What is the key consequence of bias in data selection on valuations?

• A. Leading to distorted valuations.
• B. Always improves accuracy.
• C. Only affects lender risk.
• D. Has no impact on appraisal values.

Answer: (A)

Bias in data selection means the information used to support a valuation doesn’t reflect the full market picture. When the set of comparables, market trends, or property attributes is skewed, the resulting value becomes systematically tilted rather than accurate. That tilt shows up as distorted valuations—values that don’t really represent what the property would fetch in a fair market. This isn’t about making valuations better or simply affecting one party; biases can mislead lenders, buyers, and sellers alike and undermine the credibility of the appraisal. So the key consequence is that biased data selection twists valuations away from true market value.