Abstract

Accurate prediction of house prices is crucial in the real estate industry, especially with the dynamic nature of the market and the impact of external factors like the COVID-19 pandemic. This research paper aims to explore the effectiveness of machine learning algorithms in predicting house prices, considering both house- specific features and macroeconomic variables to enhance predictive accuracy. The study utilizes datasets containing house-specific features and macroeconomic variables like interest rates, GDP, house price index, CPI, and unemployment rates. Tree-based algorithms such as random forest and XGBoost are employed to develop predictive models. The performance of the models is evaluated using metrics like mean absolute error and mean absolute percentage error to assess predictive accuracy. The results of this research are expected to provide insights into the effectiveness of machine learning algorithms in predicting house prices, highlighting the importance of incorporating macroeconomic variables for enhanced predictive accuracy. By comparing the performance of different algorithms and evaluating the impact of macroeconomic features, this study seeks to contribute to the advancement of house price prediction models, which are crucial for various stakeholders in the real estate market.