以機器學習建構房價預測模型：以臺北市房價為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：15

、訪客IP：18.97.14.89

姓名

林志祥(Chih-Hsiang Lin) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

以機器學習建構房價預測模型：以臺北市房價為例
(Constructing a housing price prediction model with machine learning: A case study of the housing market in Taipei)

相關論文

★ 不動產仲介業銷售住宅類別之成交預測模型—以不動產仲介S公司為例	★ 應用文字探勘技術建構預測客訴問題類別機器學習模型
★ 以機器學習技術建構顧客回購率預測模型：以某手工皂原料電子商務網站為例	★ 以機器學習建構股價預測模型：以台灣股市為例
★ 以機器學習方法建構財務危機之預測模型：以台灣上市櫃公司為例	★ 運用資料探勘技術於股票填息之預測模型：以台灣股市上市公司為例
★ 運用資料探勘技術優化次世代防火牆規則之研究	★ 應用資料探勘技術於電子病歷文本中識別相關新資訊
★ 應用深度學習於藥品後市場監督：Twitter文本分類任務	★ 運用電子病歷與資料探勘技術建構腦中風病人心房顫動預測模型
★ 考量特徵選取與隨機森林之遺漏值填補技術	★ 電子病歷縮寫消歧與一對多分類任務
★ 運用Meta-path與注意力機制改善個人化穿搭推薦	★ 運用機器學習技術建構核保風險預測模型：以A公司為例
★ 風扇壽命預測使用大數據分析－以 X 公司為例	★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-7-1以後開放)

摘要(中)

房價是反映城市經濟和社會發展的重要指標。近年來，臺灣房價持續上漲，其中以臺北市房價所得比最高，為房地產市場參與者在決策上帶來了挑戰。儘管機器學習技術已被應用於房價預測，但過去的國內研究存在訓練資料未全涵蓋、缺乏精準交易座標、未針對民用和商用交易資料做比較等侷限性。本研究旨在建立機器學習模型並評估表現，比較自用和商用住宅在不同類型房產住宅大樓和華廈在價格預測上特徵的差異。我們蒐集了房屋、地理和經濟三類原始資料，經過資料前處理，建立了一個包含45個預測特徵的資料集，涵蓋臺北市2016至2021年房價交易資料，共44,918筆紀錄。研究採用了隨機森林、極限梯度提升、自適應提升、類神經網路和K近鄰算法等五種機器學習，透過十折交叉驗證訓練模型，並使用五種評估指標評估模型的預測表現。結果顯示，極限梯度提升表現最好R2為0.82，進一步透過分區跑降低房價預測誤差，平均MAE降至254萬，影響房價預測的共同重要因素為房屋所在區域和建築物建造日期。影響民用房價預測的重要因素為交易目標附近的公共設施，而影響商用房價預測的則為交易移轉總面積。

摘要(英)

Housing prices are a crucial indicator of urban economic and social development. In recent years, housing prices in Taiwan have continued to rise, with Taipei City having the highest price-to-income ratio, posing challenges for real estate market participants in their decision-making processes. Although machine learning techniques have been applied to housing price prediction, previous domestic studies have had limitations such as incomplete training data coverage, lack of precise transaction coordinates, and failure to compare residential and commercial transaction data. This study aims to develop and evaluate machine learning models, comparing the features of price prediction between self-use and commercial residential buildings and apartments. We collected raw data on housing, geographic, and economic factors, and after data preprocessing, constructed a dataset with 45 predictive features, covering housing transaction data in Taipei City from 2016 to 2021, with a total of 44,918 records. The study employed five machine learning algorithms: Random Forest, Extreme Gradient Boosting, Adaptive Boosting, Neural Networks, and K-Nearest Neighbors. The models were trained using ten-fold cross-validation and evaluated with five performance metrics. The results showed that Extreme Gradient Boosting performed the best with an R² of 0.82. Further, regional partitioning reduced the prediction error, lowering the average MAE to 2.54 million NTD. The common key factors affecting price prediction were the region of the house and the construction date of the building. Important factors for predicting residential prices included the proximity of public facilities to the transaction target, while for commercial prices, the total transferred area was significant.

