總體經濟因素與空間因素對台灣都會區房價之影響

本研究主要分析總體經濟因素與空間因素對台灣都會區房價之影響，一直以來台北地區房價明顯較其他縣市還高，而近年台北市房價已高到令許多人難以負荷，政府也欲使台北市房價降低，而本研究主要分析總體經濟因素，如利率、物價、外國直接投資等因素對各都會區房價之影響為何？另一方面，也希望探討是何種因素使得房屋價格在各縣市間有不同之差異，針對此點，將利用新經濟地理學之空間因素來進行解釋。在使用模型方面，本研究以共整合檢定與向量誤差修正模型，分析總體經濟因素對房價之長短期關係，而在空間因素方面，本研究使用Panel Data固定效果模型以及FMOLS(Fully Modified Ordinary Least Squares)模型進行分析，分析結果將有助於提供政府在房市政策上之參考。本研究分析結果可觀察出人均GDP、物價指數、利率、股市、外國直接投資對各都會區房價之長短期關係；而空間因素方面顯示FMOLS模型中各縣市平均所得、服務業比例、失業率、交通建設與公司登記家數皆對都會區房價差異有顯著解釋力，而變數差分後固定效果模型中，失業率、服務業比例、公司登記家數對台灣都會區房價變動具顯著解釋力。

關鍵字

VECM ；房價差異；空間因素； Panel Data ； FMOLS

並列摘要

This study analyzes the impact of macroeconomic and spatial factors on housing prices in Taiwan’s major cities. Housing prices in Taipei City are significantly higher than other cities and have been high that many people can’t afford. Government also purports to reduce Taipei City’s housing prices using various policies. This study mainly tries to analyze the impact of macroeconomic factors, such as interest rates, CPI and FDI, on the housing prices of each metropolitan area. This study also wants to analyze the factors that cause the difference in housing prices for different cities. We use New Economic Geography to empirically interpret the impact of spatial factors on housing prices. For research model, we use the VECM to analyze the impact of macroeconomic factors on housing prices. VECM can analyze Long-run equilibrium and Short-term impact between variables. As for spatial factors, we will use the panel data fixed effect model. The empirical results demonstrate the short-term and long-term relationship among the per capita GDP, CPI, interest rates, stock market, foreign direct investment, and each metropolitan area's housing prices. For spatial factors, the results using the FMOLS model under the assumption of cointegration indicate a significant impact of average income, the proportion of the service industry, unemployment, construction and transportation, and company registration number on the housing prices of metropolitan areas. And under the assumption of no cointegration, only unemployment, the proportion of service industry, company registration number have a significant explanatory power for the housing prices in Taiwan metropolitan area. The results will provide a reference for government's housing policies in the future.