臺灣博碩士論文加值系統

住宅特徵價格模型(housing hedonic price models)被廣泛的用來估計住宅價格。模型中不僅包括住宅建物特徵，同時也包括住宅所在位置的區域特徵。由於這些特徵是具有層級(hierarchical)及巢狀內屬(nested structure)之特性，若是利用普通最小平方法OLS(ordinary least squares)對於具有內屬結構之資料進行估計，其並沒有考量其層級之特性，而是以單一層級資料作分析，嚴重違反模型中誤差項需符合獨立性的假設。
階層線性模式HLM(hierarchical linear model)是以層次分析方式來處理巢狀或內屬資料，並以隨機效果(random effect)來估計截距項及斜率項。因此，本文以台灣地區23個縣(市)地區之住宅調查統計資料作為分析樣本，並藉由HLM之五大次模型，分別探討區域特徵及建物特徵對住宅價格的影響為何?以正確反映所分析具有層級結構特性的資料。
實證結果顯示住宅建物特徵與價格的關係，會隨著縣(市)地區不同而有所差異，且區域特徵不僅對住宅價格有直接效果，亦會在住宅建物特徵與住宅價格間產生調節效果。最後並與傳統迴歸模型分析結果作一比較，其結果顯示，傳統特徵價格模型由於忽略了住宅空間效果易造成係數標準誤的低估，造成顯著性考驗高估與型Ⅰ錯誤擴大的問題。

Housing hedonic price models have been extensively used to estimate housing prices. These models consider not only the characteristics of housing structure but also the characteristics of housing location. However, these characteristics are hierarchical and in nested structures. When the ordinary least squares (OLS) method is applied to estimate the data, hierarchy of the data will not be considered. Instead, data are treated as of single-level in the analysis. Such analysis is seriously against the hypothesis of the model that the error term should be independent.
Hierarchical linear model (HLM) is to process the nested structure by hierarchical analysis and use random effect to estimate the intercept term and the slope term. Therefore, this study employed the housing statistics of 23 counties (cities) in Taiwan as the analysis sample and applied the five sub models of HLM to explore the impact of the characteristics of housing location and characteristics of housing structure on housing prices, and further reflect the hierarchical structure of the analyzed data.
The empirical results revealed that the relationship between characteristics of housing structure and housing prices significantly vary across different counties (cities), and characteristics of housing location not only have direct effect on housing prices but also have mediating effect on the relationship between characteristics of housing structure and housing prices. Finally, the empirical results were also compared with the results derived from traditional regression models. It was found that the traditional housing hedonic price model, due to its ignorance of the housing spatial effect, may underestimate the standard error of coefficients, resulting in overestimation of the significance test and expansion of Type I error.

第一章 、前言 1
第一節 、研究動機與目的 1
第二節 、研究內容與流程 5
第二章 、文獻回顧 7
第一節 、影響住宅價格之因素分析 8
第二節 、住宅特徵價格模型估計所存在的問題 12
第三節 、特徵價格模型多層次分析 17
第四節 、小結 22
第三章 、研究方法 23
第一節 、研究假設與架構 23
第二節 、實證模型設定 26
第三節 、變項設定與說明 40
第四節 、資料來源與處理 44
第四章 、實證結果分析 46
第一節 、樣本統計量之描述 46
第二節 、階層線性模式之實證結果分析 49
第三節 、小結 66
第五章 、結論與建議 67
第一節 、結論 67
第二節 、建議 70
參考文獻 72
附錄 84
圖目錄
圖 1-1本研究流程圖 6
圖 3-1 研究架構圖 25
表目錄
表3-1階層線性模型之各階層變項描述 43
表4-1個體層次變項描述統計摘要表(部份資料) 47
表4-2總體層次變項描述統計摘要表(部份資料) 48
表4-3零模型分析結果摘要表 49
表4-4零模型OLS與HLM對於縣(市)地區平均住宅價格分析結果摘要表 52
表4-5以平均數為結果的迴歸模型分析結果摘要表 53
表4-6具隨機效果的單因子共變數分析模型分析結果摘要表 55
表4-7隨機係數迴歸模型A的分析結果摘要表 56
表4-8隨機係數迴歸模型B的分析結果摘要表 57
表4-9截距項與斜率項相關係數分析結果摘要表 61
表4-10以截距及斜率為結果的迴歸模型分析結果摘要表 61
表4-11傳統迴歸分析估計結果摘要表 64
附表一、各縣市之市(區)、鎮、鄉樣本配額表 84
附表二、個體層次變項描述統計摘要表 85
附表三、總體層次變項描述統計摘要表 90
附表四、階層線性模式分析結果摘要表 95
附表五、住宅狀況調查訪問表 97

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