Milestone 3 - Model development of property prices
Belle also recently heard in the media that the proportion of sales are as follows:
· 35% of sales are apartments;
· 45% of sales are houses;
· 10% of sales are townhouses;
· 5% of sales are villas and;
· 5% of sales are other.
Belle would like to know if the sample data is statistically different to what the media is suggesting. Remember to state your assumptions and limitations of the result.
In addition to this, Belle would like you to develop a propriety model to predict Sydney property prices. To begin, Belle would like you to run a simple linear regression of
· price as the dependent variable; and
· Tincome as the independent variable.
As part of your reporting, you need to interpret the coefficients of the model and discuss whether they are economically and statistically significant. Also report the confidence interval of dependent variable (price) and interpret the results.
Next, Belle wants you to run a multiple linear regression. As part of this exercise you need to:
· Explain why a multiple linear regression is beneficial i.e. justify the need for multiple linear regression and the issues associated with running a simple linear regression. Contextualise it in the context of the cu
ent problems are there confounding factors which motivate you to do this?
· Associated with this think about the what type of model you’d like to use e.g. level-level, log-level, log-log or level-log model for each of the considered variables. You need to justify this.
· Run the model with the following independent variables:
· No of bedrooms;
· Total Income;
· Location;
· Pcondition;
· Shops; and
· Bus.
and price as the dependent variable. You should also construct at least one interaction variables but it must make sense and must be justified. Creativity will be rewarded if you do this and you (i) construct it co
ectly, (ii) justify it and (iii) provide an interpretation of this variable.
As part of the reporting requirements for the multiple linear regression:
· Interpret the coefficients.
· Define and comment whether each of the coefficients are statistically significant. Remember to state your assumptions.
· Define and comment whether each of the coefficients are economically significant. Remember to state your assumptions. You’ll also need to define what is economically significant and use a benchmark to determine this.
· Limitations and issues with your model. If you decide to use technical terms e.g. multicollinearity, homoskedasticity, bias, consistency etc. you need to explain what these terminologies are and place them in the context of your problem and how it will affect your results.