An increased awareness of the cost of negative externalities such as noise, air pollution, and human exposure, combined with the fact that urban structures are becoming denser, had led to an increasing need for reliable cost-benefit evaluations of infrastructure projects. In addition to this, there has been an increased focus on public consumption, which has required a more careful planning phase to reduce wasteful expenditure. The hedonic pricing method constitutes one of the supposedly most promising tools to evaluate negative and positive benefits from changes in infrastructure by utilizing the hedonic pricing process observed in the housing market. Houses are valued according to specific attributes and surroundings, which can be used to calculate overall benefits of infrastructure projects and to derive implicit prices of environmental loads such as noise and deterioration of amenities. The paper is organised into two parts, in which, in the first part establishing a data foundation by means of GIS and Internet technology is the primal aim. The second part of the paper focuses on econometric issues and includes an application where benefits from a newly finished metro project in the Copenhagen region are calculated. The greatest difficulty in generating data suitable for the hedonic model is to link houses and related prices with the proximity to attributes that are likely to affect prices negatively as well as positively. This is done by relating addresses to a number of thematic GIS layers used to describe residential quality. The different themes include recreational grounds, seaside and lakes, parks, sport facilities (golf courses, football fields, sports arenas, etc.) and local taxation levels to mention a few. The uncontroversial (negative) perception of traffic noise is handled through noise-buffers whereas the positive effect of being close to certain amenities are calculated by weighting the share of such amenities within different distance bands of the address. Zone-based commuter and shopping accessibility is implemented by means of log sums from an external demand model. House prices and house characteristics are extracted directly from the Internet by using java-scripts that automatically search web sites of real-estate agencies. Selling prices tend to be a fairly good reflection of the willingness-to-pay for housing in the market. Furthermore, the way data is generated makes the method independent of expensive external data sources and portable to other regions and countries. The validity of Internet data is analysed in a separate comparison with actual trade prices from the central register. In order to test the applicability of the hedonic methodology, an evaluation of the Copenhagen metro project is presented. The hedonic model estimation is carried out in a mixed regression setup, in which model parameters are allowed to follow pre-defined distributions. The mixed regression model is conceptual comparable to mixed models in discrete choice analysis (also known as error component models, and mixed logit), which is recognised to be successful in accounting for taste variation. This property is especially important in the hedonic model, since, as in stochastic assignment models, individual characteristics as income, age, and sex are not observed. The application, which calculates the overall benefit of the project as well as an implicit price of noise, illumines the strength and the cost efficiency of the method compared with traditional cost-benefit analysing tools. For the covering abstract see ITRD E124693.
Abstract