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A Bayesian approach to hedonic price analysis

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  • David C. Wheeler
  • Antonio Páez
  • Jamie Spinney
  • Lance A. Waller

Abstract

type="main" xml:lang="es"> Dos objetivos importantes en el análisis de precios hedónicos son la predicción de los precios de venta y la delimitación de submercados basada en consideraciones geográficas y funcionales. Para hacer frente a estos objetivos, en este artículo se han aplicado modelos bayesianos con coeficientes espacialmente variables para realizar un análisis de los precios de venta de la vivienda en la ciudad de Toronto (Ontario). Se evaluó el desempeño del modelo y se determinaron los patrones de los submercados indicados por los procesos de coeficiente espacial. Los resultados muestran que los modelos de proceso espacial bayesiano predicen bien los precios de venta de viviendas, proporcionan inferencias útiles con respecto a la heterogeneidad de precios dentro de un mercado, y pueden especificarse de modo que incluyan las opiniones de los expertos del mercado.

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  • David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    • Handle: RePEc:bla:presci:v:93:y:2014:i:3:p:663-683
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    Cited by:

    1. Mahdieh Yazdani, 2021. "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach," Papers 2108.02442, arXiv.org.
    2. Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.
    3. Jakob A. Dambon & Stefan S. Fahrländer & Saira Karlen & Manuel Lehner & Jaron Schlesinger & Fabio Sigrist & Anna Zimmermann, 2022. "Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-14, December.
    4. Rojas, Carolina & Páez, Antonio & Barbosa, Olga & Carrasco, Juan, 2016. "Accessibility to urban green spaces in Chilean cities using adaptive thresholds," Journal of Transport Geography, Elsevier, vol. 57(C), pages 227-240.
    5. David Rey-Blanco & Pelayo Arbués & Fernando A. López & Antonio Páez, 2024. "Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets," Environment and Planning B, , vol. 51(1), pages 89-108, January.

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