Final Course Project

Flying Around Real Estate Development: Persuading with Data Visualizations

Project Overview

The final project is an individual assignment that challenges you to apply data visualization principles to address a complex business decision with competing considerations. You will work with the case study “Flying Around Real Estate Development: Persuading with Data Visualizations” (UVA-BC-0285) to demonstrate your mastery of persuasive visualization and storytelling with data.1

The Business Challenge

You are Logan Clausen, a project manager at Pitchard Development Company (PDC), a boutique real estate development firm in San Francisco. You have identified a promising mixed-use development opportunity near a municipal airport. However, the proximity to the airport raises critical questions:

  • Does airport proximity increase or decrease property value?
  • How do noise levels (measured in CNEL decibels) impact housing prices?
  • Are there market segments that actively seek airport proximity?
  • How can you persuasively communicate the opportunity (or risk) to stakeholders?

Your task is to analyze benchmark data from two comparable California airports (Salinas and Watsonville Municipal Airports) and create compelling visualizations that support a clear recommendation about the development opportunity. The data, in Excel format, are available in the data directory of SMM635’s GitHub repository (see file real_estate.xlsx).

ImportantKey Information
  • Type: Individual project
  • Due Date: December 1, 2025 (15:59 PM)
  • Weight: 40% of final grade
  • Case: UVA-BC-0285 (available on Moodle)
  • Data: Salinas and Watsonville airport housing datasets (UVA-BC-0285X)

Learning Objectives

Through this project, you will:

  1. Apply persuasive visualization techniques to support business recommendations
  2. Design for multiple audiences with different backgrounds and information needs
  3. Handle multidimensional data (price, distance, noise levels, time)
  4. Create narrative flow that guides stakeholders through your analysis
  5. Balance analytical rigor with clarity for non-technical audiences
  6. Make defensible design choices grounded in visualization principles

Case Questions to Address

Your analysis must answer the following:

WarningRequired Analysis Questions

  1. Proximity Analysis: How does distance from the airport terminal correlate with housing values in the benchmark markets (Salinas and Watsonville)?

  2. Noise Impact Assessment: What is the relationship between Community Noise Equivalent Level (CNEL) and property values? Is the Noise Depreciation Index (NDI) of 0.45%-0.64% supported by the data?

  3. Market Segmentation: Are there patterns suggesting different market segments respond differently to airport proximity?

  4. Comparative Analysis: How do the two benchmark airports (Salinas vs. Watsonville) compare? What explains any differences?

  5. Confounding Factors: What other variables beyond noise and distance might be influencing the housing values you observe?

  6. Investment Recommendation: Should PDC pursue this development opportunity? What are the key risks and opportunities?

Deliverables

1. Executive Presentation (Primary Deliverable)

Create a professional presentation (PDF format) that includes:

  • Executive Summary (1 slide): Your recommendation and key findings
  • Context & Business Problem (1-2 slides): Situation overview
  • Data & Methodology (1-2 slides): Brief description of your analytical approach
  • Visual Analysis (6-10 slides): Your data visualizations with insights
  • Recommendation (1-2 slides): Clear action items and risk/opportunity assessment
  • Appendix (optional): Additional supporting analyses

Specifications:

  • Maximum 15 slides (excluding title and appendix)
  • Minimum 6 visualizations, maximum 12
  • Each visualization must be purposeful and support your narrative
  • Professional design consistent with business context
  • Audience: PDC partners (technical literacy varies) and potential investors

2. Technical Report (Supporting Document)

A written report (1,000 words) containing:

  • Introduction: Business context and analytical objectives
  • Data Description: Variables, ranges, potential limitations
  • Methodology: Analytical approach and design choices
  • Findings: Detailed analysis supporting each visualization
  • Design Rationale: Justification for your visualization choices based on course principles
  • Limitations & Assumptions: What your analysis cannot answer
  • Conclusion: Synthesis and recommendation

3. Reproducible Code

Well-documented code that:

  • Loads and preprocesses the datasets
  • Performs all analyses
  • Generates all visualizations in your presentation and report
  • Includes clear comments explaining each step
  • Runs without errors (provide clear setup instructions)

