An Optimal House Price Prediction Algorithm: XGBoost
This work addresses accurate house price prediction for real estate and mortgage sectors, but it is incremental as it compares existing methods without introducing new ones.
The paper tackled house price prediction as a regression task using machine learning techniques on the Ames City dataset, finding that XGBoost was the best-performing model.
An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.