Prediction of Real Estate Land Prices in the Kingdom of Bahrain

Data Mining plays an important role in prediction model development and knowledge discovery. This paper follows the Cross-Industry Standard Process for Data Mining in order to develop a prediction model for the real estate land prices in different cities and villages within the Kingdom of Bahrain. The data has been collected from different sources which include social media networks, official newspapers, and advertisement newspapers, and then it has been analyzed using WEKA data mining tool for model development and analysis. The objective of this research paper is to use data mining to help the real estate advisors and consumers in the pricing estimations of the real estate land prices, and to cope with any changes in the land prices according to the market trends. The research study considers a number of factors which affect the land price, such as: land length and width, location, and land classification. Linear Regression is used for analysis. The study shows that the model is affected by the variance of the land price between different areas within the Kingdome, and therefore a number of enhancements have been suggested for future development.