| Summary: | 碩士 === 國立臺灣大學 === 經濟學研究所 === 107 === This study is base in Taiwan house prices index from Sinyi Realty Inc, retrieving 68 samples from 2001Q1 to 2017Q4. The research discusses how macro variables influence the real estate market and the connections and causal relations among them. Chosen variables are the Taiwan house prices index, Taipei City house prices index, New Taipei City house prices index, Taoyuan City house prices index, Hsinchu City house prices index, Taichung City house prices index, Kaohsiung City house prices index, loan rate, gross domestic product growth rate, consumer price index and M2 balance.
This study was analyzed by unit root test and cointegration test, empirical results show that there is a long-term equilibrium relationship among Taiwan house prices index, six metropolitan areas house prices index, loan rate, gross domestic product growth rate, consumer price index and M2 balance.
Then, the vector error correction model shows that the house price index of Taiwan and Taipei City have a significant positive impact on the house price of the previous period. the house price index of New Taipei City, Hsinchu City and Kaohsiung City have a significant negative impact on the price of the previous period. The impact indicates that the house price index of each metropolitan area is affected by external factors in the short-term and deviates from the long-term equilibrium. It will be adjusted back to the long-term equilibrium state by its own equilibrium error correction term and the previous period''s own house price. The home loan interest rate has a significant negative impact on housing prices in Taipei, and Kaohsiung City has a significant positive impact. The gross domestic product has a significant negative impact on housing prices in Hsinchu City. The price index of Taoyuan City and Taichung City and the impact of four general economic variables are not significant. Finally, using the Granger causality test check, the causal relationship between house price index and economic variables is analyzed and different populations are found. The changes in economic variables do indeed have different correspondences with the trend of real estate prices in various cities, such as leading, backward or two-way feedback.
The results of the verification show that the variables interact closely and can be used as a reference for speculating future trends. Therefore, the bank can make predictions on real estate price fluctuations, as an indicator to manage the credit risks, manage the bank''s credit line for real estate, reduce the credit risk of undertaking the industry, and stabilize the sound development of the bank.
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