近年來由於地理資訊系統（GIS）的快速發展發，空間資料分析開始受到重視並在社會科學領域中逐漸扮演重要的角色。雖然一般的統計方法已在傳統資料分析上發展已久，然而它們卻不能有效地說明空間性資料，並且無法充分處理空間相依或空間異質性問題。一般而言，空間資料分析主要有兩個分派:模型導向學派與資料導向學派。本文研究目的在於應用空間統計方法合理且充分地評估房地產價值，研究方法包含地理統計(克利金和共克利金)、地理加權迴歸與空間特徵價格模型等，並且以台中市不動產資料進行實證探究。這項新的研究技術在不動產評價領域中將可提供更好的解析能力，使其在評價過程中或是不動產投資決策時，成為一個更強而有力的分析工具。 / In recent years, spatial data analysis has received significant awareness and played an important role in social science because of the rapid development of Geographic Information System (GIS). Although classic statistical methods are attractive in traditional data analysis, they cannot be executed seriously for spatial data. Standard statistical techniques didn’t sufficiently deal with spatial dependence or spatial heterogeneity issues. Generally, the model-driven method and the data-driven method are mainly the two branches of the spatial data analysis. The purpose of this paper is to apply spatial statistics methods including geostatistical methods (kriging and cokiging), geographically weighted regression, and spatial hedonic price models to real estate analysis. It seems to be completely reasonable and sufficient. The real estate data in Taichung city (Taiwan) is used to carry out our exploration. These techniques give better insight in the field of real estate assessment. They can apply a good instrument in mass appraisal and decision concerning real estate investment.