Price Prediction of Ames Housing Through Advanced Regression Techniques

Abstract

House price fluctuates at all times, and it is necessary to predict the future house prices for the reason that it is crucial for buyers, property investors and the economy as a whole. This article reveals the important features relating to house price and demonstrates the method of predicting house prices in Ames, Iowa through advanced regression techniques, such as correlation, feature engineering, and model building. The prediction is performed through machine learning methods based on the dataset containing almost all the features of residential houses in Ames, Iowa. The dataset was originally splitted into the ‘train set’ and the ‘test set’. Six models were chosen, LASSO Regression, Elastic Net Regression, Gradient Boosting Regression, XBoost, LightGBM, and Stacked Model, for evaluation under the five-fold cross-validation to check the performance of the algorithms and report the root-mean-square logarithmic error. Eventually, Gradient Boosting Regression Model is chosen by the lowest root-mean-square logarithmic error of 0.04615 and a low cross-validated value of 0.1174 to predict the house price to benefit buyers, investors, and the whole society.