關鍵字(中)

★ 機器學習
★ 房價預測
★ 隨機森林
★ 極限梯度提升
★ 自適應提升
★ 類神經網路
★ K近鄰算法

關鍵字(英)

論文目次

誌謝 5
摘要 I
ABSTRACT II
目錄 III
表目錄 V
圖目錄 VI
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
第二章文獻探討 4
2.1 國外文獻探討 4
2.2 國內文獻探討 10
第三章研究方法 14
3.1 資料蒐集和前處理 15
3.1.1 資料蒐集 15
3.1.2 資料前處理 17
3.2 研究變項說明 18
3.2.1 房屋變數 20
3.2.2 經濟變數 22
3.2.3 地理變數 23
3.3 機器學習技術 24
3.3.1 隨機森林 24
3.3.2 極限梯度提升 25
3.3.3 自適應增強 25
3.3.4 K近鄰算法 26
3.3.5 類神經網路 26
3.4 實驗設計 27
3.5 驗證方法 29
第四章實驗結果與分析 31
4.1 實驗結果 31
4.2 綜合討論 35
第五章結論與建議 37
5.1 研究結論與貢獻 37
5.2 研究限制 38
5.3 未來研究方向 39
參考文獻 41
中文部分 41
英文部分 42

參考文獻

丁美彤(2020)。應用機器學習與空間之鄰域特徵於房價預測。世新大學財務金融學研究所。
王思青(2021)。影響臺北市住宅房價因素之探討。國立臺北大學統計學系。
王能立(2022)。利用機器學習預測新北市房價之研究。輔仁大學資訊管理學系。
余育芳(2024)。機器學習對不動產鑑價模型的應用-以台南市為例。國立中山大學國際資產管理研究所。
邱國祥(2020)。以多元線性迴歸與機器學習模型預估不動產價格-以台中市實價登錄為例。國立中興大學應用數學系所。
洪仁進(2021)。高齡族群對以房養老金融商品使用意願初探-以中臺灣地區為例。南開科技大學福祉科技與服務管理所。
梁宏富、陳奉瑤(2016)。不動產跨區購買之溢價分析。土地經濟年刊，(27)，82-116。
許雅晶(2022)。房價預測模型-以新北市林口區為例。實踐大學資訊科技與管理學系。
陳玟寧(2022)。迴歸機器學習應用於房價預測—以臺北市實價登錄為例。明志科技大學工業工程與管理系。
廖思閔(2023)。應用機器學習於預測桃園市房價。元智大學工業工程與管理學系。
蔡沐樺(2019)。基於可解釋機器學習演算法的房屋價值評估－以高雄市為例。國立中山大學資訊管理學系研究所。
盧怡如(2017)。臺灣房屋所有權與房價對於創業的影響之研究。國立中山大學中國與亞太區域研究所。
Albawi, S., Abed Mohammed, T., & Alzawi, S. (2017). Understanding of a Convolutional Neural Network.
Anghel, I., & Hristea, A. M. (2015). Some Considerations Regarding the International Real Estate Market – Present and Future Predictions. Procedia Economics and Finance, 32, 1442-1452.
Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., & Afonso, C. (2018). Identifying real estate opportunities using machine learning. Applied sciences, 8(11), 2321.
Bourassa, S. C., Hoesli, M., Merlin, L., & Renne, J. (2021). Big data, accessibility and urban house prices. Urban Studies, 58(15), 3176-3195.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Carmona, P., Climent, F., & Momparler, A. (2019). Predicting failure in the US banking sector: An extreme gradient boosting approach. International Review of Economics & Finance, 61, 304-323.
Chen, K.-H., Ching, K.-E., Chuang, R. Y., Yang, M., & Chen, H.-C. (2020). Taiwan Semi-kinematic Reference Frame Based on Surface Deformation Model Derived from GNSS Data, 2003 to 2019. EGU General Assembly Conference Abstracts,
Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 54(6), 1-25.
Dimoski, M., & Pettersen, M. (2020). Predicting housing prices with machine learning: a macroeconomic analysis of the Norwegian housing market
Dokmanic, I., Parhizkar, R., Ranieri, J., & Vetterli, M. (2015). Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Processing Magazine, 32(6), 12-30.
Feng, D.-C., Liu, Z.-T., Wang, X.-D., Chen, Y., Chang, J.-Q., Wei, D.-F., & Jiang, Z.-M. (2020). Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction and Building Materials, 230, 117000.
Heyman, A. V., & Sommervoll, D. E. (2019). House prices and relative location. Cities, 95, 102373.
Ho, W. K. O., Tang, B.-S., & Wong, S. W. (2020). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48-70.
Hu, M., Su, Y., & Ye, W. (2019). Promoting or inhibiting: The role of housing price in entrepreneurship. Technological Forecasting and Social Change, 148, 119732.
Huston, J. H., & Spencer, R. W. (2016). The wealth effects of quantitative easing. Atlantic Economic Journal, 44, 471-486.
Ishak, A., Siregar, K., Ginting, R., & Afif, M. (2020). Orange software usage in data mining classification method on the dataset lenses. IOP Conference Series: Materials Science and Engineering,
Ja’afar, N. S., Mohamad, J., & Ismail, S. (2021). Machine learning for property price prediction and price valuation: a systematic literature review. Planning Malaysia, 19.
