Application of Machine Learning to Identify Factors Affecting Apartment Prices in Cau Giay Ward, Ha Noi City, During the 2023 - 2025 Period
DOI:
https://doi.org/10.29227/IM-2026-01-01-011Keywords:
machine learning models, apartment prices, land management, Cau Giay WardAbstract
Apartment prices are influenced by various factors, particularly within the context of highly volatile and non-transparent real estate markets. This study proposes a novel approach to identify and quantify the factors affecting apartment prices, aimed at enhancing transparency and reliability in valuation practices. The research employs several machine learning models, including Extra Trees Regressor (ETR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost), applied to a dataset of 246 apartment transaction samples in Cau Giay Ward, Hanoi City, during the 2023-2025 period, incorporating 12 influential variables. The results indicate that the ETR model outperforms other models across four out of five performance evaluation metrics. Subsequently, the study assesses the significance of these factors as quantified by the SHAP (SHapley Additive exPlanations) technique applied to the ETR model. The primary factors identified as having the greatest impact on apartment prices include: location, transaction year, developer reputation, financial factors, number of rooms, amenities, floor area, corner unit status, floor level, and market conditions. Notably, the introduction and utilization of spatial coordinate factors clarify a strong price differentiation trend along the East-West axis in the area. This research provides a new quantitative explanatory framework, contributing to increased objectivity and reliability while effectively supporting apartment management and valuation.
References
[1] Construction, M.o., Notice No. 523/TC - BXD dated October 30, 2025, on the disclosure of information on housing and the real estate market in the third quarter of 2025. 2025.
[2] Bourassa, S.C., E. Cantoni, and M. Hoesli, Spatial dependence, housing submarkets, and house price prediction. The Journal of Real Estate Finance and Economics, 2007. 35(2): p. 143 - 160.
[3] Malpezzi, S., Hedonic pricing models: a selective and applied review. Housing economics and public policy, 2003. 1: p. 67 - 89.
[4] Mullainathan, S. and J. Spiess, Machine learning: an applied econometric approach. Journal of Economic Perspectives, 2017. 31(2): p. 87 - 1 06.
[5] Varian, H.R., Big data: New tricks for econometrics. Journal of economic perspectives, 2014. 28(2): p. 3 - 28.
[6] Yoshida, T., D. Murakami, and H. Seya, Spatial Prediction of Apartment Rent using Regression - Based and Machine Learning - Based Approaches with a Large Dataset: Spatial Prediction of Apartment Rent using Regression - Based and Machine Learning - Based Approaches with a Large Dataset. The Journal of Real Estate Finance and Economics, 2024. 69(1): p. 1 - 28.
[7] Kim, W. - s., I. Choi, and M. Lee, Explainable AI - based mass appraisal: Insights from machine learning applications in Korea’s residential property market. International Journal of Strategic Property Management, 2025. 29(5): p. 350 - 376.
[8] Peng, T. - C. and C. - C. Wang, The application of machine learning approaches on real - time apartment pr ices in the Tokyo metropolitan area. Social Science Japan Journal, 2022. 25(1): p. 3 - 28.
[9] Nikodym, J., Application of machine learning methods for estimating apartment prices in the Czech Republic. 2019.
[10] Thalji, N. and M. Alwadi, Predicting Apartment Prices in Jordan Using Ensemble Machine Learning Algorithms to Support Investment and Urban Planning Decisions. International Journal of Advances in Soft Computing and its Applications, 2026. 18(1): p. 321 - 338.
[11] Chen, Y., J. Jiao, and A. Farahi, Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single - family housing price. Cities, 2023. 140: p. 104432.
[12] Noorcahya, D., et al., Prediction of Apartment Price Considering Socio Economic and Crime Rates Factors in DKI Jakarta. International Journal of Advances in Data and Information Systems, 2023. 4(2): p. 145 - 154.
[13] Hoxha, V., Comparative analysis of machine learning models in predicting housing prices: a case study of Prishtina's real estate market. International Journal of Housing Markets and Analysis, 2025. 18(3): p. 694 - 711.
[14] Gadziński, J. and A. Radzimski, The first rapid tram line in Poland: How has it affected travel behaviours, housing choices and satisfaction, and apartment prices? Journal of Transport Geography, 2016. 54: p. 451 - 463.
[15] Hiller, N. and O.W. Lerbs, Aging and urban house prices. Regional Science and Urban Economics, 2016. 60: p. 276 - 291.
[16] Chu, T., Machine learning - based predictive modelling of house prices. 2025.
[17] Ngoc, N.M., N.H. Tien, and V.M. Hieu, Factors affecting the selling price of luxury apartments in Vietnam. A quantitative analysis. International journal of business and globalisation, 2023. 10(Y): p. 1 - 19.
[18] To MInh Thu, et al., Analysis of factors affecting apartment selling prices in Bắc Ninh Province. Journal of Banking Science and Training, 2022. 238: p. 62 - 73.
[19] Liem, L.D., Analysis of factors affecting apartment prices in Ho Chi Minh City. Master’s thesis. University of Economics Ho Chi Minh City, 2019.
[20] Cuong, N.H., Factors affecting the selling prices of high - end apartments: A study in Hanoi. Doctoral dissertation. National Economics University, 2020.
[21] Khanh, D.V., Study on factors affecting the selling prices of Grade A apartments in Nam Tu Liem District, Hanoi. Journal of Natural Resources and Environment Science, 2023. 47: p. 51 - 62.
[22] Tu, B.N., T.Q. Binh, and B.T.C. Ngoc. Analyzing Mass Appraisal of Urban Residential Land with Machine Learning - A Case Study in Hanoi, Vietnam. in International Conference on Geoinformatics for Spatial - Infrastructure Development in Earth & Allied Sciences. 2023. Springer.
[23] Bilgilioğlu , S.S. and H.M. Yılmaz, Comparison of different machine learning models for mass appraisal of real estate. Survey review, 2023. 55(388): p. 32 - 43.
[24] Jafary, P., et al., Automated land valuation models: A comparative study of four machine learning and deep learning methods based on a comprehensive range of influential factors. Cities, 2024. 151: p. 105115.
[25] Breiman, L., Random forests. Machine learning, 2001. 45(1): p. 3 - 32.
[26] Geurts, P., D. Ernst, and L. Wehenkel , Extremely randomized trees. Machine learning, 2006. 63(1): p. 3 - 42.
[27] Iban, M.C., An explainable model for the mass appraisal of residences: The application of tree - based Machine Learning algorithms and interpretation of value determinants. Habitat International, 2022. 128: p. 102660.
[28] Lundberg, S.M. and S. - I. Lee, A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017. 30.
[29] Bui, T., A study of factors influencing the price of apartments: Evidence from Vietnam. Management Science Letters, 2020. 10(10): p. 2287 - 2292.
[30] Bui Ngoc Tu, Man Quang Huy, and L.P. Thuy, Comparison of machine learning models in mass land valuation: A case study in Cầu Giấy District, Hanoi. Journal of Soil Science, 2025. 80.
[31] Hoang Thi Huong, Vu Thi Thuong, and B.L. Anh, Factors affecting apartment prices in Bac Tu Liem District, Hanoi. Journal of Economics and Forecasting. Retrieved from https://kinhtevadubao.vn/yeu - to - anh - huong - den - gia - can - ho - chung - cu - tren - dia - ban - quan - bac - tu - liem - tp - ha - noi - 30212.html on December 24, 2025., 2024.
