A new Restb.ai white paper analyzes more than 1,200 real estate appraisals to reveal over $27 billion in potential hidden financial risk, according to a Restb.ai press release.

This risk is tied to flawed property condition and quality adjustments – exposing a major blind spot in the real estate and mortgage ecosystem.

The Restb.ai study uncovers widespread inconsistencies and transparency issues in how appraisers assess and adjust for a property’s physical condition and quality, two factors that directly impact property valuation, borrower equity and lender risk.

Crucially, the company stated, the white paper provides deep insight into how appraisers can leverage computer vision to mitigate risks using objective image-based scoring to detect and justify condition and quality adjustments with greater precision.

The study's findings echo recent warnings from Fannie Mae, which identified condition and quality misreporting as one of its top three appraisal quality concerns.

“The scale of flawed condition and quality adjustments in appraisals is bigger than most people realize,” Restb.ai Chief Product Officer Nathan Brannen said in the release. “Most AMCs and lenders simply don’t have a quick and easy way to check for these issues, so they ignore the problem and hope for the best. AI finally offers a solution to efficiently manage this risk.”

Unlocking new findings from appraisals

Using its proprietary computer vision technology, Restb.ai analyzed 1,271 appraisals and 6,495 comparable properties and uncovered that 1 in 3 appraisals contains a major risk tied to condition or quality adjustments that don’t match the actual property.

Additionally, nearly 3 out of 4 appraisals show warning signs of inconsistencies that could lead to inaccurate valuations, while most homes were lumped into just two categories for condition (86 percent) and quality (97 percent), making it difficult to identify the real differences that could affect property value.

Furthermore, adjustments were made even when properties had identical condition or quality scores – 11.8 percent for condition and 5.3 percent for quality – raising questions about consistency and transparency.

The study warns that these patterns can lead to systematic overvaluations or undervaluations, which carry legal, reputational and financial risks for lenders and appraisal management companies.

Supporting appraiser empowerment to reduce risk

The Restb.ai white paper on quality and condition findings come at a pivotal moment as the GSEs advance appraisal modernization and shift toward component-based scoring. The study provides indisputable statistical evidence demonstrating that AI-powered computer vision is a vital resource for appraisers – helping them tackle one of the industry’s most persistent and historically costly challenges with greater consistency, precision and confidence.

“Automated, scalable risk detection is no longer a luxury – it’s now a necessity,” Tony Pistilli, president of valuation at Restb.ai, said. “By integrating computer vision into appraisal workflows, high-risk files can be flagged by lenders earlier, protect against repurchase claims and promote fairer outcomes for consumers.”

The white paper shows that implementation of AI tools can improve appraisal quality, enhance compliance and align with evolving GSE requirements, ultimately supporting a more stable and equitable housing finance system.