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\hypersetup{pdftitle={Does the Sales Comparison Approach Actually Work? A Large-Scale Point-in-Time Validation of Comparable Selection, Price Adjustments, and Value Reconciliation}, pdfauthor={}}

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\newcommand{\WPnumber}{16}
\newcommand{\WPtitle}{Does the Sales Comparison Approach Actually Work? A Large-Scale Point-in-Time Validation of Comparable Selection, Price Adjustments, and Value Reconciliation}
\newcommand{\WPsubtitle}{Evidence from 391,000 Single-Family Transactions in Quebec, 2021--2026}
\newcommand{\WPdate}{October 2026}
\newcommand{\WPversion}{1.1}
\newcommand{\WPabstract}{%
  The sales comparison approach is the dominant method of residential valuation, yet its operating rules---how many comparables to use, how far and how far back to search, which adjustments to apply, how to weight and reconcile---rest largely on professional convention. We subject these rules to a population-scale, point-in-time backtest on \nSalesClean{} arm's-length single-family transactions in Quebec (2021--2026) matched to the municipal assessment roll. For each of \nTargetsAll{} target sales in 2024--2026 the price is masked and \nConfigs{} configurations of the approach, together with hedonic, gradient-boosting, assessment-ratio and hybrid benchmarks, are applied using only information available on the valuation date; configurations are selected on 2024 and evaluated out of sample on 2025--2026, and paired differences are tested with a cluster bootstrap over municipality--quarter cells. Adjustments dominate: the median absolute error of the five nearest sales falls from \mdapeMtwo{} with a time adjustment only to \mdapeMseven{} with a hedonic grid, and a stylised conventional grid captures most of that gain. Selecting physically similar rather than merely nearby comparables delivers the same gain without any adjustment. The optimal number of comparables is about twenty once a grid is applied, with no penalty up to fifty; short windows are dominated and the search radius matters little. Weighting and reconciliation rules matter little. The best transparent configuration chosen in 2024 reaches \mdapeMzero{} out of sample, against \mdapeMelevenB{} for a quarterly-retrained gradient-boosting model using the assessed value; the cost of transparency, \transparencyCost{} points, is precisely estimated and narrows in homogeneous submarkets, and the chosen rule ranks second among \nConfigsCov{} configurations out of sample. Accuracy is governed primarily by submarket liquidity, and a wide-net rule retaining the most similar sales adapts to it on its own.
}
\newcommand{\WPkeywords}{sales comparison approach, comparable selection, adjustment grid, automated valuation models, hedonic pricing, backtesting, residential real estate, Quebec}
\newcommand{\WPjel}{R31, R32, C52, C53, G12}

% --- Author ---
\newcommand{\WPauthor}{Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais%
}
\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
\newcommand{\WPaddress}{%
  Gatineau -- Pavillon Alexandre-Taché\\
  283, boulevard Alexandre-Taché\\
  Gatineau, Québec, Canada J9A 1L8%
}

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\input{sections/robustness}
\input{sections/conclusion}

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