<![CDATA[This study aims to analyze and forecast the movement of the Residential Property Price Index (RPPI) in Singapore using the Autoregressive Integrated Moving Average (ARIMA) time series approach. The data employed are secondary quarterly time series data covering the period from 2015 to 2025. The analysis follows the Box-Jenkins methodology, including stationarity testing, differencing, model identification through ACF and PACF correlograms, parameter estimation, diagnostic testing, and forecasting. The results indicate that the data are non-stationary at the level form but become stationary after first-order differencing. Based on the comparison of several candidate models, ARIMA(0,1,1) is selected as the most appropriate model, as it provides the lowest information criteria values among the alternatives. Diagnostic testing confirms that the residuals behave as white noise, indicating that the model is adequate for forecasting purposes. The forecasting results reveal a consistent upward trend in Singapore’s RPPI over the period 2026 to 2028. These findings suggest sustained growth in residential property prices and demonstrate that the ARIMA model effectively captures the historical pattern of the data and produces reliable forecasts. Therefore, this study contributes by providing predictive insights that can support decision-making in the property sector.]]>
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