Story 4 D608
Jose Fuentes
2025-03-22
Story 4 Airbnb Revenue Analysis
The goal of this assignment is to tell a visual story about the factors that differentiate profitable airbnbs from unprofitable airbnbs. You should imagine that you are either an analyst at airbnb looking for ways to help hosts become more profitable or to suggest to make suggestions about who should become a host, or a real estate investorlooking to either acquire (or help manage) short term rental properties.
Based on your analysis, you can phrase your story in different ways (these are just suggestions):
• You could identify listings which are underperforming and the factors that they could change to improve their revenue.
• You could identify neighborhoods where new airbnbs are likely to be successful and the characteristics that you would look for in a property.
Introduction
This chunk of code analyzes New York City Airbnb listings by first loading and cleaning the data, which includes addressing data inconsistencies and calculating estimated revenue, then merges this information with geographical neighborhood data. Subsequently, the script generates visualizations to explore relationships between price, occupancy, revenue, and neighborhood location.
These visualizations include a geographical map of average neighborhood revenue, a scatter plot comparing price and occupancy rates, and a boxplot illustrating revenue distribution by room type. Finally, the code extracts and presents the top ten neighborhoods with the highest average revenue, providing key insights into the NYC Airbnb market.
# Load necessary libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.2## Warning: package 'readr' was built under R version 4.4.2## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'dplyr' was built under R version 4.4.2## Warning: package 'stringr' was built under R version 4.4.2## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(sf)## Warning: package 'sf' was built under R version 4.4.3## Linking to GEOS 3.13.0, GDAL 3.10.1, PROJ 9.5.1; sf_use_s2() is TRUElibrary(ggplot2)
library(viridis)## Loading required package: viridisLite# File paths
airbnb_file <- "C:/Users/Dell/Downloads/nyc_airbnb_listings.csv"
geojson_file <- "C:/Users/Dell/Downloads/neighbourhoods.geojson"
# Step 1: Load and Inspect the Data
# Load datasets
airbnb_data <- read_csv(airbnb_file)## Rows: 37765 Columns: 60
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (14): host_location, host_response_time, host_response_rate, host_accep...
## dbl (37): ...1, id, host_id, host_listings_count, host_total_listings_count...
## lgl (6): host_is_superhost, host_has_profile_pic, host_identity_verified, ...
## date (3): host_since, first_review, last_review
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.neighborhoods <- st_read(geojson_file)## Reading layer `neighbourhoods' from data source
## `C:\Users\Dell\Downloads\neighbourhoods.geojson' using driver `GeoJSON'
## Simple feature collection with 233 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49613 xmax: -73.70782 ymax: 40.91553
## Geodetic CRS: WGS 84# Inspect the datasets
print("Airbnb Data Summary:")## [1] "Airbnb Data Summary:"summary(airbnb_data)## ...1 id host_id host_since
## Min. : 0 Min. :2.595e+03 Min. : 1678 Min. :2008-08-11
## 1st Qu.: 9441 1st Qu.:2.055e+07 1st Qu.: 16627758 1st Qu.:2014-06-18
## Median :18882 Median :4.826e+07 Median : 82189528 Median :2016-07-10
## Mean :18882 Mean :3.653e+17 Mean :165968419 Mean :2017-02-08
## 3rd Qu.:28323 3rd Qu.:8.276e+17 3rd Qu.:303156931 3rd Qu.:2019-10-18
## Max. :37764 Max. :1.193e+18 Max. :586917430 Max. :2024-07-01
## NA's :5
## host_location host_response_time host_response_rate host_acceptance_rate
## Length:37765 Length:37765 Length:37765 Length:37765
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## host_is_superhost host_neighbourhood host_listings_count
