AI, Confidence Scoring, and Property Intelligence
This ATTOM webinar, presented by Aaron Wagner, Head of Data Science at ATTOM, delivers a comprehensive, data-driven overview of ATTOM’s next-generation automated valuation model (AVM). The session explores how advances in machine learning, data engineering, and model design are enabling more accurate, explainable, and scalable property valuations—providing actionable insights for lenders, investors, insurers, and data-driven organizations.
During the webinar, Aaron Wagner outlines the core challenges that make residential real estate valuation uniquely complex, including inconsistent and incomplete public record data, constantly shifting market conditions, and the inherent uniqueness of individual properties. He explains how ATTOM’s AVM is designed to address these challenges by extracting stronger signals from imperfect datasets, adjusting for market movement, and modeling property-level differences to produce more reliable valuations.
The session walks through the AVM’s three-stage architecture, beginning with the creation of a clean event history. ATTOM aggregates and reconciles more than 30 years of assessor, recorder, and MLS data, filtering for true arm’s-length transactions and resolving inconsistencies across sources. This process enables the construction of a unified, high-quality dataset that forms the foundation for accurate valuation modeling.
Next, the webinar highlights how ATTOM accounts for market movement using hyperlocal, machine learning–driven price indices. These models track price trends at a granular geographic level and adjust historical transactions to current market conditions, ensuring that past sales remain relevant even in rapidly changing housing markets. By modeling localized price trajectories instead of relying on broad averages, ATTOM improves valuation precision across diverse market environments.
The final stage focuses on modeling individual property characteristics through an ensemble of machine learning models. These models evaluate factors such as property features, prior sales history, geographic context, and active listing signals, combining them into a single valuation through a dynamic, property-specific weighting approach. This adaptive modeling framework ensures that each valuation reflects the most relevant and reliable signals for that specific home.
In addition, the webinar introduces ATTOM’s approach to confidence scoring, where every valuation is delivered with a calibrated measure of uncertainty. These confidence scores are derived from the model’s historical performance on similar properties and provide a transparent range around each estimate. This allows organizations to automate decisioning workflows by distinguishing between high-confidence valuations that can be auto-approved and lower-confidence cases that may require further review.
The session also reviews performance metrics and validation approaches, including forward-looking testing against actual sale prices and third-party benchmarking. With national coverage across more than 98 million properties and strong accuracy metrics such as a low median absolute percent error and a high percentage of valuations within close range of final sale prices, the AVM is positioned as a professional-grade solution for institutional use cases.
Finally, the webinar explores how organizations can operationalize the ATTOM AVM across a wide range of applications, including mortgage lending, portfolio monitoring, capital markets analysis, insurance underwriting, and real estate analytics. Delivery options such as APIs, bulk data, and AI-native MCP integration enable flexible deployment, allowing users to access valuation insights in real time, at scale, or directly within AI-driven workflows.
This webinar also provides an overview of ATTOM’s core value proposition and breadth of data solutions. ATTOM is a one-stop shop for premium property data, offering flexible delivery solutions. Its mission is to power real estate transparency and fuel innovation across industries with the most comprehensive property data. The ATTOM Intelligence Table of Data Elements shows how ATTOM data is organized into 11 main categories. The core AI-ready Property Data provides a comprehensive foundation of property, market, and ownership data that is structured, standardized, and optimized for modern analytics and AI workflows. The newly introduced AI-Powered intelligence categories bring together four core analytics, each focused on a distinct dimension of property data, that work together to provide customers a complete foundation for analytics, modeling and decision-making.
ATTOM delivers AI-driven property intelligence built on one of the nation’s most trusted property data assets, covering 160 million U.S. properties—99% of the population. Our engineered, multi-sourced real estate data spans property tax, deeds, mortgages, foreclosure, environmental risk, property conditions, natural hazards, neighborhood insights, and geospatial boundaries, rigorously validated for advanced analytics. ATTOM supports analytics and AI-driven applications through flexible delivery options including APIs, bulk licensing, cloud delivery, and the MCP Server for AI-powered, agentic access to engineered property data—enabling organizations to automate analysis and scale property intelligence across industries.
Want to learn more about how ATTOM is redefining modern property valuation?
Click here to listen to the entire webinar.
