Vol. 83 (2023)
Special Issue - Visions of the future in real estate appraisal

Exploring farmland price determinants in Northern Italy using a spatial regression analysis

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  • Laura Giuffrida ,
  • Maria De Salvo,
  • Andrea Manarin,
  • Damiano Vettoretto,
  • Tiziano Tempesta
Laura Giuffrida
Department of Agriculture, Food and Environment, University of Catania
Maria De Salvo
Department of Veterinary Sciences, University of Messina
Andrea Manarin
Department of Land, Environment, Agriculture and Forestry, University of Padova
Damiano Vettoretto
Department of Land, Environment, Agriculture and Forestry, University of Padova
Tiziano Tempesta
Department of Land, Environment, Agriculture and Forestry, University of Padova
DOI: https://doi.org/10.36253/aestim-14986

Published 2024-04-22

Keywords

  • rural real estate market analysis,
  • farmland value,
  • spatial lag of X (SLX) model,
  • Treviso (Italy)

Copyright (c) 2023 Laura Giuffrida , Maria De Salvo, Andrea Manarin, Damiano Vettoretto, Tiziano Tempesta

Creative Commons License

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

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Abstract

Using spatial regression models, we detect determinants of farmland’s prices in a rural area located in the upper Treviso plain (Veneto region, Italy). Econometric analysis is based on a Spatial linear regression model able to account for spatial lags in the data. Estimates show which intrinsic and extrinsic characteristics have the greatest influence on price, and how buyers and sellers’ profiles also matter on the price determination. Our application fosters spatial regression models in rural real estate market analysis and appraisal, and highlights that in the area under study the farmland’s prices are significantly affected by factors that are rarely considered in the literature, such as sellers and buyers’ profiles, the land use in the context where the sold plot is located matters, the hydraulic risk of the area and the presence of large infrastructures.

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