The impact of undesirable externalities on residential property values: spatial regressive models and an empirical study

Abstract

Pollutant emissions, noise and other externalities generated by heavy infrastructures, might impact negatively on real estate values. To test this effect, this paper presents the results of an analysis based on Hedonic Linear Regression, Spatial Hedonic Linear Regression and Hedonic Geographically Weighted Regression models, carried out for the study case of the province of Taranto (Italy). The biggest steel factory in Europe is located here, and some population movements have been observed in relation to the high levels of pollution in the areas close to the factory. The variables used to measure the impact of externalities are of two types: objective indicators such as the distance from the industrial area and the levels of NO2 and PM10, and subjective indicators such as the level of pollution and noise perceived by the population. Results show that the distance from factory was a positive factor in the real estate prices although not always clearly significant, and among pollution indicators, only high levels of NO2 had a negative effect. The accessibility to employment did not prove to be a significant variable in the real estate prices, which indicates that factors related to environmental quality have a greater weight in residential location. Moreover, models including subjective indicators do not show better estimates than models considering only objective indicators. Finally, spatial regression models were useful to analyse the spatial dependence and spatial heterogeneity observed in the data.