Forecasting on House Price Index using Artificial Neural Network
Keywords:Artificial neural network, House price index
Forecasting the residential property sector is a crucial component in the decision-making process for investors and government in supporting asset allocation, developing property finance plans and implementing a relevant policy. The purpose of this study is to examine the determinants of Penang house price index and to develop a model to forecast Penang house price index in Malaysia. Estimation is done by using ordinary least square and artificial neural network method. Relevant data sets were obtained from the Monthly Statistical Bulletin, Bank Negara Malaysia and National Property Information Centre. The empirical analysis of this research is based on quarterly time series data which cover the periods from 2005Q1 to 2022Q1. The main findings reported that base lending rate and unemployment rate are negatively associated with and have significant impacts on Penang house price index. Meanwhile, gross domestic product is positively related to and has a significant impact on Penang house price index. Consumer price index shows a positive sign; however, it recorded an insignificant impact on Penang house price index. Even though there are three independent variables recorded significant impact on Penang house price index, yet gross domestic product is the most vital determinant of Penang house price index in Malaysia. The artificial neural network model was trained and tested using quarterly time series data from 2005Q1 to 2022Q1 and the model was validated using data from 2021Q1 to 2022Q1. Model validation indicates that artificial neural network has a high level of accuracy in its ability to learn, generalize, and converge time series data efficiently as well as able to generate reliable forecasting information.
Abraham, E. R., Mendes dos Reis, J. G., Vendrametto, O., Oliveira Costa Neto, P. L. D., Carlo Toloi, R., Souza, A. E. D., & Oliveira Morais, M. D. (2020). Time series prediction with artificial neural networks: An analysis using Brazilian soybean production. Agriculture, 10(10), 475.
Beale, M. H., Hagan, M. T., & Demuth, H. B. (2017). Neural Network Toolbox User's Guide. Natick, MA: The MathWorks, Inc. Retrieved from www.mathworks.com
Construction Plus Asia. (2022). Construction Forecast and Review 2H 2021 & 1H 2022: Malaysia. Retrieved from https://www.constructionplusasia.com/my/construction-forecast-and-review-2h-2021-1h2022-malaysia/
Forbes. (2018). Six fundamental human needs we need to meet to live our best lives. Retrieved from https://www.forbes.com/sites/quora/2018/02/05/six-fundamental-human-needs-we-need-to-meet-to-live-our-best-lives/?sh=76e8434344a
Garg, A. (2016). Statistical methods for estimating house price index. Journal of Business & Financial Affairs, 5(4), 1-3.
Ge, B., Ishaku, M. M., & Lewu, H. I. (2021). Research on the effect of artificial intelligence real estate forecasting using multiple regression analysis and artificial neural network: A case study of Ghana. Journal of Computer and Communications, 9(10), 1-14.
Geerolf, F., & Grjebine, T. (2014). Assessing house price effects on unemployment dynamics. CEPII Working Papers. Retrieved from https://econpapers.repec.org/paper/ciicepidt/2014-25.htm
Gregor, A. (2020). The New York Times. House hunting in Malaysia: A restored rowhouse in Penang. Retrieved from https://www.nytimes.com/2020/04/15/realestate/house-hunting-in-malaysia-a-restored-rowhouse-in-penang.html
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.
Kitapci, O., Tosun, O., Tuna, M. F., & Turk, T. (2017). The use of Artiicial Neural Networks (ANN) in forecasting housing prices in Ankara, Turkey. Journal of Marketing and Consumer Behaviour in Emerging Markets, 1(5), 4-14. Retrieved from https://ideas.repec.org/a/sgm/jmcbem/v1i5y2017p4-14.html
Latif, N. S. A., Rizwan, K. M., Rozzani, N., & Saleh, S. K. (2020). Factors affecting housing prices in Malaysia: A literature review. International Journal of Asian Social Science, 10(1), 63-67.
Lee, Y., & Azlan, M. I. B. (2022). Determinants of housing prices: Evidence from Malaysia and Singapore. Asian Journal of Research in Education and Social Sciences, 3(4), 91-106.
MathWorks. (2022). What is a neural network? 3 things you need to know. Retrieved from https://ww2.mathworks.cn/en/discovery/neural-network.html
Mohan, S., Hutson, A., MacDonald, I., & Lin, C. C. (2019). Impact of macroeconomic indicators on housing prices. International Journal of Housing Market and Analysis, 12(6), 1055-1071.
NAPIC. (2021). Malaysian House Price Index Q1-Q3 2021. National Property Information Centre. Retrieved from https://napic.jpph.gov.my/portal/web/guest/publication
Narkhede, S. (2018). Understanding descriptive statistics. Towards Data Science. Retrieved from https://towardsdatascience.com/understanding-descriptive-statistics-c9c2b0641291
Perez, C. (2019). Big Data Analytics With Neural Networks Using MATLAB. Texas: Lulu.com. Retrieved from https://books.google.com.my/books?id=6PPGDwAAQBAJ&pg=PT188&lpg=PT188&dq=multistep+prediction+for+artificial+neural+network+using+matlab&source=bl&ots=X8Rt6_lzw2&sig=ACfU3U2reJYjVtvv-ZriA00n3IsutGq8Pw&hl=en&sa=X&ved=2ahUKEwj33-in-ar5AhUd-TgGHQjfAfg4PBDoAXo
Radzi, M., & MS, M. C., Kamarudin, N., & Mohammad, IS (2012). Forecasting house price index using artificial neural network. International Journal of Real Estate Studies, 7(1), 43-48.
Rahman, M. F., & Ridzuan, A. R. (2020). Factors that determine house price index in Malaysia. International Journal of Academic Research in Accounting, Finance and Management Sciences, 10(1), 351-359. doi:10.6007/IJARAFMS/v10-i1/7224
Sukrri, N. N., Wahab, N. A., & Yusof, R. M. (2019). An enhanced house price index model in Malaysia: A Laspeyres approach. International Journal of Economics, Management and Accounting, 27(2), 373-396. Retrieved from https://journals.iium.edu.my/enmjournal/index.php/enmj/article/view/631
Sun, N. (2021). The effects of housing price on unemployment rate and stock market. International Journal of Trade, Economics and Finance, 12(5). doi:10.18178/ijtef.2021.12.5.707
Tan, Y. (2011). A hedonic model for house price in Malaysia. International Real Estate Conference, (pp. 12-15). Retrieved from http://www.prres.net/papers/tan_an_hedonic_model_for_house_prices_in_malaysia.pdf
Trofimov, I. D., Aris, N. M., & Xuan, D. C. (2018). Macroeconomic and demographic determinants of residential property prices in Malaysia. Munich Personal RePEc Archive, 1-24. Retrieved from https://mpra.ub.uni-muenchen.de/85819/1/MPRA_paper_85819.pdf
Tumbarello, P., & Wang, S. (2010). What drives house prices in Australia? A+L4584 Cross-Country Approach. 2010 International Monetary Fund, 10(291), 1-22. doi:10.5089/9781455211722.001
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