An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach
| Autor: | Nur Atiqah Rochin Demong, Farookh Khadeer Hussain, Jie Lu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Risk analysis
Property (programming) Financial risk 02 engineering and technology Data-driven Personalization Investment decisions Risk analysis (engineering) 020204 information systems 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) 020201 artificial intelligence & image processing Business Risk assessment Analysis method |
| Zdroj: | International Journal of Information Technology & Decision Making. 20:671-706 |
| ISSN: | 1793-6845 0219-6220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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