This coursework focuses on the London Housing Market. You are required to create an appropriate statistical model for predicting average London House prices.

7BUSS001W ECONOMICS FOR MA MANAGEMENT (in Module Assignment) Semester 2 2019-20
This coursework focuses on the London Housing Market. You are required to create an appropriate statistical model for predicting average London House prices. The coursework should be in report format. It is marked out of 100 marks in total and is worth 30% of the final module mark. The overall word count is 1500 words. It should be submitted electronically via Turnitin by 1pm Monday, 2nd March 2020. Details on how to submit your coursework can be found in the Module Handbook. Please be reminded of the University’s rules on plagiarism.
Instructions
1) Select variables (at least 3) which will form the basis of your model and justify their inclusion.
(10 marks)
London`s population
Average incomes
Interest rates
Affordability index
Housing stocks
International buyers
Overseas investments
Medium monthly rates

2) Obtain data sets on your chosen variables from 2000 to 2019 and input your data using the Excel Analysis ToolPak software. (10 marks)

(3) Run a multiple regression on average London House prices (y) and your chosen explanatory variables (x1, x2 and x3) and produce regression analysis output using the Excel Analysis ToolPak software.
(30 marks)
More than one explanatory variable
Y=a+b1x1+b2x2+…..+bnxn
1. Least squares method
2. Get estimate of bs
3. Iterative approach
4. Regression assumption
Multiple regression assumptions
1. There is a linear relationship between y and x
2. The regression errors have to be constant ie no autocorrelation
3. The regression errors independent of one another – no autocorrelation
4. x variables are independent of one another multicollinearity

(4) Use the regression analysis output to assess the explanatory power of the chosen variables on average house prices and the validity of the model itself. You can adopt an iterative approach by removing some variables and replacing them with others.
(50 marks)