spb/wp16_uqo

1% ============================================================================2\section{Conclusion}3\label{sec:conclusion}4% ============================================================================56The sales comparison approach is usually defended on the grounds of its logic and its transparency and criticised on the grounds of its subjectivity. This paper has asked a different question: taken as a set of explicit rules, how accurate is it, and which rules are empirically justified? Using the universe of single-family transactions in Quebec and a backtest that respects the information available on each valuation date, we have measured the accuracy of \nConfigs{} configurations of the method and of the natural alternatives on \nTargetsTest{} out-of-sample sales, with configurations chosen on a separate year and differences tested against the spatial and temporal dependence of valuation errors.78The answers are clear enough to be taught. Adjust: a grid---even a crude one---is worth four to six points of error. Choose similar comparables, not merely near ones, and search widely in space and time. Use more comparables than three: twenty or more when a grid is applied, in sparse and dense markets alike, and about ten when it is not. Do not agonise over weights or over the choice between median and mean. Use the assessed value: the ratio method is the most accurate single adjustment scheme where it applies. And report an interval, calibrated so that it means what it says---and be prepared for it to be wide. A transparent method configured on these principles and frozen in 2024 reaches a median absolute error of \mdapeMzero{} on 2025--2026 sales, within \transparencyCost{} points of the best quarterly-retrained machine-learning model given the same data, and within about a point and a half in homogeneous submarkets. The choice of that configuration is robust: the ten best configurations on 2024 differ only in the number of comparables and the radius, and all rank among the twelve best out of sample.910The results have implications for practice, for education and for regulation. For practice, they suggest that the appraiser's effort is best spent on selecting similar comparables and on estimating adjustments from the largest homogeneous pool of evidence, rather than on reconciling a handful of nearby sales by judgement. For education, they provide a set of validated rules that can be taught alongside the conventions, and a laboratory---the backtesting engine---in which students can test their own. For regulation and for the design of automated valuation models, they show that a comparable-based method can be made nearly as accurate as an opaque model in most of the market while remaining fully explainable, and that a wide-net rule retaining the most similar sales adapts to market density on its own.1112Four limitations bound the conclusions. We simulate rules; we do not observe appraisers, whose files may contain information (condition, renovations, listing history) that would improve on any rule, and whose judgement may also degrade it. We observe the characteristics of the assessment roll, not those of listings or inspections, so that adjustments for garages, pools, bedrooms and quality are untested and their effects remain in the residual error, which the within-municipality $R^2$ of about a third makes visible. We screen non-arm's-length transfers with a price-to-assessment ratio, date sales by their registration and ignore the publication lag of the register, three choices that favour every method alike but that a user of the rules in real time would have to confront. And we study single-family dwellings in one province; condominiums, income properties and other markets may yield different optima, though we expect the qualitative hierarchy---adjustment and selection first, number and radius second, weighting last---to carry over. Two extensions follow directly from the results and remain to be done: a seasonal correction of the comparables estimate, which Section~\ref{sec:stability} suggests would remove a bias of one to two points in the first and third quarters, and a re-execution of the backtest under alternative screening rules for the comparable pool. A companion project develops the recommendation system of Section~\ref{sec:recommend} into a transparent, empirically optimised comparable-sales algorithm and integrates it into a teaching platform.13