Quantitative Methods (M) Semester 1, 2020
Major Project (individual project)
1. Instructions
1) This is an individual assignment.
2) The maximum score is 50 points.
3) All numerical analysis, all tables and figures need to be done using Excel or Stata.
4) Please retain your Stata code and Excel file and make sure that they are user-friendly (use comments where necessary). Using your submitted analysis file, one should be able to produce all your results, tables, and figures.
5) The presentation of your write-up is important. Poorly presented work may result in loss of marks (up to 20 marks out of 50).
6) Please retain a copy of the project that is submitted.
7) You would need to submit:
a) Stata code (if you use it)
b) Excel file with analysis (if you use it)
c) the report (in doc, docx, or pdf format) with ‘Assignment Cover Sheet’, which must be signed (electronic signature is okay) and dated before submission; the report should be properly formatted and be similar to business report.
8) Lecturer can refuse to accept assignments, which do not have a signed acknowledgment of the University’s policy on plagiarism.
9. Any suspected plagiarism will be severely punished. This includes any student that submits copied work or any student that allows their work to be copied.
10. You must acknowledge any external material you use in your answers, e.g., material from websites, textbooks, academic journals and newspaper articles.
11. All queries for this project should be directed to Lecturer.
12. The submission deadline for the problem set is 6pm, Wednesday the 10th of June, 2020.
13. The submission must be done through MyUni.
14. Late submission will be penalized 5 points (out of 50 points) per day.
2. Agenda
Assume that now it is the end of March 2018, and you are a real estate consultant in Melbourne. The Melbourne real estate data for the 2016-2018 period was downloaded from the following website: https://www.kaggle.com/anthonypino/melbourne-housing-market.
The data set includes the following variables:
Variable Description
Suburb Suburb
Address Address
Rooms Number of bedrooms
Type H - House; U - Unit; T - Townhouse
Price Price in Australian dollars
Method Sale method:
PI - property passed in (property not sold yet);
S - property sold in auction;
SA - sold after auction;
SP - property sold prior;
VB - vendor bid (property not sold yet)
PN - sold prior not disclosed;
SN - sold not disclosed;
NB - no bid (property not sold yet);
W - withdrawn prior to auction (property not sold yet).
SellerG Real estate agent
Date Date sold
Distance Distance from CBD in Kilometres
Postcode Postcode
Bedroom2 Number of bedrooms
Bathroom Number of bathrooms
Car Number of car spots
Landsize Land size in sq. metres
BuildingArea Building size in sq. metres
YearBuilt Year the house was built
CouncilArea Governing council for the area
Lattitude Latitude
Longtitude Longitude
Regionname General region (West, North West, North, North East etc.)
Propertycount Number of properties that exist in the suburb
Given that there are two variables for the number of rooms (Rooms and Bedroom2), please use Rooms in the analysis and ignore Bedroom2.
You got a client who would like to better understand real estate market and purchase the property. Your tasks are listed below.
Task #1: Market conditions (7 points)
Your client would like to know more about the market conditions about the real estate market in Melbourne (including all suburbs). Please provide the time series graphs for quarterly sale volume (the number of sold properties) as well as mean and median sold price for the 20162018 period.
Has the average property price increased in the first quarter of 2018 compared to the last quarter of 2017?
Have the mean and median property price increased in the first quarter of 2018 compared to the first quarter of 2017? Which measure (mean or median) is more suitable to use to describe the property price evolution?
Discuss the results, including the limitations of the tests.
Task #2: Descriptive statistics (7 points)
Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to do this is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods while also noting any peculiarities within the data set. In addition, your client would like to see:
• the histogram of the sold price for the entire sample
• and the histograms of sold price for the year 2016 and the year 2017 on the same figure.
Task #3: Property price estimation (13 points)
Your client would like to purchase the following property:
• Suburb: Balwyn North
• Distance: 9.2
• Rooms: 4
• Type: House
• Car: 2
• Bathroom: 3
• Landsize: 700 • BuildingArea: 220.
You are expected to build a regression model of house prices. In doing so, make sure that you use an appropriate number of predictors to develop your estimates. Once you have constructed an appropriate model, use it to obtain:
• A point prediction of the expected property price
• A 90% interval prediction for this price.
Discuss the significance of the independent variables in your model. Suppose your client is flexible and he can drop the requirement for house are to be 220 sq. meters. Please compute:
• A point prediction of the expected property price • A 90% interval prediction for this price.
Compare the obtained results with the previous findings.
Your client’s friend is a builder, and he told your client that it is possible to extend a 3-bedroom house to a 4-bedroom house for $150,000. Would you recommend your client to buy a 3bedroom house and then renovate it rather than to buy a 4-bedroom house (assuming that after renovation the 3-bedroom house will become equivalent to a 4-bedroom house)?
Your client expects that property price increases with the number of rooms (controlling for many other factors) and would like to know whether this positive relation is impacted by the number of bathrooms.
Task #4: Hypothesis testing (7 points)
You client hear that property prices in Brighton are higher than in Balwyn North. Develop the appropriate hypotheses and test them using t-test and regression analysis. Discuss the results.
Task #5: Buying property (9 points)
Your client would like to know whether property price depends on the sale method. Would you recommend your client to purchase property prior to the auction, or in the auction, or after the auction, given the empirical data?
Your client has been told that large real estate agencies are able to sell properties at higher prices. You are asked to check this statement. Assume the following agent types:
• big (market share over the sample period in terms of number of sold properties = 6%)
• medium (1% = market share over the sample period in terms of number of sold properties 6%), and
• small (market share over the sample period in terms of number of sold properties 1%).
Would you recommend your client to take into account the agent type when buying the property?
Task #6: Limitations of the analysis (7 points)
Discuss the limitations of the analysis and how they affect your recommendations to your client.
Additional information
Our sample is likely to contain missing values, outliers and be subject to other imperfections. You may need to consider them prior to starting the analysis. Further, the sample includes properties that were not sold. You should drop those observations.
To ensure that regression residuals “behave well,” you may need to scale or transform one or more variables. For example, to use a natural logarithm value of the variable instead of its raw value.
Good luck!