ACC73002 Business Analytics and Big Data Real Estate Report

This report presents a business analytics analysis of real estate data from Safe-As-House Real Estate, focusing on non-capital cities and towns in State A. The analysis includes numerical summaries of continuous and categorical variables, such as price, internal area, bedrooms, bathrooms, and garages. The report employs multiple regression models to predict property prices, initially assessing the impact of various factors like type, internal area, bedrooms, bathrooms, and garages. Through Variance Inflation Factor (VIF) analysis, the model is refined to identify the most significant predictors, resulting in a best-fit regression model. The results reveal that bathrooms, property type, garages, and bedrooms are the most influential factors in determining property prices. The report concludes with recommendations for Safe-As-House Real Estate to prioritize these key variables when predicting future prices, providing valuable insights for both the company and potential property buyers. The report also highlights the use of descriptive analysis, data visualization, regression analysis, and model building in understanding the real estate market.