Predictive Analytics for Housing Market Trends and Valuation
DOI:
https://doi.org/10.62486/agma2025115Keywords:
Data, Computational methods, House PredictionAbstract
Introduction: The demand for housing in major cities is exceptionally high due to the concentration of offices and economic hubs in these areas. The combination of limited available land and increased demand drives house prices upward.
Objective: To accommodate this, developers are increasingly constructing residential areas on the outskirts of cities, offering easier access to transportation such as trains and highways. These developers compete by offering competitive pricing, diverse housing options, simplified mortgage processes, and attractive promotions like zero down payments. Buying a house is a significant long-term investment, as property values typically appreciate over time. Therefore, a thorough analysis is crucial when purchasing a home. Several key factors, such as location, land size, building area, and property type, play a role in determining house prices.
Method: This study adopts a quantitative approach, which involves systematically investigating phenomena by collecting measurable data and analyzing it through statistical, mathematical, or computational methods.
Result: This paper discusses the most effective techniques for data collection, pre-processing, feature extraction, model training, and evaluation. The purpose of this research method is to develop theoretical frameworks related to real-world phenomena.
Conclusions: Measurement plays a pivotal role in this quantitative study, as it is central to understanding the data and drawing meaningful conclusions. Finally, we evaluate the current state of research, identifying trends and gaps in the field
References
Cox N. DISTPLOT: Stata module to generate distribution function plot. 2017.
Liu J. Dataset Analysis and House Price Prediction. Highlights Sci Eng Technol. 2024;81:363–7. DOI: https://doi.org/10.54097/1qq43384
Lahmiri S, Bekiros S, Avdoulas C. A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization. Decision Analytics Journal 2023;6:100166. https://doi.org/10.1016/j.dajour.2023.100166. DOI: https://doi.org/10.1016/j.dajour.2023.100166
Lorenz F, Willwersch J, Cajias M, Fuerst F. Interpretable machine learning for real estate market analysis. Real Estate Economics 2023;51:1178–208. https://doi.org/10.1111/1540-6229.12397. DOI: https://doi.org/10.1111/1540-6229.12397
Kuchler T, Piazzesi M, Stroebel J. Housing market expectations☆. In: Bachmann R, Topa G, van der Klaauw W, editors. Handbook of Economic Expectations, Academic Press; 2023, p. 163–91. https://doi.org/10.1016/B978-0-12-822927-9.00013-6.
Lee J, Lee KO. Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19. J Big Data 2023;10:99. https://doi.org/10.1186/s40537-023-00786-5. DOI: https://doi.org/10.1186/s40537-023-00786-5
Gourevitch JD, Kousky C, Liao Y (Penny), Nolte C, Pollack AB, Porter JR, et al. Unpriced climate risk and the potential consequences of overvaluation in US housing markets. Nat Clim Chang 2023;13:250–7. https://doi.org/10.1038/s41558-023-01594-8. DOI: https://doi.org/10.1038/s41558-023-01594-8
Kuchler T, Piazzesi M, Stroebel J. Housing market expectations☆. In: Bachmann R, Topa G, van der Klaauw W, editors. Handbook of Economic Expectations, Academic Press; 2023, p. 163–91. https://doi.org/10.1016/B978-0-12-822927-9.00013-6. DOI: https://doi.org/10.1016/B978-0-12-822927-9.00013-6
Dou M, Gu Y, Fan H. Incorporating neighborhoods with explainable artificial intelligence for modeling fine-scale housing prices. Applied Geography 2023;158:103032. https://doi.org/10.1016/j.apgeog.2023.103032. DOI: https://doi.org/10.1016/j.apgeog.2023.103032
Shi S, Phillips PCB. Diagnosing housing fever with an econometric thermometer. Journal of Economic Surveys 2023;37:159–86. https://doi.org/10.1111/joes.12430. DOI: https://doi.org/10.1111/joes.12430
Graham J, Makridis CA. House Prices and Consumption: A New Instrumental Variables Approach. American Economic Journal: Macroeconomics 2023;15:411–43. https://doi.org/10.1257/mac.20200246. DOI: https://doi.org/10.1257/mac.20200246
Javaid HA. Improving Fraud Detection and Risk Assessment in Financial Service using Predictive Analytics and Data Mining. Integrated Journal of Science and Technology 2024;1.
Tannady H, Pahlawi N, Hernawan MA, Arta DNC, Yusuf SD. Role of Stock Performance as an Intervening Variable in a Relationship Between Profitability, Leverage, Growth and Company Value. JEMSI (Jurnal Ekonomi, Manajemen, Dan Akuntansi) 2023;9:220–5. https://doi.org/10.35870/jemsi.v9i2.1016. DOI: https://doi.org/10.35870/jemsi.v9i2.1016
Yang L, Liang Y, He B, Yang H, Lin D. COVID-19 moderates the association between to-metro and by-metro accessibility and house prices. Transportation Research Part D: Transport and Environment 2023;114:103571. https://doi.org/10.1016/j.trd.2022.103571. DOI: https://doi.org/10.1016/j.trd.2022.103571
Devine A, Yönder E. Impact of Environmental Investments on Corporate Financial Performance: Decomposing Valuation and Cash Flow Effects. J Real Estate Finan Econ 2023;66:778–805. https://doi.org/10.1007/s11146-021-09872-y. DOI: https://doi.org/10.1007/s11146-021-09872-y
Klimberg R. Fundamentals of Predictive Analytics with JMP, Third Edition. SAS Institute; 2023.
Alam I, Ali Y. Studying the effects of Türkiye earthquake disaster and its impact on real estate industry: A risk analysis based on input-output & non-linear optimization models. International Journal of Disaster Risk Reduction 2023;96:103920. https://doi.org/10.1016/j.ijdrr.2023.103920. DOI: https://doi.org/10.1016/j.ijdrr.2023.103920
Al-Okaily A, Teoh AP, Al-Okaily M. Evaluation of data analytics-oriented business intelligence technology effectiveness: an enterprise-level analysis. Business Process Management Journal 2023;29:777–800. https://doi.org/10.1108/BPMJ-10-2022-0546. DOI: https://doi.org/10.1108/BPMJ-10-2022-0546
Xu X, Zhang Y. Contemporaneous causality among residential housing prices of ten major Chinese cities. International Journal of Housing Markets and Analysis 2022;16:792–811. https://doi.org/10.1108/IJHMA-03-2022-0039. DOI: https://doi.org/10.1108/IJHMA-03-2022-0039
Xu X, Zhang Y. Network analysis of comovements among newly-built residential house price indices of seventy Chinese cities. International Journal of Housing Markets and Analysis 2024;17:726–49. https://doi.org/10.1108/IJHMA-09-2022-0134. DOI: https://doi.org/10.1108/IJHMA-09-2022-0134
Deng QS, Alvarado R, Cheng FN, Cuesta L, Wang CB, Pinzón S. Long-run mechanism for house price regulation in China: Real estate tax, monetary policy or macro-prudential policy? Economic Analysis and Policy 2023;77:174–86. https://doi.org/10.1016/j.eap.2022.11.009. DOI: https://doi.org/10.1016/j.eap.2022.11.009
Cox N. STRIPPLOT: stata module for strip plots (one-way dot plots). 2023;
house-price-prediction [Internet]. Available from: https://www.kaggle.com/datasets/srikanthladda/house-price-prediction
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Md. Awais Azam, Sakshi Rai, Md. Shams Raza (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
