Referencing Styles : Harvard Baycoast is a (fictitious) local government area (called a 'city') within greater Melbourne, Australia. It consists of a number of different suburbs, all with their own history of development. The city grew in different stages, with new suburbs gradually emerging. It covers some wealthy suburbs and some not so wealthy. As the name would indicate, the city is located on the Bay. The city stretches for several kilometres along the Bay's lovely beaches, and for several kilometres inland. About 60,000 people live in the suburbs of Baycoast. The main objective is to conduct exploratory, descriptive and causal analysis is to gain a comprehensive understanding of house prices in the Baycoast region and an understanding of the most important factors that impact prices. Your analysis will be based on a random sample of 120 houses from the city. Note that for the purpose of the assignment the unit of analysis is a ‘House’. It is defined as a stand-alone dwelling. That is, flats, apartments, etc are not included in the database. The assignment requires five separate tasks: 1. An overall view of house prices in Baycoast. 2. Identification of the main factors influencing house prices 3. Development of a multiple regression model for prices. 4. Some basic time series analysis of house prices. 5. Discuss the suitability of the data set along with other potential data sources and approaches for the purpose of this analysis. Further details of each task is given below.2 The Data The cross-sectional data collected contains a number of categorical and numerical variables which are described below: Price Selling price of house in $'000 Rooms Number of main rooms in the house Lot Size Area of the block of land (lot) in square metres Age Age of the house in years Area Area of the house in square metres Material Timber = 1, Veneer = 2, Brick = 3 To Train Distance of the house to the nearest train station (kilometres) To Bus Distance of the house to the nearest bus stop (kilometres) To Shops Distance of the house to the nearest shopping centre (kilometres) Street Street appeal as evaluated by the real estate agency: ranges from 0 (lowest appeal) to 10 (highest appeal) Storeys Number of storeys or levels in the house Style Traditional Style = 0, Non-Traditional Style = 1 Bedrooms Number of bedrooms Bathrooms Number of bathrooms Kitchen Style of kitchen: Adequate = 0, Modern = 1 Heating Central or other heating system installed: No Heat = 0, Yes Heat = 1 AirCon Air conditioning installed: No AC (No AirCon) = 0, AC (Yes AirCon) = 1 Bay Views Proportion of views of the Bay from a prominent part of the property: ranges from 0 = Nil views up to 1 = Full views Suburb Three different suburbs: 1 = Brightly, 2 = Tarron B, 3 = Millard Weekly Rent $ Actual or estimated weekly rent in $. Rental Return % Annual rate of return from rent income (Weekly rent x 52)/(Price in $'000) as a percentage (%) Condition The condition of the house in general. Very Poor = 1, Poor = 2, Good = 3, Excellent = 4 Rental Status Vacant (available for rent) = 1; Rented (currently rented) = 2; Owner (occupied by owner) = 3 In addition, time series data is available on Quarterly Median House Prices Time Period Time Period Index Quarter Quarter Description Median House Price ($'000) Median House price in $'0003 Task One – Summary of House Prices Only analyse Price by itself. The importance of other variables is considered in other tasks. You should, at the very least, thoroughly investigate relevant summary measures (and their reliability) for this variable. Also, there may well be suitable tables and graphs that will illustrate, further and more clearly, other important features of house prices. In your report you should comment, where relevant, on data location, central tendency, variability, shape and outliers for this variable. Reference: Module 1 – Topic 2 Task Two – Factors influencing house prices Analyse house prices against other variables included in the data set. Use appropriate descriptive techniques such as cross-tabulations, comparative summary measures, scatter diagrams to identify key relationships. In your report you should only include the most important factors that impact house prices (approximately between 3 – 5 factors). Reference: Module 2 – Topic 4 Task Three – Development of a multiple regression model You should follow the model building process outlined in topic 5. You are only required to consider linear relationships in the model. Each stage of developing your model should be included in your analysis. You will notice in the Baycoast spreadsheet that there are tabs called Q3-1, Q3-2, etc. These are where you place each version of your model. Note that if you have undertaken more iterations of the model then add more worksheets. The report should only include your final model and a description of its overall strength as well as the influence of each variable. Reference: Module 2 – Topics 4 and 5 Task Four – Time Series analysis Quarterly median house prices in Baycoast from Q4, 2009 to Q3, 2013 are given in QtrPriceData worksheet. Develop a multiplicative time series model to forecast median house prices for the next 4 quarters (Q4, 2013 to Q3, 2014). If the observed values for those 4 quarters are as below, calculate the MAPE of the forecast. Time Period Quarter Observed 17 2013-Q4 980 18 2014-Q1 1062 19 2014-Q2 1206 20 2014-Q3 954 Reference: Module 2 – Topic 64 Task Five – Critique the Business Research Approach Discuss the suitability of the general business research approach taken. In your response, include possible alternative approaches and other sources of (secondary) data. If the analysis was to be repeated in the future, would you recommend a different approach? Note that no actual analysis is required for this task