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To validate the functional form of a regression model used for adjustments, an appraiser plots residuals against predicted values and observes a clear inverted-U pattern. What does this pattern indicate, and what is the most defensible corrective action?

Correct Answer

B) The linear specification is misspecified; add a quadratic term for the relevant predictor (e.g., GLA²) to capture nonlinearity.

A systematic curved pattern (e.g., inverted-U) in a residual-vs-fitted plot signals nonlinearity — i.e., the true relationship is not adequately captured by a straight-line functional form. Adding a quadratic term (e.g., GLA²) is the standard remedy to model curvature. While heteroscedasticity (A) often manifests as a funnel shape, not an inverted-U; autocorrelation (C) is irrelevant in cross-sectional appraisal data (no time ordering); and automatic outlier removal (D) violates USPAP Standards Rule 2-2(c), which requires objective, documented justification for excluding data points — not mechanical truncation. AO-21 states that appraisers must assess model assumptions, including linearity, and modify specifications when diagnostic plots indicate misspecification.

Answer Options
A
Heteroscedasticity is present; apply weighted least squares to correct standard errors.
B
The linear specification is misspecified; add a quadratic term for the relevant predictor (e.g., GLA²) to capture nonlinearity.
C
Autocorrelation is occurring; use Newey–West standard errors to adjust inference.
D
Outliers are distorting results; remove all observations with residuals beyond ±3 standard errors.

Why This Is the Correct Answer

A systematic curved pattern (e.g., inverted-U) in a residual-vs-fitted plot signals nonlinearity — i.e., the true relationship is not adequately captured by a straight-line functional form. Adding a quadratic term (e.g., GLA²) is the standard remedy to model curvature. While heteroscedasticity (A) often manifests as a funnel shape, not an inverted-U; autocorrelation (C) is irrelevant in cross-sectional appraisal data (no time ordering); and automatic outlier removal (D) violates USPAP Standards Rule 2-2(c), which requires objective, documented justification for excluding data points — not mechanical truncation. AO-21 states that appraisers must assess model assumptions, including linearity, and modify specifications when diagnostic plots indicate misspecification.

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