Regression models for house price analysis

The study’s premise was to analyze and predict property prices in the US local market, taking into account various factors that influence property values. The aim was to compare and evaluate machine learning regression models to estimate housing and house prices based on the data available for a given geographical area (the US). This study analyses the relationship between attributes describing the US property market and the price of that property and then selects them accordingly. The selection aims to identify variables whose elimination will improve the properties of the regression model we wish to build. Regularisation is applied, a technique used in machine learning to prevent models from becoming too complex, which can lead to overfitting. Two regularisation techniques were proposed: LASSO and ElasticNet. Reports available from the Joint Center for Housing Studies of Harvard University have shown that many criteria affect the price of housing. On this basis, this paper proposes extensions to the model. The results of the analyses of the LASSO and ElasticNet models indicate the superior performance of LASSO in various data analysis scenarios, especially in the context of spatial and geolocation data.