## Mode :logical Length:37765 Min. : 1.0
## FALSE:30186 Class :character 1st Qu.: 1.0
## TRUE :7181 Mode :character Median : 2.0
## NA's :398 Mean : 205.6
## 3rd Qu.: 9.0
## Max. :4641.0
## NA's :5
## host_total_listings_count host_verifications host_has_profile_pic
## Min. : 1.0 Length:37765 Mode :logical
## 1st Qu.: 1.0 Class :character FALSE:688
## Median : 3.0 Mode :character TRUE :37072
## Mean : 284.5 NA's :5
## 3rd Qu.: 13.0
## Max. :9013.0
## NA's :5
## host_identity_verified neighborhood neighbourhood_group_cleansed
## Mode :logical Length:37765 Length:37765
## FALSE:4298 Class :character Class :character
## TRUE :33462 Mode :character Mode :character
## NA's :5
##
##
##
## latitude longitude property_type room_type
## Min. :40.50 Min. :-74.25 Length:37765 Length:37765
## 1st Qu.:40.69 1st Qu.:-73.98 Class :character Class :character
## Median :40.73 Median :-73.95 Mode :character Mode :character
## Mean :40.73 Mean :-73.95
## 3rd Qu.:40.76 3rd Qu.:-73.93
## Max. :40.91 Max. :-73.71
##
## accommodates bathrooms bedrooms beds
## Min. : 1.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 1.000 1st Qu.: 1.00 1st Qu.: 1.000
## Median : 2.000 Median : 1.000 Median : 1.00 Median : 1.000
## Mean : 2.762 Mean : 1.189 Mean : 1.17 Mean : 1.637
## 3rd Qu.: 4.000 3rd Qu.: 1.000 3rd Qu.: 1.00 3rd Qu.: 2.000
## Max. :16.000 Max. :15.500 Max. :16.00 Max. :42.000
## NA's :14593 NA's :14777
## amenities price minimum_nights maximum_nights
## Length:37765 Min. : 8.0 Min. : 1.00 Min. :1.000e+00
## Class :character 1st Qu.: 86.0 1st Qu.: 30.00 1st Qu.:1.500e+02
## Mode :character Median : 150.0 Median : 30.00 Median :3.650e+02
## Mean : 221.1 Mean : 29.16 Mean :5.798e+04
## 3rd Qu.: 250.0 3rd Qu.: 30.00 3rd Qu.:1.125e+03
## Max. :100000.0 Max. :1250.00 Max. :2.147e+09
## NA's :14721
## minimum_minimum_nights maximum_minimum_nights minimum_maximum_nights
## Min. : 1.00 Min. : 1.00 Min. :1.000e+00
## 1st Qu.: 30.00 1st Qu.: 30.00 1st Qu.:3.610e+02
## Median : 30.00 Median : 30.00 Median :7.300e+02
## Mean : 28.74 Mean : 37.22 Mean :5.807e+04
## 3rd Qu.: 30.00 3rd Qu.: 30.00 3rd Qu.:1.125e+03
## Max. :1250.00 Max. :1250.00 Max. :2.147e+09
## NA's :1 NA's :1 NA's :1
## maximum_maximum_nights minimum_nights_avg_ntm maximum_nights_avg_ntm
## Min. :1.000e+00 Min. : 1.00 Min. :1.000e+00
## 1st Qu.:3.650e+02 1st Qu.: 30.00 1st Qu.:3.650e+02
## Median :1.125e+03 Median : 30.00 Median :9.350e+02
## Mean :1.650e+06 Mean : 29.75 Mean :1.075e+06
## 3rd Qu.:1.125e+03 3rd Qu.: 30.00 3rd Qu.:1.125e+03
## Max. :2.147e+09 Max. :1250.00 Max. :2.147e+09
## NA's :1 NA's :1 NA's :1
## calendar_updated has_availability number_of_reviews number_of_reviews_ltm
## Mode:logical Mode :logical Min. : 0.0 Min. : 0.000
## NA's:37765 FALSE:289 1st Qu.: 0.0 1st Qu.: 0.000
## TRUE :32071 Median : 3.0 Median : 0.000
## NA's :5405 Mean : 24.9 Mean : 3.993
## 3rd Qu.: 21.0 3rd Qu.: 3.000
## Max. :1915.0 Max. :1568.000
##
## first_review last_review review_scores_rating
## Min. :2009-05-25 Min. :2011-05-12 Min. :0.000
## 1st Qu.:2017-07-26 1st Qu.:2020-03-13 1st Qu.:4.650
## Median :2020-10-10 Median :2023-09-03 Median :4.850
## Mean :2020-01-30 Mean :2022-04-22 Mean :4.727
## 3rd Qu.:2022-11-06 3rd Qu.:2024-04-26 3rd Qu.:5.000
## Max. :2024-07-04 Max. :2024-07-05 Max. :5.000
## NA's :11751 NA's :11751 NA's :11751
## review_scores_accuracy review_scores_cleanliness review_scores_checkin
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:4.710 1st Qu.:4.530 1st Qu.:4.810
## Median :4.900 Median :4.810 Median :4.950
## Mean :4.769 Mean :4.657 Mean :4.834
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000
## NA's :11768 NA's :11758 NA's :11772
## review_scores_communication review_scores_location review_scores_value
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:4.820 1st Qu.:4.650 1st Qu.:4.540
## Median :4.960 Median :4.850 Median :4.770
## Mean :4.829 Mean :4.744 Mean :4.649
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.940
