Application of the ARIMA Model in House Price Index in Malaysia

Authors

  • Muhammad Harith Ikhwan Zamri Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Seremban
  • Muhamad Afizi Rifin Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Seremban
  • Norani Amit Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Seremban

Keywords:

affordability, ARIMA model, Box-Jenkins method, housing price

Abstract

The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method.aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting house price index in Malaysia using Box-Jenkins Method. Utilizing secondary data from the National Property Information Centre (NAPIC) spanning from 1988 to 2023, the study employed the analytical method of ARIMA. The results favored the ARIMA (1,1,1) model as the best model in predicting housing price indexes. This offers an excellent forecasting model for residential properties towards gaining better understanding of their pricing dynamics and offers potential solutions to the issue of housing affordability for Malaysians.

References

Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G. (2022). 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

Agyemang, E. F., Mensah, J. A., Ocran, E., Opoku, E., & Nortey, E. N. (2023). Time series-based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints. Heliyon, 9(12). https://doi.org/10.1016/j.heliyon.2023.e22544

Alkali, M. A. (2020). Real estate forecasting model for residential market in nigeria (Unpublished doctoral dissertation). Universiti Teknologi Malaysia.

Boitan, I. A. (2016). Residential property prices’ modeling: evidence from selected european countries. Journal of European Real Estate Research.

Bozdogan, H. (1987) Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika. 52(3), 345–370. https://doi.org/10.1007/BF02294361

Darne´, O., & Diebolt, C. (2005). Non-stationarity tests in macroeconomic time series. New trends in Macroeconomics, 173–194. https://doi.org/10.1007/3-540-28556-3_9

JabatanPerumahanNegara. (2018). Dasar perumahan negara (2018-2025). Jabatan Perumahan Negara Kementerian Pembangunan Kerajaan Tempatan.

Jehani, N. A., Mastani, N. A., Saudin, S., & Ab Malek, I. (2020). A study on the relationship between house price index and its determinants in Malaysia. Malaysian Journal of Computing (MJoC), 5(2), 515-522. 10.24191/mjoc.v5i2.9466

Kaur, Jatinder, Parmar, Singh, K., Singh, & Sarbjit. (2023). Autoregressive models in environmental forecasting time series: A theoretical and application review. Environmental Science and Pollution Research, 1–25. https://doi.org/10.1007/s11356-023-25148-9

Latif, Abdul, N. S., Muhammad, R. K., Nabilah, R., & Khadijah, S. S. (2020). Factors affecting housing prices in malaysia: A literature review. International Journal of Asian Social Science, 10(1), 63–67.

Lwesya, F., Kibambila, V. (2017) A comparative analysis of the application of seasonal ARIMA and exponential smoothing methods in short run forecasting tourist arrivals in Tanzania. European Journal of Business and Management. 9(10), 56-69. 10.5958/2320-3226.2020.00001.6

Mangaleswaran, S., & Vigneshwari, S. (2020). Prediction of housing prices using machine learning, time series arima model and artificial neural network. In Icdsmla 2019: Proceedings of the 1st international conference on data science, machine learning and applications (pp. 1002–1008). https://doi.org/10.1007/978-981-15-1420-3_110

Mukopi, M. S. (2012). Prediction of housing prices: An application of the Arima model (Doctoral dissertation, University of Nairobi, Kenya).

Olanrewaju, A., & Woon, T. C. (2017). An exploration of determinants of affordable housing choice. International Journal of Housing Markets and Analysis, 10(5), 703-723.

Press Release Malaysia Property Market Report H1 2022, N. (2023, March.). Press release malaysia property market report h1 2022. Valuation and Property Services Department Ministry of Finance Malaysia.

Ramli, Fazilah, Zainal, Rozlin, Kasim, & Rozilah. (2022). Prediction supply for high-cost multi-storey house toward the development of sustainable cities. International Journal of Sustainable Construction Engineering and Technology, 13(2), 100–109.

San Ong, T. (2013). Factors affecting the price of housing in Malaysia. J. Emerg. Issues Econ. Financ. Bank, 1, 414-429.

Saudin, Sharmila, Malek, A., Isnewati, Jehani, Ashakirin, N., & Amaelya, N. (2020). Modelling of Malaysia house price index.

Tihi, N., & Popov, S. (2023). Selection of the best arima models for urban drought prediction. Fresenius Environmental Bulletin, 32(6), 2564–2572.

Yahya, N., Zainuddin, N. M. M., Sjarif, N. N. A., & Azmi, N. F. M. (2020). Correlation analysis of factors affecting the prediction of price of terrace houses in Penang, Malaysia: A case study. Open International Journal of Informatics, 8(2), 18-39. https://oiji.utm.my/index.php/oiji/article/view/37

ZZainal, R., Ramli, F., Manap, N., Ali, M., Kasim, N., Noh, H. M., & Musa, S. M. S. (2019). Price prediction model of demand and supply in the housing market. In MATEC Web of Conferences (Vol. 266, p. 06015). EDP Sciences.https://doi.org/10.1051/matecconf/201926606015

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Published

2025-08-04