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研究生： 張岳庭
Dillon-Fleshman Brandon
論文名稱： 人工智慧技術於房價預測之比較
Comparison of Artificial Intelligence Techniques in House Price Prediction
指導教授： 周瑞生
Jui-Sheng Chou
口試委員: 周瑞生
Jui-Sheng Chou
鄭明淵
Min-Yuan Cheng
曾惠斌
Hui-Ping Tserng
周建成
Chien-Cheng Chou
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 114
中文關鍵詞： 房價預測 、人工智慧 、機器學習 、資料探勘 、混和模型 、元啟發式演算法 、粒子群優化法
外文關鍵詞： House price prediction, Artificial Intelligence, Machine learning, Data mining, Hybrid model, Metaheuristic algorithm, Particle swarm optimization
相關次數： 點閱：248  下載：0 
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    Real estate is one of the most critical investments in the household portfolio, which occupies the greatest proportion of the wealth of the private household in highly-developed countries. This research conducted a comprehensive review of machine learning techniques in house price prediction. The data of the dwelling transaction price were collected from real price registration system of the Ministry of the Interior, from 2013 through to 2017 in Taipei City, Taiwan. Four well-known artificial intelligent techniques, Artificial Neural Network, Support Vector Machine, Classification and Regression Tree, and Linear Regression were implemented for developing both the baseline and ensemble models. Additionally, a hybrid model was built to compare the predictive performance with the models in baseline and ensemble scenarios. The comprehensive comparison demonstrated that the PSO-Bagging-ANNs hybrid model outperforms other models in this investigation, as well as the previously proposed models in works of literature. With the provision of multiple prediction models, users are able to determine the most suitable technique based on their backgrounds, demands, and comprehension of artificial intelligence, for the house price prediction.

    ABSTRACT ACKNOWLEDGMENT TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES ABBREVIATION AND SYMBOLS Chapter 1: Introduction 1.1 Research Background and Motivations 1.2 Research Objectives 1.3 Research Process Chapter 2: Literature Review 2.1 Traditional Real Estate Valuation Methods 2.2 Hedonic Price Theory 2.3 Artificial Intelligence in House Price Prediction Chapter 3: Methodology 3.1 Box plot 3.2 Feature Selection Method 3.3 Baseline Prediction Models 3.3.1 Artificial Neural Networks 3.3.2 Support Vector Machine for classification and regression 3.3.3 Classification and Regression Tree 3.3.4 Linear Regression 3.4 Ensemble Prediction Models 3.4.1 Voting 3.4.2 Bagging 3.4.3 Stacking 3.5 Hybrid Models 3.6 Performance Evaluation 3.6.1 Cross-Fold Validation 3.6.2 Statistic Indicators Chapter 4: House Price Dataset 4.1 Data Preprocessing 4.1.1 Data Cleaning 4.1.2 Box Plot Elimination 4.2 Feature Description 4.2.1 Feature Collection 4.2.2 Feature Selection 4.2.3 Distance Calculation 4.2.4 Description of Residential School District Chapter 5: Model Development 5.1 Baseline Prediction Models 5.2 Ensemble Prediction Models 5.3 Hybrid Prediction Model Chapter 6: Model Performance and Discussion 6.1 Performance in Baseline Models 6.2 Performance in Ensemble Models 6.3 Performance in Hybrid Model 6.4 Comprehensive Comparison and Discussion Chapter 7: Conclusion and Recommendation References Appendix A. Main codes of the data cleaning process Appendix B. The demonstration of samples after data cleaning Appendix C. Collection of features from works of literature Appendix D. Main codes for calculating the distance between the residences and the nearest 3 MRT exits Appendix E. Main codes of intercepting the distance to the nearest MRT exit Appendix F. The actual distance between the buildings and YIMBYs along with NIMBYs Appendix G. The coordinates of YIMBYs Appendix H. The coordinates of NIMBYs Appendix I. The locations of YIMBYs in Taipei city Appendix J. The locations of NIMBYs in Taipei city Appendix K. MATLAB codes of PSO-Bagging-ANNs model K.1 MATLAB_PSO_Bagging_ANNs.m K.2 Costfun.m K.3 Object_Bagging_ANNs_Fun.m K.4 PSO.m K.5 Bagging_ANNs_Model_Result.m

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