Jha, S. B., Babiceanu, R. F., Pandey, V., & Jha, R. K. (2020). Housing market prediction problem using different machine learning algorithms: A case study. arXiv preprint arXiv:2006.10092.
Jha, S. B., Pandey, V., Jha, R. K., & Babiceanu, R. F. (2020). Machine learning approaches to real estate market prediction problem: a case study. arXiv preprint arXiv:2008.09922.
Jiang, Z., & Shen, G. (2019). Prediction of house price based on the back propagation neural network in the keras deep learning framework. 2019 6th International Conference on Systems and Informatics (ICSAI),
Kurvinen, A. T., & Vihola, J. (2016). The impact of residential development on nearby housing prices. International Journal of Housing Markets and Analysis, 9(4), 671-690.
Lorenz, F., Willwersch, J., Cajias, M., & Fuerst, F. (2023). Interpretable machine learning for real estate market analysis. Real estate economics, 51(5), 1178-1208.
Martinez-Mazza, R. (2021). Causes and consequences of the housing affordability crisis.
Mohd, T., Jamil, N. S., Johari, N., Abdullah, L., & Masrom, S. (2020). An overview of real estate modelling techniques for house price prediction. Charting a Sustainable Future of ASEAN in Business and Social Sciences: Proceedings of the 3ʳᵈ International Conference on the Future of ASEAN (ICoFA) 2019—Volume 1,
Mora-Garcia, R.-T., Cespedes-Lopez, M.-F., & Perez-Sanchez, V. R. (2022). Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times. Land, 11(11), 2100.
Nunns, P. (2021). The causes and economic consequences of rising regional housing prices in New Zealand. New Zealand Economic Papers, 55(1), 66-104.
Phan, T. D. (2018). Housing price prediction using machine learning algorithms: The case of Melbourne city, Australia. 2018 International conference on machine learning and data engineering (iCMLDE),
Reisenbichler, A. (2021). The politics of quantitative easing and housing stimulus by the Federal Reserve and European Central Bank, 2008–2018. In Bricks in the Wall (pp. 190-210). Routledge.
Robnik-Šikonja, M., & Kononenko, I. (1997). An adaptation of Relief for attribute estimation in regression. Machine learning: Proceedings of the fourteenth international conference (ICML’97),
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818.
Ryczkowski, M. (2019). Money, credit, house prices and quantitative easing–the wavelet perspective from 1970 to 2016. Journal of Business Economics and Management, 20(3), 546-572.
Shkodina, I., Melnychenko, O., & Babenko, M. (2020). Quantitative easing policy and its impact on the global economy. Financial And Credit Activity-problems of theory and practice, 2, 513-521.
Suroyo, H. (2019). Penerapan Machine Learning dengan Aplikasi Orange Data Mining Untuk Menentukan Jenis Buah Mangga. Seminar Nasional Teknologi Komputer & Sains (SAINTEKS),
Tekin, M., & Sari, I. U. (2022). Real Estate Market Price Prediction Model of Istanbul. Real Estate Management and Valuation, 30(4), 1-16.
Thange, U., Shukla, V. K., Punhani, R., & Grobbelaar, W. (2021). Analyzing COVID-19 Dataset through Data Mining Tool “Orange”. 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM),
Truong, Q., Nguyen, M., Dang, H., & Mei, B. (2020). Housing Price Prediction via Improved Machine Learning Techniques. Procedia Computer Science, 174, 433-442.
Wang, F., Zou, Y., Zhang, H., & Shi, H. (2019). House price prediction approach based on deep learning and ARIMA model. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT),
Wang, P.-Y., Chen, C.-T., Su, J.-W., Wang, T.-Y., & Huang, S.-H. (2021). Deep learning model for house price prediction using heterogeneous data analysis along with joint self-attention mechanism. IEEE Access, 9, 55244-55259.

指導教授

胡雅涵

審核日期

2024-6-21

推文