Data Analytics and Visualization Projects

One of the main goals of this course is to prepare you to apply data analysis and visualization methods and tools to real-world problems in real estate and a broader built environment context. If you are interested in deeper knowledge about data science, this class will also give you a foundation to pursue future work. The final project is your opportunity to put these skills, both covered in the class and any other techniques, into practice.

Students are encouraged to work individually or in teams of up to three people. Please sign up with your group information using the Canvas Assignment provided.

Your first task is to select a project topic. Please start to think about that now! Some students may share related datasets on Ed Discussion, and I compile a list of datasets under Resources - Datasets. Also, I will be happy to brainstorm with you and suggest datasets and research questions anytime.

The project could be, for example:

Applied Data Analysis: Through exploratory data analysis and modeling, such as regression, machine learning techniques, on interesting real estate or urban datasets, to generate new insights.
Interactive Visualization: Producing interactive visualization or dashboard on multiple real estate-related datasets using R or Tableau, with the goal of generating new insights.
Methodological Exploration: A project that applies a statistical or machine learning method, which we did not cover in the course, to real estate or spatial data, and evaluates its performance.
Replication of an Academic Article: Replicate an interesting academic article in the field of real estate. You can use the same datasets with the author or not.

Ideally, projects will blend elements of these categories. Strong projects often emerge from people who have a dataset they are passionate about or a policy problem they want to investigate. If you are already engaged in research or work where real estate data plays a role, you are welcome to adapt your project to that context.

There are five deliverables, together accounting for 30% of the total grade:

Team Formation (1% of the total grade)
Project Proposal (5% of the total grade)
Draft Work Presentation (8% of the total grade)
Final Delivery (15% of the total grade)
Peer Review (1% of the total grade)

Workload Expectation

Each student is expected to contribute approximately 20 hours of high-quality work to the project over the quarter. This includes time spent on data preparation, coding, analysis, writing, and presentation. Active and equitable participation within each team is strongly encouraged.

Team Formation

Form a team (up to three members) and register via Canvas by the deadline (Oct 8, 2025).

How to create a group on Canvas?

In Course Navigation, click the People link.
Click the Add Group button.
Name your group, then Invite Students as your group members.

Project Proposal

Please write a summary of your final project on the following matters:

What are the research question(s) you want to answer?
What are the datasets you are going to use?
- Specify the temporal and spatial extent.
- What are the main variables of interest?
- Are you focusing on patterns within a single variable, or associations between multiple variables?
- Provide the source(s) of your datasets.
What data analysis and/or visualization techniques are you planning to use? Be as specific as you can!
What are the expected results of your projects by the end of this quarter?
What are the potential implications of your findings for real estate practice, policy, or the broader data science community?

Your project proposal should be submitted as a single PDF per group, no longer than 2 pages (ideally 1), and must list the names of all group members. The current deadline for the proposal is November 12, but I encourage you to submit it as early as possible!

Draft Work Presentation

In the last week, each group will prepare a 6-8 minute presentation that covers:

State the overarching research question/motivation for your analysis
Describe the data sources you use for your final project
How you have, and plan to, clean and refine your data
Explain the data analysis and/or visualization techniques you applied
Rough results of analysis and/or visualization
Any problems or challenges you have had so far

You do not need to prepare formal slides for the presentation. Instead, focus on clearly explaining your work so that your classmates can understand your approach and agree with your findings. The class and I will provide preliminary feedback on your work as well as how to appropriately wrangle your data sources. I expect that the feedback from myself and your classmates will be incorporated into your final project.

Attendance during the day of presentations is required, as well as constructive comments and evaluations to your peers.

Final Delivery

The final project may take any format (e.g., written report, website/HTML, dashboard, or poster) as long as it clearly communicates your analysis and findings. However, no matter what format your final delivery takes, you should include the following components:

Basic Info: Names, affiliation/program, and Email address of all group members.
Research Question(s): Clearly state the research question(s), motivation, and or hypothesis you are addressing.
Data Description: Describe the dataset(s) you used, including source(s), temporal and spatial scope, and key variables.
Methods: Explain the data analysis and/or visualization techniques you applied and why they are appropriate.
Results: Present your main findings, including tables, charts, or maps as appropriate.
Discussion: Interpret your results, discuss their implications, and note any limitations.
Conclusion: Summarize your insights and potential next steps.
References: Cite all data sources, articles, and tools you relied on.
Appendix (optional): Code (recommended), additional figures, or technical details.

Peer Review

We will conduct two rounds of peer review:

Draft Work Presentation Peer Review (across-group)
- During the in-class draft work presentation, each student will evaluate other groups’ projects.
- 5% of the total course grade (presentation counts for 8%) will be based on this round of peer review.
Final Delivery Peer Review (within-group)
- After the final delivery, each student will evaluate the contributions of their own group members.
- The final delivery grade for each student will be adjusted to ensure fairness in workload and contribution.