Enhanced model expands coverage by about 40% and delivers a 5.9% median error on homes not yet listed for rent

IRVINE, Calif., Nov. 3, 2026 /PRNewswire/ -- ATTOM, the leading provider of property data, AI-powered intelligence, and real estate analytics solutions, today announced enhancements to its Rental AVM, introducing an AI-powered model that estimates current monthly market rent for nearly 100 million U.S. single-family homes, condominiums, and townhouses, an increase in coverage of about 40%. The model delivers a 5.9% median absolute error on homes before they are listed for rent, helping businesses evaluate rental potential and make more informed decisions.

"Estimating rental values is difficult because leases are private and many homes have no rental history," said Rob Barber, CEO at ATTOM. "With these enhancements, we're extending the same approach driving ATTOM Intelligence, combining trusted property data with advanced AI and analytics, to many more homes, so customers get more meaningful insights and can make better-informed decisions."

Key Enhancements Include:

• Expanded Coverage: Rental estimates for nearly 100 million single-family homes, condominiums, and townhouses across all 50 states and the District of Columbia, a coverage increase of about 40%.
• Improved Accuracy: A 5.9% median absolute error on homes before they're listed for rent, with calibrated low and high values that tighten as the evidence behind an estimate grows.
• AI-Powered Modeling: Multiple machine-learning models evaluate property characteristics, property value, rental history, and recent rental activity, weighing each based on the available evidence for a property.
• Listing-Based Training: The model is trained and benchmarked on observed rental listings, cleaned to remove duplicates, placeholder rents, and unrealistic prices.

"Our enhanced Rental AVM evaluates multiple sources of rental information and adapts to the evidence available for each property," said Aaron Wagner, Head of Data Science at ATTOM. "That adaptability is what allows us to estimate rent for homes with no rental history and extend coverage to nearly 100 million properties, while keeping the ranges calibrated to the strength of the evidence behind each estimate."

Built for Real-World Applications
ATTOM's enhanced Rental AVM supports a range of business needs, including:

• Real Estate Investors and Rental Operators: Screen acquisitions, evaluate rental potential, compare rental income with property values, and support rent-setting decisions.
• Mortgage Lenders and Insurers: Assess rental income for investment-property and DSCR loans, monitor portfolio exposure, and support loss-of-rent analysis.
• Real Estate Portals and AI Developers: Integrate rental estimates into property listings, investment tools, and AI-powered applications through ATTOM's MCP Server.

Updated monthly to reflect new rental activity and changing market conditions, the enhanced Rental AVM is available through ATTOM's APIs, bulk data licensing, Snowflake, Databricks, and MCP Server.

To learn more about ATTOM's enhanced Rental AVM, visit the Rental AVM landing page or download the Rental AVM White Paper.

About ATTOM
ATTOM delivers AI-driven property intelligence built on the nation's most trusted property data repository, covering 160+ million U.S. properties—99% of the population. Our engineered, multi-sourced, semantically rich 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, AI applications, and AI agents 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, power intelligent workflows, and scale property intelligence across industries.

Media Contact:
Megan Hunt
megan.hunt@attomdata.com 

Data and Report Licensing:
949.502.8313
datareports@attomdata.com

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SOURCE ATTOM