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5 June 2025 June 2025 » M T E C H ments are present in almost three out of four appraisals. • It was challenging to determine the true variations that can impact property value because the majority of homes were grouped into only two groups based on condition (86%) and quality (97%). • There are concerns regarding consistency and transparency as adjustments were made even when homes had the same condition or quality scores (11.8% for condition and 5.3% for quality). • The repurchase risk ranged from $27.1 billion to $59.7 billion, with 33.6% of evaluations having a high risk and 73.9% having a medium risk of insufficient or missing modifica- tions. • Inaccurate conditions and quality adjustments can cause properties to be overvalued or undervalued, which can impact market stability, financing costs, and repurchase risk. • Quality control procedures can be made more effective while lowering risk by implementing automated AI analysis to quickly identify potential conditions and quality concerns. Acknowledging U.S. Appraiser Power to Lower Risk The GSEs' push for component-based scoring and appraisal modernization coincides with the release of the Restb. ai White Paper on quality and condition findings. The study offers unquestionable statistical proof that AI-powered comput- er vision is an essential tool for appraisers, enabling them to more consistently, pre- cisely, and confidently address one of the most enduring and historically expensive problems facing the sector. In order to create a more stable and equitable housing finance system, the White Paper demonstrates how the use of AI technologies can improve assessment quality, increase compliance, and comply with changing GSE criteria. Interestingly, more equivalent prop- erties are used without the appropriate condition changes than quality adjust- ments. Further, condition variations are still not appropriately accounted for in more cases than quality difficulties, even though appraisers are currently making condition changes at a rate three times faster than quality adjustments. As this study indicates, price adjustments and condition and quality evaluations are often erroneous, which invariably results in imprecise valuations and elevated risk. Given the challenge of manually distilling intricate property data into two high-level scores, these inaccura- cies make sense. Even though there are several quality assessments conducted during an ap- praisal's lifetime, it has proven difficult to consistently find and fix these problems. It is simply too time-consuming to search up images of comparable houses to make sure all properties have been appraised consistently, and it is too difficult to tell when a significant condition or quality adjustment—or lack thereof—may exist. Legal cases have demonstrated how these errors can result in inaccurate appraisals, and Fannie Mae has flagged them as common concerns. For lenders, these risks result in high financial expenses. A recent analysis by Reggora found that the average home loan repurchase rate is 0.49%, which means the lender will typically have to pay $32,288. The 33.6% of high-risk evaluations, assuming a modest estimate of 2.5 million appraisals annually, would translate into a total lender risk of almost $27 billion in repurchase expenses. The good news is that this study shows how computer vision may be used to automatically detect these problems. Even if some appraisers are still dubious about AI, its value lies in its capacity to identify possible problems early on and request a closer examination rather than waiting for inconsistencies to be discov- ered later in the evaluation process.