en.Wedoany.com Reported - IRVINE, Calif., June 9, 2026 – ATTOM, a provider of property data and AI-driven intelligence solutions, today announced the launch of ResiScore, an AI-based community intelligence product designed to rank residential areas based on expected housing market performance.

Built on technology ATTOM acquired through its January 2026 acquisition of ResiShares, ResiScore introduces a new layer of forward-looking analysis aimed at addressing a long-standing gap in real estate decision-making. While many organizations know which markets to operate in, determining which neighborhoods within those markets are most likely to outperform has been difficult to measure at scale.

ResiScore solves this challenge by assigning each residential census tract a percentile rank from 1 to 100 within its Core Based Statistical Area (CBSA) or metropolitan area, based on expected home price appreciation over a 24-month period. This approach enables users to compare neighborhoods within the same market using a standardized metric, helping prioritize areas expected to perform stronger and identify locations that may carry higher risk.

Rob Barber, CEO of ATTOM, said that ResiScore builds on the company's strategy of providing AI-driven property intelligence through its data foundation and enterprise data licensing. Customers have long relied on ATTOM for comprehensive property data, but previously lacked a consistent method to evaluate neighborhoods within markets at scale. This represents a meaningful step forward in helping them identify opportunities and manage risk.

ResiScore is part of the Market and Location Analysis category within ATTOM's AI-driven intelligence suite, designed to help organizations analyze housing trends, identify emerging opportunities, and make more informed decisions at the neighborhood level.

Aaron Wagner, Head of Data Science at ATTOM, noted that the gap between the strongest and weakest neighborhoods within a single market is often larger than the gap between markets. By ranking neighborhoods within a market by expected appreciation, ResiScore helps customers identify where upside potential is emerging and where downside risk is accumulating.

ResiScore is powered by a model trained on decades of residential property data, integrating signals such as long-term price trends, recent appreciation, price acceleration, forecasted growth, and volatility into a single composite score. The result is a forward-looking ranking that balances responsiveness and stability, helping users assess relative performance across neighborhoods without overreacting to short-term market fluctuations.

Key capabilities include: neighborhood ranking of all census tracts within a CBSA or metropolitan area; forward-looking insights based on a 24-month forecast horizon; hyper-local analysis beyond county and metro-level metrics; and identification of risk and opportunity through relative performance signals.

ResiScore supports a wide range of use cases, including investment targeting, loan and portfolio risk assessment, site selection, and market analysis. It also complements ATTOM's broader suite of AI-driven intelligence and analytics, including automated valuation models, providing a more complete view of property value and neighborhood performance. ResiScore is available through ATTOM's data delivery platforms, including bulk data licensing and Snowflake.

ATTOM delivers AI-based property intelligence built on one of the most trusted property data assets in the United States, covering 160 million U.S. properties and 99% of the population. Its engineered, multi-source real estate data encompasses property tax, deed, mortgage, foreclosure, environmental risk, property condition, natural hazard, neighborhood insights, and geospatial boundaries, rigorously validated to support advanced analytics. ATTOM supports analytical and AI-driven applications through flexible data delivery options, including APIs, bulk licensing, cloud delivery, and MCP Server for AI-driven, agentic engineered data access, enabling organizations to automate analysis and scale property intelligence across industries.

This article is compiled by Wedoany. All AI citations must indicate the source as "Wedoany". If there is any infringement or other issues, please notify us promptly, and we will modify or delete it accordingly. Email: news@wedoany.com