlines the challenges faced by a national insurance carrier regarding the accuracy of roof age information. The carrier struggled with self-reported and agent-estimated roof ages, which were often unreliable, leading to misclassified policies and pricing issues. The study presents ZestyAI's approach to analyzing roof age through advanced machine learning, utilizing building permits and aerial imagery to deliver verified assessments. The results indicated that integrating this technology led to a 1.71% reduction in the combined ratio, achieved through improved loss cost controls, better risk selection, and optimized inspections. The carrier's experience illustrates the significant impact of accurate roof age data on underwriting and pricing workflows, demonstrating how enhanced data quality can provide substantial value across the insurance lifecycle. The document concludes with the carrier's plans to further improve data quality by adopting additional property attributes and Z-PROPERTY™, ZestyAI's platform for property intelligence.