House Price Prediction Taking Account of the Influence of COVID-19

Abstract

Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level of accuracy, albeit with a minimal difference.