Abstract
Various indicators reflecting the market economy show a time series that repeatedly increases and decreases depending on external situation. If an event, such as policy change and news report, can affect time series data, it is called intervention, and in actual data analysis, a model that reflects it is required. In this study, we analyze the effect of real estate policies announced in the second half of 2017 on the change of Seoul apartment trading volume, using a seasonal ARIMA model that reflects intervention effects. The results show that real estate policies, which were announced consecutively for a period of three months from June 2017, reduced the Seoul apartment trading volume momentarily, but had no long-lasting effect. This study has significant implications in that it allows a more theoretical approach than the simple comparison of differences between pre and post the change of policies in previous studies. In addition, it is presumed that the identification of variables affecting the time series and the estimation of the degrees of changes caused thereby will contribute to establishing a stable policy in the future.
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