House Price Prediction using a Machine Learning

Abstract

Data mining is now widely used in the real estate market. The ability to extract data to extract relevant information from raw data makes it very useful to predict house prices, important housing features, and much more. Studies have shown that fluctuations in housing prices often affect homeowners and the housing market. A literature review was conducted to determine the appropriate characteristics and most effective models for real estate forecasting. The findings of this analysis confirmed the use of Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. In addition, our findings also suggest that local agents and real estate agents are key to predicting real estate prices. This research will be of great benefit, especially to housing developers and researchers, to find the most important indicators for determining housing prices and to identify the best machine learning model that will be used to conduct research in this field.