Personalised Property Investment Risk Analysis Model in the Real Estate Industry

Demong, NA; Lu, J; Hussain, FK

Personalised Property Investment Risk Analysis Model in the Real Estate Industry

Demong, NA Lu, J Hussain, FK

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Human-Centric Decision-Making Models for Social Sciences, 2014, NA, pp. 369 - 390
Issue Date:: 2014-01

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Field	Value	Language
dc.contributor.author	Demong, NA
dc.contributor.author	Lu, J
dc.contributor.author	Hussain, FK
dc.contributor.editor	Guo, P
dc.contributor.editor	Pedrycz, W
dc.date.accessioned	2015-04-01T02:42:40Z
dc.date.issued	2014-01
dc.identifier.citation	Human-Centric Decision-Making Models for Social Sciences, 2014, NA, pp. 369 - 390
dc.identifier.isbn	978-3-642-39306-8
dc.identifier.uri	http://hdl.handle.net/10453/34623
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 experts 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.
dc.publisher	Springer
dc.relation.ispartof	Human-Centric Decision-Making Models for Social Sciences
dc.subject	Risk analysis Personalisation Decision under uncertainty Real estate industry Property investment Heuristic
dc.title	Personalised Property Investment Risk Analysis Model in the Real Estate Industry
dc.type	Chapter
utslib.location	Berlin Heidelberg
utslib.for	080605 Decision Support and Group Support Systems
utslib.for	0806 Information Systems
utslib.citation.edition	NA
pubs.embargo.period	Not known
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 Software
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 - Quantum Computation and Intelligent Systems
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000
pubs.consider-herdc	false
pubs.deleted	1970-01-01T00:00:00.0+1000

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 experts 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/34623