Individual Quantitative Literacy Case Study
Assessment Task 2 – Individual Quantitative Literacy Case Study (1200 words)
Task Background
Assessment Task 2 is Fermi Problem. In Strategic Management we are using the Fermi Problem to demonstrate quantitative numeracy skills. Students will, with limited data, make effective approximate calculations modelling strategic efficiency. This is a synthetic study, and there is no right or wrong answer to the Fermi Problem, however some answers may be more detailed and accurate than others.
Task Purpose
The purpose of Assessment Task 2 is to have students undertake a strategic analysis demonstrating quantitative literacy. Quantitative literacy is a required Graduate Capability for this subject.
Quantitative literacy will be assessed within the following sub-tasks:
1) Creation of a numeric dataset based on specific organizational activities;
2) Interpretation of that numeric dataset;
3) Creating a synthetic model showing calculations with the dataset as a basis;
4) Extrapolation and explanation of outcomes for the synthetic model.
Case Background and Requirements:
You are a consultant providing advice to an industrial client who wishes to diversify their strategic portfolio by acquiring a company that currently owns thirty (30) residential properties in the suburbs of Kingsbury, Preston, Bundoora, and West Heidelberg immediately surrounding the La Trobe University Bundoora Campus.
The properties are all be four (4) bedroom, two (2) bathroom townhouses and houses, and currently service the student accommodation market.
To achieve an above average return your client needs advice on whether they should place all thirty (30) properties under the management of a real estate agent, or place them on AirBnB, or split the portfolio between a real estate agent and AirBnB.
The revenue projection period for this task is one year.
Requirements:
Provide a structured 1200 word report for your client demonstrating the following:
- Research from realestate.com.au and AirBNB
- Systematic research identifying an anticipated efficiency figure*for your client’s AirBNB listed properties
- An assumed efficiency figure*for agency managed properties of 90%
- The weekly rate of return for properties, based on your efficiency figure*, in the suburbs of Kingsbury, Preston, Bundoora, and West Heidelberg found on both realestate.com.au and AirBnB
- Calculations showing the weekly rate of return over 12 months for your client’s properties, derived from your research into realestate.com.au and AirBnB, based on the mode of tenancy which may be either whole-of-property or per room rentals
- The weekly agency costs and fees of properties managed by real estate agents or AirBNB
- Tables showing projected rental income from both realestate.com.au and AirBNB for your client’s thirty (30) properties on both whole-of-property and per room base also showing deductions for agency and AirBNB fees
- A final recommendation for both the distribution of your client’s properties and their mode of tenancy, between real estate agents and AirBnB, based on your calculations
* An efficiency figure is made up of the percentage of properties you anticipate can be reasonably continuously generating a weekly revenue
Structure:
You are to provide a response to this problem in approximately 1200 words (+/- 10%):
An introduction, main body, and conclusion must be provided.
Diagrams and calculations in tables will be essential.
Academic references are NOT required for this submission but if used must be appropriately referenced.
Students should assume their examiner is NOT an accountant or a data-analyst and must provide written explanations for each table and model.
