English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51685188      Online Users : 424
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150175


    Title: 基於機器學習與深度學習之房價預測
    Housing Price Prediction Based on Machine Learning and Deep Learning
    Authors: 胡程鈞
    Hu, Cheng-Jun
    Contributors: 呂桔誠
    林士貴

    Lyu, Jye-Cherng
    Lin, Shih-Kuei

    胡程鈞
    Hu, Cheng-Jun
    Keywords: 房價預測
    機器學習
    深度學習
    深度神經網路
    生成對抗網路
    隨機森林
    XGBoost
    Housing Price Prediction
    Machine Learning
    Deep Learning
    Deep Neural Network
    Generative Adversarial Network
    Random Forest
    XGBoost
    Date: 2024
    Issue Date: 2024-03-01 13:45:23 (UTC+8)
    Abstract: 房屋貸款是許多金融機構的重要業務,準確的房價預測對於這些金融機構是否能夠做出適宜的放款決策以及管控相關風險尤其重要。本研究運用機器學習與深度學習演算法(深度神經網路、生成對抗網路、隨機森林和XGBoost)以及線性迴歸(基準模型)來進行臺北市區域房價指數預測(Study 1),美國波士頓城鎮房價中位數預測(Study 2),以及臺北市住宅大樓每坪單價預測(Study 3)。本研究結果顯示生成對抗網路的預測成效優於線性迴歸和深度神經網路,而隨機森林和XGBoost的預測成效則更優於生成對抗網路。
    Housing loans are important businesses for many financial institutions. Accurate prediction of housing prices is crucial for these financial institutions to make appropriate lending decisions and manage associated risks. This study employs machine learning and deep learning algorithms (Deep Neural Network, Generative Adversarial Network, Random Forest, and XGBoost) and Linear Regression (Baseline Model) to predict housing price indices of districts in Taipei (Study 1), median housing prices of towns in Boston (Study 2), and housing prices per ping of residential buildings in Taipei (Study 3). The results of this research indicate that the predictive performance of Generative Adversarial Network is superior to that of Linear Regression and Deep Neural Network. However, Random Forest and XGBoost exhibit even better predictive performance than Generative Adversarial Network.
    Reference: 1. Aggarwal, K., Kirchmeyer, M., Yadav, P., Keerthi, S., and Gallinari, P. (2020). Benchmarking Regression Methods: A Comparison with CGAN. arXiv preprint arXiv:1905.12868.

    2. Antipov, E.A. and Pokryshevskaya, E.B. (2012). Mass Appraisal of Residential Apartments: An Application of Random Forest for Valuation and A CART-Based Approach for Model Diagnostics. Expert Systems with Applications, 39(2), 1772-1778.

    3. Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein Gan. arXiv preprint arXiv:1701.07875, 2(3), 4.

    4. Aycock, S.A. (2000). The Impact of Fairness, Reference Point, and Human Decision Processing on Negotiation. Journal of Financial Service professionals, 54(2), 76-81.

    5. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32.

    6. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees.

    7. Chen, T.Q. and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining (785-794).

    8. Chen, X., Wei, L., and Xu, J. (2017). House Price Prediction Using LSTM. arXiv preprint arXiv:1709.08432.

    9. Diaz III, J. (1990). The Process of Selecting Comparable Sales. The Appraisal Journal 58(4), 533-540.

    10. Diqi, M., Hiswati, M.E., and Nur, A.S. (2022). StockGAN: Robust Stock Price Prediction Using GAN Algorithm. International Journal of Information Technology, 14(5), 2309–2315.

    11. Do, A.Q. and Grudnitski, G. (1992). A Neural Network Approach to Residential Property Appraisal, The Real Estate Appraiser. 58(3), 38-45.

    12. Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1), 1-26.

    13. Embaye, W.T., Zereyesus, Y.A., and Chen, B. (2021). Predicting the Rental Value of Houses in Household Surveys in Tanzania, Uganda And Malawi: Evaluations of Hedonic Pricing and Machine Learning Approaches. Public Library of Science, 16(2), 1-20.

    14. Frew, J. and Jud, G.D. (2003). Estimating the Value of Apartment Buildings. Journal of Real Estate Research, 25(1), 77-86.

    15. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. Advances in neural information processing systems, 27.

    16. Goodman, A.C. and Thibodeau, T. (2003). Housing Market Segmentation and Hedonic Prediction Accuracy. Journal of Housing Economics, 12(3), 181-201.

    17. Gui, J., Sun, Z., Wen, Y., Tao, D., and Ye, J. (2015). A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. arXiv preprint arXiv:2001.06937.

    18. Harrison, D. and Rubinfeld, D.L. (1978). Hedonic Housing Prices and the Demand for Clean Air. Journal of Environmental Economics and Management, 5(1), 81-102.

