Data Driven Marketing in Real Estate: Forecasting House Prices and Uncovering Influential Factors
Main Article Content
Intania Prabadianti
Samidi
In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G. (2021). House Price Prediction using Random Forest Machine Learning Technique. Procedia Computer Science, 199, 806–813. https://doi.org/10.1016/j.procs.2022.01.100
Bottini, L., & Costarelli, I. (2023). Housing Satisfaction between Individual and Regional Characteristics: The Italian Case; [La soddisfazione abitativa tra caratteristiche individuali e regionali: il caso italiano]. Scienze Regionali, 22(2), 285 – 306. https://doi.org/10.14650/106228
Chai, T. (2020). Root Mean Square. In Encyclopedia of Earth Sciences Series (Vol. 2020). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-030-26050-7_280-1
Chiwuzie, A., Dabara, D. I., & Omotehinshe, O. J. (2021). Changing macroeconomic indicators and the rental values of residential properties in. Article in YBL Journal of Built Environment. https://doi.org/10.33796/ajober.5.1.01
Dwivedi, R., Gupta, R., & Pal, P. K. (2022). House Price Prediction using regression techniques. In S. V., S. V., S. M., S. M., & S. J. (Eds.), Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 (pp. 165 – 170). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICAC3N56670.2022.10074128
Edalatpanah, S. A., Hassani, F. S., Smarandache, F., Sorourkhah, A., Pamucar, D., & Cui, B. (2024). A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues. Engineering Applications of Artificial Intelligence, 129. https://doi.org/10.1016/j.engappai.2023.107531
Eny, E. P., Nimran, U., Suharyono, & Kusumawati, A. (2020). Novelty Seeking to Predict Tourist Behavior Intention In Rural Destination. Jurnal Bisnis Dan Manajemen, 7(2), 61–73. http://jurnal.unmer.ac.id/index.php/jbm
Forys, I. (2022). Machine learning in house price analysis: regression models versus neural networks. Procedia Computer Science, 207, 435–445. https://doi.org/10.1016/j.procs.2022.09.078
Gandhi, B., Jaitly, N., Vats, K., & Sagar, P. (2023). A Prediction Model for Real Estate Price Using Machine Learning. Lecture Notes in Networks and Systems, 641 LNNS, 37 – 42. https://doi.org/10.1007/978-981-99-0483-9_4
Geerts, M., vanden Broucke, S., & De Weerdt, J. (2023). A Survey of Methods and Input Data Types for House Price Prediction. In ISPRS International Journal of Geo-Information (Vol. 12, Issue 5). MDPI. https://doi.org/10.3390/ijgi12050200
Hamami, F., & Dahlan, I. A. (2024). Regression Modeling for House Price Prediction in Java Island. ICSINTESA 2024 - 2024 4th International Conference of Science and Information Technology in Smart Administration: The Collaboration of Smart Technology and Good Governance for Sustainable Development Goals, 402 – 406. https://doi.org/10.1109/ICSINTESA62455.2024.10747910
Hoxha, V. (2024). 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. https://doi.org/10.1108/IJHMA-09-2023-0120
Jafari, A., & Akhavian, R. (2019). Driving forces for the US residential housing price: a predictive analysis. Built Environment Project and Asset Management, 9(4), 515–529. https://doi.org/10.1108/BEPAM-07-2018-0100
Jáuregui-Velarde, R., Andrade-Arenas, L., Celis, D. H., Dávila-Morán, R. C., & Cabanillas-Carbonell, M. (2023). Web Application with Machine Learning for House Price Prediction. International Journal of Interactive Mobile Technologies, 17(23), 85–104. https://doi.org/10.3991/IJIM.V17I23.38073
Kang, C.-D. (2025). Effects of Access Time via Travel Modes to Urban Service Facilities on Housing Prices: Evidence from Seoul. Journal of Urban Planning and Development, 151(2). https://doi.org/10.1061/JUPDDM.UPENG-5513
Kavitha, S., Varuna, S., & Ramya, R. (2016). Proceedings of 2016 Online International Conference on Green Engineering and Technologies (IC-GET) : 19th November, 2016. Proceedings of 2016 Online International Conference on Green Engineering and Technologies (IC-GET).
Kaytan, M., & Aydilek, I. B. (2017). IDAP’17 : International Artificial Intelligence and Data Processing Symposium : September 16-17. IDAP’17 : International Artificial Intelligence and Data Processing Symposium.
Kotu, V., & Deshpande, B. (2019). Data Science: Concepts and Practice, Second Edition (Second Edition). Morgan Kaufmann Publishers.