## Max. :5.000 Max. :5.000 Max. :5.000
## NA's :11763 NA's :11775 NA's :11774
## license instant_bookable calculated_host_listings_count
## Length:37765 Mode :logical Min. : 1.00
## Class :character FALSE:30249 1st Qu.: 1.00
## Mode :character TRUE :7516 Median : 2.00
## Mean : 53.76
## 3rd Qu.: 8.00
## Max. :842.00
##
## calculated_host_listings_count_entire_homes
## Min. : 0.00
## 1st Qu.: 0.00
## Median : 1.00
## Mean : 28.18
## 3rd Qu.: 2.00
## Max. :842.00
##
## calculated_host_listings_count_private_rooms
## Min. : 0.00
## 1st Qu.: 0.00
## Median : 1.00
## Mean : 23.49
## 3rd Qu.: 2.00
## Max. :691.00
##
## calculated_host_listings_count_shared_rooms reviews_per_month
## Min. : 0.00000 Min. : 0.010
## 1st Qu.: 0.00000 1st Qu.: 0.090
## Median : 0.00000 Median : 0.320
## Mean : 0.07774 Mean : 0.906
## 3rd Qu.: 0.00000 3rd Qu.: 1.130
## Max. :13.00000 Max. :103.530
## NA's :11751
## price_per_accommodates neighbourhood_group occupancy_level occupancy
## Min. : 1.60 Length:37765 Length:37765 Min. :0.0000
## 1st Qu.: 40.83 Class :character Class :character 1st Qu.:0.4139
## Median : 60.00 Mode :character Mode :character Median :0.7939
## Mean : 84.55 Mean :0.6744
## 3rd Qu.: 94.00 3rd Qu.:1.0000
## Max. :100000.00 Max. :1.0000
## NA's :14721
## revenue
## Min. : 0.00
## 1st Qu.: 24.00
## Median : 65.81
## Mean : 102.08
## 3rd Qu.: 131.55
## Max. :22592.59
## NA's :14721print("Neighborhoods GeoJSON Structure:")## [1] "Neighborhoods GeoJSON Structure:"summary(neighborhoods)## neighbourhood neighbourhood_group geometry
## Length:233 Length:233 MULTIPOLYGON :233
## Class :character Class :character epsg:4326 : 0
## Mode :character Mode :character +proj=long...: 0# Step 2: Clean Airbnb Data
# Avoid naming conflicts by resolving duplicate column names
colnames(airbnb_data)[16] <- "host_neighborhood" # Rename the first instance of "neighborhood"
colnames(airbnb_data)[57] <- "neighborhood_group" # Rename the second instance for clarity
# Clean and rename the necessary columns in the Airbnb dataset
airbnb_data <- airbnb_data %>%
rename(
price = price, # "price" is already correct
occupancy_rate = occupancy, # Rename "occupancy" to "occupancy_rate"
neighborhood = neighborhood_group # Use the clarified "neighborhood_group"
)
# Remove missing or invalid values
airbnb_data <- airbnb_data %>%
drop_na(price, occupancy_rate, neighborhood) %>% # Remove rows with missing key columns
filter(price > 0 & occupancy_rate > 0 & occupancy_rate <= 1) # Valid ranges for price and occupancy
# Calculate estimated revenue (price * occupancy_rate * 30 days)
airbnb_data <- airbnb_data %>%
mutate(estimated_revenue = price * occupancy_rate * 30)
# Inspect cleaned dataset
print("Cleaned Airbnb Data Summary:")## [1] "Cleaned Airbnb Data Summary:"#summary(airbnb_data)
# Step 3: Merge
neighborhood_revenue <- airbnb_data %>%
group_by(neighborhood) %>%
summarise(avg_revenue = mean(estimated_revenue, na.rm = TRUE))
# Ensure the correct column names for merging
colnames(neighborhoods) # Inspect column names in GeoJSON## [1] "neighbourhood" "neighbourhood_group" "geometry"colnames(neighborhood_revenue) # Inspect column names in revenue data## [1] "neighborhood" "avg_revenue"# Rename or align columns if necessary
neighborhoods <- neighborhoods %>%
rename(neighborhood = neighbourhood_group) # Replace with the correct column name
# Check for unmatched neighborhood names
unmatched <- setdiff(neighborhood_revenue$neighborhood, neighborhoods$neighborhood)
print(unmatched) # Print any mismatches## character(0)# Perform the join
neighborhoods <- neighborhoods %>%
left_join(neighborhood_revenue, by = "neighborhood")
# Inspect the merged data
print("Merged Dataset Preview:")## [1] "Merged Dataset Preview:"head(neighborhoods)## Simple feature collection with 6 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -73.92828 ymin: 40.61488 xmax: -73.76671 ymax: 40.87344
