The effect of google drive distance and duration in residential property in Sydney, Australia

Nejad, MZ; Lu, J; Asgari, P; Behbood, V

The effect of google drive distance and duration in residential property in Sydney, Australia

Nejad, MZ Lu, J Asgari, P Behbood, V

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Publication Type:: Conference Proceeding
Citation:: Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016, 2016, pp. 646 - 655
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Nejad, MZ	en_US
dc.contributor.author	Lu, J	en_US
dc.contributor.author	Asgari, P	en_US
dc.contributor.author	Behbood, V	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016, 2016, pp. 646 - 655	en_US
dc.identifier.isbn	9789813146969	en_US
dc.identifier.uri	http://hdl.handle.net/10453/64597
dc.description.abstract	© 2016 by World Scientific Publishing Co. Pte. Ltd. Predicting the market value of a residential property accurately without inspection by professional valuer could be beneficial for vary of organization and people. Building an Automated Valuation Model could be beneficial if it will be accurate adequately. This paper examined 47 machine learning models (linear and non-linear). These models are fitted on 1967 records of units from 19 suburbs of Sydney, Australia. The main aim of this paper is to compare the performance of these techniques using this data set and investigate the effect of spatial information on valuation accuracy. The results demonstrated that tree models named eXtreme Gradient Boosting Linear, eXtreme Gradient Boosting Tree and Random Forest respectively have best performance among other techniques and spatial information such drive distance and duration to CBD increase the predictive model performance significantly.	en_US
dc.relation.ispartof	Uncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016	en_US
dc.relation.isbasedon	10.1142/9789813146976_0102	en_US
dc.title	The effect of google drive distance and duration in residential property in Sydney, Australia	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 by World Scientific Publishing Co. Pte. Ltd. Predicting the market value of a residential property accurately without inspection by professional valuer could be beneficial for vary of organization and people. Building an Automated Valuation Model could be beneficial if it will be accurate adequately. This paper examined 47 machine learning models (linear and non-linear). These models are fitted on 1967 records of units from 19 suburbs of Sydney, Australia. The main aim of this paper is to compare the performance of these techniques using this data set and investigate the effect of spatial information on valuation accuracy. The results demonstrated that tree models named eXtreme Gradient Boosting Linear, eXtreme Gradient Boosting Tree and Random Forest respectively have best performance among other techniques and spatial information such drive distance and duration to CBD increase the predictive model performance significantly.

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http://hdl.handle.net/10453/64597