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Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model

Hossam H. Mohamed, Ahmed H. Ibrahim, Omar A. Hagras

Abstract


There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decision-makers to use.

 

Doi: 10.28991/CEJ-SP2023-09-04

Full Text: PDF


Keywords


Residential; Real Estate; Price; Decision Makers; Deep Learning.

References


Sirmans, G. S., Macpherson, D. A., & Zietz, E. N. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 3–43. doi:10.1080/10835547.2005.12090154.

Li, L. (2015). Prices and Bubbles: Factors Affecting the Chinese Real Estate Market. Master Thesis, Lund University, Lund Sweden.

Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843–2852. doi:10.1016/j.eswa.2008.01.044.

Borowiecki, K. J. (2009). The Determinants of House Prices and Construction: An Empirical Investigation of the Swiss Housing Economy. International Real Estate Review, 12(3), 193–220. doi:10.53383/100112.

Rafiei, M. H., & Adeli, H. (2016). A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units. Journal of Construction Engineering and Management, 142(2), 04015066, 1–8. doi:10.1061/(asce)co.1943-7862.0001047.

Limsombunc, V., Gan, C., & Lee, M. (2004). House Price Prediction: Hedonic Price Model vs. Artificial Neural Network. American Journal of Applied Sciences, 1(3), 193–201. doi:10.3844/ajassp.2004.193.201.

Khalafallah, A. (2008). Neural network based model for predicting housing market performance. Tsinghua Science and Technology, 13(S1), 325–328. doi:10.1016/s1007-0214(08)70169-x.

Rahman, M. M. (2008). Australian housing market: causes and effects of rising price. Proceedings of the 37th Australian Conference of Economists (ACE 2008), 28 Sptember-4 October, 2008, Gold Coast, Australia.

Candas, E., Kalkan, S. B., & Yomralioglu, T. (2015). Determining the factors affecting housing prices. FIG Working Week, 17-21 May, 2015, Sofia, Bulgaria.

Malpezzi, S. (2008). Hedonic Pricing Models: A Selective and Applied Review. Housing Economics and Public Policy, 67–89. doi:10.1002/9780470690680.ch5.

Yusof, A., & Ismail, S. (2012). Multiple Regressions in Analyzing House Price Variations. Communications of the IBIMA, 1–9. doi:10.5171/2012.383101.

Pow, N., Janulewicz, E., & Liu, L. (2014). Applied Machine Learning Project 4 Prediction of real estate property prices in Montréal. Course project, COMP-598, Fall/2014, McGill University, Montreal, Canada.

Gustafsson, A. & Wogenius, S. (2014). Modelling apartment prices with the multiple linear regression model. Degree Project in Applied Mathematics and Industrial Economics, Royal Institute of Technology, School of Engineering Sciences, Stockholm, Sweden.

Ozgur, C., Hughes, Z., Rogers, G., & Parveen, S. (2016). Multiple linear regression applications in real estate pricing. International Journal of Mathematics and Statistics Invention (IJMSI), 4(8), 30-59.

Dai, H., Xue, G., & Wang, W. (2014). An Adaptive Wavelet Frame Neural Network Method for Efficient Reliability Analysis. Computer-Aided Civil and Infrastructure Engineering, 29(10), 801–814. doi:10.1111/mice.12117.

Story, B. A., & Fry, G. T. (2014). A structural impairment detection system using competitive arrays of artificial neural networks. Computer-Aided Civil and Infrastructure Engineering, 29(3), 180–190. doi:10.1111/mice.12040.

Butcher, J. B., Day, C. R., Austin, J. C., Haycock, P. W., Verstraeten, D., & Schrauwen, B. (2014). Defect detection in reinforced concrete using random neural architectures. Computer-Aided Civil and Infrastructure Engineering, 29(3), 191–207. doi:10.1111/mice.12039.

