Final Project

Load necessary libraries for continous presentation of Final Project

library(tidyverse)

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library(plotly)

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Attaching package: 'plotly'

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library(RColorBrewer)
library(viridis)

Loading required package: viridisLite

library(GGally)

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library(leaflet)

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Load dataset from working directory to the global environment,and make headers lower_case and remove space

setwd("C:/Users/Owner/OneDrive/Desktop/Data110")
# Suppress all messages when reading the CSV file
airbnb_2025 <- suppressMessages(read_csv("airbnb_washington_dc,2025.csv", show_col_types = FALSE))
names(airbnb_2025)<- gsub( " ","_",tolower(names(airbnb_2025)))
head(airbnb_2025)

# A tibble: 6 × 18
     id name        host_id host_name neighbourhood_group neighbourhood latitude
  <dbl> <chr>         <dbl> <chr>     <lgl>               <chr>            <dbl>
1  3686 Vita's Hid…    4645 Vita      NA                  Historic Ana…     38.9
2  3943 Historic R…    5059 Vasa      NA                  Edgewood, Bl…     38.9
3  4197 Capitol Hi…    5061 Sandra    NA                  Capitol Hill…     38.9
4  4529 Bertina's …    5803 Bertina   NA                  Eastland Gar…     38.9
5  5589 Cozy apt i…    6527 Ami       NA                  Kalorama Hei…     38.9
6  7103 Lovely gue…   17633 Charlotte NA                  Spring Valle…     38.9
# ℹ 11 more variables: longitude <dbl>, room_type <chr>, price <dbl>,
#   minimum_nights <dbl>, number_of_reviews <dbl>, last_review <date>,
#   reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
#   availability_365 <dbl>, number_of_reviews_ltm <dbl>, license <chr>

Filter/clean the data to handle missing values.

airbnb_clean <- airbnb_2025 |>
  select(price, minimum_nights, number_of_reviews, reviews_per_month, availability_365,number_of_reviews_ltm,latitude, longitude,name, neighbourhood ) |>
  filter(!is.na(price) & !is.na(reviews_per_month)) |>
  mutate(price = as.numeric(price))  # convert if needed

Use ggpairs() for regression insight

ggpairs(
  airbnb_clean,
  columns = 1:5,
  lower = list(continuous = wrap("smooth", method = "lm", se = FALSE)),
  diag  = list(continuous = wrap("densityDiag")),
  upper = list(continuous = "cor"),
  progress = FALSE
)

Visualize booking frequency by location.

Using leaflet package,allows for interactive mapping where listings and its details are seen by just mouse clicking.

# define your color palette
pal_reviews2 <- colorNumeric(
  palette  = magma(7),
  domain   = airbnb_clean$number_of_reviews_ltm,
  na.color = "transparent"
)

leaflet(data = airbnb_clean) |>
  addTiles() |>
  setView(lng = -77.0369, lat = 38.9072, zoom = 12) |>
  addCircleMarkers(
    lng            = ~longitude,
    lat            = ~latitude,
    radius         = ~sqrt(number_of_reviews_ltm) * 2 ,
    color          = "#472988",
    fillColor      = ~pal_reviews2(number_of_reviews_ltm),
    fillOpacity    = 0.7,
    popup          = ~paste0(
      "<b>Name:</b> ", name, "<br/>",
      "<b>Neighborhood:</b> ", neighbourhood, "<br/>",
      "<b>Price (USD):</b> $", price, "<br/>",
      "<b>Reviews/mo:</b> ", round(reviews_per_month, 2), "<br/>",
      "<b>Avail (days/yr):</b> ", availability_365
    ),
    clusterOptions = markerClusterOptions()   # clusters ~3K points for performance
  ) |>
  addLegend(
    position = "bottomright",
    pal      = pal_reviews2,
    values   = ~number_of_reviews_ltm,
    title    = "Reviews (Last 12 mo)",
    opacity  = 1
  )

Reflections on Visualizations

What the visualizations represent: Average Price by Neighborhood: Bar charts show which top and bottom DC neighborhoods command higher or lower nightly rates, highlighting Dupont Circle’s premium pricing.

Interactive Booking Frequency Map: A Leaflet map with circle markers sized and colored by reviews in the last 12 months flags hotspots of demand—particularly Dupont Circle, Capitol Hill, and Georgetown.

Interesting patterns or surprises:

Weak explanatory power of our regression (Adjusted R²≈1.2%) shows price is driven far more by unobserved factors—amenities, property size, interior quality, and exact location (e.g., proximity to Metro or monuments)—than by listing-level controls alone.I also noticed some outlier listings charge extraordinarily high rates (> $1,000/night), pulling up neighborhood averages and underscoring the long right‐tail in price distributions.

Limitations & Wish List

Expanded Use of Categorical Variables: Due to time constraints, I wasn’t able to fully incorporate all available categorical predictors—such as room_type, calculated_host_listings_count, or detailed neighborhood groupings—which could have added valuable nuance to both the regression and visualizations.

Incomplete Amenities Data: Airbnb’s public data did not include many listing amenities (e.g., “wifi”, “kitchen”, “pool”, “air conditioning”), yet these features often drive price variation. Having a complete amenities checklist would likely improve the accuracy of any price‐prediction model.

Bibliography

Airbnb. (2024, April 12). Our support for sensible, short-term rental policies for renters. Airbnb Newsroom. https://news.airbnb.com/our-support-for-sensible-short-term-rental-policies-for-renters/

OpenAI. (2023). ChatGPT (GPT-4) [Large language model]. https://chat.openai.com/