查詢結果分析
來源資料
頁籤選單縮合
題名 | 貝氏多層次模型在臺灣不動產市場估價之應用--以臺北市住宅建物為例=An Application of Bayesian Inference in the Real Estate Market--A Case Study of Taipei Collective Housing |
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作者 | 林祖嘉; 馬毓駿; Lin, Chu-chia; Ma, Yu-chun; |
期刊 | 住宅學報 |
出版日期 | 20120600 |
卷期 | 21:1 2012.06[民101.06] |
頁次 | 頁1-18 |
分類號 | 554.89 |
語文 | chi |
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; |
中文摘要 | 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。 |
英文摘要 | 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. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。