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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
dc.contributor.author | Chao-Feng Chiang | en |
dc.contributor.author | 江兆峰 | zh_TW |
dc.date.accessioned | 2021-06-17T07:04:37Z | - |
dc.date.available | 2021-01-20 | |
dc.date.copyright | 2021-01-20 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2021-01-11 | |
dc.identifier.citation | [1] Chin-Oh Chang, Steven C.L. Farr. (1993) A Study of Real Estate Transaction Price. Journal of Housing Study No. 1, pp. 75-97 [2] Jane P. Brown, Haiyan Song, Alan McGillivray. (1997) Forecasting UK house prices: A time varying coefficient approach. Economic Modelling Volume 14, Issue 4 pp. 529-548 [3] Kao Ming-Chih, Chang Chin-Oh, Tsaih Rua-Huan. (1999) The Application of Neural Network to Real Estate Appraisal. [4] Wang Sung-Min, Sun Li-Chun. (1999) 都會地區房價之特徵價格分析-以台北市信義區為例. [5] Li Husiao-Lung. (2002) 出租公寓租金之價格預測 – 複迴歸分析與類神經網路之比較 [6] Wei Ju-Lung. (2003) 類神經網路於不動產價格預估效果之研究 [7] Lee Chia Chang. (2004) The Study on the Investigation and Predictive model of New Hosing Price for the Readjustment Area-The Case Studies in the Hu Wei Liao and the Jheng Zin Liao, Tainan. [8] Visit Limsombunchai, Christopher Gan, Minsoo Lee. (2004) House Price Prediction: Hedonic Price Model vs. Artificial Neural Network. American Journal of Applied Sciences 1(3) pp. 193-201 [9] Peddy Pi-Ying Lai. (2007) Applying the Artificial Neural Network in Computer-assisted Mass Appraisal. Journal of Housing Study No. 16 pp. 43-65 [10] Fu-Yin Wu. (2008) The Determinants of Auction House Price - An Empirical Case Study in Kaohsiung City. [11] Budy Resosudarmo, Arief Anshory Yusuf. (2008) Does clean air matter in developing countries' megacities? A hedonic price analysis of the Jakarta housing market, Indonesia. Ecological Economics 68(5) pp. 1398-1407 [12] Hua Ching Chun. (2010) 電腦大量估價模型於實務應用之探討. 金融聯合徵信雜誌 風險管理類 April. 2010 pp. 27-36 [13] Liu Shih-Hsu, Hsieh Meng-Hsun. (2012) Artificial Neural Network on Court Auction Houses. [14] Ahmad Shazrin Mohamed Azmia, Raz Faeizi Azharb, Abdul Hadi Nawawi. (2012) The Relationship between Air Quality and Property Price. Procedia - Social and Behavioral Sciences 50 pp. 839 – 854 [15] Rangan Gupta, Stephen M. Miller. (2012) The Time-Series Properties of House Prices: A Case Study of the Southern California Market. J Real Estate Finan Econ 44 pp. 339–36 [16] James Bergstra, Dan Yamins, David D. Cox. (2017) Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. Proc. of the 12th Python in Science Cong. [17] Mu Jingyi, Wu Fang, Zhang Aihua. (2014) Housing Value Forecasting Based on Machine Learning Methods. Abstract and Applied Analysis pp. 648047 [18] Roland Füss, Joachim Zietz. (2016) The economic drivers of differences in house price inflation rates across MSAs. Journal of Housing Economics 31, 2016 pp. 35-53 [19] Joshua Aizenman, Yothin Jinjarak, Huanhuan Zheng. (2016) House Valuations and Economic Growth: Some International Evidence. NBER Working Paper No. 22699, 2016 [20] Daniel M. Sullivan Working Paper. (2016) The True Cost of Air Pollution: Evidence from House Prices and Migration. [21] Tianqi Chen, Carlos Guestrin. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [22] Li Li, Kai-Hsuan Chu. (2017) Prediction of real estate price variation based on economic parameters. 