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Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

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  • Arnab Bhattacharjee
  • Eduardo Castro
  • João Marques

Abstract

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes the approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, applied to the urban housing market of Aveiro (Portugal) at two different spatial scales, provides exciting inferences on the spatial structure of the housing market. RÉSUMÉ L'hétérogénéité spatiale, la dépendance spatiale et l’échelle spatiale sont des caractéristiques clé de l'analyse spatiale dans les marchés de l'immobilier. Toutefois, la pratique habituelle de la modélisation de la dépendance spatiale comme étant le résultat d'interactions spatiales par le biais d'une matrice de poids spatiaux n'est souvent pas satisfaisante. Alors que les estimateurs existants des matrices de poids spatiaux sont basés sur des données de panel ou des ventes répétées, la présente communication adopte le principe d'un cadre transversal. Plus spécifiquement, sur la base d'une définition à priori des sub-marchés de l'immobilier, et de l'hypothèse d'un modèle multifactoriel, nous créons une méthodologie de probabilité maximale pour estimer des modèles hédoniques qui facilitent les connaissances de l'hétérogénéité spatiale et des interactions spatiales. Cette méthodologie, basée sur une analyse des facteurs orthogonaux, appliquée au secteur de l'immobilier urbain à Aveiro (Portugal) à deux échelles spatiales différentes, fournit des inférences excitantes en ce qui concerne la structure spatiale du secteur de l'immobilier. EXTRACTO La heterogeneidad, dependencia y escala espaciales constituyen características clave del análisis espacial de los mercados de la vivienda. No obstante, la práctica común de modelar la dependencia espacial como algo generado por interacciones espaciales a través de una matriz conocida de pesos espaciales, a menudo, no es satisfactoria. Aunque los estimadores existentes de matrices de pesos espaciales se basan en ventas repetidas o datos de panel, este estudio lleva el planteamiento a un marco de corte transversal. Específicamente, basados en una definición a priori de los submercados de la vivienda y en la presuposición de un modelo de múltiples factores, desarrollamos una metodología de probabilidad máxima para estimar modelos hedónicos, que facilita la comprensión de la heterogeneidad espacial y las interacciones espaciales. La metodología, basada en el análisis estadístico de factores ortogonales y aplicada al mercado de la vivienda urbana de Aveiro (Portugal) en dos escalas espaciales diferentes, proporciona interesantes inferencias sobre la estructura espacial del mercado de la vivienda.

Suggested Citation

  • Arnab Bhattacharjee & Eduardo Castro & João Marques, 2012. "Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 133-167, March.
  • Handle: RePEc:taf:specan:v:7:y:2012:i:1:p:133-167
    DOI: 10.1080/17421772.2011.647058
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    2. David M. Vetter & Kaizô I. Beltrão & Rosa M. R. Massena, 2013. "The Impact of the Sense of Security from Crime on Residential Property Values in Brazilian Metropolitan Areas," Research Department Publications IDB-WP-415, Inter-American Development Bank, Research Department.
    3. Charles-Olivier Amédée-Manesme & Michel Baroni & Fabrice Barthélémy & Francois des Rosiers, 2017. "Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach," Urban Studies, Urban Studies Journal Limited, vol. 54(14), pages 3260-3280, November.
    4. Arnab Bhattacharjee & Taps Maiti & Dennis Petrie, 2014. "Spatial structures of health outcomes and health behaviours in Scotland: Evidence from the Scottish Health Survey," SEEC Discussion Papers 1401, Spatial Economics and Econometrics Centre, Heriot Watt University.
    5. Vidoli, Francesco & Pignataro, Giacomo & Benedetti, Roberto, 2022. "Identification of spatial regimes of the production function of Italian hospitals through spatially constrained cluster-wise regression," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    6. Wei-Ling Tsou & Chen-Yi Sun, 2021. "Consumers’ Choice between Real Estate Investment and Consumption: A Case Study in Taiwan," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    7. Joao Lourenço Marques & Eduardo Castro & Arnab Bhattacharjee & Paulo Batista, 2012. "SPATIAL HETEROGENEITY ACROSS SUBMARKETS: Housing submarket in an urban area of Portugal," ERSA conference papers ersa12p1111, European Regional Science Association.
    8. Bhattacharjee, Arnab & Jensen-Butler, Chris, 2013. "Estimation of the spatial weights matrix under structural constraints," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 617-634.
    9. Arnab Bhattacharjee & Eduardo Castro & Taps Maiti & João Marques, 2014. "Endogenous spatial structure and delineation of submarkets: A new framework with application to housing markets," SEEC Discussion Papers 1403, Spatial Economics and Econometrics Centre, Heriot Watt University.
    10. Miryam S. Merk & Philipp Otto, 2022. "Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    11. Polixeni Iliopoulou & Elissavet Feloni, 2022. "Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece," Geographies, MDPI, vol. 2(1), pages 1-21, February.
    12. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
    13. Tao Zhou & Rui Zhao & Yulin Zhou, 2017. "Factors Influencing Land Development and Redevelopment during China’s Rapid Urbanization: Evidence from Haikou City, 2003–2016," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    14. Bhattacharjee, Arnab & Maiti, Taps & Petrie, Dennis, 2014. "General equilibrium effects of spatial structure: Health outcomes and health behaviours in Scotland," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 286-297.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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