Which method can estimate a feature's contribution to price in a large data set while holding other included characteristics constant?
Correct Answer
D) A properly specified multiple regression model
A suitably specified regression can estimate conditional associations between a feature and price while accounting for included predictors. Data quality, variation, omitted influences and validation determine whether that coefficient supports an adjustment. ZIP-code mean prices, feature-mention counts and gross-price ranking alone do not estimate the specified conditional relationship.
Why This Is the Correct Answer
Option D is correct because a properly specified multiple regression model estimates the relationship between a feature and price while accounting for the other included characteristics. Its coefficient for the feature reflects that conditional relationship. Whether the coefficient supports an adjustment still depends on data quality, enough variation, omitted influences and validation.
Why the Other Options Are Wrong
Option A: An overall ZIP-code mean price without feature information
Option A, an overall ZIP-code mean price without feature information, cannot measure a feature's contribution because it does not even record which properties have the feature or control for other differences.
Option B: A count of listing advertisements mentioning the feature
Option B, a count of listing advertisements mentioning the feature, measures how often agents mention the feature, not how much it affects price.
Option C: A ranking of the gross sale prices from high to low
Option C, a ranking of gross sale prices from high to low, orders sales by price but does not separate the feature's effect from size, location or other characteristics.
Regression holds the rest still
Multiple regression asks: if only this feature changed and the other included traits stayed the same, how would price move?
How to use: Use this to recognize regression as the method that controls for other characteristics.
Exam Tip
Look for the phrase holding other characteristics constant; it points to a multivariable statistical method rather than simple averages or rankings.
Common Mistakes to Avoid
- -Trusting a regression coefficient without checking data quality or omitted variables
- -Using area averages that ignore the feature being measured
- -Confusing how often a feature is advertised with what it adds to price
Concept Deep Dive
Analysis
This question tests which method can isolate a feature's contribution while holding other characteristics constant in a large data set. Multiple regression estimates how price changes with one variable while controlling for the other variables included in the model. The other options either ignore features or measure something other than price contribution.
Background Knowledge
Multiple regression analysis models sale price as a function of several property characteristics at once. Each coefficient estimates the change in price associated with a one-unit change in that variable, holding the other included variables constant. Fannie Mae recognizes statistical analysis and modeling as ways to support adjustments, but the model must be well specified and validated.
Real-World Application
An appraiser with access to several hundred recent sales in a county wants to support a garage adjustment. A regression including size, age, location and garage stalls estimates the price associated with each stall. The appraiser checks the model's fit and data quality before using the result in the grid.
More Sales Comparison Questions
Supported adjustments are: non-market financing $10,000, GLA $36/sf, garage $3,500 per car, pool/spa $12,500. A comparable identical to the 2,800 sf subject sold for $267,000 with non-market financing. What is its adjusted price?
Using GLA $36/sf and non-market financing $10,000: a 3,100 sf comparable sold for $277,000 with non-market financing; the subject has 2,800 sf. Bath count differences are not adjusted. What is the adjusted price?
Using garage $3,500 per car and pool/spa $12,500: a comparable with a 2-car garage and a pool/spa sold for $268,000. The subject has a 3-car garage and no pool. What is the adjusted price?
Using GLA $36/sf, garage $3,500 per car and pool/spa $12,500: a 2,600 sf comparable with a 2-car garage and a pool/spa sold for $264,000. The subject is 2,800 sf with a 3-car garage and no pool. What is the adjusted price?
In a neighborhood with declining values and marketing times over six months, what must Fannie Mae lenders confirm about the sales comparison analysis?
A house is a 4,000 square foot home in an area of 2,000 square foot homes. How must the appraiser treat this over-improvement in the sales comparison grid?
According to the Interagency Appraisal and Evaluation Guidelines, which items should an appraisal’s market conditions discussion include?
A comparable listed at $420,000 and sold for $399,000. What sale-to-list price ratio (sale price ÷ list price) does this indicate?
An appraiser wants to describe the neighborhood boundaries in a Fannie Mae report. Which approach is acceptable?
Which of these is listed by Fannie Mae as a sales or financing concession?
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Previous Question
A verified pair of cash-equivalent sales in the same market has C2 at $672,000 and C4 at $615,000. Rights, terms, time and other material characteristics are matched. A suitable comparable sold for $598,000. The subject is C2; the comparable is C4. Assume the observed contribution applies and no other adjustments. What signed condition adjustment and adjusted comparable price result?
Next Question
Adjusted comparable indications are $402,000, $408,000 and $413,000. A reviewer says choose the lowest solely because the subject has inferior appeal, although that appeal difference is already reflected in all adjustments. What is the appropriate response?