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Expert systems and mass appraisal

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  • John Kilpatrick

Abstract

Purpose - The purpose of this paper is to examine the usefulness of a heuristic expert system, to show its applicability to real‐world valuation problems, and to suggest several avenues for statistical testing. Design/methodology/approach - The expert systems follow a traditional sales adjustment grid format, with sufficient data for non‐parametric testing. Findings - The paper finds that, while non‐parametric statistics provide weaker results than traditional (e.g. hedonic regression) modeling, the technique provides a statistically testable model useful in situations with limited data and/or poorly characterized probability functions. Practical implications - This paper addresses the conundrum faced by real estate valuers on the lack of statistical underpinnings of traditional heuristic models. Originality/value - This is one of the first empirical studies in the valuation literature exploring statistical characterization of heuristic valuation methods.

Suggested Citation

  • John Kilpatrick, 2011. "Expert systems and mass appraisal," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 29(4/5), pages 529-550, July.
    • Handle: RePEc:eme:jpifpp:v:29:y:2011:i:4/5:p:529-550
    DOI: 10.1108/14635781111150385
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    Citations

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    Cited by:

    1. Patrick Krieger & Carsten Lausberg, 2021. "Entscheidungen, Entscheidungsfindung und Entscheidungsunterstützung in der Immobilienwirtschaft: Eine systematische Literaturübersicht [Decisions, decision-making and decisions support systems in r," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 7(1), pages 1-33, April.
    2. Jannet C. Bencure & Nitin K. Tripathi & Hiroyuki Miyazaki & Sarawut Ninsawat & Sohee Minsun Kim, 2019. "Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    3. Demetris Demetriou, 2017. "A spatially based artificial neural network mass valuation model for land consolidation," Environment and Planning B, , vol. 44(5), pages 864-883, September.
    4. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    5. Demetriou, Demetris, 2018. "Automating the land valuation process carried out in land consolidation schemes," Land Use Policy, Elsevier, vol. 75(C), pages 21-32.
    6. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    7. Sebastian Gnat & Mariusz Doszyn, 2020. "Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1230-1245.

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