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Welcome to our Airbnb Insights: A Look at U.S. Travel & Tourism Trends dashboard. This tool is designed for Airbnb hosts and property managers who want a clearer view of how demand, prices, and guest behavior vary across major U.S. cities.
Use the tabs above to explore:
New York City Neighborhood Hotspots – which boroughs and areas see the most guest activity.
Seasons of Stay – when Airbnb bookings spike or slow down over the calendar year.
How Property Type Impacts Performance – how entire homes, private rooms, shared rooms, and hotel-style listings differ in price and demand.
Minimum Night Requirements & Demand – how length-of-stay rules affect bookings and reviews.
The Impact of COVID-19 – how listing availability and activity changed from 2019–2024.
This page highlights the neighborhoods in New York City that show the highest Airbnb demand. Using the reviews_per_month variable as an indicator of guest activity, we identified the top three neighborhoods where Airbnb listings experience the most consistent turnover. These high-demand areas offer valuable insight into where hosts and property managers may see the greatest opportunity for short-term rental performance.
The visualization shows clear differences in demand across neighborhoods. The top three areas consistently generate higher monthly review activity, indicating stronger guest turnover and more frequent bookings. This suggests that these neighborhoods may benefit from higher visibility, stronger tourism appeal, or greater listing density. Understanding these patterns helps highlight where Airbnb activity is concentrated within the city and how hosts in these areas may experience more predictable demand throughout the year.
This page visualizes how active Airbnb guests are throughout the year by tracking the number of NYC listings that received at least one review each month. Because reviews follow real guest stays, this interactive time-series plot helps hosts see when demand rises, dips, or spikes across the calendar.
Across most of the timeline, monthly Airbnb activity in NYC stays under roughly 2,000 listings receiving reviews, indicating a steady but manageable level of guest traffic. Over time, the line trends upward, suggesting growing tourism demand and a recovering short-term rental market.
One of the most notable features is a sharp spike in Fall 2025, especially September 2025, where review activity jumps well above typical months. This surge likely reflects a mix of seasonal travel (fall tourism, university move-ins), business trips, and high-profile events like New York Fashion Week and major conferences. For hosts, this period represents a prime window to raise prices, tighten minimum-night rules, and prepare for faster guest turnover, while quieter months may call for more flexible pricing or longer-stay strategies.
This side panel provides additional context for the interactive time-series plot and helps Airbnb hosts interpret the seasonal patterns in monthly review activity.
NYC Airbnb guest activity remains relatively steady for most months, generally staying below 2,000 listings receiving reviews. The overall upward trend points to recovering travel demand, while the dramatic spike in Fall 2025—especially September—highlights an unusually busy period likely driven by tourism peaks, university schedules, and major city events.
Higher-activity months present opportunities to increase prices or limit stays to maximize revenue. Slower months may benefit from more flexible pricing, discounts, or extended-stay strategies. Planning maintenance, deep cleans, and restocking around these patterns can help hosts operate more efficiently.
This page analyzes how Airbnb property types differ in price, guest ratings, and demand across Chicago, Los Angeles, and New York City. By comparing entire homes, private rooms, shared rooms, and hotel-style listings, this section helps hosts see which property categories attract the most activity and command the highest prices.
| Property Type | Avg. Price (USD) | Avg. Reviews/Month | Number of Listings |
|---|---|---|---|
| Hotel room | 17577.35 | 0.90 | 181 |
| Entire home/apt | 277.68 | 1.52 | 34495 |
| Private room | 158.44 | 1.39 | 13495 |
| Shared room | 101.65 | 1.35 | 266 |
What the Visuals Show
Entire homes/apartments consistently command the highest nightly prices across all three cities. Private rooms appear as the most active listings, with the highest reviews per month, indicating fast turnover and budget-friendly appeal. Hotel-type listings vary widely, reflecting differences between traditional hotels and hybrid Airbnb–hotel units. Shared rooms remain a niche but low-cost offering.
Demand Patterns
Private rooms show strong activity at lower prices, while entire homes cluster at higher prices with fewer stays, but still generate substantial revenue due to higher nightly rates.
- Entire homes: Optimize for higher pricing, longer stays, and group- or amenity-focused positioning.
- Private rooms: Leverage high turnover with dynamic pricing, fast guest communication, and strong review management.
- Hotel-style units: Highlight unique features compared to traditional hotels to justify pricing differences.
- Shared rooms: Emphasize affordability, safety, and social appeal.
Understanding how property type influences both pricing power and guest demand helps hosts position their listings more effectively across competitive urban markets.
Shorter minimum stays attract more guests, while longer requirements raise prices but reduce bookings. Minimum night rules directly influence how often listings are used by tourists in NYC.
This chart shows how Airbnb listings changed across NYC boroughs from 2019–2024. Listings dropped during COVID-19 (2020–2021) and gradually recovered afterward, revealing how different boroughs were affected. These trends provide a quick view of short-term rental availability and market shifts.
Shows how Airbnb listing availability changed across NYC boroughs from 2019 to 2024, highlighting trends and disruptions during the COVID-19 period.
This dashboard was created using Quarto in RStudio, and the R Language and Environment.
The dataset used to create this dashboard was downloaded from Yahoo Finance and Kaggle
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