Real Estate Price Prediction
Keywords:
Real Estate, Machine Learning, Deep Learning, Market Dynamics, Investment AnalysisAbstract
Real estate price predictions are critical for stakeholders, including investors and developers, because they have a considerable impact on investment decisions and market stability. In order to fill in the shortcomings in earlier approaches, this work presents a novel methodology by utilizing deep learning (DL) and machine learning (ML) techniques to improve real estate price forecast accuracy. We used the "House Prices 2023 Dataset" from Kaggle, which contains 168,000 entries of Pakistani property data. Our methodology included extensive data preparation, feature engineering, and the use of various algorithms, including Linear Regression, Gradient Boosting, Random Forest, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN). The models were tested using MSE, RMSE, R-squared, and accuracy. KNN outperformed the other models, with a lower RMSE of 13.79 and a higher R-squared value of 0.85, indicating improved predictive accuracy. RF also produced impressive results, with an accuracy of 80%. Handling complicated feature interactions, guaranteeing model scalability, and controlling hardware resources were all challenges that suggested possibilities for future improvement. As a result, our research offers a solid foundation for raising forecasting accuracy in fluctuations in the market and emphasizes the possibility of utilizing ML approaches for better real estate price prediction.
References
J. Zaki, A. Nayyar, S. Dalal, and Z. H. Ali, “House price prediction using hedonic pricing model and machine learning techniques,” Concurr. Comput. Pract. Exp., vol. 34, no. 27, p. e7342, Dec. 2022, doi: 10.1002/CPE.7342.
H. Vu Minh, T. Nguyen Hoang, D. Le Doan Minh, and N. Nguyen Minh, “The relevance of factors affecting real estate investment decisions for post pandemic time,” Int. J. Bus. Glob., vol. 1, no. 1, p. 1, 2022, doi: 10.1504/IJBG.2022.10056378.
D. Mahmoudinia and S. M. Mostolizadeh, “(A)symmetric interaction between house prices, stock market and exchange rates using linear and nonlinear approach: the case of Iran,” Int. J. Hous. Mark. Anal., vol. 16, no. 4, pp. 648–671, Aug. 2023, doi: 10.1108/IJHMA-01-2022-0008/FULL/XML.
D. Broxterman and T. Zhou, “Information Frictions in Real Estate Markets: Recent Evidence and Issues,” J. Real Estate Financ. Econ. 2022 662, vol. 66, no. 2, pp. 203–298, Aug. 2022, doi: 10.1007/S11146-022-09918-9.
J. Phangestu and S. Tinggi Manajemen PPM Jakarta, “The Effects of Resource Management to Firm’s Performance Through Exploratory and Exploitative Innovations (An Empirical Research on Real Estate Developer Firms in Indonesia),” pp. 128–133, Aug. 2020, doi: 10.2991/AEBMR.K.200812.023.
K. W. D. Hoang, “Machine learning methods in finance: Recent applications and prospects,” Eur. Financ. Manag., vol. 29, no. 9, pp. 1657–1701, 2023, [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1111/eufm.12408
S. H. Zulkarnain, A. S. Nawi, M. A. Esquivias, and A. Husin, “Determinants of housing prices: evidence from East Coast Malaysia,” Int. J. Hous. Mark. Anal., vol. ahead-of-print, no. ahead-of-print, 2024, doi: 10.1108/IJHMA-10-2023-0139/FULL/XML.
N. Baptista, J. F. Januario, and C. O. Cruz, “Social and Financial Sustainability of Real Estate Investment: Evaluating Public Perceptions towards Blockchain Technology,” Sustain. 2023, Vol. 15, Page 12288, vol. 15, no. 16, p. 12288, Aug. 2023, doi: 10.3390/SU151612288.
D. Broxterman, D. Gatzlaff, M. Letdin, G. S. Sirmans, and T. Zhou, “Introduction to Special Issue: Topics Related to Real Estate Market Efficiency,” J. Real Estate Financ. Econ., vol. 66, no. 2, pp. 197–202, Feb. 2023, doi: 10.1007/S11146-022-09928-7/METRICS.
S. Ziweritin, C. Chimezie Ukegbu, T. A. Oyeniran, and I. O. Ulu, “A Recommendation Engine to Estimate Housing Values in Real Estate Property Market,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 9, no. 1, pp. 1–7, 2021, doi: 10.26438/ijsrcse/v9i1.17.
S. H. Lee, J. H. Kim, and J. H. Huh, “Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach,” Symmetry 2021, Vol. 13, Page 616, vol. 13, no. 4, p. 616, Apr. 2021, doi: 10.3390/SYM13040616.
W. K. O. Ho, B. S. Tang, and S. W. Wong, “Predicting property prices with machine learning algorithms,” J. Prop. Res., vol. 38, no. 1, pp. 48–70, Jan. 2021, doi: 10.1080/09599916.2020.1832558.
B. Al Kurdi, H. Raza, S. Muneer, M. B. Alvi, N. Abid, and M. T. Alshurideh, “Estate Price Predictor for Multan City Townships Using Marching Learning,” Int. Conf. Cyber Resilience, ICCR 2022, 2022, doi: 10.1109/ICCR56254.2022.9996072.
X. Xu and Y. Zhang, “Residential housing price index forecasting via neural networks,” Neural Comput. Appl. 2022 3417, vol. 34, no. 17, pp. 14763–14776, May 2022, doi: 10.1007/S00521-022-07309-Y.
