House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning
DOI:
https://doi.org/10.33317/ssurj.504Keywords:
Real Time Data, Machine Learning, Housing Sale Price PredictionAbstract
Pakistan’s real estate market has a large impact in GDP growth. Investment in real estate sector in Pakistan is encumbered with lucrative opportunities. The market demand for housing is ever increasing year by year. House sales prices keep on changing and increasing frequently, so there is a need for a system to forecast house sales prices in the future. Several factors that influence house sales price includes; location, physical attributes, number of bedrooms as well as several other economic factors. One of the main motivation of choosing Karachi for the house prediction is that Karachi is capital of Sindh and it has significant importance in country's economic as it is the major commercial and industrial center of Sindh. It is one of the main contribution of the work is that through this the house prediction model based on DHA Karachi data is developed and as per best of our knowledge till today there is no prediction of housing for the country’s important has been developed. has This research paper mainly focuses on real time Defense Housing Authority (DHA) Karachi data, applying different regression algorithms like Decision tree, Random forest and linear regression to find the sales price prediction of the house and compare the performance of these models. Random Forest algorithm gives 98% of accuracy. The proposed work will be very much helpful for the common people, real-estate people, investors and builders to inform them about making decision of selling or buying at Defense Housing Authority (DHA) Karachi.
References
. Alvarez, F., Roman-Rangel, E., & Montiel, L. V. (2022). Incremental learning for property price estimation using location-based services and open data. Engineering Applications of Artificial Intelligence, 107, 104513.
. Gao, G., Bao, Z., Cao, J., Qin, A. K., & Sellis, T. (2022). Location-centered house price prediction: A multi-task learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2), 1-25.
. Mallikarjuna, B., Addanke, S., & Sabharwal, M. (2022). An Improved Model for House Price/Land Price Prediction using Deep Learning. In Handbook of Research on Advances in Data Analytics and Complex Communication Networks (pp. 76-87). IGI Global.
. Renigier-Biłozor, M., Źróbek, S., Walacik, M., Borst, R., Grover, R., & d’Amato, M. (2022). International acceptance of automated modern tools use must-have for sustainable real estate market development. Land Use Policy, 113, 105876.
. Ho, W. K., Tang, B. S., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48-70.
. Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021). Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, 104919.
. Satish, G. N., Raghavendran, C. V., Rao, M. S., & Srinivasulu, C. (2019). House price prediction using machine learning. Journal of Innovative Technology and Exploring Engineering, 8(9), 717-722.
. Alfiyatin, A. N., Febrita, R. E., Taufiq, H., & Mahmudy, W. F. (2017). Modeling house price prediction using regression analysis and particle swarm optimization. International Journal of Advanced Computer Science and Applications, 8(10), 323-326.
. Chen, Z. H., Tsai, C. T., Yuan, S. M., Chou, S. H., & Chern, J. (2015, August). Big data: Open data and realty website analysis. In 2015 8th International Conference on Ubi-Media Computing (UMEDIA) (pp. 84-88). IEEE.
. Kuvalekar, A., Manchewar, S., Mahadik, S., & Jawale, S. (2020, April). House Price Forecasting Using Machine Learning. In Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST).
. Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42(6), 2928-2934.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2022 Lata Bai Gokalani, Bhagwan Das, Dilip Kumar Ramnani, Mahender Kumar, Mazhar Ali Shah (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.