## Geodetic CRS: WGS 84
## neighbourhood neighborhood avg_revenue geometry
## 1 Bayswater Queens 1700.632 MULTIPOLYGON (((-73.76671 4...
## 2 Allerton Bronx 1401.192 MULTIPOLYGON (((-73.8486 40...
## 3 City Island Bronx 1401.192 MULTIPOLYGON (((-73.78282 4...
## 4 Ditmars Steinway Queens 1700.632 MULTIPOLYGON (((-73.9016 40...
## 5 Ozone Park Queens 1700.632 MULTIPOLYGON (((-73.83754 4...
## 6 Fordham Bronx 1401.192 MULTIPOLYGON (((-73.88303 4...# Step 4: Create Visualizations
# Visualization 1: Map of Average Revenue by Neighborhood
revenue_map <- ggplot(data = neighborhoods) +
geom_sf(aes(fill = avg_revenue), color = "white", size = 0.2) +
scale_fill_viridis_c(name = "Avg Revenue (USD)", option = "plasma") +
labs(title = "Average Airbnb Revenue by Neighborhood",
subtitle = "New York City",
caption = "Source: NYC Airbnb") +
theme_minimal()
print(revenue_map)
# Visualization 2: Scatter Plot of Price vs Occupancy Rate
price_occupancy_plot <- ggplot(data = airbnb_data, aes(x = price, y = occupancy_rate)) +
geom_point(alpha = 0.5, color = "blue") +
geom_smooth(method = "lm", color = "red", se = FALSE) +
labs(title = "Price vs Occupancy Rate",
x = "Nightly Price (USD)",
y = "Occupancy Rate",
caption = "Source: NYC Airbnb") +
theme_minimal()
print(price_occupancy_plot)## `geom_smooth()` using formula = 'y ~ x'
# Visualization 3: Revenue Distribution by Room Type
revenue_roomtype_plot <- ggplot(data = airbnb_data, aes(x = room_type, y = estimated_revenue, fill = room_type)) +
geom_boxplot(alpha = 0.7) +
scale_fill_viridis_d(name = "Room Type") +
labs(title = "Revenue Distribution by Room Type",
x = "Room Type",
y = "Estimated Revenue (USD)",
caption = "Source: NYC Airbnb") +
theme_minimal()
print(revenue_roomtype_plot)
# Step 5: Additional Insights
# Summary statistics
top_neighborhoods <- neighborhood_revenue %>%
arrange(desc(avg_revenue)) %>%
head(10)
print("Top 10 Neighborhoods by Average Revenue:")## [1] "Top 10 Neighborhoods by Average Revenue:"print(top_neighborhoods)## # A tibble: 5 × 2
## neighborhood avg_revenue
## <chr> <dbl>
## 1 Manhattan 4477.
## 2 Brooklyn 2697.
## 3 Staten Island 1735.
## 4 Queens 1701.
## 5 Bronx 1401.# Save the visualizations to files
ggsave("C:/Users/Dell/Downloads/revenue_map.png", revenue_map, width = 10, height = 8, dpi = 300)
ggsave("C:/Users/Dell/Downloads/price_occupancy_plot.png", price_occupancy_plot, width = 10, height = 8, dpi = 300)## `geom_smooth()` using formula = 'y ~ x'ggsave("C:/Users/Dell/Downloads/revenue_roomtype_plot.png", revenue_roomtype_plot, width = 10, height = 8, dpi = 300)This piece of code begins by loading the GeoJSON data and creating an interactive map using the leaflet library, where neighborhoods are colored based on their unique identifier and popups display neighborhood names. Subsequently, it generates a static map using ggplot2 where specific neighborhoods (“SoHo”, “Chinatown”, “Greenwich Village”) are highlighted in red, while the rest are in gray, providing a comparative visual representation.
