An additive sale-price model includes a linear months-before-effective-date predictor alongside relevant property controls. What is its intended analytical role?
Correct Answer
C) Represent a conditional linear price trend across sale dates
An elapsed-time predictor can represent a fitted time trend conditional on other predictors. Its sign depends on whether time increases forward or measures months before the effective date. A single linear term does not separately identify recurring seasonal effects, and market attribution requires suitable data and controls. It is not the property’s remaining economic life.
Why This Is the Correct Answer
Option C is correct because a linear months-before-effective-date predictor represents a fitted price trend across sale dates, conditional on the other property controls in the model. Its sign depends on how time is coded: here, more months before the effective date means an older sale.
Why the Other Options Are Wrong
Option A: Directly measure physical deterioration of each building
Option A says the variable directly measures physical deterioration of each building. Sale date is not a measure of building condition; deterioration relates to the improvements, not to when the sale occurred.
Option B: Directly measure remaining economic life
Option B says the variable directly measures remaining economic life. Remaining economic life is a property characteristic, while months before the effective date describes timing of the sale.
Option D: Isolate a recurring seasonal cycle in sale prices
Option D says the variable isolates a recurring seasonal cycle in sale prices. A single linear term produces a straight-line trend and cannot separately capture prices that rise and fall with the seasons.
Time term tracks the trend
A straight time variable draws a straight trend line through prices over the sale dates, nothing more.
How to use: Use this when a question asks what a time or sale-date variable measures in a model.
Exam Tip
Match each variable to what it describes: sale date variables capture market trends, while age or condition variables capture physical characteristics.
Common Mistakes to Avoid
- -Reading a time variable as a measure of depreciation
- -Expecting a linear time term to capture seasonal swings
- -Ignoring how time was coded when interpreting the sign
Concept Deep Dive
Analysis
This question tests what a linear elapsed-time variable does in a sale-price regression. With property characteristics controlled, a single time term captures a straight-line price trend across sale dates. It does not measure building condition, economic life or seasonal cycles.
Background Knowledge
Sale prices often need adjustment to a common date to account for time trending. In a regression, a time variable estimates the price change associated with time, holding other included characteristics constant. A linear term assumes a steady rate of change; capturing seasonality requires additional terms such as seasonal indicators.
Real-World Application
An appraiser builds a regression of neighborhood sales over the past two years, including a variable for months before the effective date. The estimated coefficient suggests a monthly price change that helps support time adjustments for older comparables.
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.
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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?
Compared with analyzing one matched sale pair for one feature, what can a suitably specified multiple regression do?
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.
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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.
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