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.