A model predicts a subject's value at $412,000 while the sales comparison grid indicates $455,000. What is the appropriate response?
Correct Answer
D) Examine both for the cause of the divergence
Why this is correct: The appropriate response is to examine both for the cause of the divergence. A significant discrepancy ($43,000) indicates a potential problem in one or both analyses that must be investigated before reconciliation, as the original explanation notes. Why the other choices are wrong: You should not automatically report the regression figure as more objective. You should not simply average the two indications, as this buries the problem. You should not discard the grid solely because the model uses more data; the grid may contain valid qualitative adjustments. Exam tip: A large gap between value indications is a red flag. Your job is to diagnose why, not to mechanically average them.
Why This Is the Correct Answer
Examining both analyses for the cause of the divergence is what reconciliation actually requires, and it is the only response that can improve the conclusion rather than merely produce one. The investigation typically reveals a specific, fixable problem, after which the appraiser can weight the indications knowledgeably and explain that weighting. It also protects against the possibility that the error lies in the grid rather than the model. Whatever the outcome, the reasoning belongs in the report.
Why the Other Options Are Wrong
Option A: Report the regression figure as more objective
Objectivity is not a property of arithmetic; a regression embeds the appraiser's choices about variables, functional form, market area, and estimation sample, and it inherits every error in the underlying data. Calling the model objective mistakes the absence of visible judgment for the absence of judgment. It also abandons the grid's advantage, which is that individual sales were verified and adjusted with knowledge of the specific properties.
Option B: Average the two indications into one figure
Averaging conceals the discrepancy instead of resolving it and produces a figure neither analysis supports. Reconciliation is a weighing of indications based on reliability, never a mechanical mean, and an average of a sound indication and a flawed one is simply a less sound indication. It also leaves the underlying defect in place to recur on the next assignment.
Option C: Discard the grid, since the model uses more data
Sample size is not the criterion for reliability; relevance and data quality are. A grid of four verified, well-matched sales can easily outperform a model fitted on hundreds of loosely comparable transactions, particularly where condition and quality drive value and are poorly captured by coded data. Discarding the grid also discards the qualitative judgment that a model cannot supply.
A Gap Is a Clue
Treat a divergence the way a mechanic treats a warning light. It is not an inconvenience to be silenced, it is information about where to look. Averaging is putting tape over the light.
How to use: In any conflicting-indications item, choose the diagnostic answer. Eliminate options that pick a winner by category, average the figures, or discard an analysis for a reason unrelated to its reliability.
Exam Tip
The word average is nearly always wrong in reconciliation questions, whether the subject is two models, three approaches, or several comparables. Look for weigh, analyze, or reconcile instead.
Common Mistakes to Avoid
- -Averaging conflicting indications rather than diagnosing them
- -Assuming a statistical output is more objective than a supported grid
- -Failing to explain in the report why one indication received more weight
Concept Deep Dive
Analysis
A $43,000 spread on a roughly $430,000 property is about ten percent, which is too large to wave through and too specific to ignore. Reconciliation requires the appraiser to analyze the indications produced by the approaches and techniques applied, considering the appropriateness of each to the assignment, the accuracy of the data behind each, and the quantity of evidence supporting each. A divergence of this size is diagnostic information: it says one analysis is missing something the other captured. Common culprits are a model that omits condition or quality because those variables are hard to code, a grid resting on comparables whose concessions were never verified, a model estimated on a market area broader than the subject's, a grid with unsupported adjustments, or simply a data error in one input. Diagnosing the cause usually improves both analyses, and whatever the appraiser concludes, the report must explain the reasoning that led to the final opinion rather than presenting a number that appeared from nowhere.
Background Knowledge
You need the reconciliation criteria of appropriateness, accuracy, and quantity of evidence, and the rule that reconciliation weighs rather than averages indications. You should also understand the typical weaknesses of automated and statistical models, particularly poor treatment of condition, quality, and view, and the requirement that the report explain the reasoning supporting the value opinion.
Real-World Application
An appraiser facing a $43,000 gap discovers her model coded the subject's condition as average when a recent full renovation put it well above that, and that two grid comparables carried unadjusted seller concessions. She corrects both, watches the indications converge to within two percent, and explains the diagnosis and the final weighting in her reconciliation.
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:
People Also Study
Real Estate Market
13.6% of exam
Property Description
11.8% of exam
Land or Site Valuation
4.5% of exam
Sales Comparison Approach
16.4% of exam
Cost Approach
13.6% of exam