AirBnB Investment Optimization Data Analysis

Project Timeline:

8 weeks (March 2025 - April 2025)

Lisa Tran, Sarah Lin, Heidi Tsui

Team:

Excel, SQL, Tableau and Python

Toolkit:

Role:

Data Cleaner & Data Visualizer

Cleaned datasets, Tableau dashboards, Final recommendation report for listing optimization and third-location investment strategy, Presentation

Deliverables:

Background

We are a consulting team for an Airbnb host seeking to improve listing performance and identify a profitable location for future investment. The goal was to analyze historical data and provide actionable insights to increase occupancy and revenue.

Objective

Increase our Airbnb client’s occupancy rate by 15% at the two current listings and expand to the third location to diversify their portfolio

Datasets used

  1. (Primary Airbnb USA 2024 DataSet [Location & Amenities]): https://www.kaggle.com/datasets/paramvir705/airbnb-data

  2. (Secondary Airbnb USA 2024 Calendar Dataset [Seasonality] ): https://insideairbnb.com/get-the-data/

Methodology

  1. Cleaning the data using a variety techniques in all four software (Excel, SQL, Tableau, and Python)

  2. Create visualizations and analyze them to help aid our AirBnB investment strategy pitch

Data Visualizations

We conducted a multiple regression analysis to measure how different listing characteristics impact revenue. The results revealed that the number of amenities and review scores had the most significant positive correlation with revenue, while the number of reviews showed a slight negative correlation—suggesting that beyond a certain point, reviews plateau in impact. These insights helped us prioritize optimization strategies such as enhancing amenities and maintaining high-quality guest experiences to boost ratings.

To enhance data exploration, we built an interactive Tableau dashboard with dynamic filters, tooltips, and visuals segmented by city, pricing, amenities, and listing type. This allowed stakeholders to drill into patterns such as average revenue per city, review counts, and property types associated with higher earnings. The dashboard supports both high-level overviews and granular exploration, making it a practical tool for investors evaluating Airbnb performance metrics.

Data Analysis

Short Summary: Our analysis revealed that enhancing listings with high-value amenities (like 24-hour check-in and wireless internet) and increasing the number of customer reviews significantly boost occupancy and revenue. We recommended leveraging dynamic pricing tools to align rates with seasonal demand, implementing targeted marketing strategies, and optimizing property features based on guest behaviour data. For portfolio growth, we advised expansion into under-saturated, regulation-friendly markets such as Louisville, Peoria, or Michigan City, which offer high rental yields and long-term demand stability.

These insights are then used to create and backup our Amplify (Seasonal & Amenity Optimization) and Ignite (Third Location Expansion) strategy. Link to our full presentation can be seen visiting the following link Full Analysis/Presentation

Reflections

This project deepened my understanding of data storytelling and stakeholder-driven insights. From cleaning over a million rows of listing data to designing interactive dashboards, I recognized how small decisions in data preparation directly affect the clarity of insights. Collaborating on listing optimization and market strategy also sharpened my ability to translate technical findings into actionable business recommendations.

I was able to achieve the following:

  • Strengthened skills in Excel, SQL, Tableau and Python for large dataset cleaning and transformation.

  • Practiced translating regression and correlation results into visual, decision-ready takeaways.

  • Gained experience designing dashboards for real client use cases.

Conclusion

Our analysis helped identify the key drivers of revenue and occupancy in Airbnb listings—amenities, pricing, guest capacity, and review ratings. Cities like New York and LA led in revenue, but investment potential was higher in smaller, less saturated markets like Peoria and Louisville. Through interactive dashboards, location analysis, and pricing insights, we provided a clear roadmap to improve Jack’s current listings and guide future expansion.

Key takeaways include:

  • Amenities and review scores are strong predictors of Airbnb revenue.

  • Dynamic pricing aligned with seasonality increases profitability.

  • Smaller markets with high yield and relaxed regulations offer strong expansion potential.

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