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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| 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.
Keywords: Artificial neural network; House price index |
| References |
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