Land Price Prediction System Using Case-based Reasoning

Real estate price prediction is very complex process. Big data and machine learning technology have been introduced in many research areas, and they are also making such an attempt in the real estate market. Although real estate price forecasting studies is actively conducted, using support vector machine, machine learning algorithm, AHP method, and so on, validity and accuracy are still not reliable.In this research, we propose a Case-Based Reasoning system using regression analysis to allocate weight of attributes. This proposed system can support to predict the real estate price based on collecting public data and easily update the knowledge about real estate. Since the result shows error rate less than 30% through the experiment, this algorithm gives better performance than previous one. By this research, it is possible for help decision-makers to expect the real estate price of interested area.