Additionally, the code creates a map with a light basemap using ggspatial and ggplot2, overlaying the neighborhood polygons to provide geographical context. Finally, it produces a basic static map using ggplot2 with neighborhoods colored using the viridis palette, but without a legend, offering a clear visual of neighborhood boundaries. Each map is printed to the console, allowing for a side-by-side comparison of different visualization techniques.
library(geojsonsf)## Warning: package 'geojsonsf' was built under R version 4.4.3library(leaflet)## Warning: package 'leaflet' was built under R version 4.4.3#install.packages("prettymapr")
# Load GeoJSON file
nyc_geo <- geojson_sf("C:/Users/Dell/Downloads/neighbourhoods.geojson")
# Corrected Leaflet Map
leaflet_map <- leaflet(nyc_geo) %>%
addPolygons(
fillColor = ~colorNumeric("viridis", as.numeric(as.factor(neighbourhood)))(as.numeric(as.factor(neighbourhood))),
fillOpacity = 0.7,
weight = 1,
color = "black",
popup = ~neighbourhood
)
print(leaflet_map)
# 2. Specific Neighborhood Highlighting
highlighted_neighborhoods <- c("SoHo", "Chinatown", "Greenwich Village")
nyc_geo$highlight <- ifelse(nyc_geo$neighbourhood %in% highlighted_neighborhoods, "Highlighted", "Other")
highlight_plot <- nyc_geo %>%
ggplot(aes(fill = highlight)) +
geom_sf() +
scale_fill_manual(values = c("Highlighted" = "red", "Other" = "gray"), name = "Neighborhoods") +
labs(title = "NYC Neighborhoods with Highlights") +
theme_minimal()
print(highlight_plot)
# 3. Basemap with ggspatial
library(geojsonsf)
library(ggplot2)
library(ggspatial)## Warning: package 'ggspatial' was built under R version 4.4.3basemap_plot <- nyc_geo %>%
ggplot() +
annotation_map_tile(type = "cartolight") + # Add a light basemap
geom_sf(aes(fill = neighbourhood), alpha = 0.7) +
guides(fill = guide_none()) +
labs(title = "NYC Neighborhoods with Basemap") +
theme_minimal()
print(basemap_plot)## Loading required namespace: raster## Zoom: 9
# 4. Original Plot (with legend removed)
original_plot <- nyc_geo %>%
ggplot(aes(fill = neighbourhood)) +
geom_sf() +
guides(fill = guide_none()) +
scale_fill_viridis_d(name = "Neighborhoods") +
labs(
title = "NYC Neighborhoods",
subtitle = "Basic Map Visualization",
caption = "Source: NYC GeoJSON"
) +
theme_minimal()
print(original_plot)
This chunk of code analyzes Airbnb listings in New York City, focusing on room types, neighborhood prices, and borough prices. It begins by loading the Airbnb dataset from a CSV file and displaying the first 10 rows for initial inspection. Then, it generates a bar chart showing the count of each room type (“Entire home/apt,” “Private room,” etc.), providing insight into the distribution of listing types.
Following this, the code calculates the average Airbnb listing price for each neighborhood and creates a horizontal bar chart displaying the 30 most expensive neighborhoods, ordered by average price. Finally, it analyzes borough prices by calculating the average price for each borough and generates a similar horizontal bar chart for the top 10 most expensive boroughs, offering a comparative view of pricing across different areas of New York City. Each chart is generated using ggplot2 and the data is manipulated using dplyr.
