shak9345/airbnb_New_York_prices

Airbnb NYC Price Analysis

Project Overview

This project explores Airbnb listings in New York City in order to understand which factors are most closely related to listing prices. The analysis is based on exploratory data analysis (EDA) and focuses on identifying patterns between listing price and variables such as location, room type, reviews, minimum nights, and host-related characteristics.

Research Question

What factors influence Airbnb prices in New York City?

Dataset

The dataset contains around 49,000 Airbnb listings and 16 variables. It includes both numerical and categorical data, such as:

• price
• room_type
• neighbourhood_group
• neighbourhood
• minimum_nights
• number_of_reviews
• reviews_per_month
• availability_365
• latitude
• longitude

This combination makes the dataset suitable for exploring how different listing characteristics are associated with Airbnb prices.

Data Quality and Cleaning

Before beginning the analysis, the dataset was inspected for missing and invalid values.

Main cleaning decisions:

• Missing values in reviews_per_month were filled with 0
• Listings with price = 0 were treated as unrealistic and replaced with the mean valid price
• Identifier-like columns such as id, host_id, name, and host_name were removed from the analytical dataset
• Because the price variable contained many extreme outliers, most of the visual analysis focused on listings up to the 95th percentile of price

At first, the 99th percentile was considered, but the plots were still difficult to interpret due to the large number of extreme outliers. The 95th percentile provided clearer and more readable visualizations.

Main Insights

The analysis suggests that location and room type are the strongest factors associated with Airbnb prices in New York City.

Key findings:

• Listings in Manhattan tend to be more expensive than listings in the other boroughs
• Certain neighborhoods such as Tribeca, NoHo, and DUMBO show especially high average prices
• Entire homes/apartments are significantly more expensive than private rooms and shared rooms
• The combination of location and room type explains price differences better than either variable alone
• Minimum nights show some relationship with price, but the effect is weaker
• Number of reviews does not appear to strongly increase price
• Most numerical variables have only weak linear correlations with price

Key Visualizations

The notebook includes several visualizations used to explore the research question, including:

• Room type distribution
• Neighborhood group distribution
• Price distribution
• Price by room type
• Price distribution by neighborhood group
• Average price by neighborhood and room type
• Geographical distribution of prices
• Top 10 most expensive neighborhoods
• Correlation matrix

Example Visualizations

Price by Room Type

This visualization shows a clear difference between room types. Entire homes/apartments have the highest prices, private rooms are cheaper, and shared rooms are usually the cheapest.

Price Distribution by Neighborhood Group

This graph shows that location matters strongly. Manhattan has the highest overall price distribution, while the Bronx and Staten Island tend to have lower price levels.

Average Price by Neighborhood and Room Type

This plot highlights that price is influenced by the combination of neighborhood and room type, not by only one factor.

Conclusion

Based on the analysis, the main factors influencing Airbnb prices in New York City are location and room type. Listings in more central and high-demand areas tend to be more expensive, and listings offering an entire home or apartment are priced significantly higher than private or shared-room options.

Other variables, such as minimum nights, number of reviews, and availability, appear to have weaker or less consistent relationships with price. Overall, the dataset suggests that Airbnb prices in New York are driven mainly by spatial and structural listing characteristics.

Limitations

This is an exploratory analysis, so the results show associations rather than causal relationships.

Some limitations of the dataset:

• It does not include apartment size, number of bedrooms, amenities, or interior quality
• It does not capture seasonal demand or local tourism events
• Some conclusions may be affected by variables that are not available in the dataset

Future Work

Future work could extend this analysis by:

• building predictive models for Airbnb price
• testing feature importance more formally
• enriching the dataset with additional property-level features such as number of bedrooms, amenities, or distance from major attractions