How Atlanta’s Geography Shapes Its Airbnb Landscape

Abstract

In 2017, Atlanta ranked among the top three urban markets for the highest return on investment (ROI) in Airbnb’s annual report by Rented.com. The study considered various factors, including real estate prices, insurance, taxes, maintenance costs, average annual ROI, and the popularity of the destination. To better understand the factors influencing Airbnb listing prices in this booming shared economy, this project employs both a General Linear Model (GLM) and a Geographically Weighted Regression (GWR) model using a dataset of 5,581 Airbnb listings in Metro Atlanta.

The GWR method is selected to capture the relationship between Airbnb listing prices and factors that vary locally. Price determinants were chosen based on previous studies, including variables such as rating, age of the listing, proximity to popular landmarks and public transport (measured by Euclidean distance to highways and the city center), and the number of reviews.

Data Sources:

• Airbnb Listings: 5,581 samples from the Atlanta metropolitan area, sourced from Airdna (https://www.airdna.co/market-data/app/us/georgia/atlanta/overview).
• MARTA Bus Stops and Rail Stations: Data from the ARC Open Data Portal (https://catalog.data.gov/dataset?metadata_type=geospatial&_metadata_type_limit=0&q=Bus%20Routes).
• Highways and Major Roads: Sourced from the Atlanta Regional Commission (http://opendata.atlantaregional.com/).
• Local Attractions: Data from the ARC Open Data Portal (http://opendata.atlantaregional.com/).

Variable Descriptions:

• Price: Ratio of listed price per night to the maximum number of guests (USD).
• Reviews: Number of reviews for each Airbnb listing.
• H-Distance: Euclidean distance to the nearest major road or highway (miles).
• C-Distance: Euclidean distance to Atlanta City Center (miles).
• Age: Number of months since the listing was established.
• Rating: Overall rating score of the Airbnb listing.

Methods:

The study focuses on the City of Atlanta as the study area. The dataset, initially comprising 5,581 Airbnb listings from the Metro Atlanta area across 20 counties, was sourced from Airdna, which gathers data from Airbnb.com. After filtering out listings with no transactions or poor maintenance, a refined subset of 312 samples was used. Figure 1 illustrates the spatial distribution of these Airbnb listings in Atlanta. The GWR method was employed to analyze the relationship between listing price and locally varying factors, with price determinants selected based on previous research on Airbnb pricing.

Conclusions:

The analysis revealed that a significant concentration of Airbnb listings—53%—are located within the city center of Atlanta. While the General Linear Model (GLM) indicated that rating and the number of reviews significantly impact price, it failed to capture the influence of geographical variables. The Geographically Weighted Regression (GWR) model, however, effectively highlighted these spatial relationships.

The results show that downtown and midtown areas command higher prices due to their proximity to transportation, popular landmarks, and other attractions, as illustrated in Figure 2. The coefficient indicates a decreasing trend in price as listings move away from the city center, with the exception of the North Atlanta region, particularly near Sandy Springs, where factors like exceptional ratings and proximity to highways contribute to higher prices.

This study provides valuable insights for stakeholders, such as Airbnb, in developing more effective pricing strategies for listings with better access to transportation, whether public transit or major highways.