An appraiser uses time-adjusted sale prices as the dependent variable rather than raw prices. What does this accomplish?
Correct Answer
B) It removes market movement from what is modelled
Why this is correct: Using time-adjusted sale prices as the dependent variable removes the effect of market-wide price changes over time from the analysis. This allows the regression model to isolate and estimate the value contribution of physical and locational characteristics alone. Why the other choices are wrong: It does not automatically increase R-squared; it may increase or decrease it depending on the data. It does not eliminate the need for sale verification. It does not allow the use of unverified sales. Exam tip: You can model price change in two ways: 1) Use raw prices and include time as an independent variable, or 2) Use time-adjusted prices and exclude time. Both aim to isolate physical/locational value.
Why This Is the Correct Answer
Option B states the purpose exactly: converting prices to time-adjusted equivalents strips market-wide movement out of the variable being modeled. With that source of variation removed, the coefficients on square footage, bath count, garage capacity, and location speak to those features rather than to when the transaction closed. The appraiser must then avoid double counting by keeping a time variable out of the same model.
Why the Other Options Are Wrong
Option A: It increases the R-squared of the model automatically
Fit can improve, worsen, or stay flat depending on how well the time adjustment was derived and how much of the price variation was time-related. A poorly estimated adjustment injects noise and can lower the coefficient of determination. Nothing about the technique guarantees a better-fitting model, and treating a higher figure as automatic proof of quality is its own error.
Option C: It eliminates the need to verify each transaction
Verification is about confirming that each transaction happened as reported, at that price, with those terms, and with no atypical motivation. Nothing in a mathematical adjustment for time confirms any of that. Skipping verification would leave the model built on unconfirmed prices no matter how they were transformed.
Option D: It allows the sample to include unverified sales
Unverified sales remain unverified after arithmetic is applied to them. Including them would import unknown financing concessions, non-arm's-length transfers, and reporting errors straight into the coefficients. Time adjustment changes the value of a number, not its reliability.
Freeze the Clock First
Time-adjusting the prices freezes every sale at the effective date. Once the clock is frozen, whatever price differences remain must come from the properties themselves.
How to use: When a stem asks what a data transformation accomplishes, name the variation it removes. Removing time from the prices leaves physical and locational variation for the model to explain.
Exam Tip
Watch for options that promise a statistical technique will improve a fit measure automatically. Statistics questions on this exam reward understanding of purpose, not faith in outputs.
Common Mistakes to Avoid
- -Including both time-adjusted prices and a time variable in the same model
- -Deriving the time adjustment from an unsupported assumption about appreciation
- -Reading a high coefficient of determination as proof the model is credible
Concept Deep Dive
Analysis
Regression estimates how each property characteristic contributes to price, but sale prices also embody when the sale happened, and in a moving market that timing effect can swamp the physical variables. There are two clean ways to deal with it: leave prices raw and put a time variable into the model, or adjust each price to the effective date first and leave time out of the model. Time-adjusting the dependent variable does the second, so the price differences the model is left to explain come from location and physical characteristics rather than from market movement. What it does not do is relieve the appraiser of deriving a defensible time adjustment in the first place, usually from resales, from a matched-pairs analysis across time, or from a market trend study.
Background Knowledge
You need the basics of regression: dependent variable, independent variables, and coefficients as marginal contributions. You also need to know how market conditions adjustments are derived and why they must be either applied to the prices or represented by a time variable, but not both.
Real-World Application
Analyzing eighteen months of sales in an appreciating market, an appraiser derives a monthly rate of change from resales of the same properties, brings every sale forward to the effective date, and then models the adjusted prices against living area, lot size, and condition.
More Statistics Questions
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?
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?
In a market study, what does a frequency distribution of sale prices show?
An appraiser includes months elapsed since each sale as a variable in a price model. What is this intended to capture?
An appraiser presents a statistical analysis in a report. What must accompany it for the reader to weigh it?
An R-squared of 0.86 in a sales model indicates that:
Which measure would best summarize the most common lot size in a subdivision?
Paired sales analysis and regression differ mainly in that regression:
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