An application of the ARIMA model to real-estate prices in Hong Kong

Introduction The foundation for fundamental analysis is supply and demand for the commodity in question. Fundamental analysis involves modelling the supply-demand relationships in a market. Analysis of investments, including commodity futures, can be labelled as fundamental or technical. Supply and demand are far more difficult to analyse and model than generally realized. To provide useful results, the analyst cannot deal only with static equilibrium conditions for a commodity but must incorporate dynamic influences also. If markets were perfectly efficient, the practice of either fundamental or technical analysis as a predictive tool would be in vain. No one could hope to earn consistently higher returns than a naïve investor could do. This research attempts to identify the relationships within the system of real estate market being modelled. Using the ARIMA model, these relationships become equations in an interlinked system of equations. While some property analysts use fundamental analysis to try to determine the direction of the market trend, it seems that few can apply technical analysis for timing their entry and exit from market positions. In general, fundamental analysis is useful to gauge the long-term market factors, whereas technical analysis is important for dealing with shorter-term influences. Forecasting the real estate market is neither an easy task to accomplish nor susceptible to naïve, mechanistic approaches. Nevertheless, any forecasting approach that consistently provides better odds than those from tossing a coin in making the correct investment decision should merit careful examination. In fact, technical analysis has been developed and applied to finance for many years, and this research will apply the Box-Jenkins methodology – ARIMA model to the study of Hong Kong's real estate prices. In a time series, it is important to identify the data series in the following processes: (a) are the data random?; (b) do the data have a trend?; (c) model identification and (d) testing for model adequacy. If a series is random, the correlation between successive values in a time series is close to zero. However, if the observations of time series are statistically dependent on or related to one another, then the Box-Jenkins (ARIMA) methodology is appropriate. By looking at autocorrelation coefficients for time lags of more than one period, one can determine additional information on how values of a given time series are related. This method produces forecasts that are likely to be more accurate than the forecasts