Two variables in a market study show a strong correlation. What may the appraiser conclude?
Correct Answer
B) That they move together, whatever the reason
Why this is correct: The original explanation emphasizes that correlation measures how two variables move together, but does not imply causation. They may be related due to a third factor or coincidence. Why the other choices are wrong: That one of the two variables causes the other is a common fallacy; correlation does not equal causation. That both are required in any pricing model is not necessarily true; correlated variables may provide redundant information. That the relationship will persist in future is an assumption not guaranteed by past correlation. Exam tip: Correlation ≠ Causation. Don't assume a correlated variable is a direct driver of value without further evidence.
Why This Is the Correct Answer
Why this is correct: The original explanation emphasizes that correlation measures how two variables move together, but does not imply causation. They may be related due to a third factor or coincidence. Why the other choices are wrong: That one of the two variables causes the other is a common fallacy; correlation does not equal causation. That both are required in any pricing model is not necessarily true; correlated variables may provide redundant information. That the relationship will persist in future is an assumption not guaranteed by past correlation. Exam tip: Correlation ≠ Causation. Don't assume a correlated variable is a direct driver of value without further evidence.
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?
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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 α = 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?
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