現行不動產大量估價主要以特徵價格模型為基礎進行價格之預估，而常以鄰里、轄區或次市場虛擬變數或是與特定公共設施之距離作為控制區位價值之變數。惟僅以次市場變數之係數或是距離特定公共設施距離之係數衡量樣本之區位價值，則因係數之僵化性弱化或低估區位對不動產價格之影響，導致大量估價模型之精度難以突破。 本研究以區位價值波面之概念建立條件式殘差擬合變數，從空間角度評估各樣本之區位價值並以量化數值呈現各樣本區位價值之高低，在細膩處理區位價值下模型之預估能力相對提升。實證結果顯示，整體模型之絕對誤差平均值為10.1%，而10%、20%誤差命中率達62.9%、87.9%，相對優於過去研究之模型預估能力；另外，經過區域侷限性測驗發現，條件式殘差擬合變數修正模型不受次市場之侷限，對於是否劃分模型次市場已不影響模型之預估能力，且經由實證發現，當實價登錄樣本愈趨豐富時，模型之預估能力將更加提升，值得作為後續建立大量估價模型之參考。 / Hedonic model is the most commonly-used tool for real estate mass appraisal, and neighborhoods, districts or sub-market dummies or the distance from the specific public facilities are the common variables used to control the value of location in the model. However, controlling the location value by these ways leads to the coefficient rigidities, making it possible to underestimate the value of the location. This research sets up the conditional-selected residual fitting variable by the concept of location value response surface, and estimates the location value from the spatial perspective. The result shows that the MAPE of the model is 10.1%, and the hit-rate of 10% and 20% come to 62.9% and 87.9%, having significant improvement compared with the past studies. Besides, by the confinement test of sub-market, it has been proved that the CRF modified model successfully gets rid of confinement from the sub-market, and whether dividing sub-markets or not no longer affects the prediction capability of the model. Another test giving us new images that, when the train data gets richer as time goes, the prediction capability of the model gets higher as well.