Keywords:-
Article Content:-
Abstract
House rent estimation remains a challenge due to inconsistent pricing methods, reliance on manual valuation, and the absence of intelligent systems for determining fair rent values. Traditional approaches used by house agents and property owners often result in price variations and inaccurate rent estimations. This study developed a House Rent Prediction System using stack ensemble to estimate annual house rent values based on housing characteristics. The dataset used for the study was obtained from house agents and secondary sources and was preprocessed through data cleaning, encoding, and feature scaling using StandardScaler. The developed system was implemented using Streamlit and included functionalities such as manual prediction, property visualization, house comparison, prediction history, market analytics, and property carousel display. Model evaluation was carried out using MAE, MSE, RMSE, R² Score, and K-Fold Cross Validation. The results showed that the developed system was effective in predicting house rent values and can support intelligent housing valuation and decision-making in community development.
References:-
References
Akinola S. O., Osunade O., Abiola O. A. and Adebisi R. O.(2026). Development of a Multimodal Artificial Intelligence Framework for Forest Fire Prediction University of Ibadan Journal of Science and Logics in ICT Research (UIJSLICTR),Vol. 16 No. 1, pp. 202–212
Choy, L. H. T., & Ho, W. K. O. (2023). The use of machine learning in real estate research. Land, 12(4), 740. https://doi.org/10.3390/land12040740.
Deng, L. (2025). Real estate valuation with multi-source image fusion and enhanced machine learning pipeline. PLOS ONE, 20(5), e0321951. https://doi.org/10.1371/journal.pone.0321951.
Ogundunmade, T. P., Abidoye, M., & Olunfunbi, O. M. (2023). Modelling residential housing rent price using machine learning models. Modern Economics & Management, 2, 14. https://doi.org/10.53964/mem.2023014.
Zong, H., & Song, J. (2023). Research on the prediction model of house rent based on machine learning. . Proceedings of the International Conference on Big Data Blockchain and Economy Management., https://doi.org/10.4108/eai.19-5-2023.2334250.
Trinkaus, O., & Kauermann, G. (2023). Can machine learning algorithms deliver superior models for rental guides?. AStA Wirtschafts-und Sozialstatistisches Archiv, 17, 305–330.
Cao, S., Liao, W., & Huang, J. (2024). Research on renting price prediction based on machine learning. . Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference., https://doi.org/10.4108/eai.8-12-2023.2344718.
Chen, W., Farag, S., Butt, U., & Al-Khateeb, H. . (2024). Leveraging machine learning for sophisticated rental value predictions: A case study from Munich, Germany. Applied Sciences, 14(20), 9528. https://doi.org/10.3390/app14209528.
Hu, G., & Tang, Y. (2023). GERPM: A geographically weighted stacking ensemble learning-based urban residential rents prediction model. Mathematics, 11(14), 3160. https://doi.org/10.3390/math11143160.
Oluyele, S., Akingbade, J., Akinode, V., & Idoghor, R. (2024). Prediction of urban house rental prices in Lagos, Nigeria: A machine learning approach. ABUAD Journal of Engineering Research and Development, 7(2), 216–228.
Khan, M. K., Khan, A. U., Ahmed, S., & Ali, W. (2026). A robust stacked ensemble approach for house price prediction. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 63(2).
Naz, R., Jamil, B., & Ijaz, H. (2024). Machine learning, deep learning, and hybrid approaches in real estate price prediction: A comprehensive systematic literature review. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 61(2), 129–144. https://doi.org/10.53560/PPASA(61-2)863.
Nor, M. I., Audu, B., & Mohamed, A. D. (2025). Investigating the impact of property characteristics, cost of living, and environmental factors on rental prices in Baidoa's climate-affected real estate market: A hybrid approach using hedonic regression and neural networks. Frontiers in Sustainable Cities, 7, Article 1511761. https://doi.org/10.3389/frsc.2025.1511761
Sharma, H., Harsora, H., & Ogunleye, B. (2024). An optimal house price prediction algorithm: XGBoost. . Analytics, 3(1), 30–45. https://doi.org/10.3390/analytics3010003.
Geerts, M., vanden Broucke, S., & De Weerdt, J. (2023). A Survey of Methods and Input Data Types for House Price Prediction. ISPRS International Journal of GeoInformation, 12(5), 200. https://doi.org/10.3390/ijgi12050200.