Personalised property investment risk analysis model in the real estate industry

Demong, NAR; Lu, J; Hussain, FK

Personalised property investment risk analysis model in the real estate industry

Demong, NAR Lu, J

Hussain, FK

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Publication Type:: Chapter
Citation:: 2014, 502 pp. 369 - 390
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Demong, NAR	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	2014, 502 pp. 369 - 390	en_US
dc.identifier.isbn	9783642393068	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37966
dc.description.abstract	Property investment in the real estate industry entails high cost and high risk, but provides high yield for return on investment. Risk factors in the real estate industry are mostly uncertain and change dynamically with the surrounding developments. There are many existing risk analysis tools or techniques that help investors to find better solutions. Most techniques available refer to expert's opinions in ranking and weighting the risk factors. As a result, they create misinterpretation and varying judgments from the experts. In addition, investment purposes differ between investors for both commercial and residential properties. There is therefore a need for personalisation elements to enable investors to interact with the analysis. This chapter presents a personalised risk analysis model that enables investors to analyse the risk of their property investments and make correct decisions. The model has three main components: investor, decision support technologies, and the data. Real world data from the Australian real estate industry is used to validate the proposed model. © 2014 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-642-39307-5_15	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Personalised property investment risk analysis model in the real estate industry	en_US
dc.type	Chapter
utslib.citation.volume	502	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	0806 Information Systems	en_US
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/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.deleted	1970-01-01T00:00:00.0+1000
pubs.publication-status	Published	en_US
pubs.volume	502	en_US

Abstract:

Property investment in the real estate industry entails high cost and high risk, but provides high yield for return on investment. Risk factors in the real estate industry are mostly uncertain and change dynamically with the surrounding developments. There are many existing risk analysis tools or techniques that help investors to find better solutions. Most techniques available refer to expert's opinions in ranking and weighting the risk factors. As a result, they create misinterpretation and varying judgments from the experts. In addition, investment purposes differ between investors for both commercial and residential properties. There is therefore a need for personalisation elements to enable investors to interact with the analysis. This chapter presents a personalised risk analysis model that enables investors to analyse the risk of their property investments and make correct decisions. The model has three main components: investor, decision support technologies, and the data. Real world data from the Australian real estate industry is used to validate the proposed model. © 2014 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/37966