The Airbnb dataset analysis highlights significant variations in listing prices, availability, and review patterns across New York City neighborhoods, offering valuable insights for market stakeholders.

source: kaggle 

Given the dataset, Powerdrill detects and analyzes the metadata, then gives these relevant inquiries:

1. Listing Analysis:

• What is the average price of listings in different neighborhoods?

• How does the availability of listings vary across different neighborhoods?

2. Review Insights:

• What is the average number of reviews per listing?

• How do review ratings correlate with the price of the listing?

3. Neighborhood Trends:

• Which neighborhood has the highest number of listings?

• How do the average prices compare between Manhattan and Brooklyn?

4. Booking and Policies:

• What percentage of listings are instant bookable?

• How does the cancellation policy affect the number of bookings?

5. Temporal Trends:

• What is the trend in the number of reviews over the years?

• How has the average price of listings changed over time?

6. Room Types:

• What is the distribution of different room types (e.g., Entire home/apt, Private room)?

• How does the price vary between different room types?

7. Geographical Insights:

• What are the latitude and longitude ranges for listings in New York City?

• Are there any geographical clusters of high-priced listings?

8. Service Fees:

• What is the average service fee for different price ranges?

• How do service fees vary across different neighborhoods?

Listing Analysis

Average Price of Listings in Different Neighborhoods

The analysis of the Airbnb dataset reveals that the average price of listings varies significantly across different neighborhoods. The average prices range from as low as 107.67 to 1045.00, with a mean average price of approximately $622.48. This variation in pricing can be attributed to factors such as location desirability, proximity to key attractions, and local amenities.

Key Observations:

• Highest Average Price: Neighborhoods like Arden Heights have higher average prices, reaching up to $804.889.

• Lowest Average Price: Conversely, neighborhoods like Allerton have more affordable options, with an average price of $636.344.

Average Availability of Listings in Different Neighborhoods

The availability of listings also shows considerable variation across different neighborhoods. The average availability ranges from 0 days to 365 days annually, with a mean average availability of 168.19 days.

Key Observations:

• High Availability: Neighborhoods like Arrochar show higher availability, with an average of 228.981 days, suggesting less frequent bookings or seasonal variations.

• Low Availability: In contrast, neighborhoods like Astoria have lower availability, averaging around 140.91 days, which could indicate higher demand or more consistent bookings throughout the year.

Visual Analysis

The bar charts provided visually underscore the disparities in both price and availability across neighborhoods. These visualizations help in quickly identifying which neighborhoods are more expensive or have higher availability, aiding stakeholders in making informed decisions regarding property investments or accommodations.

Overall, the data provides valuable insights into the dynamics of the Airbnb market across different neighborhoods, highlighting the economic and availability factors that could influence both hosts and guests in the platform.

Review Insights

Average Number of Reviews per Listing

• Average Reviews: The average number of reviews per listing is 27.48. This indicates the typical engagement level of users with the listings.

Correlation between Review Ratings and Listing Price

• Correlation Coefficient: The correlation between the review rate number and the price is -0.00457609.

• Interpretation: This value suggests that there is no significant correlation between the review ratings and the listing prices. The review ratings do not tend to increase or decrease with higher or lower prices, indicating that other factors might influence the review ratings more than the price itself.

Neighborhood Trends

Neighborhood with the Highest Number of Listings

• Neighborhood: Bedford-Stuyvesant

• Number of Listings: 7937

• Key Insight: Bedford-Stuyvesant has the highest number of listings compared to other neighborhoods.

Comparison of Average Prices Between Manhattan and Brooklyn

• Average Price in Brooklyn: $626.56

• Average Price in Manhattan: $622.44

• Key Insight: Brooklyn has a slightly higher average listing price compared to Manhattan, with a difference of approximately $4.12.

Summary

The analysis reveals that Bedford-Stuyvesant is the most populated neighborhood in terms of listings, indicating a high level of residential or rental activity. Additionally, when comparing the average prices of listings, Brooklyn is marginally more expensive than Manhattan. This information could be valuable for potential renters, buyers, or investors looking at trends in these areas.

Booking and Policies

Instant Bookable Listings Percentage

• Percentage of Instant Bookable Listings: The data indicates that 0% of the listings are instant bookable. This suggests that none of the listings in the dataset are available for instant booking.

Impact of Cancellation Policy on Number of Bookings

• Cancellation Policy and Booking Frequency:

• Flexible Policy: Listings with a flexible cancellation policy have the highest average number of reviews, approximately 27.56. This suggests that these listings are slightly more popular among users, potentially due to the leniency in cancellation which could encourage more bookings.

• Moderate Policy: Listings with a moderate cancellation policy have an average of 27.47 reviews, indicating a moderately high frequency of bookings.

• Strict Policy: Listings with a strict cancellation policy have the lowest average number of reviews, about 27.34. This could imply that the strict nature of the policy might deter some users from booking these listings.

Summary

• The dataset shows that there are no instant bookable listings, which could affect user convenience and booking immediacy.

