When a statistical model and a separate applicable appraisal analysis produce different indications, why investigate their assumptions and evidence together?
Correct Answer
B) The comparison may reveal limitations or errors in either analysis
When two applicable analyses differ, examining their data, assumptions and limitations can identify reasons for the disagreement. Neither statistics nor conventional sales comparison is automatically superior. The analyst should evaluate credibility for the assignment rather than assume one must always replace the other or that reconciliation is relevant only to lending. Statistical fit alone is not complete validation.
Why This Is the Correct Answer
Option B is correct because comparing two differing analyses can reveal limitations or errors in either one, such as poor data, unrealistic assumptions or a misspecified model. Neither statistical analysis nor conventional sales comparison is automatically superior. Investigating both lets the appraiser judge which indication is more credible for the assignment.
Why the Other Options Are Wrong
Option A: Statistics are prohibited as a primary analytical method
Option A says statistics are prohibited as a primary analytical method. Statistical methods are an accepted analytical tool when properly applied and supported; no general prohibition exists.
Option C: Traditional analysis is more accurate than statistical analysis
Option C says traditional analysis is more accurate than statistical analysis. Neither approach is automatically more accurate; credibility depends on the data, assumptions and how well each analysis fits the assignment.
Option D: Reconciliation applies to lending assignments rather than other uses
Option D says reconciliation applies to lending assignments rather than other uses. Reconciling differing indications is part of sound appraisal practice for many intended uses, not just lending.
Disagreement is a clue
When two analyses disagree, treat it as a clue to investigate both, not a contest with an automatic winner.
How to use: Use this to pick answers that call for examining both analyses' assumptions and evidence.
Exam Tip
Be wary of answers declaring one method always superior or limiting reconciliation to one type of assignment.
Common Mistakes to Avoid
- -Assuming the statistical model is automatically correct
- -Assuming traditional analysis is automatically more accurate
- -Treating reconciliation as relevant only to lending work
Concept Deep Dive
Analysis
This question tests reconciliation thinking when two applicable analyses disagree. Rather than assuming one method wins, the appraiser examines the data, assumptions and limitations of both. The disagreement itself is useful evidence that something may need correcting or explaining.
Background Knowledge
Reconciliation is the process of evaluating differing value indications and reaching a supported conclusion. When a statistical model and a traditional analysis diverge, the difference can point to data errors, omitted variables or flawed adjustments. Statistical fit alone is not complete validation.
Real-World Application
An appraiser's regression model indicates a value well above her paired-sales analysis. Reviewing both, she finds the model lacked a variable for a busy road that affects the subject, and she reconciles toward the better-supported indication.
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Ordered sale prices are $200,000, $220,000, $240,000, $280,000, $310,000, $330,000, $350,000, $370,000, $390,000, $410,000, $430,000, $450,000, $470,000, $490,000, $510,000, $530,000, $550,000, $580,000, $800,000, $950,000. Compute a 15% winsorized mean at each tail: replace the lowest 3 observations with the smallest retained value and the highest 3 with the largest retained value. Average all 20 resulting observations and round to the nearest dollar.