{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:49:01Z","timestamp":1777528141427,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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 R2 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.<\/jats:p>","DOI":"10.3390\/data10090135","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:09:32Z","timestamp":1756080572000},"page":"135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Real Estate Prices Using Machine Learning in Bosnia and Herzegovina"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1740-3637","authenticated-orcid":false,"given":"Zvezdan","family":"Stojanovi\u0107","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, European University Br\u010dko District, 76100 Br\u010dko, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dario","family":"Gali\u0107","sequence":"additional","affiliation":[{"name":"Department of Interdisciplinary Areas, Faculty of Dental Medicine and Health, 31000 Osijek, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hava","family":"Kahri\u0107","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Kallos University, 75000 Tuzla, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1080\/00420980220135545","article-title":"Estimating neighbourhood effects in house prices: Towards a new hedonic model approach","volume":"39","author":"Tse","year":"2002","journal-title":"Urban Stud."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1111\/j.1475-4932.2009.00544.x","article-title":"Australian house prices: A comparison of hedonic and repeat-sales measures","volume":"85","author":"Hansen","year":"2009","journal-title":"Econ. 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