Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors

Jafary, Peyman; Shojaei, Davood; Rajabifard, Abbas; Ngo, Tuan

doi:10.1016/j.cities.2024.105115

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2024

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Cited by 5 publications

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Analyzing Mass Appraisal of Urban Residential Land with Machine Learning - A Case Study in Hanoi, Vietnam

Tu,

Binh,

Ngoc

2024

Lecture Notes in Civil Engineering

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Analyzing Mass Appraisal of Urban Residential Land with Machine Learning - A Case Study in Hanoi, Vietnam

Tu,

Binh,

Ngoc

2024

Lecture Notes in Civil Engineering

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AI-driven valuation: a new era for real estate appraisal

Topraklı

2024

JERER

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PurposeThis paper explores the emergence of artificial intelligence (AI) in real estate valuation, analysing its potential to enhance accuracy, efficiency and transparency in the appraisal process and examines the implications of this technological shift for various stakeholders.Design/methodology/approachThis research adopts a comprehensive literature review approach, drawing upon existing research in real estate, computer science and related fields. A systematic analysis of scholarly publications and industry trends was conducted to examine the underlying technologies, diverse applications, potential benefits, inherent limitations and future trends associated with AI-driven valuation models, including machine learning and deep learning methods.FindingsThe study reveals that AI-powered valuation models offer significant advantages over traditional appraisal methods, including enhanced accuracy, increased efficiency, reduced costs and improved risk management. However, critical challenges related to data bias, algorithmic transparency (the “black box” problem), and the need for human oversight must be addressed to ensure responsible and effective AI implementation.Originality/valueThis paper provides insights into the transformative potential of AI in real estate valuation for a wide range of stakeholders. Real estate professionals can gain a better understanding of how AI can enhance their decision-making processes, improve efficiency and mitigate risk. Appraisers will benefit from the paper’s analysis of evolving skillsets required in the AI era, while lenders and investors will gain a clearer understanding of how AI-driven valuation can strengthen risk assessment and portfolio management. Policymakers can utilize this research to inform the development of ethical guidelines and regulations for AI adoption in real estate appraisal.

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Mapping of Utility-Scale Solar Panel Areas From 2000 to 2022 in China Using Google Earth Engine

Lyu,

Li,

Wei

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors

Cited by 5 publications

References 59 publications

Analyzing Mass Appraisal of Urban Residential Land with Machine Learning - A Case Study in Hanoi, Vietnam

Analyzing Mass Appraisal of Urban Residential Land with Machine Learning - A Case Study in Hanoi, Vietnam

AI-driven valuation: a new era for real estate appraisal

Mapping of Utility-Scale Solar Panel Areas From 2000 to 2022 in China Using Google Earth Engine

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