🏖️ Mallorca Airbnb Data Analysis

📊 Built for: Statistics – University of the Balearic Islands (UIB)
📆 Year: 2024/25 🧑‍💻 Team Members: Michele Vincenzo Gentile, Finn Dicke & Angus Pelegrin

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📖 Project Overview

This project presents an in-depth statistical analysis of Airbnb listings in Mallorca 🏝️ across four distinct time periods:

• 📅 2023-12-17
• 📅 2024-03-23
• 📅 2024-06-19
• 📅 2024-09-13

Through this analysis, we dive into descriptive statistics, hypothesis testing, temporal trends, and explore whether Zipf's Law applies to Airbnb review comments.

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🎯 Objectives

The project aims to answer 7 key statistical questions:

1. 📊 Descriptive Statistics

   • Calculate descriptive statistics for price and number_of_reviews grouped by municipality and time period.
   • Present results using kableExtra tables.

2. 📈 Normality Assessment

   • For Pollença and Palma (2024-09-13), study the normality of price (filtered between 50 and 400) and number_of_reviews:
     • Plot histograms 📊, kernel density functions, and normal density overlays.

3. 💰 Price Comparisons

   • Test if the mean price in Pollença is greater than in Palma for all periods, filtering prices between 50 and 400 euros.
   • Perform hypothesis testing, calculate the p-value and confidence interval.

4. 🕒 Temporal Price Changes

   • Compare mean price in Palma between 2023-12-17 and 2024-03-23:
     • Test if prices were lower in December 2023.
     • Provide boxplots for comparison and comment on results.

5. ⭐ Proportion of High Ratings

   • For the period 2024-03-23, test if the proportion of apartments with review_scores_rating > 4 in Palma and Pollença are equal or different.
   • Construct a confidence interval for the difference in proportions.

6. 📅 Periodic Comparison of Ratings

   • Compare the proportion of apartments with review_scores_rating > 4 in Palma between 2023-12-17 and 2024-03-23.

7. 🔢 Zipf's Law Analysis

   • Investigate whether Zipf’s Law applies to the comment lengths of Airbnb listings in Palma (2023-12-17).
   • Conduct a linear regression of frequency vs rank of comment lengths.
   • Justify results both statistically 📊 and graphically 📉.

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🌍 Data Sources

• 📡 Platform: Inside Airbnb
• 🗺️ Location: Mallorca, Spain
• 🕒 Time Periods:
  • December 17, 2023
  • March 23, 2024
  • June 19, 2024
  • September 13, 2024

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🗂️ Repository Structure

Here’s how the project is organized:

Mallorca-Airbnb-Analysis/
│
├── data/             # Raw and processed data files
├── R/                # R scripts for each analysis question
├── reports/          # Final reports in Rmarkdown and HTML
└── docs/             # Project documentation

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🛠️ Required R Packages

Make sure the following R packages are installed before running the project:

Package	Description
tidyverse	Data manipulation and visualization tools
kableExtra	Enhanced tables in Rmarkdown
sf	Spatial data handling for mapping
tmap	Thematic mapping for geospatial data
stringr	String manipulation
stats	Statistical functions and hypothesis tests

📦 Installation

1. Clone the repository:

   git clone https://github.com/mivige/Airbnb.git
   cd Airbnb

2. Install the required R packages:

   install.packages(c("tidyverse", "kableExtra", "sf", "tmap", "stringr", "stats"))

3. Ensure the data files are in the data/ directory.

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📝 License

This project is licensed under the MIT License. Feel free to use, modify, or distribute the code as needed! 🎓

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👥 Team Members

Developed with 📊, 🧠, and a hint of ✨ by:

• Michele Vincenzo Gentile
• Finn Dicke
• Angus Pelegrin

🏫 University of the Balearic Islands (UIB)

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🎉 If you enjoyed this project or found it helpful, feel free to give it a ⭐ on GitHub! Suggestions and feedback are always welcome. 😊