Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/222892
Title: RELATING ECONOMIC VARIABLES TO FUTURE PRICES IN SINGAPORE �S PRIVATE RESIDENTIAL PROPERTY MARKET
Authors: LIN QIWEI TIMOTHY
Keywords: Real Estate
RE
Cheng Fook Jam
2012/2013 RE
Correlation
Economic variables
Price
Regression
Singapore residential property price
Issue Date: 29-Apr-2013
Citation: LIN QIWEI TIMOTHY (2013-04-29). RELATING ECONOMIC VARIABLES TO FUTURE PRICES IN SINGAPORE �S PRIVATE RESIDENTIAL PROPERTY MARKET. ScholarBank@NUS Repository.
Abstract: This study examines the correlation between identified key economic variables and private residential property prices in Singapore from 1993 to 2012. The significant correlations were established through Pearson’s Correlation Coefficient. The variables were then tested for its suitability for use in a forecasting model using the Hierarchical Multiple Regression. While significant correlations can be established between key economic variables and price levels in the private residential property market of Singapore, they may not be statistically significant enough to be used in a model to determine future price fluctuations. A review of existing literature has identified concerns with the accuracy and suitability of existing models to forecast changes in property prices. The study found that while variables may initially be statistically significant enough to be predictor variables for a forecasting model, the addition of another variable might cause the earlier statistically significant variable to lose its significance in the model. The limitation of this research is that the variables used were not tested for their causal relations, stationarity and collinearity. As such, the results are not suitable for use in a forecasting model. The method used in this study to examine if the variables were suitable for use in a forecasting model could be, on further refinement into a more complete and advanced treatment, be used to evaluated the accuracy and quality of the information used in existing and future forecasting models.
URI: https://scholarbank.nus.edu.sg/handle/10635/222892
Appears in Collections:Bachelor's Theses

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