Maps

Proponents of Airbnb and other short-term-rental (STR) platforms often describe such rentals as having minimal negative impact upon communities. A key part of this position is the assertion that STRs mostly compete with large hotel chains in neighborhoods already flooded with tourists and/or business travelers. While the qualitative portion of the Mediating Platforms research project analyzes the conflicting positions of industry representatives, community activists, and labor groups about STR impacts (Monahan 2021), we have also mapped changes to neighborhoods over time.

 

Looking at our three case-study cities (Boston, Austin, and San Francisco), we find significant spillover of Airbnb listings into neighborhoods without large hotel chains.[1] This indicates, at a minimum, that some neighborhoods are being inundated with Airbnb listings, which contributes to the depletion of housing and rental stock for city residents, resident displacement, and often reduced city tax bases.

 

In the maps below, you can see changes in Airbnb listings in Boston, Austin, and San Francisco between 2015 and 2019. We map the position of both large hotel chains and Airbnb listings to show their relative locations. For each city and year, we include

  • Unweighted heat maps that highlight the total number of Airbnb properties to make concentrations easier to spot.

  • Weighted heat maps that highlight the total number of nights available within a year to make gestalt impacts more apparent. We interpret the “total number of nights available” as a proxy for resident displacement because even if the properties aren’t being rented, they are still effectively off the market for residents.  

2019.png

To contextualize some of these trends, here are Airbnb’s total numbers and total nights available in the most affected neighborhoods of our case-study cities.

2019

2015

2015.png

Platform mediation occurs in multiple ways. First, preexisting regulation and zoning ordinances on hotels can modulate the location of Airbnb listings. Because Airbnbs aren’t subject to the same regulations as hotels in cities like San Francisco, for instance, this allows them to operate in some areas of tourist demand where large hotels cannot. Second, the relative “walkability” of city neighborhoods is correlated with a density of Airbnb listings, which can be seen if one compares our maps below to those generated by WalkScore.com. Third, how residents and policymakers respond to Airbnbs can differ according to local cultures, which is something we explore in more detail in our interview-based research (e.g., Monahan 2021).

Methods note: Airbnb listing data were acquired from Inside Airbnb. Hotel data were acquired from STR’s global hotel census database. Mapping was performed using ArcMap.

Boston2015UNweighted.jpg

Boston (2015)

Unweighted heat map

Boston2015weighted.jpg

Boston (2015)

Weighted heat map

Boston2019UNweighted.jpg

Boston (2019)

Unweighted heat map

Boston2019weighted.jpg

Boston (2019)

Weighted heat map

Austin2015UNweighted.jpg

Austin (2015)

Unweighted heat map

Austin2015weighted.jpg

Austin (2015)

Weighted heat map

Austin2019UNweighted.jpg

Austin (2019)

Unweighted heat map

Austin2019weighted.jpg

Austin (2019)

Weighted heat map

SF2015UNweighted.jpg

San Francisco (2015)

Unweighted heat map

SF2015weighted.jpg

San Francisco (2015)

Weighted heat map

SF2019UNweighted.jpg

San Francisco (2019)

Unweighted heat map

SF2019weighted.jpg

San Francisco (2019)

Weighted heat map

Suggested citation for maps:
Platform Mediation Project (PI: Torin Monahan). 2021. “Visualizations of Airbnb Listings and Hotels in Boston, Austin, and San Francisco.” https://www.platformmediation.com/maps.

 

Footnote:

[1] The concentration somewhat remains around large hotel chains, which are often located in downtown and/or tourist locations, but the spillover effect is discernible.