Abstract
This paper aims to test various machine learning algorithms on a real-world dataset of office real estate and identify the most accurate one. A comprehensive analysis was conducted using proprietary data on office real estate in Poland, obtained from a leading market intelligence provider specializing in commercial property analytics, covering a 20-year observation period. The research results indicate that among six tested algorithms including Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Gaussian Naive Bayes (GNB), Support Vector Machines (SVM), the Decision Tree Classifier (DT) appears to be the best-fit algorithm for selecting factors to estimate office real estate prices.
Paper Type
Poster
DOI
10.62036/ISD.2025.73
Performance comparison of machine learning algorithms for accurate office real estate price estimation: suggested approaches
This paper aims to test various machine learning algorithms on a real-world dataset of office real estate and identify the most accurate one. A comprehensive analysis was conducted using proprietary data on office real estate in Poland, obtained from a leading market intelligence provider specializing in commercial property analytics, covering a 20-year observation period. The research results indicate that among six tested algorithms including Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Gaussian Naive Bayes (GNB), Support Vector Machines (SVM), the Decision Tree Classifier (DT) appears to be the best-fit algorithm for selecting factors to estimate office real estate prices.
Recommended Citation
Maślankowski, J. & Rymarzak, M. (2025). Performance comparison of machine learning algorithms for accurate office real estate price estimation: suggested approachesIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.73