An appraiser includes months elapsed since each sale as a variable in a price model. What is this intended to capture?
Correct Answer
C) The change in market conditions over time
Why this is correct: Including a variable for the number of months since each sale (time variable) in a regression model is a direct method to estimate and capture the effect of changing market conditions over the sales period, holding other property characteristics constant. Why the other choices are wrong: "The physical depreciation of the improvements" is wrong; physical depreciation is related to age and condition, not simply the passage of time between sale dates. "The remaining economic life of each building" is wrong; that is not measured by months since sale. "The seasonal pattern in buyer preferences" is wrong; a simple time variable in months would not specifically isolate seasonal effects. Exam tip: In regression, a time variable estimates market trends, often called the "time adjustment."
Why This Is the Correct Answer
A months-elapsed variable captures the change in market conditions over the period spanned by the data, which is the regression method of deriving a time adjustment. Its coefficient tells the appraiser how much prices moved per month with property characteristics held constant. That figure can then be used to bring older comparables forward to the effective date. The technique is preferred where enough data exists, because it draws on the full sample rather than a few resales.
Why the Other Options Are Wrong
Option A: The physical depreciation of the improvements
Physical deterioration relates to the age and condition of the improvements, which a separate age variable addresses. A house does not deteriorate because time passed between two different properties' sale dates. Confusing the two variables would double count age or leave market movement unmeasured.
Option B: The remaining economic life of each building
Remaining economic life is a function of effective age and total economic life for a specific building, estimated from condition and market acceptance rather than from transaction timing. Nothing about months elapsed since a sale speaks to how long a building will remain economically useful. The concepts share the word time and nothing else.
Option D: The seasonal pattern in buyer preferences
Isolating seasonality requires variables that identify the month or quarter of sale, such as a set of dummy variables, since a simple linear count of months elapsed models a steady trend rather than a repeating cycle. Seasonal effects are real in many residential markets, but capturing them takes a different specification. The option names a plausible objective achieved by a different technique.
Age Is the Building, Months Are the Market
Two time variables live in every price model and they must not be confused. Age measures how old the house is, feeding depreciation. Months since sale measures when the deal happened, feeding market conditions.
How to use: When a stem names a time-related variable, ask whether it describes the property or the transaction. Property means depreciation; transaction means market conditions.
Exam Tip
A linear months variable models a steady trend. If a question involves cyclical or seasonal patterns, expect the answer to involve dummy variables or a different specification.
Common Mistakes to Avoid
- -Confusing an age variable with a months-since-sale variable
- -Expecting a linear time variable to capture seasonality
- -Adopting a regression time coefficient without cross-checking it against paired resales
Concept Deep Dive
Analysis
In a hedonic price model each independent variable is meant to hold one dimension of difference constant so the others can be estimated cleanly. Physical characteristics such as gross living area, age, bath count, and lot size handle the property differences. A variable measuring months elapsed since each sale handles a different dimension entirely: it lets the model estimate how much prices moved over the data period, holding physical characteristics constant. The resulting coefficient is a market conditions or time adjustment expressed as dollars per month or, in a logarithmic specification, as a percentage rate. This is the regression counterpart to deriving a time adjustment from paired resales, and it has the advantage of using the whole dataset rather than the handful of properties that happened to sell twice. Age and months since sale must be kept distinct: age measures how old the building is, which speaks to depreciation, while months since sale measures when the transaction occurred, which speaks to the market.
Background Knowledge
You need the structure of a hedonic regression model with a dependent price variable and independent property and transaction characteristics, and the interpretation of coefficients as marginal effects. You should also know the alternative methods of deriving a market conditions adjustment, including paired resales and segmented trend analysis, and how dummy variables capture categorical or seasonal effects.
Real-World Application
An appraiser modeling 180 sales over two years includes months elapsed alongside living area, age, baths, and lot size. The time coefficient indicates about $520 per month, which she converts to an annual rate, cross-checks against her paired resales, and uses to support her market conditions adjustment.
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?
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