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題名 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
作者 胡程鈞
Hu, Cheng-Jun
貢獻者 呂桔誠<br>林士貴
Lyu, Jye-Cherng<br>Lin, Shih-Kuei
胡程鈞
Hu, Cheng-Jun
關鍵詞 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
日期 2024
上傳時間 1-Mar-2024 13:45:23 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
國際金融碩士學位學程
111ZB1009
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111ZB1009
資料類型 thesis
dc.contributor.advisor 呂桔誠<br>林士貴zh_TW
dc.contributor.advisor Lyu, Jye-Cherng<br>Lin, Shih-Kueien_US
dc.contributor.author (Authors) 胡程鈞zh_TW
dc.contributor.author (Authors) Hu, Cheng-Junen_US
dc.creator (作者) 胡程鈞zh_TW
dc.creator (作者) Hu, Cheng-Junen_US
dc.date (日期) 2024en_US
dc.date.accessioned 1-Mar-2024 13:45:23 (UTC+8)-
dc.date.available 1-Mar-2024 13:45:23 (UTC+8)-
dc.date.issued (上傳時間) 1-Mar-2024 13:45:23 (UTC+8)-
dc.identifier (Other Identifiers) G0111ZB1009en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150175-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際金融碩士學位學程zh_TW
dc.description (描述) 111ZB1009zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 摘要 i Abstract ii Table of Contents iii List of Tables v List of Figures vi 1 Introduction 1 2 Literature Review 6 2.1 Hedonic Pricing Theory 6 2.2 Manual Housing Valuation Methods 7 2.3 Housing Price Prediction Models 9 2.4 Generative Adversarial Network 12 3 Methodology 16 3.1 Linear Regression 16 3.2 Deep Neural Network 17 3.3 Generative Adversarial Network 24 3.4 Random Forest 26 3.5 XGBoost 30 3.6 Evaluation Metrics 34 4 Empirical Studies 37 4.1 Study 1 37 4.2 Study 2 43 4.3 Study 3 48 5 Conclusion and Future Work 56 5.1 Conclusion 56 5.2 Future Work 58 Reference 63zh_TW
dc.format.extent 1544488 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111ZB1009en_US
dc.subject (關鍵詞) 房價預測zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 深度神經網路zh_TW
dc.subject (關鍵詞) 生成對抗網路zh_TW
dc.subject (關鍵詞) 隨機森林zh_TW
dc.subject (關鍵詞) XGBoostzh_TW
dc.subject (關鍵詞) Housing Price Predictionen_US
dc.subject (關鍵詞) Machine Learningen_US
dc.subject (關鍵詞) Deep Learningen_US
dc.subject (關鍵詞) Deep Neural Networken_US
dc.subject (關鍵詞) Generative Adversarial Networken_US
dc.subject (關鍵詞) Random Foresten_US
dc.subject (關鍵詞) XGBoosten_US
dc.title (題名) 基於機器學習與深度學習之房價預測zh_TW
dc.title (題名) Housing Price Prediction Based on Machine Learning and Deep Learningen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.zh_TW