INSTALACION EXPERIMENTAL

The valuation of environmental goods is challenging since they do not attract direct supply and demand functions in the market. Several econometric techniques have, therefore, been developed to estimate the indirect costs and benefits of environmental goods. Both the stated and the revealed preference methods have been

used to assess the indirect cost and risk of natural disasters in the economic literature. Freeman (2003) discussed the advantages and disadvantages of both approaches. Stated preference methods, which include the contingent valuation method and choice experiments, are based on interviews or surveys to assess the willingness to pay (WTP) for reducing the risk of natural disasters such as floods. A disadvantage of stated preference methods however is that respondents may be biased. Additionally, choice models have not been found to be appropriate for identifying underlying preference structures, as too many factors can influence housing choices (Timmermans et al., 1994).

The revealed preference method is based on actual consumer behaviour in the market. This model applies the assessment of the WTP value for different scenarios. Most of the housing market studies have adopted the revealed preference approach where environmental amenities and dis-amenities are exogenously determined. Individual consideration regarding flood risks, for example, may be heterogeneous with other similar housing characteristics.

The HP valuation method is one of the widely accepted methods used to estimate the indirect costs of natural hazards including floods. The history of HP price models and its applications goes back to the 1960s, with the seminal work by Rosen (1974) making a major contribution to the application of hedonic theory which has been further developed by other researchers, particularly Anselin (1988), Freeman (2003) and Griffith et al. (2003). The HP model has been evolved to assess the contribution of a product’s different characteristics that ultimately determine its market price. The theory and its application has since become a standard tool in environmental valuation due to its well-established theoretical framework. The HP price function can be used to estimate the overall price indices of a property and to estimate the consumer demand for a property’s different attributes. Its wide application includes estimation of willingness to pay for environmental amenities (open space, proximity to parks, forest and natural habitats etc.), estimation of the cost of environmental pollution (air pollution, water pollution, proximity to industrial zones and highways, etc.), estimation of the cost of natural disasters (flood, earthquake, tsunami etc.) and impacts of climate change as discussed above. The flexibility in its application over a long period of time has led to the advancement of

the methodology which reflects the applicability of the model in addressing different economic issues.

More recently, a number of HP price studies have suggested that in a cross- sectional HP price analysis, the value of a property in one location may also be affected by the property value in its neighbouring area. In this sense observations made at different locations may not be independent. The observations measured at nearby locations may have a higher correlation than observations made at locations further away. This phenomenon is called spatial autocorrelation or spatial heterogeneity. According to Anselin (2009), the spatial econometric technique dates back three decades, but its application became common in the mid-2000s. However, commercial econometric software is yet to be developed to easily estimate spatial statistics. Anselin & Hudak (1992) have reviewed possible software options to address spatial issues. With recent contributions, the application and theory of spatial econometrics is further evolving (see, for example, Anselin, 1988, 1990 and 2009; Anselin & Hudak, 1992; LeSage, 1997; Cameron, 2006; Mallios et al., 2009).

Ignoring this spatial effect may cause ordinary least square (OLS) estimation to be either inconsistent or inefficient, which suggests that the spatial effect is one of the major econometric issues in the estimation of HP models. Hayunga & Pace (2010) found that commercial property markets show a spatial correlation due to inherent geographical characteristics. In recent studies, spatial variability has been captured in hedonic estimation using spatial econometric techniques (see, for example, Hayunga & Pace, 2010; Osland, 2010; Mallios et al., 2009; Cameron, 2006). The spatial variability can be incorporated in hedonic models by including spatial lagged dependent variables and a spatial error term. Mallios et al.’s (2009) application of spatial hedonic models in the valuation of irrigation water concluded that spatial methods are more efficient and consistent compared to traditional OLS estimation. Instead of using spatial econometric models, studies such as Fortheringham et al. (1998) and Duarte & Tamez (2009) adopted geographically- weighted regression methods to capture the spatial effects, whereas others have considered market segmentation to minimise the spatial bias on estimations (see, for example, Dorsey et al., 2010; Wilhelmsson, 2004).

Muller & Loomis (2008) compared spatial and non-spatial HP pricing models to examine negative externalities based on all possible neighbourhood characteristics

in a relatively small study area. Contradictorily the results did not show any significant difference between two estimated coefficients although the data used were spatially correlated. Interestingly, Neill et al. (2007) compared OLS and maximum likelihood estimation (MLE) and concluded that the MLE method outperforms the traditional OLS method since MLE estimates consider spatial variability. The remainder of this chapter discusses the theoretical background of the HP model and the econometric issues of spatial autocorrelation, heterocedasticity, multicolinearity and selection of the functional form.