現今不動產價格估算往往是由不動產估價師利用專業的知識、豐富的經驗、相關的資料與精準的判斷，才能夠算出合理的不動產價格。但卻因為不同的估算師也會有不一樣的結果，缺乏一套客觀的標準。本研究利用特徵價格法的特徵因子為評估基準，參考國內外相關文獻，找出影響不動產價格的特徵因子，並且利用統計方法(SPSS)篩選出顯著影響房價的特徵因子，然後利用演化式模糊多元適應性雲形迴歸(Evolutionary Fuzzy Multivariate Adaptive Regression Splines, EFMARS)建立不動產估價模型，藉由模式訓練與測試，找出輸入(特徵因子)與輸出(單價)的映射關係，推論合理的不動產價格。本研究特徵因子經過文獻整理與統計方法篩選過後，總共10個特徵因子，接著以台北市大安區與中正區蒐集2014年1月與2014年12月之歷史交易資料總共406筆。將訓練與測試資料隨機分成10組，利用交叉驗證準則(Cross Validation)的概念進行訓練與測試。預測結果以中正區之整體預測準確度優於大安區，在平均絕對百分比誤差(Mean Absolute Percent Error, MAPE)值方面皆小於10%，顯示預測結果屬於精準的預測，亦即預測模式具備解釋房價的能力。最後將EFMARS與人工智慧(Artificial Intelligence, AI)等方法進行比較，其結果亦優於類神經網路(Neural network, NN)、迴歸分析(Regression)與支持向量機(Support Vector Machine, SVM)，表示本研究應用EFMARS來推論不動產價格具有一定的可信度。

Nowadays, the real estate prices are often estimates by the Real Estate Appraisers use of professional knowledge, experience, relevant information and precise judgment. But the different estimators will have different results. Nevertheless, this estimation is vulnerable due to human bias. To eliminate possible human bias made by appraisers. The research objective is to establish an Evolutionary Fuzzy Multivariate Adaptive Regression Splines (EFMARS) model, based on the hedonic pricing concept, to predict urban real estate price. Literature review summarizes 10 features that commonly show up for the hedonic pricing approach. The data collection targets at historical housing transactions in Taipei city Daan and Zhong Cheng district from Jan, 2014 to Dec, 2014. The total of 406 were collected. The study assessed model performance using the k-fold cross validation method and a stratified 10-fold cross validation approach. The results demonstrate that the proposed model reaches under 10% in MAPE. Compared with other modules, the result is also better than the Support Vector Machine (SVM), Neural Network (NN) and Regression, display model has a certain credibility.

[1] 蔡爾逸, "應用支撐向量機(SVM)於都市不動產價格預測之研究," 碩士, 營建管理研究所, 國立中央大學, 2012.
[2] 魏如龍, "類神經網路於不動產價格預估效果之研究," 碩士, 地政系, 國立政治大學, 2003.
[3] 林英彥, 不動產估價: 文笙書局股份有限公司, 2006.
[4] 楊謙柔, "都市住環境設施評價模式之研究," 博士, 建築及都市計畫研究所, 中國文化大學, 2009.
[5] 陳治勳, "應用模糊類神經網路於房地產價格之研究─以北、高兩市為例," 碩士, 不動產經營系, 國立屏東商業技術學院, 2008.
[6] 劉玉婷, "應用迴歸分析及類神經網路建構不動產估價模式-以台中市住宅為例," 碩士, 營建工程系, 國立雲林科技大學, 2009.
[7] Y. E. Hamzaoui and J. A. H. Perez, "Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process," in Artificial Intelligence (MICAI), 2011 10th Mexican International Conference on, 2011, pp. 175-181.
[8] X. Hu and M. Zhong, "Applied research on real estate price prediction by the neural network," in Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on, 2010, pp. 384-386.
[9] H. Selim, "Determinants of house prices in Turkey: Hedonic regression versus artificial neural network," Expert Systems with Applications, vol. 36, pp. 2843-2852, 2009.
[10] 黃于祐, "台北市房價影響因素之空間分析-地理加權迴歸方法之應用," 碩士, 都市計畫研究所, 國立台北大學, 2008.
[11] 李宣佑, "顧客需求導向對都會區不動產特徵價格影響之研究," 碩士, 營建管理研究所, 國立中央大學, 2014.
[12] 黃韋憲, "都會區住宅用地價格評估模式之建構," 碩士, 營建管理研究所, 國立中央大學, 2010.
[13] L. Li, L. Xiao, and J. W. Xiao, "Factor and Regional Difference Analysis on Real Estate Price," Technology for Education and Learning, vol. 136, pp. 317-323, 2012.
[14] C. He, W. Zhang, and J. Zeng, "Setting reasonable housing price based on fuzzy math," in Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on, 2011, pp. 5211-5214.
[15] 陳慧茹, "應用模糊理論於不動產估價之研究," 碩士, 環境資訊及工程學系, 國防大學, 2014.
[16] 蔡奇宏, "GIS資料庫應用於國有土地估價迴歸分析之研究," 碩士, 地球科學研究所, 國立成功大學, 2007.
[17] 程麟崴, "以案例式推理建構不動產估價模式-以台北市大安區主流不動產商品為例," 碩士, 營建工程系, 國立雲林科技大學, 2006.
[18] M.-Y. Cheng and M.-T. Cao, "Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams," Engineering Applications of Artificial Intelligence, vol. 28, pp. 86-96, 2014.
[19] M.-Y. Cheng and M.-T. Cao, "Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines," Applied Soft Computing, vol. 22, pp. 178-188, 2014.
[20] J. H. Friedman, "Multivariate Adaptive Regression Splines," The Annals of Statistics, vol. 19, pp. 1-67, 1991.
[21] D. Karaboga and B. Akay, "A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems," Applied Soft Computing, vol. 11, pp. 3021-3031, 4// 2011.
[22] D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, pp. 687-697, 1// 2008.
[23] D. Karaboga and B. Gorkemli, "A combinatorial Artificial Bee Colony algorithm for traveling salesman problem," in Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, 2011, pp. 50-53.
[24] 柯千禾, "演化式模糊類神經推論模式於營建管理決策之研究," 博士, 營建工程系, 國立台灣科技大學, 2002.
[25] D.E. Goldberg, K. Deb, H. Kaegupta, and G. Harik, "Rapid, accurate optimization of difficult problems using fast messy genetic algorithms," 1993.
[26] 葉怡成, 類神經網路模式應用與實作, 9 ed.: 儒林出版社, 2009.
[27] C. Bishop, Pattern Recognition and Machine Learning: Springer-Verlag New York, 2006.
[28] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," presented at the Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2, Montreal, Quebec, Canada, 1995.
[29] 潘配淮, "應用演化式最小平方差支持向量機推論模式(ELSIM)推估橋梁維修經費-以新北市為例," 碩士, 營建工程系, 國立台灣科技大學, 2013.