Compared with analyzing one matched sale pair for one feature, what can a suitably specified multiple regression do?
Correct Answer
C) Estimate conditional associations for several predictors at once
Multiple regression estimates the conditional association of several predictors with sale price from one data set, whereas a single matched pair isolates one difference only if all other influences are controlled. Regression does not remove the need for data verification, cannot estimate several coefficients from two observations, and an association alone does not prove causation.
Why This Is the Correct Answer
Option C is correct because a suitably specified multiple regression estimates the conditional association of several predictors with sale price at once from one data set. A single matched pair isolates one feature difference only when all other influences are controlled, so regression can address several features simultaneously.
Why the Other Options Are Wrong
Option A: Remove the need to verify the underlying transactions
Option A says regression removes the need to verify the underlying transactions, but regression results are only as reliable as the data entered. Verification remains essential.
Option B: Produce reliable estimates from only two observations
Option B claims regression produces reliable estimates from only two observations, but several coefficients cannot be estimated from two data points. Regression needs enough observations to estimate its parameters with reasonable precision.
Option D: Prove that each estimated association between price and a feature is causal
Option D says regression proves each estimated association between price and a feature is causal, but regression measures association. An association alone does not prove causation.
Pairs Isolate One, Regression Many
A matched pair looks at one difference at a time; regression looks at many differences together.
How to use: Use this contrast when comparing paired sales analysis with multiple regression.
Exam Tip
Reject answers claiming regression proves causation or replaces verification; its advantage is estimating several conditional associations at once.
Common Mistakes to Avoid
- -Believing regression proves cause and effect
- -Thinking regression makes data verification unnecessary
- -Assuming very few observations can support many coefficients
Concept Deep Dive
Analysis
This question tests what multiple regression can do compared with a single matched pair. A matched pair isolates one difference only if all other influences are controlled, while regression estimates the conditional association of several predictors at once from one data set. It does not remove the need for verification or prove causation.
Background Knowledge
Paired sales analysis compares two sales that differ mainly in one feature to isolate its contribution. Multiple regression extends this idea by estimating many contributions together from a larger data set, while still depending on good data, adequate sample size and sound model specification.
Real-World Application
An appraiser lacks clean matched pairs for garage, pool and view differences in a neighborhood. Using regression on 120 verified sales, the appraiser estimates all three conditional associations together, then checks them against other market evidence.
More Statistics Questions
Sale prices are $270,000, $290,000, $300,000, $310,000, $330,000. Treat the listed observations as the complete population and divide by n when computing the standard deviation. Report to the nearest dollar.
A sales sampling frame systematically excludes a relevant part of the target market. What is the principal risk?
An appraiser estimates a market-wide price trend using only sales data from one brokerage that specializes in luxury homes, without checking how other segments are represented. What is the principal concern?
The base-period median sale price is $400,000 and the current-period median is $460,000. Compute the price relative defined as current ÷ base, as a decimal to three places.
The assessment-to-sale ratios are 0.8, 0.85, 0.9, 0.92, 0.95, 0.98, 1.02, 1.05, 1.08, 1.1, 1.15, 1.2. Calculate COD as [average absolute deviation from the median ÷ median]×100. For even n, average the two middle ratios. Report the COD as a percentage to two decimals.
Assessment-to-sale ratios are 0.94, 0.96, 0.98, 1, 1.01. Compute the arithmetic mean. Report as a decimal ratio to three places.
Market data show a first quartile sale price of $405,000 and a third quartile sale price of $495,000. What is the interquartile range, to the nearest dollar?
Assessment-to-sale ratios are 0.96, 0.98, 0.99, 1.01. Compute the arithmetic mean. Report as a decimal ratio to three places.
Given first quartile $400,000, median $440,000 and third quartile $500,000, compute the trimean (Q1+2×median+Q3)/4. Report to the nearest dollar.
The positive observations are 10, 15, 22.5. Compute their simple geometric mean (product of all observations)^(1/n). Report as a dimensionless ratio to three decimals.
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