    19. Ho, W.K.O., Tang, B., and Wong S.W. (2021). Predicting Property Prices with Machine Learning Algorithms. Journal of Property Research, 38(1), 48-70.

    20. Hsieh, C.F. and Lin, T.C. (2021). Housing Price Prediction by Using Generative Adversarial Networks. In 2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 49-53.

    21. Huang, H., Yu, P.S., and Wang, C. (2018). An Introduction to Image Synthesis with Generative Adversarial Nets. arXiv preprint arXiv:1803.04469.

    22. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

    23. Lin, H., Chen, C., Huang, G., and Jafari, A. (2021). Stock Price Prediction Using Generative Adversarial Networks. Journal of Computer Science, 17(3), 188-196.

    24. Liu, B., Lv, J., Fan, X., Luo, J., and Zou, T. (2022). Application of an Improved DCGAN for Image Generation. Mobile Information Systems, 2022.

    25. Liu, Z., Song, A., Sabar, N., Qin, K., Izuhara, T. (2023). Evolution Enhancing Property Price Prediction by Generating Artificial Transaction Data. In Proceedings of the Conference on Genetic and Evolutionary Computation, 739-742.

    26. Lusht, K.M. (1996). A Comparison of Prices Brought by English Auctions and Private Negotiations. Journal of Real Estate Economics, 24(4), 517-530.

    27. Mackmin, D. (1985). Is There a Residential Valuer in The House? Journal of Valuation, 3(4), 384-390.

    28. Maliene, V. (2011). Specialized Property Valuation: Multiple Criteria Decision Analysis. Journal of Retail & Leisure Property, 9, 443–450.

    29. Mirza, M. and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784.

    30. Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., and French, N. (2003). Real Estate Appraisal: A Review of Valuation Methods. Journal of Property Investment and Finance, 21(4), 383-401.

    31. Park, B. and Bae, J.K. (2015). Using Machine Learning Algorithms for Housing Price Prediction: The Case of Fairtax County, Virginia Housing Data. Expert Systems with Applications, 42(6), 2928-2934.

    32. Rico-Juan, J.R. and de La Paz, P.T. (2021). Machine Learning with Explainability or Spatial Hedonics Tools? An Analysis of The Asking Prices in The Housing Market in Alicante, Spain. Expert Systems with Applications, 171, 114590.

    33. Romero, R.A.C. (2017). Generative Adversarial Network for Stock Market Price Prediction. CD230: Deep Learning, Stanford University, 5.

    34. Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy, 82(1), 34-55.

    35. Rosenblatt, F. (1957). The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain. Psychological Review, 65(6), 386-408.

    36. Soltani, A., Heydari, M., Aghaei, F., and Pettit, C.J. (2022). Housing Price Prediction Incorporating Spatio-Temporal Dependency into Machine Learning Algorithms. Cities, 131(4), 103941.

    37. Tanaka, F.H.K.D.S. and Aranha, C. (2019). Data Augmentation using GANs. arXiv preprint arXiv:1904.0913.

    38. Tay, D.P.H. and Ho, D.K.H. (1991). Artificial Intelligence and The Mass Appraisal of Residential Apartments. Journal of Property Valuation and Investment, 10, 525 -539.

    39. Xu, X. and Zhang, Y. (2021). House Price Forecasting with Neural Networks. Intelligent Systems with Applications, 12, 200052.

    40. Yilmaz, B. (2023). Housing GANs: Deep Generation of Housing Market Data. Computational Economics, 1-16.

    41. Yiu, C.Y., Tang, B.S., Chiang, Y.H., and Choy, L.H.T. (2006). Alternative Theories of Appraisal Bias. Journal of Real Estate Literature, 14(3), 321-344.

    42. Yu, L., Jiao, C., Xin, H., Wang, Y., and Wang, K. (2018). Prediction on Housing Price Based on Deep Learning. International Journal of Computer and Information Engineering, 12(2), 90-99.

    43. Zhang, B., Sui, W., Huang, Z., Qi, M., and Li, M. (2023). Normalizing Flow based Uncertainty Estimation for Deep Regression Analysis. Available at SSRN 4698811.

    44. Zhang, K., Zhong, G., Dong, J., Wang, S. and Wang, Y. (2018). Stock Market Prediction Based on Generative Adversarial Network. Procedia Computer Science 147, 400-406.

    45. Zheng, T., Song. L., Wang, J., Teng, W., Xu, X., and Ma, C. (2020). Data Synthesis Using Dual Discriminator Conditional Generative Adversarial Networks for Imbalanced Fault Diagnosis of Rolling Bearings. Measurement, 158(1), 107741.
    Description: 碩士
    國立政治大學
    國際金融碩士學位學程
    111ZB1009
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111ZB1009
    Data Type: thesis
    Appears in Collections:[國際金融碩士學位學程] 學位論文

    Files in This Item:

    File SizeFormat
    100901.pdf1508KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback