IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v10y2025i9p135-d1730825.html

Predicting Real Estate Prices Using Machine Learning in Bosnia and Herzegovina

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zvezdan Stojanović & Dario Galić & Hava Kahrić, 2025. "Predicting Real Estate Prices Using Machine Learning in Bosnia and Herzegovina," Data, MDPI, vol. 10(9), pages 1-15, August.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:9:p:135-:d:1730825
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/10/9/135/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/10/9/135/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James Hansen, 2009. "Australian House Prices: A Comparison of Hedonic and Repeat‐Sales Measures," The Economic Record, The Economic Society of Australia, vol. 85(269), pages 132-145, June.
    2. Elli Pagourtzi & Vassilis Assimakopoulos & Thomas Hatzichristos & Nick French, 2003. "Real estate appraisal: a review of valuation methods," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 21(4), pages 383-401, August.
    3. Soo-Jin Kim & Seung-Jong Bae & Min-Won Jang, 2022. "Linear Regression Machine Learning Algorithms for Estimating Reference Evapotranspiration Using Limited Climate Data," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lozano, Francisco Javier, 2025. "Índice de precios de viviendas nuevas en Santiago basado en la metodología de ventas repetidas [New housing price index in Santiago based on the repeat-sales methodology]," MPRA Paper 127115, University Library of Munich, Germany.
    2. Inga Dailidienė & Inesa Servaitė & Remigijus Dailidė & Erika Vasiliauskienė & Lolita Rapolienė & Ramūnas Povilanskas & Donatas Valiukas, 2023. "Increasing Trends of Heat Waves and Tropical Nights in Coastal Regions (The Case Study of Lithuania Seaside Cities)," Sustainability, MDPI, vol. 15(19), pages 1-21, September.
    3. Bocart, Fabian & Hafner, Christian, 2012. "Volatility of price indices for heterogeneous goods," LIDAM Discussion Papers ISBA 2012019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Walacik Marek, 2025. "Methodology for Homogenous Market Area Determination HAD2 - Mission Accomplished," Real Estate Management and Valuation, Sciendo, vol. 33(1), pages 113-124.
    5. Edward Oczkowski, 2016. "Analysing Firm-level Price Effects for Differentiated Products: The Case of Australian Wine Producers," Australian Economic Papers, Wiley Blackwell, vol. 55(1), pages 43-62, March.
    6. Juergen Deppner & Benedict Ahlefeldt-Dehn & Eli Beracha & Wolfgang Schaefers, 2025. "Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 71(2), pages 314-351, August.
    7. Christian Gillitzer & Jin Cong Wang, 2015. "Housing Wealth Effects: Cross-sectional Evidence from New Vehicle Registrations," RBA Research Discussion Papers rdp2015-08, Reserve Bank of Australia.
    8. Marcel-Cristian Voia & Thi Hong Thinh Doan, 2019. "What We Should Know About House Reconstruction Costs?," The Journal of Real Estate Finance and Economics, Springer, vol. 58(3), pages 489-516, April.
    9. Unel, Fatma Bunyan & Yalpir, Sukran, 2023. "Sustainable tax system design for use of mass real estate appraisal in land management," Land Use Policy, Elsevier, vol. 131(C).
    10. Matthew Read & Chris Stewart & Gianni La Cava, 2014. "Mortgage-related Financial Difficulties: Evidence from Australian Micro-level Data," RBA Research Discussion Papers rdp2014-13, Reserve Bank of Australia.
    11. Moritz Stang & Bastian Krämer & Cathrine Nagl & Wolfgang Schäfers, 2023. "From human business to machine learning—methods for automating real estate appraisals and their practical implications [Vom Vergleichswertverfahren zum maschinellen Lernen – Methoden zur automatisi," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(2), pages 81-108, October.
    12. Guo, Xiaoyang & Zheng, Siqi & Geltner, David & Liu, Hongyu, 2014. "A new approach for constructing home price indices: The pseudo repeat sales model and its application in China," Journal of Housing Economics, Elsevier, vol. 25(C), pages 20-38.
    13. Callan Windsor & Gianni La Cava & James Hansen, 2014. "Home Price Beliefs in Australia," RBA Research Discussion Papers rdp2014-04, Reserve Bank of Australia.
    14. Süreyya Özöğür Akyüz & Birsen Eygi Erdogan & Özlem Yıldız & Pınar Karadayı Ataş, 2023. "A Novel Hybrid House Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1215-1232, October.
    15. repec:hum:wpaper:sfb649dp2012-039 is not listed on IDEAS
    16. Robert Hill & Radoslaw Trojanek, 2020. "House Price Indexes for Warsaw: An Evaluation of Competing Methods," Graz Economics Papers 2020-08, University of Graz, Department of Economics.
    17. Paul Frijters & Benno Torgler & Christian Gillitzer & Jin Cong Wang, 2016. "Housing Wealth Effects: Cross-sectional Evidence from New Vehicle Registrations," The Economic Record, The Economic Society of Australia, vol. 92, pages 30-51, June.
    18. Chmielewska, Aneta & Walacik, Marek & Grover, Richard, 2025. "Property valuation principles – How policy changes can be detrimental to urban development," Land Use Policy, Elsevier, vol. 150(C).
    19. Kirill Solovev & Nicolas Prollochs, 2021. "Integrating Floor Plans into Hedonic Models for Rent Price Appraisal," Papers 2102.08162, arXiv.org.
    20. Alicia N. Rambaldi & Cameron S. Fletcher & Kerry Collins & Ryan R.J. McAllister, 2013. "Housing Shadow Prices in an Inundation-prone Suburb," Urban Studies, Urban Studies Journal Limited, vol. 50(9), pages 1889-1905, July.
    21. Costello, Greg & Fraser, Patricia & Groenewold, Nicolaas, 2011. "House prices, non-fundamental components and interstate spillovers: The Australian experience," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 653-669, March.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:10:y:2025:i:9:p:135-:d:1730825. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.