  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.
