Housing Price Analysis Using Linear Regression and Logistic Regression: A Comprehensive Explanation Using Melbourne Real Estate Data

Abstract

The use of machine learning aided techniques to analyze real estate data is emerging as a trending research topic and has attracted a lot of interests from both industry and academia. In this paper, both linear regression and logistic regression algorithms are comprehensively reviewed and used to analyze realistic housing price data from the Melbourne real estate market. The trials of the learning process show that the optimum model coefficients which minimize the error function can always be efficiently obtained. The results demonstrate that the trained linear regression model can very accurately predict the average housing price. Also, using the logistic regression algorithm, the houses sold at different councils are precisely classified into different categories.