特徵價格法在住宅大量估價模型中的延伸—分量迴歸之應用

• Main Authors: 張怡文, Chang, Yi Wen
• Language: 中文
• Published: 國立政治大學
• Subjects: 不動產估價; 大量估價; 特徵價格法; 分量迴歸; Real estate appraisal; Mass appraisal; Hedonic price theory; Quantile regression
• Online Access: http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0094257024%22.

特徵價格模型是傳統常被使用於不動產大量估價的模型，由於模型將造成所有價位的不動產其特徵都具有同樣的邊際價格而無法解釋現實不動產特徵的各種可能狀況，故引發本研究利用分量迴歸建立大量估價模型之動機。研究利用台灣不動產成交行情公報的資料進行台北市大廈的實證分析，針對特徵價格法的延伸與估價準確度做檢視。嘗試應用分量迴歸建立大量估價模型，討論住宅特徵對於價格的邊際影響力於不同價位的住宅是否存在差異，並討論分量迴歸模型的估價精確度。研究採用交互驗證法與重複實驗30次討論模型的估計效果，並利用平均絕對百分比誤差（MAPE）以及命中率（Hit Rate）做為模型預測優劣程度的衡量標準，以討論分量迴歸模型是否可以較最小平方特徵價格模型有更為準確的估計表現。實證首先探討價格分量之下各住宅屬性對於價格的影響狀況，得到大部分住宅特徵對於價格的邊際影響力的確會因住宅價位的不同而有所差異。在估價準確度的部份，經測試得到利用分量迴歸建立大量估價模型的估價效果達研究的預期目標，且其估計表現優於最小平方特徵價格模型。 === 藉由分量迴歸模型，得到隨著住宅價位的增加，坪數與屋齡對於價格的影響力並非呈現一致的趨勢；坪數輪廓與屋齡輪廓出現轉折也為變數增加二次項變數的原因得到實證依據。重複實驗30次的整體表現，分量迴歸模型的MAPE較最小平方迴歸模型低了1.687％；誤差落在正負10％的Hit Rate較最小平方迴歸模型高了3.81％；誤差落在正負20％的Hit Rate較最小平方迴歸模型高了5.14％。30次的實證為分量迴歸模型的估價表現更優於最小平方迴歸模型得到較具說服力的結果。 === Hedonic pricing models are traditionally used for real estate automated valuation models. Because the conditional mean calculated by OLS does not give a complete description of the relationship between dependent variable and independent variables, which leads to the motive of this study. This study inspects the extension of hedonic pricing models and appraisal accuracy, and we attempt to apply quantile regression to real estate automated valuation models and discuss the difference of the marginal contribution in each individual characteristic under different price level. Our study adopts cross validation and repeats empirical process for 30 times, and we use MAPE and hit rate to evaluate accuracy and argue if quantile regression models have better estimation. The empirical results show that the marginal contribution of housing area and age changes with price level; the turning points of area curve and age curve show empirical evidence for including square variables. The entirety performance of repeated experiments points out that the MAPE of quantile regression model is 1.687% lower than OLS model; as error ranged between 10% to -10%, the hit rate of quantile regression model is 3.81% higher than OLS model; as error ranged between 20% to -20%, the hit rate of quantile regression model is 5.14% higher than OLS model. The 30 times experiment of quantile regression models shows a much more persuasive result than OLS models.