Accepted languages: Tableau,2 R, Python

4. README File

A markdown file explaining:

  • How to set up the environment (packages/libraries needed)
  • How to run your code
  • Description of output files
  • Any known issues or dependencies

Data Description

You will work with two datasets:

Salinas Municipal Airport Dataset

  • Properties analyzed: Houses at varying distances from terminal
  • Distance range: 0.64 to 3.67 miles from terminal
  • Noise categories:
    • ≥60 dB CNEL
    • 55-60 dB CNEL
    • <55 dB CNEL
  • Variables: Property values, distances, noise levels, time series data

Watsonville Municipal Airport Dataset

  • Properties analyzed: Houses at varying distances from terminal
  • Distance range: 0.71 to 2.02 miles from terminal
  • Noise categories:
    • ≥65 dB CNEL
    • 60-65 dB CNEL
    • <60 dB CNEL
  • Variables: Property values, distances, noise levels, time series data
TipUnderstanding CNEL (Community Noise Equivalent Level)

CNEL measures 24-hour noise exposure with penalties for evening/night noise:

  • Quiet urban area (day): 50 dB
  • Quiet urban area (night): 40 dB
  • Countryside (night): 20-25 dB
  • Discomfort threshold: >120 dB
  • Pain threshold: >130 dB
  • Rocket launch: ~200 dB

For context: rustling leaves ≈ 20 dB, normal conversation ≈ 60 dB, lawnmower ≈ 90 dB

Visualization Requirements

Mandatory Visualizations

Your analysis must include at least:

  1. One visualization showing the relationship between distance and property value
  2. One visualization showing the relationship between noise levels and property value
  3. One comparative visualization contrasting Salinas and Watsonville
  4. One multidimensional visualization integrating 3+ variables

Design Principles to Demonstrate

Your visualizations should showcase:

NoteExpected Design Competencies
  • Clarity: Clear titles, labels, legends, and annotations
  • Appropriate chart types: Match visualization to data type and question
  • Hierarchy: Guide the viewer’s attention to key insights
  • Context: Provide reference points and benchmarks where helpful
  • Integrity: Honest representation without distortion
  • Polish: Professional appearance suitable for executive presentation

Tools

You may use any combination of:

  • R: ggplot2, plotly, shiny, patchwork, gganimate
  • Python: matplotlib, seaborn, plotly, altair, bokeh
  • Tableau: For interactive components (export to PDF for submission)
  • Design tools: Illustrator, Figma for final polish (optional)

Submission Requirements

What to Submit

Upload a single compressed folder (.zip or .tar.gz) named LastName_FirstName_FinalProject containing:

LastName_FirstName_FinalProject/
├── presentation.pdf          # Executive presentation (required)
├── report.pdf               # Technical report (required)
├── code/                    # All code files
│   ├── analysis.R (or .py, .tbm)
│   ├── visualizations.R (or .py, .tbm)
│   └── utils.R (optional helper functions)
├── data/                    # Processed data (if applicable)
│   └── processed_data.csv
├── figures/                 # Generated visualizations
│   ├── figure1.png
│   ├── figure2.png
│   └── ...
└── README.md               # Setup and execution instructions

Where to Submit

  • Platform: Moodle (Final Course Project submission link)
  • Deadline: December 1, 2025, 15:59 PM

Evaluation Criteria

Your project will be assessed on:

Visualization Quality (40%)

  • Appropriateness: Chart types match data and questions
  • Design excellence: Professional, polished, clear
  • Insight generation: Visualizations reveal meaningful patterns
  • Technical execution: Accurate, honest representation
  • Accessibility: Considerate of diverse audiences

Analytical Rigor (25%)

  • Depth of analysis: Thorough exploration of relationships
  • Methodology: Sound analytical approach
  • Critical thinking: Consideration of confounds and limitations
  • Data handling: Appropriate preprocessing and transformations

Storytelling & Persuasion (20%)

  • Narrative flow: Logical progression of ideas
  • Clarity of recommendation: Clear, actionable conclusions
  • Audience awareness: Appropriate for PDC partners and investors
  • Persuasiveness: Compelling case supported by evidence

Technical Quality (10%)

  • Code quality: Clean, documented, reproducible
  • Completeness: All deliverables present and functional
  • Documentation: Clear README and comments

Professionalism (5%)

  • Presentation polish: Executive-ready materials
  • Writing quality: Clear, concise, error-free
  • Organization: Well-structured submission

These criteria will be assessed based on the submitted material and individual presentation.

