住宅屬性之價值評估：應用特徵價格模型於台中市中古屋市場

隨著國人之所得增加，對於住宅要求也隨之提高，住宅不再只是單純提供居住之功能，還要擁有各種屬性以便能提供多元的生活機能及良好環境品質等，而這些能夠滿足國人對於住宅需求的各種屬性，自然而然就成為決定住宅價格高低的重要因素，然而住宅為複合式商品，即住宅總價乃是由住宅所包含各種屬性價值加總而來，對於其包含的各種屬性如格局、車位甚至是外在公設等等並沒有個別決定價格的交易市場，使得個別屬性的價格變的難以判斷，更無法進一步討論影響住宅價格的重要因素。因此本文採用2007年台中市真實房屋交易價格資料，以特徵價格理論為理論基礎，並將資料分為集合式住宅及獨立式住宅兩類，用半對數迴歸模型、Box-Cox模型來探討住宅的各種屬性之隱含價格，藉此討論影響住宅價格的重要因素。研究結果發現：1). 兩模型對於集合式住宅價格均有良好的解釋能力，且由Box-Cox模型之轉換變數得知該集合式住宅資料有顯著的非線性關係。2). 集合式住宅之屬性之中，土地、房間數、廳堂數、衛浴數、電梯、總樓高、屋齡、車位、體育館、新市政中心、汙水處理廠、殯儀館、鐵路兩側等變數在兩個模型之中均為顯著，因此這些變數為影響集合式住宅房價之重要因素。3). Box-Cox模型對獨立式住宅價格的解釋能力高於半對數模型，且由Box-Cox模型之轉換變數得知該獨立式住宅資料有顯著的非線性關係。4). 獨立式住宅屬性之中建坪、土地、房間數、衛浴數、車位、國小、公園、新市政中心、機關用地、鐵路兩側等變數在兩個模型當中均為顯著，為影響獨立式住宅房價之重要因素。

關鍵字

集合式住宅獨立式住宅特徵價格理論

並列摘要

The growing personal and family income in Taiwan has already enhanced the requirements of house to an extent that a house should not only function as a shelter or residence but also has various attributes for providing diversified living functions and good quality of environment. The attributes that could satisfy the demands of people are the important factors in determining the price of a house. However, a house is a complex commodity, that is, the price of a house is the total amount of the values in terms of various attributes contained in the house. There is no market that could individually determine the prices of these various attributes such as room pattern, parking lot or even the external public facilities. Therefore, this research adopts the house market prices in Taichung City in 2007 as basic data, and applies hedonic price theory to decompose and identify the prices of attributes in order to reveal the importance factors in hourse prices. The research uses the semilogarithm regression model and Box-Cox model to explore the implicit prices of house attributes and discuss the important factors that might affect the price of a house. The house market is divided into two types as “apartment house market” and “town house market”because people usually have different consideration on purchasing different type of house. The results find that : 1) both models have good explanatory capability on the house market. From the conversion variables of Box-Cox model, the house prices show significant non-linear correlations with attributes in apartment house market; 2) apartment attributes including the square footage of land, number of rooms, numbers of dinning rooms, number of bathrooms, elevator, number of stories, age of house, parking lot, and location around sport field, or new city hall center, or sewage treatment plant, or funeral home, or railroad are significant factors to affect prices in both models; 3) the explanatory capability in Box-Cox model is better than the semilogarithm regression model. From the conversion variables of Box-Cox model the house prices show significant non-linear correlations with attributes in town house market; 4) town house attributes including floor space, the square footage of land, number of rooms, number of bathrooms, parking lot, location around elementary schools, or parks, or new city hall center, of government organizations, or railroad are significant factors to affect prices in both models.