Vol. 87 (2025)
Original Articles - Appraisal and Rural Economics

Using a spatial econometric approach to identify the main determinants and spillover effects of residential property prices in La Spezia (Italy)

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  • Laura Giuffrida,
  • Giuseppe Cucuzza,
  • Daniela Tavano,
  • Francesca Salvo,
  • Giovanni Signorello,
  • Maria De Salvo
Laura Giuffrida
Department of Agriculture, Food and Environment, University of Catania, Italy
Giuseppe Cucuzza
Department of Agriculture, Food and Environment, University of Catania, Italy
Daniela Tavano
Department of Environmental Engineering, University of Calabria, Italy
Francesca Salvo
Department of Environmental Engineering, University of Calabria, Italy
Giovanni Signorello
Department of Agriculture, Food and Environment, University of Catania, Italy
Maria De Salvo
Department of Veterinary Sciences, University of Messina, Italy
DOI: https://doi.org/10.36253/aestim-17865

Published 2025-10-29

Keywords

  • Spatial autocorrelation,
  • Econometric spatial models,
  • Real estate market analysis

Copyright (c) 2025 Laura Giuffrida, Giuseppe Cucuzza, Daniela Tavano, Francesca Salvo, Giovanni Signorello, Maria De Salvo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

We employ a spatial econometric approach to investigate the factors influencing residential property prices in La Spezia province (Italy). Unlike traditional hedonic models, which often overlook spatial dependencies, our methodology explicitly accounts for spatial autocorrelation, thereby yielding more robust and accurate estimates. Diagnostic spatial tests reveal significant spatial dependence in both property prices and context variables. To address this, we adopt the Spatial Durbin Error Model (SDEM), using a first-order Queen contiguity weight matrix. This model not only enhances explanatory power but also improves predictive accuracy. By incorporating spatial effects, the SDEM enables the disentanglement of direct and spillover influences, offering a more comprehensive understanding of the determinants of property prices. The findings demonstrate the importance of spatially-aware models not only in the formulation of effective housing policies and urban development strategies but also in appraisal practices, where they improve the accuracy of real estate valuation.

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