An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach

Nur Atiqah Rochin Demong, Jie Lu () and Farookh Khadeer Hussain ()
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Nur Atiqah Rochin Demong: Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam, 42300 Selangor, Malaysia†Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street Broadway, NSW 2007, Australia
Jie Lu: #x2020;Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street Broadway, NSW 2007, Australia
Farookh Khadeer Hussain: #x2020;Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street Broadway, NSW 2007, Australia

International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 02, 671-706

Abstract: Risk assessment analysis for investment decisions largely depends on expert judgment using traditional approaches and is lacking in considering investors’ different preferences and limitations. This paper proposes an adaptive personalized property investment risk analysis (APPIRA) method to identify the property investment determinants using a data-driven and personalized approach to weight the risk factors using the multicriteria decision model for optimal solutions. Result for predictive modeling using value prediction technique that measures the median house price depicts that the best method used was nonseasonal ARIMA. Furthermore, classification technique indicates that in each of the three selected suburbs, different property characteristics determined the rental properties desirable. As shown in result, for the investors who plan to invest in property for rental purposes, they need to choose townhouse type or property to make it rentable while for Vaucluse, terrace houses. These results can be applied into practice and will benefit the property industry directly.

Keywords: Risk analysis; property investment; data-driven; optimal solution; multicriteria decision approach; personalization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219622021500115

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