Peyman is a PhD student at the Department of Infrastructure Engineering, University of Melbourne.

His PhD thesis is on the application of Building Information Modelling (BIM) in property valuation. His research aims to develop an integrated property valuation method leveraging BIM potentials in different phases of valuation. It considers the physical, geographical, environmental, socio-economic and legal factors affecting property valuation. 

He received his master’s degree in GIS and bachelor’s degree in Geomatics Engineering, and has contributed to a variety of research and practical projects in the field of geospatial technology, GIS and Remote Sensing.

Peyman has published several articles in high-ranked scientific journals and conferences, and has undertaken work experience at Food and Agriculture Organization (FAO) of the United Nations, as a GIS and Remote Sensing Expert.

PhD Start and End Dates
December 2021 to December 2025

Publications

• October 2024 – Data-driven strategies for affordable housing: a hybrid genetic algorithm-machine learning optimization model in the Melbourne metropolitan area
• August 2024 – Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors
• June 2024 – Automating property valuation at the macro scale of suburban development: A multi-step method based on spatial imputation techniques, machine learning and deep learning
• May 2024 – Analysing factors affecting land prices in urbanised areas using machine learning: A basis for future 3D property valuations
• January 2024 – Deep learning-based spatiotemporal analysis and clustering of house price fluctuations and appreciation levels in the different suburbs of Metropolitan Melbourne (Paper ID 21)
• December 2022 – BIM and real estate valuation: challenges, potentials and lessons for future directions
• October 2022 – A framework to integrate BIM with artificial intelligence and machine learning-based property valuation methods