國內近年來有許多文獻在進行特徵價格模型預測時，避免樣本中存在異常點會造成模型估計值產生偏差，會使用統計軟體進行異常點檢測，但皆是直接將檢測出的異常點刪除，未加以著墨探究這些異常點的特徵結構、成因及特色等。因此，本研究透過統計檢定方法，探討刪除異常點前後整體樣本的特徵結構變化，並以個別估價觀點加以探討住宅交易樣本異常點的成因與特色，藉此歸納出實價登錄資料未揭露的重要特徵，以及迴歸模型搜尋疑似申報不實案件之可行性。 透過敘述統計及樣本結構差異檢定結果發現，異常樣本的離散程度相對原始樣本與正常樣本較大，且經過刪除異常點的正常樣本特徵結構差異程度縮小；異常點的形成可能受到區位變數無法反映實際情況及樣本群聚程度影響，也可能因模型未納入某些重要的特徵變數，而使隱含該變數的樣本被判斷為異常點；異常樣本與正常樣本的成交總價、土地坪數、建物坪數、總樓層、所在樓層及屋齡等變數平均數、變異數及中位數有顯著差異。 藉由個案分析結果歸納，可能因異常個案的住宅屬性存在整幢大樓住商混合使用、特殊鄰居、附屬建物占比過高、高總價豪宅產品、都更效益、增建效益、裝潢效益、約定專用空間效益、樓層高度挑高、獨特視野景觀或特殊區位條件；外部環境存在鄰近嫌惡設施或迎毗設施；交易情況存在買方身分特殊之影響，但受限於實價登錄未要求登載並揭露這些特徵，故模型未考量這些因素對價格的影響，使得模型可能將隱含這些特徵的樣本判斷為異常點，並進而影響模型預測結果。另外也發現，實價登錄資料存在登載錯誤及價格申報不實的情況，且可能被模型判斷為異常點。 / Many literatures use statistics-way to detect outliers in preventing any extreme deviation in hedonic price model prediction. Nevertheless, deleting the outliers instead of investigation into the structures, causes and features. Hence, this thesis studies the feature structures variation of the sample before and after deleting the outliers and with the valuations by appraisers’ perspective to inquire into the factors and features of the outliers in residential transactions. Thereby to summarize the significant features that are not disclosed by real price registration and feasibility in searching the possible false declaration of price by regression. Through descriptive statistics and sample structural difference parametric and nonparametric test shows the discreteness level of singular (outliers only) samples is greater than the primary (outliers including) and normal (outliers deleting) samples and the feature structure variation lessened after deleting the outliers in normal samples. The formation of outliers may be influenced by location variable not able to reflect actual circumstances and level of clustering in samples. Maybe some significant variables are not subsumed into the model, which leads to the judgement of samples with this variable to be outliers. The mean, variance and median in total traded price, land size, building size, total floors, exact floor and house age of singular samples are notably different with normal ones. With the analysis of cases, the possible reasons may be residential and commercial mixed-use in building, peculiar neighbors, high proportion of accessory building, luxury houses, urban renewal benefits, building addition benefits, interior decoration benefits, agreed space benefits, high-ceiling benefits, unique view or location, YIMBY and NIMBY property in environment and special relationship between the buyer and seller. Nevertheless, due to the nondisclosure of these features in real price registration that the model does not take these into consideration. That leads to the judgement of samples with these features as outliers and affects the model prediction. Also the registration error and false declaration in price may also be judged as outliers.