Geographically Weighted Regression is a statistical technique for real estate market analysis, particularly adequate in order to identify homogeneous areas and to define the marginal contribution that the geographical location gives to the market value of the properties. In this paper a GWR has been applied, in order to verify the robustness of the real estate sample, this for the subsequent individuation of progressive real estate sub-samples in able to detect and to identify possible potential market premium in real estate exchange and rent markets for green buildings [21, 22, 23, 24, 25, 26, 27, 28]. The model has been built on a large real estate dataset, related to the trades of residential real estate units in the city of Reggio Calabria (Calabria region, Southern Italy).

Geographically Weighted Regression for the Post Carbon City and Real Estate Market Analysis: A Case Study

MASSIMO Domenico Enrico;MUSOLINO Mariangela;
2019

Abstract

Geographically Weighted Regression is a statistical technique for real estate market analysis, particularly adequate in order to identify homogeneous areas and to define the marginal contribution that the geographical location gives to the market value of the properties. In this paper a GWR has been applied, in order to verify the robustness of the real estate sample, this for the subsequent individuation of progressive real estate sub-samples in able to detect and to identify possible potential market premium in real estate exchange and rent markets for green buildings [21, 22, 23, 24, 25, 26, 27, 28]. The model has been built on a large real estate dataset, related to the trades of residential real estate units in the city of Reggio Calabria (Calabria region, Southern Italy).
978-3-319-92098-6
Post carbon city, Green buildings, Geographically Weighted Regression (GWR), Spatial Statistics, Real estate market analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/12795
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