Veros Real Estate Solutions (Veros®), a leading provider of collateral property valuation and risk management solutions, has released a groundbreaking new research paper which examines prevailing methodologies for testing the accuracy and performance of Automated Valuation Models (AVMs), a vital tool in the housing finance market. The paper, titled "Optimizing AVM Testing Methodologies," was thoroughly researched by Veros’ Senior Research Economist, Reena Agrawal. It highlights key challenges and proposes an improved approach that aligns with real-world mortgage lending practices.
As AVMs continue to play a critical role in property valuations for housing finance and risk assessment, the accuracy of AVM testing methodologies is increasingly under scrutiny. Veros’ research paper provides an in-depth analysis of three testing approaches—purchase transactions testing, refinance/HELOC appraisal testing, and pre-MLS listing testing—assessing their strengths, limitations, and influences on reported AVM performance metrics such as P10 and hit rate.
Key findings from the research paper include:
- Data Access & Timing Matter – A representative and accurate evaluation hinges on when the test is conducted and how much information the AVM can access.
- AVM Use Cases Matter – This paper emphasizes the critical importance of considering AVM use cases when evaluating AVM performance.
- A New Approach – This research aims to address some common and previously unchallenged opinions on AVM testing as it aims to significantly enhance the transparency and applicability of AVM testing results.
"Some current AVM testing practices have inherent biases that can skew performance evaluations and misrepresent real-world accuracy," said David Rasmussen, EVP Operations at Veros. "The research by our Senior Research Economist underscores the need for a standardized, data-driven approach that aligns with how AVMs are actually used in lending decisions. By introducing a methodology that evaluates true AVM performance, we can ensure a more level playing field and improve the market’s understanding and confidence in automated valuations."
The research paper underscores the importance of continuous validation and the need for testing methodologies to evolve alongside advancements in technology and data availability within the property valuation spectrum. By challenging existing assumptions and proposing a more representative testing framework, this research contributes to a deeper understanding of AVM capabilities and their role in the housing finance market. Veros invites industry stakeholders, lenders, regulators, and AVM providers to review the research paper and join the discussion on refining AVM testing standards.
The full research paper is available for download at: AVM Method Testing on VeroVALUE
About Veros Real Estate Solutions (Veros®)
A mortgage technology innovator since 2001, Veros is a proven leader in enterprise risk management and collateral valuation services. The firm combines predictive technology, data analytics, and industry expertise to deliver advanced automated solutions that control risk and increase profits throughout the mortgage industry, from loan origination to servicing and securitization. Veros’ services include automated valuation, fraud and risk detection, portfolio analysis, forecasting, and next-generation collateral risk management platforms. Veros is the primary architect and technology provider of the GSEs’ Uniform Collateral Data Portal® (UCDP®). Veros also works closely with the FHA to support its Electronic Appraisal Delivery (EAD) portal. The company is also making the home-buying process more efficient for our nation’s Veterans through its appraisal management work with the Department of Veterans Affairs. For more information, visit veros.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20250605442569/en/
"Some current AVM testing practices have inherent biases that can skew performance evaluations and misrepresent real-world accuracy." — David Rasmussen, EVP Operations at Veros
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