Krämer, B., Nagl, C., Stang, M., & Schäfers, W. (2023). Explainable AI in a Real Estate Context – Exploring the Determinants of Residential Real Estate Values. Journal of Housing Research, 32, 1–42. https://doi.org/10.1080/10527001.2023.2170769
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. In Nature (Vol. 521, Issue 7553, pp. 436–444). Nature Publishing Group. https://doi.org/10.1038/nature14539
Lenaers, I., Boudt, K., & De Moor, L. (2024). Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques. International Journal of Housing Markets and Analysis, 17(1), 96–113. https://doi.org/10.1108/IJHMA-11-2022-0172
Mishra, R., Singh, R., & Govindan, K. (2022). Net-zero economy research in the field of supply chain management: a systematic literature review and future research agenda. In … Journal of Logistics Management. emerald.com. https://doi.org/10.1108/IJLM-01-2022-0016
Mukhlishin, M. F., Saputra, R., & Wibowo, A. (2017). Predicting house sale price using fuzzy logic, Artificial Neural Network and K-Nearest Neighbor. Proceedings - 2017 1st International Conference on Informatics and Computational Sciences, ICICoS 2017, 2018-January, 171 – 176. https://doi.org/10.1109/ICICOS.2017.8276357
Njo, A., Sugiarto, H. D., & Prayogo, D. (2025). Artificial intelligence algorithms to predict housing market prices in Surabaya, Indonesia. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-01-2025-0022
Rahmatillah, I., & Sudirman, I. D. (2023). A Deep Learning Approach to Predict Housing Market Price Trends in Bandung. 2023 1st IEEE International Conference on Smart Technology: Advances in Smart Technology for Sustainable Well-Being, ICE-SMARTec 2023, 140 – 145. https://doi.org/10.1109/ICE-SMARTECH59237.2023.10461965
Rampini, L., & Re Cecconi, F. (2021). Artificial intelligence algorithms to predict Italian real estate market prices. Journal of Property Investment and Finance. https://doi.org/10.1108/JPIF-08-2021-0073
Sabrina Abdul Latif, N., Muhammad Rizwan Majeed, K., Rozzani, N., & Khadijah Saleh, S. (2020). Factors Affecting Housing Prices in Malaysia: A Literature Review. International Journal of Asian Social Science, 10(1), 63–67. https://doi.org/10.18488/journal.1.2020.101.63.67
Sanyal, S., Kumar Biswas, S., Das, D., Chakraborty, M., & Purkayastha, B. (2022). Boston House Price Prediction Using Regression Models. 2022 2nd International Conference on Intelligent Technologies, CONIT 2022. https://doi.org/10.1109/CONIT55038.2022.9848309
Saraswathi, P., Prabha, M., Jhoshitha, N. A., & Prathiksaa, J. G. (2024). House price prediction with advanced machine learning techniques. In M. N.K. & R. S. (Eds.), AIP Conference Proceedings (Vol. 3214, Issue 1). American Institute of Physics. https://doi.org/10.1063/5.0239036
Statista. (2023). Real estate in Indonesia.
Statista. (2024). Residential real estate in Indonesia.
Szumilo, N., Laszkiewicz, E., & Fuerst, F. (2017). The spatial impact of employment centres on housing markets. Spatial Economic Analysis, 12(4), 472 – 491. https://doi.org/10.1080/17421772.2017.1339119
Teoh, E. Z., Yau, W. C., Ong, T. S., & Connie, T. (2023). Explainable housing price prediction with determinant analysis. International Journal of Housing Markets and Analysis, 16(5), 1021–1045. https://doi.org/10.1108/IJHMA-02-2022-0025
Trang, L. H., Huy, T. D., & Le, A. N. (2021). Clustering helps to improve price prediction in online booking systems. International Journal of Web Information Systems, 17(1), 45–53. https://doi.org/10.1108/IJWIS-11-2020-0065
van Ruijven, K., & Tijm, J. (2024). Do People Value Environmental Goods? Evidence From the Dutch Housing Market. Journal of Real Estate Literature, 32(2), 57–102. https://doi.org/10.1080/09277544.2024.2322781
Verma, A., Gera, P., Singhal, S., & Mohapatra, A. K. (2023). Advanced Regression Models for Accurate House Price Prediction: An Analysis of Performance and Interpretability. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023. https://doi.org/10.1109/ICCCNT56998.2023.10307946
Wang, F., & Ran, G. (2019). Excessive Financial Support, Real Estate Development and Macroeconomic Growth: Evidence from China. Emerging Markets Finance and Trade, 55(11), 2437–2447. https://doi.org/10.1080/1540496X.2018.1555463
Wang, Q., Lin, B., & Tan, C. (2024). A modified spatial house price to income ratio and housing affordability drivers study: using the post-LASSO approach. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-12-2023-0169
Warisse, C. (2017). Economic Review - June 2017 - Analysis of the developments in residential property prices : Is the Belgian market overvalued ?
Whelan, S., Atalay, K., Barrett, G., Cigdem-Bayram, M., & Edwards, R. (2023). Transitions into home ownership: a quantitative assessment. AHURI Final Report, 404, 1–96. https://doi.org/10.18408/ahuri7327101
Wu, H., Jiao, H., Yu, Y., Li, Z., Peng, Z., Liu, L., & Zeng, Z. (2018). Influence factors and regression model of urban housing prices based on internet open access data. Sustainability (Switzerland), 10(5). https://doi.org/10.3390/su10051676
Zhang, H., Li, Y., & Branco, P. (2023). Describe the house and i will tell you the price: House price prediction with textual description data. Natural Language Engineering. https://doi.org/10.1017/S1351324923000360
Zhang, Y., Rahman, A., & Miller, E. (2023). Longitudinal modelling of housing prices with machine learning and temporal regression. International Journal of Housing Markets and Analysis, 16(4), 693–715. https://doi.org/10.1108/IJHMA-02-2022-0033
Zhao, J., & Zhang, B. (2021). Application and Study of artificial neural network intelligent control. Journal of Physics: Conference Series, 1948(1). https://doi.org/10.1088/1742-6596/1948/1/012003
Zhao, Y., Chetty, G., & Tran, D. (2019). 2019 IEEE Symposium Series on Computational Intelligence : IEEE SSCI 2019 : December 6-9, 2019, Xiamen, China. 1396–1401.
Zheng, Y. (2023). Community resilience and house prices: A machine learning approach. Finance Research Letters, 58. https://doi.org/10.1016/j.frl.2023.104400
Zulkifley, N. H., Rahman, S. A., Ubaidullah, N. H., & Ibrahim, I. (2020). House price prediction using a machine learning model: A survey of literature. International Journal of Modern Education and Computer Science, 12(6), 46–54. https://doi.org/10.5815/ijmecs.2020.06.04