• Listings with more lenient cancellation policies (flexible and moderate) tend to have higher booking frequencies as indicated by the average number of reviews, suggesting that users prefer properties with less stringent cancellation terms.

Temporal Trends

Trend in the Number of Reviews Over the Years

Observations:

• The data shows a significant spike in the number of reviews around the year 2020, with a peak reaching approximately 1.8 million reviews.

• There are two notable peaks observed: one around 2020 and a smaller one around 2040.

• Outside of these peaks, the number of reviews remains relatively low, close to zero.

Conclusion:

• The trend indicates that there were periods of extremely high activity in 2020 and 2040, possibly due to specific events or changes in market dynamics.

• The general trend, aside from these peaks, suggests a low and stable number of reviews annually.

Trend in the Average Price of Listings Over Time

Observations: 

• The average price of listings shows considerable fluctuation over the years.

• There is a noticeable decline in average prices starting around 2012, stabilizing somewhat until a sharp increase in recent times.

• The prices range from as low as approximately 185 to 920.

Conclusion:

• The average price of listings has experienced volatility, with a general downward trend until recent sharp increases.

• This could reflect changes in market conditions, economic factors, or shifts in consumer preferences over the years.

Visual Insights:

• The line charts provided visually support the observations, clearly showing the spikes and trends in both the number of reviews and average prices over the specified periods.

Overall Summary:

The analysis of both the number of reviews and average prices over time reveals significant temporal fluctuations, which could be influenced by a variety of external factors affecting consumer behavior and market dynamics.

Room Types

Distribution of Different Room Types

• Most Common Room Type: The dataset predominantly features 'Entire home/apt' and 'Private room' types, with counts of 53,701 and 46,556 respectively. These two categories dominate the market.

• Least Common Room Types: 'Shared room' and 'Hotel room' are significantly less common, with only 2,226 and 116 listings respectively.

Price Variation Between Room Types 

• Highest Average Price: 'Hotel room' has the highest average price at approximately $668.47, indicating it might offer more exclusive or premium accommodations.

• Comparable Pricing: 'Entire home/apt' and 'Private room' have similar average prices around $625, suggesting a competitive pricing strategy between these common room types.

• Moderately Priced: 'Shared room' stands slightly higher than the lowest average at about $634.13, which might reflect a niche market or specific conditions that slightly elevate its pricing.

Visual Insights

Bar Chart Analysis: The provided bar charts visually reinforce the numerical data, clearly showing the dominance of 'Entire home/apt' and 'Private room' in availability and the higher pricing of 'Hotel rooms'.

Overall, the data suggests a market heavily skewed towards entire homes and private rooms, with pricing that reflects a premium for hotel rooms and a competitive landscape for the more common accommodation types.

Geographical Insights

Geographical Range of Listings in New York City

• Latitude Range: The listings in New York City span from a minimum latitude of 40.4998 to a maximum latitude of 40.917.

• Longitude Range: The listings extend from a minimum longitude of -74.2498 to a maximum longitude of -73.7052.

Geographical Clusters of High-Priced Listings

• Cluster Analysis: The listings have been grouped into clusters based on their geographical location and price.

Price Distribution Across Clusters:

• Cluster 0: Average price of $631.38, located at latitude 40.7287 and longitude -73.9497.

• Cluster 1: Higher average price of $862.28, located at latitude 40.7276 and longitude -73.9497.

• Cluster 2: Lowest average price of $166.73, located at latitude 40.7281 and longitude -73.9499.

• Cluster 3: Moderate average price of $400.96, located at latitude 40.7285 and longitude -73.9497.

• Cluster 4: Highest average price of $1088.01, located at latitude 40.7275 and longitude -73.9493.

Visual Representation

• Scatter Plot of Listings: The geographical range of listings is visually represented, showing the spread across different latitudes and longitudes in New York City.

• Color-Coded Scatter Plot for Price Clusters: A color-coded scatter plot illustrates the geographical clusters of high-priced listings, with colors representing different price ranges, facilitating an easy visual understanding of how prices vary across different areas.

Overall, the analysis provides a clear view of the geographical distribution and price variation of listings in New York City, highlighting specific areas with higher-priced listings and the overall spread across the city.

Service Fees

Average Service Fee by Price Range

The analysis of the average service fee across different price ranges shows a clear trend: as the price range increases, the average service fee also increases. This is evident from the data provided and the bar chart visualization. The service fee starts at a lower value in the '0-100' price range and progressively increases, reaching its highest in the '1101-1200' price range.

Average Service Fee by Neighborhood

The average service fee varies significantly across different neighborhoods. The bar chart visualization and the data indicate that some neighborhoods have higher average service fees, while others are relatively lower. This variation could be influenced by factors such as the location desirability, type of accommodations available, and local pricing strategies.

Key Observations:

• Higher Price Ranges Correspond to Higher Service Fees: The service fees increase consistently as the accommodation prices increase.

• Variability Across Neighborhoods: There is considerable variability in service fees across different neighborhoods, suggesting a localized pricing strategy that could be influenced by multiple socio-economic factors.

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