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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/35902


    Title: 台北市新推個案訂價之時間與空間相依性分析
    Temporal and spatial dependence of new construction in Taipei city-a study of product pricing
    Authors: 紀凱婷
    Chi, Kai Ting
    Contributors: 張金鶚
    詹士樑
    紀凱婷
    Chi, Kai Ting
    Keywords: 空間相依性
    空間迴歸模型
    產品訂價
    spatial dependence
    spatial autocorrelation
    product pricing
    Date: 2007
    Issue Date: 2009-09-18 16:20:42 (UTC+8)
    Abstract: 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.
    Reference: 一、中文文獻
    1.白金安、張金鶚，1996，「預期景氣變動對預售屋與成屋價格差異影響之研究」，『中國財務學刊』，3(2)：99-114。
    2.艾兆蕾，2005，「影響住宅區地價因素之空間分析─以鄉鎮與縣市為例」，世新大學管理學院經濟學系碩士學位論文。
    3.林潤華、周素卿，2005，「『臺北信義豪宅』及其生產集團--信義計畫區高級住宅社區之生產者分析」，『地理學報』，40：17-43。
    4.沈庭增，2006，「都市仙丹─臺北市『小套房』之空間生產與消費」，臺灣大學建築與城鄉研究所碩士論文。
    5.林育聖，2004，「建商商譽與產品訂價之差異分析」，政治大學地政學系碩士論文。
    6.林秋瑾，1996，「穩健性住宅租金模式之探討-異常點分析」，『住宅學報』，4:51-72
    7.李福輝，2000，「捷運車站周邊住宅產品供給特性之研究─以竹圍、紅樹林、淡水站為例─」，中國文化大學建築及都市計畫研究所碩士論文。
    8.許獻叡，2005，「台中市豪宅投資方案評估模式之研究」，朝陽科技大學建築及都市設計研究所碩士論文。
    9.陳慈仁，2001，「台北市資訊軟體業與網際網路服務業區位分佈之研究」，臺灣大學建築與城鄉研究所碩士論文：臺北。
    10.陳靜怡，2007，「都市地區人口老化之現象分析與空間分佈探討-以台南市為例」，第三屆住宅學會青年學者論壇論文：台南。
    11.鄒欣樺，2007，「建商不動產表價與議價策略之探討—景氣時機、個案區位、及建商類型分析」，『管理評論』，26(3)：47-69。
    12.張金鶚，2003，『房地產投資與市場分析－理論與實務』，台北：華泰文化事業有限公司。
    13.張秀玲，2001，「整合空間資訊科技與土地大量估價作業之研究」，國立成功大學都市計劃研究所碩士論文
    14.黃紹東，2004，「台南市東區住宅價格之空間自我迴歸分析」，成功大學都市計劃研究所碩士論文：台南。
    15.黃景昇，1998，「影響建商房屋銷售訂價之研究」，政治大學地政學系碩士論文。
    16.楊宗憲，2003，「住宅市場之產品定位分析—建商推案行為之研究」，『住宅學報』，12(2)：123-139。
    二、外文文獻
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    5.Benser ,C. (2002) “A Spatial Autoregressive Specification with a Comparable Sales Weighting”, JRER, Vol. 24, 193-210.
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    7.Can, A. ,1990, “The Measurement of Neighborhood Dynamics in Urban House Prices.”, Economic Geography.
    8.Case, B., J. Clapp, R. Dubin and M. Rodriguez (2004). “Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models,” Journal of Real Estate Finance and Economics, 29(2), 167-191.
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    10.Dubin,R., R.K. Pace and T. G..Thibodeau,1999, “Spatial Autoregression Techniques for Real Estate Data,” Journal of Real Estate Literature,17(1),79-95.
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    Description: 碩士
    國立政治大學
    地政研究所
    95257012
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095257012
    Data Type: thesis
    Appears in Collections:[Department of Land Economics] Theses

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