Please use this identifier to cite or link to this item: https://doi.org/10.1108/02637470510618406
Title: Autoregressive analysis of Singapore's private residential prices
Authors: Chin, L. 
Fan, G.-Z. 
Keywords: Autoregressive processes
Box Jenkins
Housing
Prices
Residential property
Singapore
Issue Date: 2005
Source: Chin, L.,Fan, G.-Z. (2005). Autoregressive analysis of Singapore's private residential prices. Property Management 23 (4) : 257-270. ScholarBank@NUS Repository. https://doi.org/10.1108/02637470510618406
Abstract: Purpose - The purpose of this paper is to examine the nature of Singapore's private housing market with respect to its price movement using time series models. Design/methodology/approach - This paper analyses the price dynamics in the Singapore private housing market using the integrated autoregressive-moving average modeling coupled with outlier detection and autoregressive conditional heteroskedasticity modeling techniques. Findings - The paper finds that private house prices are better modeled as an ARIMA (1, 1, 0) model with corresponding dummy variables. This suggests that housing prices may be characterized as the combination of a stationary cyclical component and a non-stationary stochastic growth component over the past almost three decades. This affirms that the Singapore's private housing market is characterised by the weak-form inefficiency. Research limitations/implications - The results show that even though ARIMA with dummy variables performs better to ARIMA with ARCH in dynamic performance, there is only marginal improvement on the original model. This suggests that the method for selecting intervention variables in the ARIMA modeling is worth further research with the aim of improving its predictive ability. Originality/value - This paper incorporates the detection of outliers and intervention procedure in the modeling in order to analyse the impacts of extraordinary events such the recent Asian financial crisis and excessive market speculation on property prices and take them into consideration in forecasting price changes. © Emerald Group Publishing Limited.
Source Title: Property Management
URI: http://scholarbank.nus.edu.sg/handle/10635/46087
ISSN: 02637472
DOI: 10.1108/02637470510618406
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