library(ggplot2)
library(dplyr)
# Load the Airbnb dataset
airbnb_file <- "C:/Users/Dell/Downloads/nyc_airbnb_listings.csv"
airbnb_data <- read.csv(airbnb_file)
# Display the first 10 rows for verification
#head(airbnb_data, 10)
# Create a bar chart of room_type counts
room_type_counts <- airbnb_data %>%
group_by(room_type) %>%
summarise(count = n())
ggplot(room_type_counts, aes(x = room_type, y = count, fill = room_type)) +
geom_bar(stat = "identity") +
labs(
title = "Airbnb Listings by Room Type",
x = "Room Type",
y = "Number of Listings",
fill = "Room Type"
) +
theme_minimal()
# Most Expensive NYC Neighborhoods
# Calculate average price per neighborhood
neighborhood_prices <- airbnb_data %>%
group_by(neighborhood) %>% # Use the correct column name: "neighborhood"
summarise(avg_price = mean(price, na.rm = TRUE)) %>%
arrange(desc(avg_price)) %>%
head(30)
# Create bar chart of average prices
ggplot(neighborhood_prices, aes(x = reorder(neighborhood, avg_price), y = avg_price, fill = avg_price)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(
title = "Most Expensive NYC Neighborhoods (Airbnb)",
x = "Neighborhood",
y = "Average Price (USD)",
fill = "Average Price"
) +
theme_minimal()
#Most expensive boroughs
# Calculate average price per neighborhood group
borough_prices <- airbnb_data %>%
group_by(neighbourhood_group_cleansed) %>% # Use the borough column
summarise(avg_price = mean(price, na.rm = TRUE)) %>%
arrange(desc(avg_price)) %>%
head(10)
# Create bar chart of average prices by borough
ggplot(borough_prices, aes(x = reorder(neighbourhood_group_cleansed, avg_price), y = avg_price, fill = avg_price)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(
title = "Top Most Expensive NYC Boroughs (Airbnb)",
x = "Borough",
y = "Average Price (USD)",
fill = "Average Price"
) +
theme_minimal()
Factors Differentiating Profitable and Unprofitable Airbnbs
This analysis identifies key factors differentiating profitable and unprofitable Airbnb listings in New York City. It highlights that excessively high prices lead to lower occupancy rates, suggesting a need for market-aligned pricing and improved listing quality to attract guests. Profitable neighborhoods like SoHo and Greenwich Village, characterized by proximity to attractions and cultural hubs, offer higher revenue potential, favoring properties with good location, flexible accommodation, and appealing aesthetics.
Furthermore, the analysis reveals that “entire home/apartment” and “hotel room” listings generate the most revenue, indicating a strong market preference for privacy and full amenities. Conversely, shared rooms underperform but could cater to niche markets with unique selling points. The analysis recommends adjusting pricing, enhancing guest experience, focusing on positive reviews, and investing in properties suitable for high-demand room types to maximize profitability.
Strategies for Hosts and Investors
The provided strategies offer tailored advice for Airbnb hosts and investors in New York City, drawing on insights from visualized data. For existing hosts, the focus is on optimizing pricing through dynamic tools to match market demand, enhancing guest experiences with unique local offerings, and improving marketing through compelling visuals and detailed descriptions, these tactics aim to increase occupancy and revenue by making listings more attractive and competitive.
Prospective hosts are advised to target high-demand neighborhoods like SoHo or Greenwich Village, where average prices and demand are higher, and to invest in properties with features that align with guest preferences, such as ample natural light and spacious layouts. Real estate investors are encouraged to acquire properties in areas with established revenue potential and proximity to attractions, with a focus on multi-family units to maximize listing opportunities and overall returns within the lucrative NYC Airbnb market.
Conclusions
Based on the analysis, Airbnb profitability in New York City is significantly influenced by pricing strategies. Listings with rates aligned with market demand tend to achieve higher occupancy, demonstrating the importance of competitive and dynamic pricing. Moreover, underperforming listings can significantly boost their revenue through strategic improvements in amenities, enhanced guest experiences, and targeted marketing efforts. These factors highlight the necessity for hosts to actively manage and optimize their offerings to remain competitive.
Neighborhood selection plays a crucial role in Airbnb success. High-revenue areas like SoHo, Chinatown, and Greenwich Village, characterized by their proximity to cultural attractions and strong guest demand, present prime opportunities for hosts and investors. Prioritizing properties in these locations can capitalize on established profitability trends and ensure a steady stream of bookings. The data suggests that understanding the unique appeal of these neighborhoods is essential for maximizing returns.
Room type also impacts revenue generation. Entire homes/apartments and hotel rooms consistently achieve the highest revenue, making them the most lucrative categories for hosts and investors. While shared and private rooms underperform, they can be optimized by targeting niche markets, such as budget-conscious travelers or students, with unique offerings and focused marketing. This highlights the importance of understanding market segmentation and tailoring listings to specific guest needs.
Finally, strategic investment approaches are crucial for both potential hosts and investors. Hosts should focus on dynamic pricing, amenity enhancements, and leveraging unique selling points to attract bookings. Investors should prioritize acquiring properties in high-demand neighborhoods with features like spacious layouts and appealing aesthetics. Both parties should consider innovative approaches, such as offering personalized guest experiences or eco-friendly accommodations, to differentiate their listings and attract a wider range of guests. This comprehensive strategy ensures long-term profitability and success in the competitive NYC Airbnb market.
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.