Application of machine learning techniques in estimating the construction cost of residential buildings in the middle East region

The recent global economic crisis resulted in a crash in the real estate market. This has underscored the significance of accurately predicting construction costs for real estate units in the near-term economy. Over the past few decades, there has been limited research conducted on forecasting the construction costs of housing units. Effective cost forecasting, particularly during the early stages, can play a crucial role in minimizing expenses and ensuring the viability of a project. In recent years, the utilization of machine learning techniques has witnessed a significant surge. These techniques offer generalized solutions and demonstrate favorable performance in terms of effort, time, and cost. Therefore, the objective of this paper is to compare the capabilities of different machine learning methods in estimating real estate construction costs. As part of the study, multiple economic variables and indices will be used as inputs for the machine learning models. The paper will also assess and discuss the performance of each technique by comparing it against actual measurements. Ultimately, the aim is to identify the most suitable approach for such cost estimation tasks.