International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 12 (December 2025), Pages: 237-243

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 Original Research Paper

Application of machine learning algorithms in real estate valuation

 Author(s): 

 Tsolmon Sodnomdavaa 1, Erdenetsogt Gurbazar 1, *, Tegshjargal Sodnomdavaa 2

 Affiliation(s):

  1Department of Economics, Mandakh University, Ulaanbaatar, Mongolia
  2Management School, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0009-0009-6713-3215

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.12.021

 Abstract

Accurately valuing residential properties is difficult in transitional economies because market data are fragmented, many transactions occur informally, and price trends are unstable. These conditions reduce the reliability of traditional appraisal methods. This study systematically compares nine machine learning models, covering regression, kernel-based, ensemble, and deep learning approaches, using a dataset of 9,326 housing listings from Ulaanbaatar, Mongolia. The methodology includes extensive hyperparameter tuning, five-fold cross-validation, and district-level validation to ensure robust findings. Model performance was assessed on a separate test set using R², Mean Squared Error (MSE), and Mean Absolute Error (MAE). The deep neural network (DNN) achieved the highest accuracy (R² = 0.918; MSE = 0.051), outperforming both XGBoost and Random Forest, while Support Vector Regression (SVR) showed the weakest results. The most influential price factors were district, total area, floor level, garage availability, and number of windows. Some interior characteristics, such as parquet or tile flooring, were linked to lower prices, suggesting a buyer preference for more modern designs. The study also presents a Docker-based web application for real-time price prediction, demonstrating the practical value of these models in settings with limited data. By examining Mongolia’s secondary housing market, this research offers new evidence on the potential of machine learning to improve transparency and support decision-making in real estate valuation.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Housing valuation, Machine learning, Deep neural network, Price prediction, Transitional economies

 Article history

 Received 24 July 2025, Received in revised form 23 November 2025, Accepted 1 December 2025

 Acknowledgment

The authors would like to express their sincere gratitude to Mandakh University for providing financial and institutional support for this research. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Sodnomdavaa T, Gurbazar E, and Sodnomdavaa T (2025). Application of machine learning algorithms in real estate valuation. International Journal of Advanced and Applied Sciences, 12(12): 237-243

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 Figures

  Fig. 1  Fig. 2  Fig. 3

 Tables

  Table 1  Table 2 

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