影響不動產流動性因素之探討-以台北巿為例

本研究擬探討不動產住宅巿場成交與否及住宅巿場之流動性變動受何種因素影響且哪些因素為顯著之影響。因而以台北巿12個行政區不動產之資料進行篩選，選擇住宅巿場產品中之公寓、華廈及大樓作為實證研究對象，以瞭解影響住宅巿場之成交、不成交及住宅巿場流動性之因素受何種影響較深。其中以二元邏輯特迴歸模型分析影響住宅巿場成交與否之因素項目；以複迴歸模型探討影響住宅巿場影響流動性因素之原因。實證結果發現，在二元邏輯特迴歸分析中，住宅巿場成交與否之結果顯示出寸土寸金的大台北地區，民眾在預算有限下，價格仍為考量之因素，以「建物總面積」、「有無車位」等具有顯著性；若注重住宅之寧適性、管理狀況者則會購買大樓或華廈之產品。在住宅巿場流動性部分，以成交案例中的銷售天數來研究並以複迴歸模型分析，實證結果發現，平均銷售天數為103天，銷售天數主要集中於100天以下為大宗，100天以內的銷售天數其議價率區間範圍為百分之零至百分之四十左右。與流動性相關之因素為「總面積」、「議價率」、「總樓層」、「屋齡」、「是否有車位」及「營建類股價指數」等因素具有顯著性。最後，本研究建議未來可建立住宅巿場的流動性指數可與房價指數作為搭配互為參考，將可提高政府部門或相關研究人員運用數據時可提高資料之準確性。。

關鍵字

二元邏輯特；複迴歸；流動性；住宅產品；住宅巿場；成交；不成交

並列摘要

This research examines the types of factors affecting housing market transactions and liquidity changes and identifies which factors are of significance. This is conducted by screening real estate data from 12 administrative districts in Taipei City, followed by selecting the apartments, condominiums, and high-rise apartments among the products of the housing market as empirical research subjects to understand which factors are of greater influence. Binary logistic regression model is used to analyze the influencing factors of housing market transactions; multiple regression model is used to examine the reasons affecting the housing market liquidity. Empirical results from the binary logistic regression analysis show that housing market transactions in the greater Taipei area where every inch of the land is worth an inch of gold, for members of the public who have limited budget, price is the factor for consideration. The “total building area” and “car space availability” are of significance in the consideration. For those who place importance on the amenities and management, they will purchase high-rise apartments or condominium products. On housing market liquidity, research is based on the number of days the property is on the market (Days on Market, DOM) obtained from successful cases and analysis was conducted using the multiple regression model. Empirical results show that the average DOM was 103 days. DOM for bulk products is mainly concentrated under 100 days and their price negotiation scope is from 0% to around 40%. Related factors to liquidity, such as “total area,” “price negotiation rate,” “total number of stories,” “housing age,” “parking space availability” and “construction index” are factors of significance. Lastly, this research suggests that in the future the housing market liquidity index and the house price index can be matched for mutual reference. This can enhance accuracy of information when the government departments or related researchers are using the data.