Forecasting Multan estate prices using optimized regression techniques

Keywords: regression methods, machine learning, plot prediction, hyper parameter tuning


Purchasing a house or plot has become a complicated task for an average person due to budget constraints and market situations. An individual does not know the prices of the plots and gets trapped by a middle man. This paper proposes a solution for this problem by predicting the plot prices using a machine learning approach, leveraging Multiple Linear Regression, Gradient Boosting Regression, and Random Forest regression techniques. This work compares the performance of these three algorithms by hyper-parameter tuning using gird search, and random search for checking which one is adequate in terms of  scores and error rates. Factors that influence the prices of the plots include plot covered area, physical condition of the plot, area, and population. Gradient boosting regression has surpassed all other machine learning methods, achieving the lowest error rates and highest R-squared score of 0.987 with grid search. The resultant predictive systems can help the folk in three ways. 1) safety from deception 2) budget oriented instant information, and 3) time saving.

Author Biography

Alvi, The Islamia University of Bahwalpur

Muhammad Bux Alvi is an Assistant Professor in the Department of Computer Systems Engineering Engineering, The Islamia University of Bahawalpur, Pakistan. He received his Bachelor of Engineering from QUEST, Nawabshah, Pakistan in 2002, Master of Engineering from MUET, Pakistan in 2010. He is the author of 07 International Journals/Conference papers. His research interests are focused on the field of data mining, sentiment analysis, machine learning, and Roman Sindhi resource development.

How to Cite
Akhter, A., Alvi, M. B., & Alvi, M. (2022). Forecasting Multan estate prices using optimized regression techniques. University of Sindh Journal of Information and Communication Technology , 5(4), 166-173. Retrieved from