What is the risk when two independent variables in a model are themselves highly correlated?
Correct Answer
C) Their individual coefficients become unreliable
Why this is correct: This describes multicollinearity. When independent variables (like living area and room count) are highly correlated, the regression model cannot isolate their separate effects on the dependent variable. The overall model fit (R-squared) may remain high, but the estimated coefficient for each correlated variable becomes unstable and unreliable for individual adjustment. Why the other choices are wrong: 'R-squared falls to zero for the whole model' is wrong because multicollinearity does not destroy overall model fit. 'The dependent variable can no longer be chosen' is incorrect; the dependent variable is defined by the problem. 'The regression cannot be computed at all' is false; computation proceeds, but the results for individual predictors are problematic. Exam tip: Remember, multicollinearity affects the reliability of individual coefficients, not the model's ability to compute or its overall explanatory power.
Why This Is the Correct Answer
Why this is correct: This describes multicollinearity. When independent variables (like living area and room count) are highly correlated, the regression model cannot isolate their separate effects on the dependent variable. The overall model fit (R-squared) may remain high, but the estimated coefficient for each correlated variable becomes unstable and unreliable for individual adjustment. Why the other choices are wrong: 'R-squared falls to zero for the whole model' is wrong because multicollinearity does not destroy overall model fit. 'The dependent variable can no longer be chosen' is incorrect; the dependent variable is defined by the problem. 'The regression cannot be computed at all' is false; computation proceeds, but the results for individual predictors are problematic. Exam tip: Remember, multicollinearity affects the reliability of individual coefficients, not the model's ability to compute or its overall explanatory power.
More appraisal-statistical-methods Questions
A price index rises from 100 to 121 over two years. What compound annual rate does this represent?
A sample of four sales drawn from a market with 200 annual transactions is:
A set of comparable sales has a mean of $250,000 and a standard deviation of $20,000. What is the coefficient of variation?
A property sold for $400,000 and resold three years later for $463,050 with no physical change. What compound annual rate does this indicate?
An appraiser includes both 'total room count' and 'bedroom count' as independent variables in a regression model estimating single-family home sale prices. The variance inflation factor (VIF) for 'bedroom count' is calculated as 12.3. What is the most appropriate appraisal action based on this result?
An appraiser runs a regression of sale price on GLA, age, and a binary variable for 'renovated' (1 = yes, 0 = no). The estimated coefficient for 'renovated' is $18,400 with a standard error of $6,200 and a t-statistic of 2.97. Assuming a two-tailed test at Ξ± = 0.05 and 42 degrees of freedom, what conclusion is supported regarding the market's recognition of renovations?
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?
A histogram of neighborhood sale prices shows two distinct peaks. What does this most likely mean?
What does it mean to validate a regression model?
An appraiser includes months elapsed since each sale as a variable in a price model. What is this intended to capture?
People Also Study
Valuation Principles & Procedures
25% of exam
Property Description & Analysis
20% of exam
Market Analysis & Highest/Best Use
15% of exam
Appraisal Math & Statistics
15% of exam
USPAP (Ethics & Standards)
15% of exam