Valligent, a Veros Software company providing real estate valuation solutions, has enhanced their traditional appraisal quality control process with BiasCHECK, a technology-based risk analysis solution integrated into Valligent’s appraisal review to quickly detect if an appraisal may be at risk for potential valuation bias or quality issues that need deeper analysis.
BiasCHECK utilizes automated valuation models (AVM), risk scoring, and content-enabled word scans provided by Veros Real Estate Solutions (Veros®), a Valligent sister company. It helps Valligent’s quality control team prioritize and expedite any additional due diligence without a negative impact on average traditional appraisal turn times or costs to customer.
The first component of BiasCHECK is VeroVALUE, an automated valuation model (AVM) used to detect if there is any potential for over- or undervaluation of the subject property. VeroVALUE has undergone testing by Veros economists, demonstrating its absence of racial bias and its consistent accuracy across minority communities, making it both fair and accurate.
The second component is VeroSCORE, which detects deficiencies or red flags that could potentially compromise overall appraisal quality. Lastly, a bias word scan examines the appraisal report field-by-field for acceptable terminology. It detects if there is any presence of racial, political, religious, and other potentially prohibitive words. If the word scan indicates the presence of potentially biased words, prioritization is given for further due diligence.
“BiasCHECK is a unique appraisal review tool powered by reliable technology. Utilizing it as part of our quality control process helps ensure Valligent’s traditional appraisals contain data customers can trust and confidently use to make collateral decisions,” said Charles Rumfola, President of Valligent. “We all have a responsibility to make the mortgage lending process as fair and accurate as possible, and with BiasCHECK, Valligent is taking a proactive step to protect lenders and borrowers against potential bias.”
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