MA609 Business Analytics and Data Intelligence

Introduction

Property prices have been seen to sky rocket in the recent decade in various cities of the world. There are various factors that have been perceived to be the major drivers of these house prices. Several researches have come up with models which various factors considered to have significant influence on house prices in order to be able to predict house prices. In Melbourne Australia, the scenario has been the same. It is for this reason that this research report has collected data on house prices in Melbourne so as to establish a model that can be used to determine house prices.

Understanding the dataset

The Melbourne housing data used in this report contained several variables. So in order to understand the dataset, the variables were categorized into numerical variables and nominal variables. Numerical variables are those variables that take values as numbers while nominal variables are variables that are non-numeric or they are just names. In this dataset not every variable was used. Only variables that were important to the report were used. The nominal variables included method (how the property was sold), type (type of house), sellerG (real estate agent) and region. The numerical variables on the other side included number of rooms of the house, price in dollars, distance from CBD, property count in the suburb, number of bedrooms, number of bathrooms, number of car spots, land size and building area size.

The multiple regression results are as shown in table 6 above. From the look of p-values for the independent variables, it can be said that all the independent variables were significant predictors of the house price. The R-squared value was found to be 0.3903 meaning that the predictor variables are able to explain 39.03% of the variation that occurs in the dependent variable which is house price.

The model produced above is important for any business that is dealing in property since with the model the price of houses can be predicted. Important significant variables to be considered by sellers and buyers can also be found in the model.

Conclusion

From this research report, various conclusions can be made. It can be concluded that the house prices in Melbourne are not normally distributed and most of them are between 500,000 and 3,500,000 dollars. This report also concludes that bathroom, car pot, land size, distance and bedroom2 are significant predictors of house price.

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