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CIB Conferences

Abstract

Property valuation is a critical component of real estate management, influencing decisions on investments, sales, and financing. Traditional methods, such as Linear Regression and Multiple Regression Analysis, often struggle to address the complexities of modern property markets. In response, Artificial Intelligence (AI) offers innovative solutions by utilising advanced models for more accurate predictions of property value. This paper presents a Systematic Literature Review (SLR) using Scopus database, focusing on 37 selected papers. It evaluates the effectiveness of key AI models in property valuation, assessing their predictive power, interpretability, robustness, and flexibility, and examines how well these models generate reliable valuations and their accuracy. The key models were referred to as the “Valuation Four”: Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Regression Models (RM). The review highlights each model’s ability to handle complex real estate data, with SVM and RF demonstrating superior accuracy. At the same time, DT excels in interpretability, making it more user-friendly for decision-making. Regression-based models continue to serve as useful benchmarks but are less effective for intricate datasets. The findings of this study provide valuable insights for real estate professionals and policymakers by identifying the AI models that support precise and reliable property valuation on basis of dataset and factors considerations. This contributes to the ongoing evolution of automated valuation methods and helps advance data-driven decision-making in real estate.

The paper will be presented:

Online

Primary U.N. Sustainable Development Goals (SDG)

Sustainable Cities and Communities - - Make cities and human settlements inclusive, safe, resilient and sustainable

Primary CIB Task Group OR Working commission

W116 – Smart and Sustainable Built Environments

Secondary CIB Task Group OR Working commission

W069 – Residential Studies

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