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.
Highlights
Many urban areas are exposed to high levels of negative externalities such as air pollution, poor water quality or the presence of toxic components
Five types of hedonic models were estimated to assess the influence of undesired externalities on dwelling prices: hedonic multiple linear regression (HLR), spatial autoregressive (SAR) in the dependent variable, spatial autoregressive in the error term (SEM), spatial autoregressive in the dependent variable and in the error term (SAC) and geographically weighted regression (GWR)
The models, estimated using data collected in the province of Taranto, were compared to control the presence of spatial relationships between observations and to test if the presence of the industrial area and the ILVA steel factory was a significant factor explaining real estate values
Summary
Many urban areas are exposed to high levels of negative externalities such as air pollution, poor water quality or the presence of toxic components. The former is defined as the existence of a functional relationship between what occurs at a point in space and what occurs at neighbouring points, whereas the latter is defined as the lack of spatial structural stability in the parameters of the model Both effects could be present in the context of real estate data due to factors such as the existence of different housing markets, the propagation effects of market prices in nearby areas or the omission in the hedonic function of relevant variables with spatial characteristics. The case study considered in this research is the province of Taranto (Southern Italy) which is one of the most polluted cities in Western Europe (Lucifora et al, 2015) due to the emission of the ILVA steel factory, the industrial seaport and an oil refinery plant located nearby The effect of such undesirable externalities on housing prices has been measured using two classes of indicators:. The results achieved could be included in a LUTI model used by governmental and other institutional bodies to assess public policies aimed at effectively managing the effect of heavy infrastructure on residential location and trip generation in the study area
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