ECF5410 Take-Home-Exercise 1 - Task Description

1 Tasks

• The dataset for this exercise is airbnb.csv.

• Using a simple linear regression framework, estimate a hedonic pricing model for airbnb properties of the general form . (using daily rental price).

• X is a vector of property characteristics. You can decide, which property characteristics (i.e. variables) you want to include.

• Estimate the hedonic pricing model for 1) the whole sample, 2) apartments only and 3) houses only. Interpret the estimated coefficients of 1-2 variables of your choice. Do you find any differences in the estimated coefficients between the apartment and the houses subsamples?

• Assume that you are working for a company or government organisation that operates in the Australian short-term rental, or real-estate markets (e.g. Developer, ACCC, stayz, airnbnb, Bunnings, Real Estate Agents etc.). Your supervisor has asked you to analyse the data and use the results to engage with the company’s/organisation’s audience/customers on social media.

• Choose one (or more) of the results from above. Describe the result(s) and its implications for your audience in non-technical terms in a short statement (e.g. social media post; 100-200 words).

• Create a graph (scatter plot, histogram etc.) using ggplot to provide visual support for your statement.

• Important: Correlation does not imply causation! Do NOT use causal language (X leads to Y, one more bedroom increases the daily price by AUD 156.67). Instead Use for example ‘is associated with’, ‘is correlated with’ etc.

• Include the story in your RMarkdown file.

• Submit the RMarkdown via moodle/Week 2/Take Home Exercise by Monday March 15, 12:00pm