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 [21–28]. 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; Del Giudice, Vincenzo; De Paola, Pierfrancesco; Forte, Fabiana; Musolino, Mariangela; Malerba, Alessandro. - 100:(2019), pp. 142-149. (Intervento presentato al convegno 3rd International New Metropolitan Perspectives. Local Knowledge and Innovation dynamics towards territory attractiveness through the implementation of Horizon/Europe2020/Agenda2030, 2018 tenutosi a ita nel 2018) [10.1007/978-3-319-92099-3_17].

Geographically weighted regression for the post carbon city and real estate market analysis: A case study

Del Giudice, Vincenzo;De Paola, Pierfrancesco
;
Forte, Fabiana;
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 [21–28]. 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).
2019
9783319920986
Geographically weighted regression for the post carbon city and real estate market analysis: A case study / Massimo, Domenico Enrico; Del Giudice, Vincenzo; De Paola, Pierfrancesco; Forte, Fabiana; Musolino, Mariangela; Malerba, Alessandro. - 100:(2019), pp. 142-149. (Intervento presentato al convegno 3rd International New Metropolitan Perspectives. Local Knowledge and Innovation dynamics towards territory attractiveness through the implementation of Horizon/Europe2020/Agenda2030, 2018 tenutosi a ita nel 2018) [10.1007/978-3-319-92099-3_17].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/722551
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