S. S. S. Das, M. E. Ali, Y. F. Li, Y. Bin Kang, and T. Sellis, “Boosting house price predictions using geo-spatial network embedding,” Data Min. Knowl. Discov., vol. 35, no. 6, pp. 2221–2250, Nov. 2021, doi: 10.1007/S10618-021-00789-X/METRICS.
A. Nouriani and L. Lemke, “Vision-based housing price estimation using interior, exterior & satellite images,” Intell. Syst. with Appl., vol. 14, p. 200081, May 2022, doi: 10.1016/J.ISWA.2022.200081.
A. Yousif, S. Baraheem, S. S. Vaddi, V. S. Patel, J. Shen, and T. V. Nguyen, “Real estate pricing prediction via textual and visual features,” Mach. Vis. Appl., vol. 34, no. 6, pp. 1–13, Nov. 2023, doi: 10.1007/S00138-023-01464-5/METRICS.
T. Li, T. Akiyama, and L. Wei, “Constructing a highly accurate price prediction model in real estate investment using LightGBM,” Proc. - 4th Int. Conf. Multimed. Inf. Process. Retrieval, MIPR 2021, pp. 273–276, 2021, doi: 10.1109/MIPR51284.2021.00051.
V. Gampala, N. Y. Sai, and T. N. Sai Bhavya, “Real-Estate Price Prediction System using Machine Learning,” Proc. - Int. Conf. Appl. Artif. Intell. Comput. ICAAIC 2022, pp. 533–538, 2022, doi: 10.1109/ICAAIC53929.2022.9793177.
D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 1, no. 4, pp. 140–147, Dec. 2020, doi: 10.38094/JASTT1457.
H. Tyralis and G. Papacharalampous, “Boosting algorithms in energy research: a systematic review,” Neural Comput. Appl. 2021 3321, vol. 33, no. 21, pp. 14101–14117, Apr. 2021, doi: 10.1007/S00521-021-05995-8.
C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artif. Intell. Rev., vol. 54, no. 3, pp. 1937–1967, Mar. 2021, doi: 10.1007/S10462-020-09896-5/METRICS.
M. Ihme, W. T. Chung, and A. A. Mishra, “Combustion machine learning: Principles, progress and prospects,” Prog. Energy Combust. Sci., vol. 91, p. 101010, Jul. 2022, doi: 10.1016/J.PECS.2022.101010.
G. G.-A. Roozbeh Valavi, Jane Elith, José J. Lahoz-Monfort, “Modelling species presence-only data with random forests”, [Online]. Available: https://nsojournals.onlinelibrary.wiley.com/doi/10.1111/ecog.05615
A. Sekulić, M. Kilibarda, G. B. M. Heuvelink, M. Nikolić, and B. Bajat, “Random Forest Spatial Interpolation,” Remote Sens. 2020, Vol. 12, Page 1687, vol. 12, no. 10, p. 1687, May 2020, doi: 10.3390/RS12101687.
D. Borup, B. J. Christensen, N. S. Mühlbach, and M. S. Nielsen, “Targeting predictors in random forest regression,” Int. J. Forecast., vol. 39, no. 2, pp. 841–868, Apr. 2023, doi: 10.1016/J.IJFORECAST.2022.02.010.
H. C. et Al., “A Comparative Study between the Parameter-Optimized Pacejka Model and Artificial Neural Network Model for Tire Force Estimation,” J. Auto-vehicle Saf. Assoc., vol. 13, no. 4, pp. 33–38, 2021.
N. Mohamed, M. Bajaj, S. K. Almazrouei, F. Jurado, A. Oubelaid, and S. Kamel, “Artificial Intelligence (AI) and Machine Learning (ML)-based Information Security in Electric Vehicles: A Review,” Proc. - 2023 IEEE 5th Glob. Power, Energy Commun. Conf. GPECOM 2023, pp. 108–113, 2023, doi: 10.1109/GPECOM58364.2023.10175817.
A. Alcañiz, D. Grzebyk, H. Ziar, and O. Isabella, “Trends and gaps in photovoltaic power forecasting with machine learning,” Energy Reports, vol. 9, pp. 447–471, Dec. 2023, doi: 10.1016/J.EGYR.2022.11.208.
T. Ma, J. Mou, H. Yan, and Y. Cao, “A new class of Hopfield neural network with double memristive synapses and its DSP implementation,” Eur. Phys. J. Plus 2022 13710, vol. 137, no. 10, pp. 1–19, Oct. 2022, doi: 10.1140/EPJP/S13360-022-03353-8.
S. Uddin, I. Haque, H. Lu, M. A. Moni, and E. Gide, “Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction,” Sci. Reports 2022 121, vol. 12, no. 1, pp. 1–11, Apr. 2022, doi: 10.1038/s41598-022-10358-x.
S. Zhang, “Challenges in KNN Classification,” IEEE Trans. Knowl. Data Eng., Oct. 2021, doi: 10.1109/TKDE.2021.3049250.
S. Zhang and J. Li, “KNN Classification With One-Step Computation,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 3, pp. 2711–2723, Mar. 2023, doi: 10.1109/TKDE.2021.3119140.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 50SEA
This work is licensed under a Creative Commons Attribution 4.0 International License.
