Semi-Parametric Tools for Spatial Hedonic Models: An Introduction to Mixed Geographically Weighted Regression and Geoadditive Models
Growing evidence in the economic literature of the multiplicity of amenity sources linked to space and location of houses. Amenity sources can be related to the distance to different public services, commercial facilities, noteworthy landmarks, tourist area, etc. Using monocentric models based on the single distance to Central Business District (CBD) is not sufficient to assess the effect of urban sprawl on land prices. Dominant models in economy fail to account for amenity values, congestion cost or social expenditure for public infrastructure (Brueckner 2001). The specification of hedonic OLS models may be quite difficult when numerous distance measures are candidate regressors, especially with respect to the colinearity issue. GAM is a powerful and flexible tool to explore distance effects with large samples.
The upscale and downscale extension of available spatial descriptors provided by many public and private organizations has resulted in an explosion in the use of GIS in the last five years. This increased use of GIS by public administrations facilitates the introduction of finer descriptors of landscape, public services and social neighbourhoods in hedonic models. Working on large samples with a lot of finer descriptors of the environment and neighborhood involves dealing with numerous local effects. In this case it is necessary to use multiple scales to analyze the structure of house prices (Quigley 1995). Locally weighted regressions such as Geographically Weighted Regressions (GWR) or MGWR, and GAM, enable estimation of spatially varying and invariant implicit prices in hedonic analysis.
KeywordsSpatial Autocorrelation House Price Generalize Additive Model Geographically Weight Regression Central Business District
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