Personalised Property Investment Risk Analysis Model in the Real Estate Industry

Demong, Nur Atiqah Rochin; Lu, Jie; Hussain, Farookh Khadeer

doi:10.1007/978-3-642-39307-5_15

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The current relevant research mainly draws on mathematics, operations research, economics, and computer science to analyze and evaluate risks, such as arbitrage pricing theory, mathematical statistics, the Monte Carlo method [1], fuzzy mathematics [2], grey system theory [3] and other methods. Demong N Lu J, Hussain F [4] proposed a new uncertainty measure for risk factors based on multidimensional data models and data mining techniques. Das I, Bhattacharya K, Canizares C [5] established a novel relationship between the sensitivity index and investors' profit to assess project investment risk.…”

Section: Introductionmentioning

confidence: 99%

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Lan,

Wu,

et al. 2023

Atlantis Highlights in Intelligent Systems

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The digital transformation of the economy represents the general trend. In order to effectively control the investment risk of grid digitization projects and adopt risk-coping strategies with foresight, construct an investment risk evaluation model for grid digitization projects by optimizing the kernel function parameters and regularization parameters of least squares support vector machines through the Archimedes algorithm. Questionnaires and expert judgment are used to analyze the risk factors facing digitization projects' investment environment and establish an investment risk evaluation system. A fuzzy hierarchical analysis method is applied to evaluate the investment risk of 40 completed projects according to the actual engineering situation, and the evaluation results are normalized and processed as the input vector of the evaluation model for training. The results show that the Archimedes optimization algorithm improves the least squares support vector machine model prediction with an average absolute percentage error of 3.5026%, which can more accurately assess the riskiness of digital projects and provide a reference basis for digital project investment risk control.

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Section: Introductionmentioning

confidence: 99%

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Lan,

Wu,

et al. 2023

Atlantis Highlights in Intelligent Systems

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“…Having an accurate estimation of market price of residential property is important for different users such as investors who face choices among housing securities and other investment opportunities aim to have a precise price estimate. Some researches investigate the risk of investment in real estate industry that they mention the importance of accurate estimation of real estate value ( [14], [15] and [16]). In this study, we conduct a comprehensive experiment to investigate and analysis existing popular machine learning models for property valuation on real world data samples.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Google Drive Distance and Duration in Residential Property in Sydney, Australia

Nejad¹,

Lu²,

Asgari³

et al. 2016

Uncertainty Modelling in Knowledge Engineering and Decision Making

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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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Personalised Property Investment Risk Analysis Model in the Real Estate Industry

Cited by 2 publications

References 17 publications

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

The Effect of Google Drive Distance and Duration in Residential Property in Sydney, Australia

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