Cocola-Gant, Agustin, and Antonio Paolo Russo. “Tourism Gentrification in the Age of Airbnb.” The Routledge Handbook of Tourism and Gentrification, edited by Agustin Cocola-Gant and J. Mark Souther, Routledge, 2023, pp. 1-18.
This chapter synthesizes recent scholarship on tourism gentrification with specific attention to how platforms like Airbnb produce new forms of neighborhood change. The authors distinguish between “classic” gentrification, driven by residential migration, and “tourism gentrification,” driven by short-term mobility, arguing that Airbnb blurs this boundary by turning residential space into tourist space. They introduce the concept of “platform-mediated displacement,” where displacement occurs not through eviction but through the gradual conversion of housing stock such that communities become uninhabitable for long-term residents. For this project, this source is invaluable because it directly theorizes the link between listing concentration, which the Inside Airbnb data visualizes, and community effect, which the ACS data approximates. The “platform-mediated displacement” framework helps argue that concentration is a process with documented consequences for the existing population of people.
Safransky, Sara. “Geographies of Algorithmic Violence: Redlining the Smart City.” International Journal of Urban and Regional Research, vol. 44, no. 2, 2020, pp. 200-218. Wiley Online Library, https://doi.org/10.1111/1468-2427.12833.
While not about Airbnb specifically, this article provides essential framing for how data infrastructures can reproduce racial and spatial inequality. Safransky examines how property technology platforms use algorithms and data to value urban space, arguing that these systems often reinscribe historical patterns of redlining and disinvestment under the pretense of neutrality. She shows how data-driven valuations create feedback loops where areas deemed “desirable” by platforms receive investment and attention, while areas deemed “undesirable” are further marginalized. For this project, this source argues that Airbnb data is not a neutral representation of reality but a constructed dataset that makes certain communities visible as tourist destinations and renders others invisible. When the project maps listing concentrations, it is also mapping where the platform deems valuable and likewise, where it does not. This source exemplifies power and silence in apparent and tangible ways.
Santa Monica Conservancy. “History of Santa Monica.” Santa Monica Conservancy, https://smconservancy.org/explore-santa-monica/historic-places/history-of-santa-monica/. Accessed 28 Feb. 2026.
This source provides a detailed historical narrative of Santa Monica, from its indigenous Tongva inhabitants and the symbolism of its name to its development as a resort town, industrial site, and modern urban area. Compiled by a preservation advocacy organization, it offers a community-focused perspective on the city’s evolution. We use this source in the Santa Monica CCD case study to substantiate our quantitative Airbnb data in the area’s long history as a tourist destination and residential community. From the founders’ failed port ambitions to its emergence as a beach resort, from wartime industrial boom to postwar revitalization, all narratives demonstrate that Santa Monica has continuously been reshaped by external forces. This historical context deepens the humanistic inquiry of our project by showing that the current transformation driven by short-term rentals is not unprecedented, but rather the ongoing negotiation of a community’s identity as a place for visitors and a home for residents.
Wachsmuth, David, and Alexander Weisler. “Airbnb and the Rent Gap: Gentrification Through the Sharing Economy.” Environment and Planning A: Economy and Space, vol. 50, no. 6, 2018, pp. 1147-1170. SAGE Journals, https://doi.org/10.1177/0308518X18778038.
This study introduces the concept of the “rent gap” to explain how Airbnb accelerates gentrification by converting long-term housing into short-term accommodations. The authors argue that short-term rentals allow landlords to capture higher returns than traditional rental markets, creating incentive to displace long-term residents. Using New York City data, they demonstrate that Airbnb listings concentrate in gentrifying neighborhoods and that entire-home listings are overrepresented in areas with rent gaps. For this Los Angeles project, this source provides the theoretical framework for understanding commodification not just as an economic transaction but as a spatial process that displaces communities. The rent gap concept helps explain why the Inside Airbnb data shows concentration in certain neighborhoods and how they are precisely where the gap between long-term rental income and short-term tourist income is largest. This source also models how to pair listing data with demographic data to make claims about neighborhood change.
Zhang, Zhihua, and Rachel J. C. Fu. “Spatial Distribution of Airbnb Supply in Los Angeles.” Tourism Analysis, vol. 27, no. 4, 2022, pp. 467-477. Ingenta Connect, https://doi.org/10.3727/108354222X16571659728565.
This study directly addresses the first half of the research question by analyzing where Airbnb listings concentrate in Los Angeles and what neighborhood characteristics predict that concentration. Using exploratory spatial data analysis and spatial regression models on data from 2014 to 2019, the authors find that Airbnb supply is “positively clustered” with a clear “center-periphery pattern,” exhibiting high-high clusters in central areas like Hollywood, low-low clusters in surrounding areas. They identify determinants including resident population, housing units, income, rent, points of interest, and distance to Hollywood. For this project, this source provides both methodological precedent, as the project extends their analysis with 2024 data, and findings to compare our outcomes against. The identification of specific determinants helps answer why certain areas become commodified, linking spatial patterns to the demographic and economic variables the ACS data captures. This source bridges the two primary datasets by showing how listing locations relate to neighborhood characteristics.
Freepik. “Neighbors on Balconies Concept.” Freepik, https://www.freepik.com/free-vector/neighbours-balconies-quarantine-concept_7765110.htm. Accessed 28 Feb. 2026.
“Los Angeles.” Inside Airbnb, https://insideairbnb.com/los-angeles/. Accessed 28 Feb. 2026.
“Los Angeles County, California.” United States Census Bureau, https://data.census.gov/profile/Los_Angeles_County,_California?g=050XX00US06037. Accessed 28 Feb. 2026.
Macrovector. “American Cityscapes Set.” Freepik, https://www.freepik.com/free-vector/american-cityscapes-banners-set_3796392.htm. Accessed 28 Feb. 2026.
—. “Family Morning Routine Flat Concept.” Freepik, https://www.freepik.com/free-vector/family-morning-routine-flat-concept-with-father-son-making-bed-vector-illustration_33771931.htm. Accessed 28 Feb. 2026.
—. “Stock Market Exchange Set.” Freepik, https://www.freepik.com/free-vector/stock-market-cartoon-icons-set-with-people-trading-online-isolated-vector-illustration_63439384.htm. Accessed 28 Feb. 2026.
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