Airbnb San Francisco Market Analysis

Course: MSMI 603 – Applied Statistics in Marketing Intelligence
Authors: Sean McDevitt, Taiyi Huang, Jayce Guan, Quan Nguyen
Date: 2024-12-12

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🚀 Project Goals

• Analyze Airbnb San Francisco data to answer:
  1. Which listing types should Airbnb recruit?
  2. Should Airbnb focus on individuals or companies as hosts?
  3. What amenities should Airbnb incentivize?

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📁 Files

• Final Project.R: All R code (data cleaning, analysis, modeling, visualization)
• Stats Final Project Written Report.docx: Full report (methods, results, recommendations)
• SF_Listings.csv: Airbnb listings data (required to run code)
• SF_Reviews.csv: Airbnb guest reviews (required to run code)

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🔧 How to Run

1. Clone this repo:

   git clone https://github.com/your-username/airbnb-sf-market-analysis.git
   cd airbnb-sf-market-analysis

2. Place SF_Listings.csv and SF_Reviews.csv in your working directory.
3. Open Final Project.R in RStudio.
4. Run code blocks in order to reproduce all tables and plots.

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📊 Main Results

• Entire homes get the highest guest ratings.
• Individual hosts outperform company hosts, even controlling for amenities.
• Only outdoor furniture showed a significant positive effect on ratings; Wi-Fi and TV were linked with lower satisfaction.

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📈 Visualizations

• Average ratings by room type
• Host type vs. ratings boxplots
• Amenity impact regression plots
• Word frequency of amenities in reviews

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📬 Contact

Questions?
Email: quannguyen0161@gmail.com

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_{Project for the MSMI program at University of San Francisco.}