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題名 貝氏多層次模型在台灣不動產市場估價之應用─以台北市住宅建物為例
其他題名 An Application of Bayesian Inference in the Real Estate Market –A Case Study of Taipei Collective Housing
作者 林祖嘉;馬毓駿
Lin,Chu-Chia;Ma,Yu-Chun
貢獻者 政大經濟系
關鍵詞 特徵方程式;貝氏分析;馬可夫鏈蒙地卡羅法
hedonic equation;Bayesian inference;Markov Chain Monte Carlo
日期 2012-06
上傳時間 17-Sep-2013 10:00:09 (UTC+8)
摘要 在房地產價格估計的領域當中,特徵方程式是最常被應用來估計建物價格的工具之一,然因特徵估價法是建構在線性迴歸的基礎之上,對於建物特徵與建物價格關係的描述過於簡化,同時實務上存在諸多無法量化的因素,致使模型容易產生異質變異的現象,而現有的非參數模型有時過於複雜,且使用上的限制亦多。針對上述問題,本文嘗試採用多層次貝式模型來彌補線性模型的缺陷,有別於多數研究將區位視為建物特徵之一的假設,本文由區位不同造成異質變異的角度切入,重新呈現建物特徵與建物價格的非單調性關係。實證結果指出多數的建物特徵對建物價格的影響,多因區位而產生變化,且時呈不同方向,同時在異質變異現象獲得舒緩後,建物價格估價的精確度亦獲得顯著提升。
How to estimate housing prices precisely has always been an important issue in the real estate
     market. Most studies adopt parametric or non-parametric methods to deal with problems such as heteroskedasticity or non-monotonic phenomena which come from less influential attributes or from characteristics which can not easily be realized. Researchers have attempted to adopt certain methods such as non-parametric methods to recover from these failures but they still do not work well. This paper therefore tries to re-examine the issue of heteroskedasticity in the housing price model. By using data for collective housing-type buildings in Taipei, this study employs the Hierarchical Bayesian model to bridge the relationship between attributes and housing prices.By means of a random effect device, the location effect gives rise to a non-monotonic effect on regressors that affect housing prices. Besides, capturing the heteroskedasticity effects results in the Bayesian model providing a better estimation than OLS.
關聯 住宅學報, 21(1), 1-18
資料類型 article
dc.contributor 政大經濟系en_US
dc.creator (作者) 林祖嘉;馬毓駿zh_TW
dc.creator (作者) Lin,Chu-Chia;Ma,Yu-Chunen_US
dc.date (日期) 2012-06en_US
dc.date.accessioned 17-Sep-2013 10:00:09 (UTC+8)-
dc.date.available 17-Sep-2013 10:00:09 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2013 10:00:09 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/61060-
dc.description.abstract (摘要) 在房地產價格估計的領域當中,特徵方程式是最常被應用來估計建物價格的工具之一,然因特徵估價法是建構在線性迴歸的基礎之上,對於建物特徵與建物價格關係的描述過於簡化,同時實務上存在諸多無法量化的因素,致使模型容易產生異質變異的現象,而現有的非參數模型有時過於複雜,且使用上的限制亦多。針對上述問題,本文嘗試採用多層次貝式模型來彌補線性模型的缺陷,有別於多數研究將區位視為建物特徵之一的假設,本文由區位不同造成異質變異的角度切入,重新呈現建物特徵與建物價格的非單調性關係。實證結果指出多數的建物特徵對建物價格的影響,多因區位而產生變化,且時呈不同方向,同時在異質變異現象獲得舒緩後,建物價格估價的精確度亦獲得顯著提升。en_US
dc.description.abstract (摘要) How to estimate housing prices precisely has always been an important issue in the real estate
     market. Most studies adopt parametric or non-parametric methods to deal with problems such as heteroskedasticity or non-monotonic phenomena which come from less influential attributes or from characteristics which can not easily be realized. Researchers have attempted to adopt certain methods such as non-parametric methods to recover from these failures but they still do not work well. This paper therefore tries to re-examine the issue of heteroskedasticity in the housing price model. By using data for collective housing-type buildings in Taipei, this study employs the Hierarchical Bayesian model to bridge the relationship between attributes and housing prices.By means of a random effect device, the location effect gives rise to a non-monotonic effect on regressors that affect housing prices. Besides, capturing the heteroskedasticity effects results in the Bayesian model providing a better estimation than OLS.
en_US
dc.language.iso en_US-
dc.relation (關聯) 住宅學報, 21(1), 1-18en_US
dc.subject (關鍵詞) 特徵方程式;貝氏分析;馬可夫鏈蒙地卡羅法en_US
dc.subject (關鍵詞) hedonic equation;Bayesian inference;Markov Chain Monte Carloen_US
dc.title (題名) 貝氏多層次模型在台灣不動產市場估價之應用─以台北市住宅建物為例zh_TW
dc.title.alternative (其他題名) An Application of Bayesian Inference in the Real Estate Market –A Case Study of Taipei Collective Housing-
dc.type (資料類型) articleen