代写BCPM0097: REAL ESTATE DATA ANALYSIS代写Web开发

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 an end-of-the-module mandatory 2500-word research project. The project's contents should align with a relevant research question that should be 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?

3. Use of AI:

The following category of AI can/cannot be used:

Category 2 – AI tools can be used in an assistive role.

For examples of each category, please go to Using AI tools in assessment

Category 2 - Students are permitted to use AI tools for specific defined processes to support the development of specific skills as required by the assessment, such as data analysis, transcription, translation, generating insights, giving feedback on content, or proofreading content.

Students must acknowledge where they have used AI within their coursework.

4. Assessment sequence and weighting:

L1) Remember the basic principles of how to interpret data.

L2) Understand the challenges of drawing conclusions from data.

L3) Apply the basic econometric concepts to data analysis.

L4) Analyse real estate data using R.

L5) Create visualisations adapted to their objectives.

5. Format:

This assignment has a limit of 2000 words (excluding tables, figures, references and appendices). All sources and references should be acknowledged using the Harvard referencing system.

There is a 10% leeway for the word limit: submissions that are within 10% over or under the word count won’t be penalised.

Type of content                                                                    Counts towards the word limit

Table of contents                                                                   No

Reference list or bibliography at the end                                   No

Cover page                                                                            No

Diagrams, annotated pictures, figures and any other visuals        No

Appendices                                                                            No

Abstract                                                                                 No

Acknowledgements                                                                  No

Footnotes                                                                               Yes

Tables in the main text                                                             No

In-text citations                                                                       Yes

6. Marking Criteria:

Contribution to previous research                   10%

Quality of the data description                   10%

Quality of visualisations                           10%

Applicability of the methods                       20%

Quality of the interpretation of the results             20%

Understanding of the necessary elements (and shortcomings) in causal inference            15%

Replicability of the proposal. Clarity of the code                    5%

Overall presentation. Clarity and coherence of the arguments. Accuracy, style. and grammar             10%