This project analyzes Airbnb listings data in New York City, exploring revenue estimates, pricing trends, and neighborhood performance using Power BI and Python.
The dataset is derived from the public Airbnb Open Data available for NYC (2019).
It includes over 48,000 listings with attributes like:
neighbourhood_group(borough)priceroom_typeavailability_365number_of_reviewsreviews_per_monthlisting coordinates- and more.
Original source: Kaggle Airbnb
🔹 Total Revenue Estimate: $148M
🔹 Average Reviews/Month: 1.37
🔹 Listings with Reviews: 39K+
🔹 Average Price: $152
🔹 Top Boroughs: Manhattan & Brooklyn
🔹 Peak Season: June–July
🔹 Most Popular Room Type: Entire Home/Apt in Manhattan (61%)
The interactive dashboards (created using Power BI) include:
- 💸 Revenue Trends by Year and Neighborhood
- 🗺️ Listing Density Map
- 🏘️ Average Price by Borough
- 📈 Monthly Review Trend
- 🛏️ Room Type Distribution
- 🧮 Price Trend Over Time
- Power BI: Dashboard creation and data visualization
- Python (Pandas, Matplotlib, Seaborn): Data cleaning and exploratory analysis
- Jupyter / Colab: Used for prototyping
- Excel / CSV: Dataset preprocessing
- What areas generate the highest Airbnb revenue in NYC?
- Which room types are most profitable?
- How do prices and reviews change over time?
- How does seasonality impact booking activity?