A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
On current neural network applications involving spatial modelling of property prices
Abstract In recent years, the neural network modellingtechnique has become a serious alternative toand extension of more conventional propertyvalue modelling approaches. The neural networkis a nonlinear and flexible (i.e., model-free,non/semi-parametric) regression technique thatdoes not require a priori specifiedformal theory to work on. Instead, the idea –although not confined to this particular groupof methods – is to allow for only aposteriori support for theory. The aim of thearticle is to evaluate the pros and cons ofneural network models of property valuation(particularly the `self-organizing map', SOM)in comparison with hedonic models, and toprovide some examples of the application of theSOM method. Of particular interest is howdifferent locational, environmental, and socialfactors impact housing market segments andhouse price levels. It is argued that theseobjectives are conveniently handled with amethod based on the SOM. Some ideas forfollow-up are also presented for this method.
On current neural network applications involving spatial modelling of property prices
Abstract In recent years, the neural network modellingtechnique has become a serious alternative toand extension of more conventional propertyvalue modelling approaches. The neural networkis a nonlinear and flexible (i.e., model-free,non/semi-parametric) regression technique thatdoes not require a priori specifiedformal theory to work on. Instead, the idea –although not confined to this particular groupof methods – is to allow for only aposteriori support for theory. The aim of thearticle is to evaluate the pros and cons ofneural network models of property valuation(particularly the `self-organizing map', SOM)in comparison with hedonic models, and toprovide some examples of the application of theSOM method. Of particular interest is howdifferent locational, environmental, and socialfactors impact housing market segments andhouse price levels. It is argued that theseobjectives are conveniently handled with amethod based on the SOM. Some ideas forfollow-up are also presented for this method.
On current neural network applications involving spatial modelling of property prices
Kauko, Tom (author)
2003
Article (Journal)
English
BKL:
56.00$jBauwesen: Allgemeines
/
56.81$jWohnungsbau$XArchitektur
/
74.72
Stadtplanung, kommunale Planung
/
74.72$jStadtplanung$jkommunale Planung
/
56.00
Bauwesen: Allgemeines
/
74.60$jRaumordnung$jStädtebau: Allgemeines
/
74.60
Raumordnung, Städtebau: Allgemeines
/
56.81
Wohnungsbau
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