Getting Started

Step 1: Understand the Context

  1. Read the case study carefully
  2. Research real estate development and airport proximity effects
  3. Understand CNEL and NDI measurements
  4. Identify your key stakeholders and their concerns

Step 2: Explore the Data

  1. Load and examine both datasets
  2. Check for data quality issues
  3. Calculate summary statistics
  4. Create exploratory visualizations (not for final submission)
  5. Identify interesting patterns and relationships

Step 3: Develop Your Argument

  1. Form a hypothesis about the development opportunity
  2. Identify what evidence you need to support it
  3. Design visualizations that reveal this evidence
  4. Consider counterarguments and alternative explanations
  5. Sketch your narrative arc

Step 4: Create & Refine

  1. Build your visualizations
  2. Write your report and presentation
  3. Iterate based on self-critique
  4. Test with a peer (optional but recommended)
  5. Polish and finalize

Resources & Support

Office Hours

  • When: Wednesdays 15:00-17:00, or by appointment
  • Where: Faculty office or online (schedule via email)
  • What to discuss: Methodology, design feedback, technical issues

Academic Integrity

WarningImportant Reminders
  • This is an individual project - all work must be your own
  • You may discuss concepts with classmates but not share code or visualizations
  • Cite all sources including online tutorials, Stack Overflow answers, or AI assistance
  • Use provided data - do not supplement with external datasets
  • Original visualizations - do not copy designs from online examples
  • Violations will be handled according to university academic integrity policies

Success Tips

TipRecommendations for Excellence
  1. Start early: Complex decisions require time to develop nuanced arguments
  2. Iterate often: First drafts of visualizations are rarely final versions
  3. Seek feedback: Use office hours to validate your approach
  4. Think like your audience: Partners care about ROI, investors about risk
  5. Question your assumptions: What if your initial hypothesis is wrong?
  6. Design with purpose: Every element should serve your narrative
  7. Test your code: Make sure everything runs before submission
  8. Proofread everything: Typos undermine professional credibility
  9. Save incrementally: Version control or frequent backups
  10. Keep it simple: Clarity beats complexity

Frequently Asked Questions

Q: Can I use interactive visualizations? A: Yes, but your final presentation must be a PDF. You can include static screenshots of interactive visualizations with annotations explaining the interactive features.

Q: How technical should the presentation be? A: Balance is key. Your audience includes both technical and non-technical stakeholders. Explain concepts clearly without oversimplifying or using jargon unnecessarily.

Q: Should I recommend for or against the development? A: Either conclusion can be correct if well-supported by evidence. The quality of your reasoning and visualization matters more than your specific recommendation.

Q: Can I use external data sources? A: No. Use only the provided Salinas and Watsonville datasets. You may reference external information for context (e.g., NDI research) but not add external data to your analysis.

Q: What if I find data quality issues? A: Document them in your limitations section and explain how you handled them. Real-world data is messy.

Q: How many visualizations should I include? A: Quality over quantity. 6-10 excellent visualizations that tell a coherent story are better than 15 mediocre ones.

Q: Can I work with others? A: This is an individual project. You may discuss general concepts but all analysis, code, and visualizations must be your own original work.

This project is your opportunity to demonstrate mastery of data visualization principles in a realistic business context. Focus on creating visualizations that don’t just show data, but reveal insights and persuade stakeholders. Good luck!

Footnotes

  1. Electronic copy of the case available in the “Case Studies” section of SMM635’s Moodle Page.↩︎

  2. If you use Tableau, include your .tbm file in the submission package.↩︎