A BIM and machine learning integration framework for automated property valuation

Su, Tengxiang 2022. A BIM and machine learning integration framework for automated property valuation. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Property valuation contributes significantly to market economic activities, while it has been continuously questioned on its low transparency, inaccuracy and inefficiency. With Big Data applications in real estate domain growing fast, computer-aided valuation systems such as AI-enhanced automated valuation models (AVMs) have the potential to address these issues. On the one hand, while the advantages of Machine Learning for property valuation have been recognized by researchers and professionals, the predictive accuracy and model interpretability of current AVMs still need to be improved. On the other hand, the benefits and opportunities of BIM for property valuation have gradually captured the attention, but little effort has been made on standard data interpretation and information exchange in property valuation process. This thesis presents a novel system that leverages a holistic data interpretation, facilitates information exchange between AEC projects and property valuation, and an improved AVM for property valuation. A BIM and Machine Learning (ML) integration framework for automated property valuation was proposed which contains an IFC extension for property valuation, an IFC-based information extraction and an automated valuation model based on genetic algorithm optimized machine learning (GA-GBR). This research contributes to managing information exchange between AEC projects and property valuation and enhancing automated valuation models. The main findings indicated the proposed BIM-ML system: (1) in terms of

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Engineering
Uncontrolled Keywords:	Property valuation, Machine learning, Building, information modelling (BIM), Industry foundation class (IFC),Information exchange, Genetic algorithm
Related URLs:	https://orca.cardiff.ac.uk/id/eprint/141...
Date of First Compliant Deposit:	29 September 2022
Last Modified:	29 Sep 2022 10:50
URI:	https://orca.cardiff.ac.uk/id/eprint/152958

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