An In-Depth Data Analysis of Airbnb Listings

Insights: The dataset contains information on Airbnb listings in NYC, including columns like price, host_name, neighborhood, room_type, and more.

There were a total of 200 unique neighborhoods in NYC as of 2017.

Three room types were rented out in 2017: Entire home/apt, shared room, and private room.

Highest Price Listed by a Host in Each Neighborhood and Room Type:

The highest prices ($10,000) listed by a host in NYC were in Astoria and Greenpoint. Kathrine listed a private room with the highest price, while Erin listed an entire home.

Neighborhoods with Generally High Fees:

​The neighborhood with the highest average price was Woodrow ($700), indicating generally high fees. On the other hand, the cheapest neighborhood was Graniteville ($20).

Expensive Room Types:

On average, entire homes/apartments were the most expensive room type, while shared rooms were the cheapest.

Number of properties listed by each host and their average prices

Some hosts listed multiple properties, and the average price varied among hosts

Conclusion

Through the analysis of the Airbnb listings in NYC, we gained several insights into the pricing, neighborhoods, and room types. The findings revealed the highest and lowest listed prices, the neighborhoods with higher average prices, and the room types that were more expensive. Additionally, we explored the relationship between the minimum nights spent and the average price and identified hosts with multiple properties and their average prices.

Challenges

Some challenges encountered during the analysis include:

Dealing with missing values: The dataset might contain missing or incomplete information, requiring careful handling or imputation techniques.

Data quality issues: Inconsistent data formats, outliers, or incorrect values may affect the analysis and require data cleaning and preprocessing steps.

Interpretation of results: The analysis provides insights based on the available data but may not capture the entire picture. Additional context and external factors might be necessary to make informed conclusions.