Predicting Real Estate Prices Using Machine Learning in Bosnia and Herzegovina

Zvezdan Stojanović (), Dario Galić and Hava Kahrić
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Zvezdan Stojanović: Department of Electrical Engineering, Faculty of Engineering, European University Brčko District, 76100 Brčko, Bosnia and Herzegovina
Dario Galić: Department of Interdisciplinary Areas, Faculty of Dental Medicine and Health, 31000 Osijek, Croatia
Hava Kahrić: Department of Electrical Engineering, Faculty of Engineering, Kallos University, 75000 Tuzla, Bosnia and Herzegovina

Data, 2025, vol. 10, issue 9, 1-15

Abstract: The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly in artificial intelligence and machine learning, have enabled more precise predictions of real estate prices. Machine learning allows computers to recognize patterns in data and create models that can predict prices based on the characteristics of the property, such as location, square footage, number of rooms, age of the building, and similar features. The aim of this paper is to investigate how the application of machine learning can be used to predict real estate prices. A machine learning model was developed using four algorithms: Linear Regression, Random Forest Regression, XGBoost, and K-Nearest Neighbors. The dataset used in this study was collected from major online real estate listing portals in Bosnia and Herzegovina. The performance of each model was evaluated using the R 2 score, Root Mean Squared Error (RMSE), scatter plots, and error distributions. Based on this evaluation, the most accurate model was selected. Additionally, a simple web interface was created to allow for non-experts to easily obtain property price estimates.

Keywords: real estate prices; machine learning algorithms; prediction model (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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