特徵價格法通常以多變數迴歸分析法來建立房地產估價公式。這種方法相當簡便，但有幾個缺點，不易理解、不通用性、不具有應用性及模型彈性差。本研究目的在於提出一個逐步分解迴歸分析法來克服上述缺點。本研究考慮的因子包括：(1)捷運站距離之影響因子k1，代表交通的便利性對每坪單價的影響係數，(2) 徒步生活圈內的超商數之影響因子k2，代表生活機能的便利性對每坪單價的影響係數，(3) 屋齡之影響因子k3，代表屋齡對每坪單價的影響係數，（4）成交年月之影響因子k4，代表成交年月對每坪單價的影響係數，（5）二度空間因子k5，代表空間區位對每坪單價的影響係數。考慮的區域有新店區、淡水區、文山區、北投區四區，分別建立模型。研究果顯示，四個區域的房地產估價20%誤差命中率均在70%以上。 Multivariate regression analysis is usually employed to establish real estate valuation formula. This approach is quite simple, but it has a few drawbacks including being difficult to understand the meaning of the coefficients, cannot be universal to another areas, cannot be used in comparison approach, and without good flexibility. The purpose of this study is to propose the stepwise decomposition regression analysis to overcome these shortcomings. The factors considered in this study include The factor of the distance to the nearest MRT station which represents the impact of transportation function to the price per unit area. The factor of the number of convenience stores in the living circle on foot which represents the impact of living function to the price per unit area. The factor of the age of house which represents the impact of the quality of the house to the price per unit area. The factor of transaction date which represents the impact of market trend to the price per unit area. The factor of the geographic coordinates which represent the impact of spatial location to the price per unit area. The results showed that the 20% error hit rates of real estate valuation were greater than 70% for all the four testing areas in Taipei City and New Taipei City.
