estimate a model of vacancy for Central London offices and a model of rents for City of London offices using multiple regression analysis. Using e-view!. estimate a model of vacancy for Central London offices and a model of rents for City of London offices using multiple regression analysis. Using e-view!.
This assignment focuses on model building and the empirical estimation of models with regression techniques. You are required to specify and estimate a model of vacancy for Central London offices and a model of rents for City of London offices using multiple regression analysis. The outcome of this assignment is to present your preferred models, carry out appropriate tests to support their strength and/or their limitations and interpret and discuss the implications of your findings.
The following real estate data and economic series are provided for the period 1980 to 2015 in a separate XL file.
- Nominal office rents in the City of London
- Take up of office space in the City of London
- New office completions – City of London
- Office vacancy rate in Central London
- GDP (gross domestic product) in the UK
- Employment in financial and business services in the UK
- Employment in finance, insurance and real estate in the UK
- CPI (consumer price index)
The start date of the data series varies due to lack of data in the earlier years. The source of the real estate data is Cushman & Wakefield and the source of the economic data is the Office for National Statistics.
The tasks for this assignment include:
- Outline succinctly the stages in the modelling process at the beginning of the assignment.
- Provide information about how you have derived your final models for office vacancy and rents.
- Explain the regression results (interpretation of coefficients, t-ratios, residual sum of squares, R2, regression F-statistic).
- Carry out autocorrelation and unit root tests for the independent variables in your models and comment on the findings.
- Compute the actual and fitted values as well as the errors. Comment on fitted values and errors.
- Check whether your best models pass the key diagnostics tests (normality, serial correlation, heteroskedasticity, RESET).
- Discuss whether the models you have constructed are subject to a structural break.
- Comment on any other issues you consider important to highlight (relating to data, models, their estimation or other).
- Interpretation of your models and practical use.
- What do your models imply for future office vacancy and office rents in the City of London. Calibrate your answers with views from the industry.
You must write a report focusing on the tasks above. You can copy and paste e-views outputs or print screens into your word document. Brief comments should accompany e-views regression and test results. The length of the essay should not exceed 2,500 words.
- Understanding the theory of vacancy and rent determination 25%
- Essential understanding in model building and basic regression analysis. 25%
- Basic knowledge of e-views and running regressions in practice. 25%
- Clarity in the interpretation and presentation of results. 25%
Data are courtesy of Cushman and Wakefield for the sole purpose of this assignment. As such the data should be treated in strict confidence and they should not be disseminated or used for any other purpose. Files with the data must be deleted after submission.
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