This boxplot illustrates the variation in median prices associated with the top 20 most frequently used keywords in Airbnb listing titles across New York City. Notably, despite its frequent usage, the keyword ‘luxury’ is associated with lower median prices, while ‘village’ and ‘quiet’ tend to command higher prices, highlighting the influence of specific descriptive terms on pricing perceptions. This visualization underscores the impact of keyword selection on potential pricing strategies within the Airbnb market.
This map visualization presents the average Airbnb listing prices across different neighborhoods in New York City. Prices are indicated by the color intensity of the circle markers, with darker colors representing higher prices. The top ten neighborhoods with the highest average prices are marked with additional details in popups that display the exact average price when clicked. This interactive map allows users to visually compare pricing across the city and identify high-value areas at a glance.
This facet plot explores the relationship between the number of reviews a listing has and its pricing across the five neighbourhoods with the highest count of listings in New York City. Each panel represents one neighbourhood and displays a scatterplot with a linear regression line, helping to visualize how consumer feedback (in the form of total reviews) might correlate with pricing strategies in different parts of the city. This analysis can reveal if more popular (frequently reviewed) listings tend to charge more or less, depending on the local market dynamics.
This boxplot displays the relationship between the minimum nights required for an Airbnb listing and its nightly price. Each box represents the price distribution for a specific minimum night requirement, showing the median price and the interquartile range. This visualization highlights how the length of stay required affects pricing, potentially offering insights into pricing strategies for short vs. long-term stays. It helps viewers understand pricing trends and anomalies associated with different stay durations in New York City Airbnb listings.
The first plot, “Population Density vs. Airbnb Price,” explores the relationship between population density and the average price of Airbnb listings across the top 30 neighborhoods in New York City by the number of Airbnb listings. Each point represents a neighborhood, with the x-axis indicating the total population and the y-axis representing the average Airbnb price. The blue line depicts the trend line fitted through the data points, providing insights into how population density influences pricing.
The second plot, “Vacancy Rates vs. Airbnb Price,” examines how vacancy rates in different neighborhoods correlate with the average price of Airbnb listings. Each point on the plot represents a neighborhood, with the x-axis showing the vacancy rate and the y-axis indicating the average Airbnb price. The vacancy rate in each neighborhood is calculated by dividing the number of vacant housing units by the total number of housing units, providing a measure of housing availability relative to demand. The blue line represents the trend line fitted to the data, offering insights into how vacancy rates impact Airbnb pricing across neighborhoods in New York City.