台灣的不動產市場中，有高比率的建商屬於開發少案或僅開發一案的建設公司，而眾多一案建商存在的現象更是不動產新推個案市場中為大家所垢病。經初步的統計分析，在民國八十二年至九十一年間，一案建商的家數比率超過了六成，顯示在台灣的不動產市場中一案建商的存在，的確是很嚴重的問題。而缺乏過去推案表現及公司聲譽的累積，眾多的一案建商所推出之個案是如何能被市場所接受？一案建商是否會透過訂價行為的改變與其他類型建商從事競爭？這是本研究所要探討的問題。 由國內外相關文獻可以看出，商譽較差的建商可能透過降低價格的方式與其他建商競爭，或者是避免與其他建商在相同地區推案，以避免價格上的競爭。本研究依據建商過去的推案情形，將建商分為：一案建商、一般建商及穩健型建商，並依相關理論與文獻建立了三個研究假說。實證結果顯示在控制其他變數下，不同類型建商的產品訂價是具有差異的。其中一案建商的產品訂價相較一般建商，每坪價格會低約5.56%，而穩健型建商在產品訂價上，每坪價格則會比一般建商高出約6.79%。而實證結果也指出，不同類型建商的訂價差距會隨著區域市場特性的差異及房地產市場景氣的變化而有不同。

鑒於過去文獻可知，由於同一地區內的鄰近住宅擁有相同區位及市場特性，因而不動產價值存在高度相依性。空間相依性的產生往往是因為近鄰區域內的住宅有相似的建築結構（往往在同一個時間所興建），以及享有相同社會服務。由於建商在產品策略決策上會參考同一時間內鄰近競爭個案的產品策略，所以鄰近的新推個案會有相似的建築特徵以及相似的產品訂價。因此新推個案的訂價與鄰近的個案產生相關性，而新推個案訂價的相依性程度也會隨著時間距離遞減。 　　本文的目的在於將空間和時間的相依性最適地納入新推個案的訂價模型。採用582個台北市建商推案樣本進行實證。本研究分別以Moran’s I值和LISA值兩項指數來檢測空間自相關，並且比較傳統OLS迴歸模型、空間落遲模型，以及空間誤差模型三個模型的預測能力。此外，我們以不同的空間和時間的加權矩陣納入空間誤差模型中討論。 　　研究結果顯示，考量空間相依性之空間迴歸模型其解釋能力明顯優於一般傳統迴歸模型。而比起空間統計模型，時空迴歸模型更可以提高估計新推個案訂價的準確性。此外，研究結果亦顯示考慮時空交互影響的時空迴歸模型乃為新推個案訂價的最佳推估模式。 / It is well-known from the literature that the values of real estates are highly dependent on their locational and market characteristics of the buildings in adjacent areas. Spatial dependence mainly derives from factors that buildings at nearby properties have similar structural features (which were often developed at the same time) and often share the same social welfare. As developers in making decisions on product strategy will make reference to the strategy of nearby products of competitive cases which developed during the same time, therefore, within a certain period of time, the adjacent new construction will often have similar construction attributes as well as similar products pricing. Not only the pricing of a new construction is likely to be related to the pricing of adjacent new construction, but also the pricing of a new construction would be prone to autocorrelation decays in accordance with time distance. The aim of this paper is to analyze on how to take this temporal and spatial dependence into account in the pricing model of the new construction in the most appropriate way. We use a database of 582 asking prices of real estate developers in Taipei city. Two indices for measuring spatial autocorrelation are considered including (i) Moran’s I Index and (ii) LISA’s Index. We compared the predictive ability of three models including (i) OLS model, (ii) spatial lag model, and (iii) spatial error model. Moreover, we discussed the different temporal and spatial weight matrices in the spatial error model. According to our research results, we concluded that spatial statistical models obviously perform better than the traditional OLS model. Temporal and spatial statistical models would provide more accurate predictions on the pricing of a new construction than spatial statistical models do. The research result reveals that the best pricing model of the new construction is temporal and spatial statistical models which include temporal and spatial correlation.