2017 International Conference on Applied System Innovation (ICASI) [23] Jiao Yang Wu. (2017) Housing Price prediction Using Support Vector Regression. Master's Projects. 540. [24] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. (2017) LightGBM: a highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems pp. 3149–3157 [25] Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin. (2017) CatBoost: unbiased boosting with categorical features. [26] Scott M. Lundberg, Su-In Lee. (2017) A Unified Approach to Interpreting Model Predictions. 31st Conference on Neural Information Processing Systems (NIPS) [27] Jinze Li. (2018) Monthly Housing Rent Forecast based on LightGBM (Light Gradient Boosting) Model. International Journal of Intelligent Information and Management Science 7(6). [28] Yixuan Ma, Zhenji Zhang, Alexander Ihler, Baoxiang Pan. (2018) Estimating Warehouse Rental Price using Machine Learning Techniques. International Journal of Computers, Communications Control (IJCCC) 13(2) pp. 235-250 [29] J. J. Wang et al. (2018) Predicting House Price with a Memristor-Based Artificial Neural Network. IEEE Access, vol. 6, pp. 16523-16528. [30] Li chuen-sheng, Li shia-ya, chang ke-jia. (2018) 基於遺傳算法改進的 BP 神經網絡房價預測分析 [31] Fu X, Jia T, Zhang X, Li S, Zhang Y (2019) Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning. PLOS ONE 14(5): e0217505. [32] Law, Stephen, Brooks Paige, and Chris Russell. (2019) “Take a Look Around.” ACM Transactions on Intelligent Systems and Technology 10.5 pp. 1–19. [33] Hiroki Nakamura. (2019) Relationship among land price, entrepreneurship, the environment, economics, and social factors in the value assessment of Japanese cities. Journal of Cleaner Production 217, 2019 pp. 144-152 [34] Xin He, Kaiyong Zhao, Xiaowen Chu. (2020) AutoML: A Survey of the State-of-the-Art. arXiv:1908.00709 Online Resources: [1] Ministry of the Interior R.O.C (Sept 2019) Department of Land Administration, Actual Price Registration open data. Available at: https://plvr.land.moi.gov.tw/DownloadOpenData [2] 591 Housing Transaction Platform (Sept 2019) Available at: https://www.591.com.tw/ [3] Central Bank of Republic of China (Feb 2020) Statistic and Publications. Available at: https://www.cbc.gov.tw/en/mp-2.html [4] Taipeiecon (Feb 2020) Growth Rate of Real GDP in Taiwan. Available at: https://www.taipeiecon.taipei/econ_obs_cont.aspx?MmmID=3001 CatID=2 MSid=2001 [5] Environmental Protection Administration Executive Yuan of R.O.C. (May 2020) Environmental and Biological Monitoring and Daily Air Quality. Available at: https://erdb.epa.gov.tw/DataRepository/EnvMonitor/AirQualityMonitorMonData.aspx?topic1=%E5%A4%A7%E6%B0%A3 topic2=%E7%92%B0%E5%A2%83%E5%8F%8A%E7%94%9F%E6%85%8B%E7%9B [6] Google (Feb 2020) Google Trend. Available at: https://trends.google.com.tw/trends/?geo=TW [7] Google API (Feb 2020) Google Place API. Available at: https://developers.google.com/places/web-service/intro [8] Google API (Feb 2020) Google Geocoding API. Available at: https://developers.google.com/maps/documentation/geocoding/intro [9] Intellectual Property Office R.O.C. (Feb 2020) Chinese-English Technical Patent Glossary. Available at: https://paterm.tipo.gov.tw/IPOTechTerm/doIPOTechTermIndex.do | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72727 | - |