Patil, P., Shah, D., Rajput, H. & Chheda, J. (2020). House Price Prediction Using Machine Learning and RPA. International Research Journal of Engineering and Technology, 7(3), 5560-5563.

Cheung, K. S., Yiu, C. Y., & Xiong, C. (2021). Housing Market in the Time of Pandemic: A Price Gradient Analysis from the Covid-19 Epicentre in China. Journal of Risk and Financial Management, 14(3), 108. doi:10.3390/jrfm14030108.

Ho, W. K. O., Tang, B. S., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. doi:10.1080/09599916.2020.1832558.

Hu, M. R., Lee, A. D., & Zou, D. (2021). COVID-19 and Housing Prices: Australian Evidence with Daily Hedonic Returns. Finance Research Letters, 43. doi:10.1016/j.frl.2021.101960.

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. doi:10.3390/land11112100.

Corsini, K. R. (2009). Statistical analysis of residential housing prices in an up and down real estate market: a general framework and study of Cobb County, GA. Ph.D. Thesis, Georgia Institute of Technology, Atlanta, United States.

Haider, M., & Miller, E. J. (2000). Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques. Transportation Research Record: Journal of the Transportation Research Board, 1722(1), 1–8. doi:10.3141/1722-01.

Jian, D. S., & Zhang, X. W. (2012). The Real Estate Economics. Shanghai: University of Finance and Economics, 23-78.

Ding, J. (2014). An empirical analysis of factors affecting Chinese Real Estate prices. Central China Normal University. Lingnan Journal of Banking, Finance and Economics, 5(1), 1-13.

Cao, Z. L. (2003). The General Theory of Real Estate Economics. Peking University Press, Beijing, China.

Bartlett, J. E., Kotrlik, J. W. K. J. W., & Higgins, C. (2001). Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 1-43.

Statistical Package for Social Sciences (SPSS). (2017). Ver. 25.0, IBM Corp, Armonk, New York, United States.

Gab-Allah, A. A., Ibrahim, A. H., & Hagras, O. A. (2015). Predicting the construction duration of building projects using artificial neural networks. International Journal of Applied Management Science, 7(2), 123–141. doi:10.1504/IJAMS.2015.069259.

Mohmad, H. H., Ibrahim, A. H., & El Nagar, H. H. (2016). Pre-Tender cost estimate of agriculture subsurface drainage projects. International Journal of Applied Management Science, 8(4), 271–289. doi:10.1504/IJAMS.2016.080314.

El Touny, A. S., Ibrahim, A. H., & Mohamed, H. H. (2021). An integrated sustainable construction project’s critical success factors (Iscsfs). Sustainability (Switzerland), 13(15). doi:10.3390/su13158629.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12, 2825-2830.

Mishra, C., & Gupta, D. L. (2016). Deep Machine Learning and Neural Networks: An Overview. International Journal of Hybrid Information Technology, 9(11), 401–414. doi:10.14257/ijhit.2016.9.11.34.

Yu, D., & Deng, L. (2011). Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP. IEEE Signal Processing Magazine, 28(1), 145–154. doi:10.1109/msp.2010.939038.

Shen, D., Wu, G., & Suk, H.-I. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19(1), 221–248. doi:10.1146/annurev-bioeng-071516-044442.

Xu, A., Liu, Z., Guo, Y., Sinha, V., & Akkiraju, R. (2017). A New Chatbot for Customer Service on social media. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. doi:10.1145/3025453.3025496.

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246. doi:10.1093/bib/bbx044.

Rafiei, M. H., & Adeli, H. (2018). Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of Construction Engineering and Management, 144(12), 1–9. doi:10.1061/(asce)co.1943-7862.0001570.

Chollet, F. (2021). Deep learning with Python. Simon and Schuster, Manhattan, New York, United States.


Full Text: PDF

DOI: 10.28991/CEJ-SP2023-09-04

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Copyright (c) 2022 Omar A. Hagras

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