Paired sales analysis and regression differ mainly in that regression:
Correct Answer
C) Uses many observations to estimate several effects at once
Why this is correct: Regression analysis is a statistical method that uses many data observations (sales) to estimate the simultaneous effect of multiple property characteristics (like size, age, location) on value. This is its key distinction from paired sales analysis, which isolates the effect of a single variable by comparing only two nearly identical sales. Why the other choices are wrong: "Cannot be applied to residential property data" is wrong because regression is commonly used with residential data. "Requires only two matched sales to isolate a variable" describes paired sales analysis, not regression. "Eliminates the need to verify individual sales" is wrong; regression still requires verified, reliable data. Exam tip: Remember: Paired sales = few sales, one variable. Regression = many sales, many variables.
Why This Is the Correct Answer
Why this is correct: Regression analysis is a statistical method that uses many data observations (sales) to estimate the simultaneous effect of multiple property characteristics (like size, age, location) on value. This is its key distinction from paired sales analysis, which isolates the effect of a single variable by comparing only two nearly identical sales. Why the other choices are wrong: "Cannot be applied to residential property data" is wrong because regression is commonly used with residential data. "Requires only two matched sales to isolate a variable" describes paired sales analysis, not regression. "Eliminates the need to verify individual sales" is wrong; regression still requires verified, reliable data. Exam tip: Remember: Paired sales = few sales, one variable. Regression = many sales, many variables.
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:
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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?
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