BCPM0097: REAL ESTATE DATA ANALYSIS
1. Introduction:
This document includes important information regarding your summative assessment. Please read this document in full and refer to it while preparing your assignment.
This coursework has one part, with a weighting of 100, marked on a scale of 100.
Please note that this is an INDIVIDUAL coursework.
2. Assessment Brief:
The Real Estate Data Analysis module assessment entails a mandatory 2500-word research project at the end of the module. The project's contents should align with a relevant research question presented in the introduction.
The project will cover all the areas of the module's learning outcomes. The objective is for the student to answer a relevant research question. The question has to be broadly related to Real Estate and have an empirical and causal approach.
The student can use any dataset presented during the course or develop a new one. The dataset must either have been supplemented during the course or be publicly available. The student must be able to provide visualisations for the descriptive statistics on the data.
To answer the research question, the student must use at least one of the methods learned during the course.
The project should provide at least preliminary results. The results should provide not only the numerical results but an interpretation and implications of their meaning. These results must also account for any potential shortcomings in the analysis.
The student must provide the scripts for complete replication of the project. This script. has to provide full replicability of all the project's tables, graphs, and results. It is vital that the script. starts from a raw dataset and allows it to reach the same results as in the project handed. So, the student should check that the code can replicate the research before submitting the assessment.
Students can submit a project proposal during the course to provide further guidance. This is further detailed in section 7.
The final research project report should contain the following sections:
1. Introduction. Contextualise the problem and give examples.
2. Previous Research. What has been done on this topic? What differentiates your proposal from theirs? What are you contributing?
3. Data. What data did you use and why? What is the best way to visualise your data?
4. Method / Identification strategy. How will you solve it? What are the assumptions and shortcomings of this method concerning the research? What is your principal equation/regression to use?
5. Results. Tables/figures/numbers and interpretation of the results.
6. Discussion. What do you conclude? What could have been done better in your research?