Applying Machine Learning Models for Forecasting House Prices – A Case of the Metropolitan City of Karachi

Hyder Ali Khan; Junaid Rehman

doi:10.52633/jemi.v5i3.318

Authors

Hyder Ali Khan Mohammad Ali Jinnah University, Karachi, Pakistan. Author
Junaid Rehman Mohammad Ali Jinnah University, Karachi, Pakistan. Author

DOI:

https://doi.org/10.52633/jemi.v5i3.318

Keywords:

House Price Forecasting, Machine Learning Models, Karachi Real Estate Market, Multivariate Regression, Random Forest

Abstract

The real estate market is a crucial component of the economy, and the accurate prediction of house prices is essential for buyers, sellers, investors, and policymakers in order to make informed decisions. Machine learning algorithms can provide a more accurate & efficient approach to predicting house prices by meaningfully utilizing the vast amount of available housing data. The research was aimed at addressing this gap in the extant literature by collecting firsthand data through web scraping from zameen.com and developing a user-friendly interface for accurate price estimation. To this end, seven machine learning algorithms that included: Ada Boost, Gradient Boosting, Random Forest, Ridge Regression, Lasso Regression, Elastic-Net, and Neural Network were evaluated for their performance based on metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) value. The findings showed that the Random Forest model, showing the highest R-square value, performed better than any other model. This suggested that in order to reliably forecast the house price using the given data of the Karachi market, the Random Forest model would be suitable for implementing the price prediction platform. In this regard, the additional GUI features further enhanced the usability, accessibility, and user-friendliness of the price prediction model platform that was proposed for sellers, buyers, and investors of property. Overall, the findings of this research have significant implications for the real estate sector, financial institutions, and government, offering valuable insights for more informed financial decision-making in the dynamic real estate market of Karachi, Pakistan.

Author Biographies

Hyder Ali Khan, Mohammad Ali Jinnah University, Karachi, Pakistan.

Mohammad Ali Jinnah University, Karachi, Pakistan.
Junaid Rehman, Mohammad Ali Jinnah University, Karachi, Pakistan.

Assistant Professor, School of Business Administration, Mohammad Ali Jinnah University, Karachi, Pakistan.

References

Abdulal, A., & Aghi, N. (2020, August 13). House Price Prediction. Kristianstad University Research Portal. https://researchportal.hkr.se/en/studentTheses/house-price-prediction-5

Ahtesham, M., Bawany, N.Z., & Fatima, K. (2020). House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan, 21st International Arab Conference on Information Technology (ACIT), 2020.

Adetunji, A. B., Akande, O. N., Ajala, F. A., Oyewo, O., Akande, Y. F., & Oluwadara, G. (2022). House Price Prediction using Random Forest Machine Learning Technique. Procedia Computer Science, 199, 806–813. https://doi.org/10.1016/j.procs.2022.01.100

Dimoski, M., & Pettersen, M. (2020). Predicting housing prices with machine learning: A macroeconomic analysis of the Norwegian housing market [Master thesis]. https://openaccess.nhh.no/nhh-xmlui/handle/11250/2734788

Donoho, D. (2017). 50 Years of Data Science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734

Faggella, D. (2020, February 26). What is Machine Learning? - An Informed Definition. Emerj Artificial Intelligence Research. https://emerj.com/ai-glossary-terms/what-is-machine-learning/

Garg, K., Das, N.N. & Aggrawal, G. (2023). A Review On: Autism Spectrum Disorder Detection by Machine Learning Using Small Video, 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT), 2023.

Housing Prices Continue to Soar in Many Countries Around the World. (2021, October 18). IMF. https://www.imf.org/en/Blogs/Articles/2021/10/18/housing-prices-continue-to-soar-in-many-countries-around-the-world

Isaac, A. (2022). Combining Machine Learning models to predict House Prices.

Jessica, A., Raj, F.C. & Sankaran, J. (2023). Credit Card Fraud Detection Using Machine Learning Techniques, 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), 2023.

Jordà, Ò., Schularick, M., & Taylor, A. M. (2014). The Great Mortgaging: Housing Finance, Crises, and Business Cycles (Working Paper No. 20501). National Bureau of Economic Research. https://doi.org/10.3386/w20501

Kaewchada, S., Ruang-On, S., Kuhapong, U. & Songsri-in, K. (2023). Random forest model for forecasting vegetable prices: a case study in Nakhon Si Thammarat Province, Thailand, International Journal of Electrical and Computer Engineering (IJECE).

Kintzel, J. D. (2019). Price Prediction and Deep Learning in Real Estate Valuation Models.

Komagome-Towne, A. (2016). Models and Visualizations for Housing Price Prediction. California State Polytechnic University, Pomona.

Leamer, E. E. (2015). Housing Really Is the Business Cycle: What Survives the Lessons of 2008–09? Journal of Money, Credit and Banking, 47(S1), 43–50. https://doi.org/10.1111/jmcb.12189

Levantesi, S. & Piscopo, G. (2020). The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach, Risks, 2020.

LeCun, Y. (2022). A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27.

Miller, N., Peng, L., & Sklarz, M. (2011). House Prices and Economic Growth. Journal of Real Estate Finance and Economics, 42. https://doi.org/10.1007/s11146-009-9197-8

Pakistan Economic Survey. (2022). Pakistan Economic Survey 2021-22 Real Estate Highlights.

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sarojamma, B., & AnilKumar, K. (2018). A Study On Comparison Among Ridge, Lasso, And Elastic Net Regressions. 6(1).

Stewart, M. (2020). The Limitations of Machine Learning. Medium. https://towardsdatascience.com/the-limitations-of-machine-learning-a00e0c3040c6

Tzanis, G., Katakis, I., Partalas, I., & Vlahavas, I. (2006). Modern Applications of Machine Learning.

Zhang, Q. (2021). Housing Price Prediction Based on Multiple Linear Regression. Scientific Programming, 2021, e7678931. https://doi.org/10.1155/2021/7678931

Zhan, C., Liu, Y., Wu, Z., Zhao, M., Chow, T.W.S. (2023). A hybrid machine learning framework for forecasting house prices, Expert Systems with Applications.