Valligent Adds Risk Analysis to Its Real Estate Valuation System

Last year, Veros, which markets property valuation and collateral risk management solutions, claimed that there was no racial bias in its automated valuation model. The topic has come up again in a different sense. One of its owned companies, Valligent, which produces real estate valuation software, announced the addition of a product they call 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,” according to the company’s press release. There are three components to BiasCHECK: • The first is an automated valuation model called VeroVALUE. It is supposed to detect the potential for overvaluation or undervaluation of a 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 company wrote. • Second is VeroSCORE, “which detects deficiencies or red flags that could potentially compromise overall appraisal quality.” • Third is a word scan for biased terms, looking at each field for “acceptable terminology.” It looks for prohibited racial, political, religious, and other potentially problematic words. “If the word scan indicates the presence of potentially biased words, prioritization is given for further due diligence,” the company says. “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, in prepared remarks. “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.”