dc.description.abstract | 在台灣地區的學術界鮮少使用較新的梯度提升決策樹(GBDT)針對房地產鑑價的應用進行研究,此外,針對不同的變數類別在各大縣市的貢獻度比較則更少。本篇論文採納國內外針對房地產特徵變數的建議,加入了地理位置、時間刻度、總體經濟指標、生活圈、空氣污染與線上行為的變數類別於模型之中,並使用輕量級梯度提升機器(lightGBM)和SHAP的分析套件來展開研究方法。本論文的目的是逐一的評估各大變數類別在全台灣七大主要城市的貢獻度表現,並同時檢測資料集的“時效性”存在與否與對預測結果產生的影響。研究結果發現,地理區位和生活圈的變數可獨立存在為模型主要參考依據,而資料的時效性充斥著整個資料集,建議未來房地產的相關應用應盡可能的更新訓練資料集,或在資產重新估價的專案上,挑選訓練資料時應盡可能包含欲估價的時間範疇。 | zh_TW |
dc.description.abstract | There are few studies in Taiwan using latest GBDT models on real estate market and are even less of them making feature comparison across individual cities. Draw from the studies of previous scholars in Taiwan and oversea, we included feature categories from location, time, economy, living area, pollution and online behavior and utilized the related analysis toolkit of lightGBM and SHAP value. The purpose of this study is to evaluate the contribution of different categories on major cities in Taiwan and also to examine the timeliness of dataset when training a model. The result turned out to be location and living area standalone are most contributing categories. And, data timeliness issue is all over the dataset, it is suggested to train a most up-to-date possible model for predicting the future or covering the re-appraisal period when choosing the training set. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:04:37Z (GMT). No. of bitstreams: 1 U0001-0801202115323700.pdf: 5577701 bytes, checksum: 8df6bba695de01ab022d9bde5360e4e7 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | Contents 摘要 3 ABSTRACT 4 ACKNOWLEDGEMENT 5 CONTENTS 6 LIST OF FIGURES 7 LIST OF TABLES 7 CHAPTER 1 INTRODUCTION 8 1.1 RESEARCH BACKGROUND AND MOTIVATION 8 1.2 RESEARCH PURPOSE 10 1.3 DURATION OF DATA 11 1.4 RESEARCH WORKFLOW AND THESIS ORGANIZATION 12 CHAPTER 2 LITERATURE REVIEW 13 2.1 MODELS USED IN THE HOUSING MARKET 13 2.2 FEATURE CATEGORIES USED IN HOUSING APPRAISAL 16 CHAPTER 3 RESEARCH METHOD 21 3.1 DATASET 21 3.2 FEATURE ENGINEERING 26 3.3 MODEL 32 3.4 DATASETS SPLITTING 36 3.5 TESTING TIMELINESS OF DATASETS 37 3.6 RESEARCH WORKFLOW AND EVALUATION 40 CHAPTER 4 RESULT 42 4.1 MODEL PERFORMANCE 42 4.2 BASIC, MACRO, GEOGRAPHICAL RELATED FEATURES 47 4.3 DATA TIMELINESS 56 4.4 PERFORMANCE IN DIFFERENT CITIES 61 CHAPTER 5 CONCLUSION AND FUTURE WORK 74 5.1 CONCLUSION 74 5.2 FUTURE WORK 75 CHAPTER 6 REFERENCE 76 APPENDIX 84 | |
dc.language.iso | zh-TW | |
dc.title | 以機器學習方法研究臺灣各大縣市影響房地產之特徵 | zh_TW |
dc.title | The Research of Significant Factors of House Price in Taiwan Using Machine Learning Models | en |
dc.type | Thesis | |
dc.date.schoolyear | 109-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳建錦(Chien-Chin Chen) | |
dc.contributor.oralexamcommittee | 周子元(Zi-Yuan Zhou) | |
dc.subject.keyword | 機器學習,輕量級梯度提升機器(lightGBM),房屋鑑價,SHAP分析,台灣實價登錄系統, | zh_TW |
dc.subject.keyword | Machine Learning,lightGBM,housing appraisal,SHAP analysis,Taiwan Actual Price Registration System, | en |
dc.relation.page | 87 | |
dc.identifier.doi | 10.6342/NTU202100032 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2021-01-12 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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