Department Environmental Social Sciences
Valuing water resources in Switzerland through the housing market
Switzerland has a great appreciation for green space and water. With the increase in urbanization, the demand for environmental quality and the natural environmental becomes more important as well. Location of housing therefore plays an essential role in the price setting. The goal of this research is to assess the value of different environmental amenities, as well as structural house and neighbourhood characteristics, applying the hedonic pricing method to the housing market in Switzerland. With hedonic pricing we will be able to identify to what extent each characteristic explains the variation in house prices. A variety of new water related amenities will be included, namely water abundance and distances to the nearest lake, river, bathing site and wastewater treatment plant. Furthermore, a spatial component will be incorporated by using spatially explicit data and using a spatial version of the hedonic price model. Eventually, the study will give insight in the relative role of water in the capital value of houses across Switzerland.
In a follow-up study we will zoom in on several regions in Switzerland that differ from each other in terms of biogeography, water abundance and socio-economic characteristics. Using the benefit transfer method, the goal is to assess whether the spatial hedonic price functions are transferable within and across the different regions. It furthermore enables us to identify the role of spatial effects in this transferability.
Main Results
Valuing water resources in Switzerland using a hedonic price model
In this paper, linear and spatial hedonic price models are applied to the housing market in Switzerland, covering all 26 cantons in the country over the period 2005-2010. Besides structural house, neighborhood and socio-economic characteristics, we include a wide variety of new environmental characteristics related to water to examine their role in explaining variation in sales prices. These include water abundance, different types of water bodies, the recreational function of water and water disamenity. Significant spatial autocorrelation is found in the estimated models, as well as non-linear effects for distances to the nearest lake and large river. Significant effects are furthermore found for water abundance and the distance to large rivers, but not to small rivers. Although in both linear and spatial models water related variables explain less than one percent of the price variation, the distance to the nearest bathing site has a larger marginal contribution than many neighborhood related distance variables. The housing market shows to differentiate between different water related resources in terms of relative contribution to house prices, which could help the housing development industry make more geographically targeted planning activities.
