Web-Based Property Value Prediction Utilizing Random Forest Algorithms

Abstract

Property price prediction is a complex problem and has many influencing factors. The lack of applications that can facilitate property appraisers in predicting property values makes it difficult know prices quickly and accurately, so that it can disrupt the balance and efficiency of the property market. Research with the title Development of a Web-based Property Value Prediction System can provide a clearer picture or recommendation about the selling value of property in Surabaya based on physical characteristics, position and location. This research was conducted using one of the machine learning algorithm models, namely Random Forest for data processing and modelling. The data used includes information about land area, road width, designation zone, and indication of land value. The results of the prediction will be displayed on the website by utilising the Laravel frameworks. Evaluation of the developed model showed promising results, with an accuracy of 83% in predicting property values. This shows that the system has the potential to help property appraisers determine property values more effectively.