Identification of locational influence on real property values using data mining methods

The value of real estate is an important matter for municipal authorities, since property tax is one of their main budget sources. Its estimation tends to be a complex process, owing to the diversity of factors affecting it. One of those factors is property location, which embraces the geographic re...

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Autores principales:	Edson Melanda, Andrew Hunter, Michael Barry
Formato:	article
Lenguaje:	DE EN FR IT PT
Publicado:	Unité Mixte de Recherche 8504 Géographie-cités 2016
Materias:	property assessment modeling/modelling economic appraisal econometrics data mining regression classification Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/aac0f6c617984384b975a238a2e982eb
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id	oai:doaj.org-article:aac0f6c617984384b975a238a2e982eb
record_format	dspace
spelling	oai:doaj.org-article:aac0f6c617984384b975a238a2e982eb2021-12-02T11:18:01ZIdentification of locational influence on real property values using data mining methods1278-336610.4000/cybergeo.27493https://doaj.org/article/aac0f6c617984384b975a238a2e982eb2016-02-01T00:00:00Zhttp://journals.openedition.org/cybergeo/27493https://doaj.org/toc/1278-3366The value of real estate is an important matter for municipal authorities, since property tax is one of their main budget sources. Its estimation tends to be a complex process, owing to the diversity of factors affecting it. One of those factors is property location, which embraces the geographic relationship between the property and the surrounding local amenities. Hedonic modelling is frequently applied to estimate the value of a property; to consider the influence of property location within such models, the region under analysis is usually divided into homogeneous areas. This division can introduce a bias (a particular vision) related to the modifiable areal unit problem. Our intent in this paper is to apply data mining techniques to address a possible valuer bias, a particular valuer’s vision, in the current City of Calgary assessment model. Employing the decision tree technique, one locational attribute (Sub-Neighbourhood) was represented by the (x, y) coordinates of the properties, with approximately 96% correct classification with respect to their City of Calgary sub-neighbourhood designation. By adopting the regression tree technique, we show that it is possible to explain approximately 73% variability of the Sale Price attribute, using only the attribute Sub-Neighbourhood or the (x, y) coordinates as input. In general, the results showed a consistent relationship between property value and location. Additionally, the sale price patterns of actual properties do not conform strictly to the politico-administrative units adopted by the city. Those patterns usually cross the unit boundaries limits or are mixed inside a unit. Our results suggest that using a property’s spatial coordinates, instead of political-administrative subdivisions, to express its location, would lead to more accurate results and not incur the possibility of bias.Edson MelandaAndrew HunterMichael BarryUnité Mixte de Recherche 8504 Géographie-citésarticleproperty assessmentmodeling/modellingeconomic appraisaleconometricsdata miningregression classificationGeography (General)G1-922DEENFRITPTCybergeo (2016)
institution	DOAJ
collection	DOAJ
language	DE EN FR IT PT
topic	property assessment modeling/modelling economic appraisal econometrics data mining regression classification Geography (General) G1-922
spellingShingle	property assessment modeling/modelling economic appraisal econometrics data mining regression classification Geography (General) G1-922 Edson Melanda Andrew Hunter Michael Barry Identification of locational influence on real property values using data mining methods
description	The value of real estate is an important matter for municipal authorities, since property tax is one of their main budget sources. Its estimation tends to be a complex process, owing to the diversity of factors affecting it. One of those factors is property location, which embraces the geographic relationship between the property and the surrounding local amenities. Hedonic modelling is frequently applied to estimate the value of a property; to consider the influence of property location within such models, the region under analysis is usually divided into homogeneous areas. This division can introduce a bias (a particular vision) related to the modifiable areal unit problem. Our intent in this paper is to apply data mining techniques to address a possible valuer bias, a particular valuer’s vision, in the current City of Calgary assessment model. Employing the decision tree technique, one locational attribute (Sub-Neighbourhood) was represented by the (x, y) coordinates of the properties, with approximately 96% correct classification with respect to their City of Calgary sub-neighbourhood designation. By adopting the regression tree technique, we show that it is possible to explain approximately 73% variability of the Sale Price attribute, using only the attribute Sub-Neighbourhood or the (x, y) coordinates as input. In general, the results showed a consistent relationship between property value and location. Additionally, the sale price patterns of actual properties do not conform strictly to the politico-administrative units adopted by the city. Those patterns usually cross the unit boundaries limits or are mixed inside a unit. Our results suggest that using a property’s spatial coordinates, instead of political-administrative subdivisions, to express its location, would lead to more accurate results and not incur the possibility of bias.
format	article
author	Edson Melanda Andrew Hunter Michael Barry
author_facet	Edson Melanda Andrew Hunter Michael Barry
author_sort	Edson Melanda
title	Identification of locational influence on real property values using data mining methods
title_short	Identification of locational influence on real property values using data mining methods
title_full	Identification of locational influence on real property values using data mining methods
title_fullStr	Identification of locational influence on real property values using data mining methods
title_full_unstemmed	Identification of locational influence on real property values using data mining methods
title_sort	identification of locational influence on real property values using data mining methods
publisher	Unité Mixte de Recherche 8504 Géographie-cités
publishDate	2016
url	https://doaj.org/article/aac0f6c617984384b975a238a2e982eb
work_keys_str_mv	AT edsonmelanda identificationoflocationalinfluenceonrealpropertyvaluesusingdataminingmethods AT andrewhunter identificationoflocationalinfluenceonrealpropertyvaluesusingdataminingmethods AT michaelbarry identificationoflocationalinfluenceonrealpropertyvaluesusingdataminingmethods
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Identification of locational influence on real property values using data mining methods

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