Introduction :

Once you’ve settled into your home for the next few days, dropped off your bags, and step outside to explore the area where you’ll be staying, what do you actually observe? A quiet residential street, a neighbor starting their day off with a walk, perhaps a “For Rent” sign fading in the sun or a key lockbox attached to a gate. The surface is calm, but beneath it is an obscured reality of increased housing costs, decreased long-term stock for locals, and community displacement, all trickling effects of the platform economy. This project pulls back the curtain on your Airbnb stay to ask an overarching question:

Where is Airbnb concentrated in Los Angeles County, and how do these concentrations align with the commodification of different community profiles?

Using data from Inside Airbnb and the U.S. Census Bureau’s American Community Survey, we mapped over 45,000 listings across Los Angeles County and layered demographic details of race, educational attainment, income, and poverty status to understand not just where short-term rentals cluster, but who lives in the communities hosting them. What we found is a city divided: some neighborhoods accept Airbnb as a natural extension of tourism and thrive, while others experience it as a commercial force reshaping residential life.

This project asks three smaller interconnected questions to answer the overarching one.

( I ) First, where are Airbnbs concentrated in Los Angeles County?

( I I ) Second, when we account for housing availability by calculating Airbnb listings as a percentage of total housing units in each subdivision county, what do the demographics of the subdivision county indicate about the communities that sustain the highest proportional impact?

( I I I ) And third, what patterns present themselves when we overlay census data onto these Airbnb-dense areas? What is the social and temporal context for who lives in the neighborhoods most transformed by short-term rentals and how do their characteristics differ from communities where the platform has barely penetrated?

Presentation :

( I ) First, where are Airbnbs concentrated in Los Angeles County?

Our first visualization is a horizontal bar chart showing straight Airbnb counts by neighborhood, color-coded by CCD and sorted in descending order. Long Beach tops the list with 1,906 listings, followed by Hollywood at 1,758, Venice at 1,529, and Santa Monica at 1,243. While this answers the initial question, the methodology is misleading as it emphasizes sheer quantity without regard for respective city size and its effect on housing stock. As Zhang and Fu demonstrate in their spatial analysis of Airbnb supply in Los Angeles, the distribution of listings follows a center-periphery pattern, with high-density clusters concentrated in central areas while peripheral neighborhoods show significantly lower density (Zhang and Fu 467). Their findings support our observations that Airbnb supply is not randomly distributed but exhibits positive spatial clustering, which means that listings tend to aggregate in specific areas rather than spreading evenly across the county. Our raw count data aligns with this pattern, showing that neighborhoods with amenities and cultural attractions suited for tourism consistently host the highest numbers of listings.

Viz 1. Airbnb Listings by Neighborhood. Click ‘CCD Name’ > ‘Keep Only’ to filter for Airbnb listings in the CCD. Click ‘Exclude’ to filter for all other CCD Airbnb listings.

( I I ) Second, when we account for housing availability by calculating Airbnb listings as a percentage of total housing units in each subdivision county, what do the demographics of the subdivision county indicate about the communities that sustain the highest proportional impact?

Our second visualization addresses the misleading approach used in our first visualization by calculating Airbnb listings as a percentage of total housing units in each CCD, using DP05 housing estimates. Here the Santa Monica CCD rises as the county’s most impacted region, with over 5,000 Airbnbs occupying just over 53,000 total housing units, accounting for 9.5% of its housing stock allocated to short-term rental use. The Agoura Hills-Malibu CCD follows at 4.8%, and South Bay Cities CCD at 4.2%. Meanwhile, the San Fernando Valley CCD hosts over 6,000 Airbnbs in absolute terms, however sees that proportion diluted by its immense housing supply of over 620,000 units to just 0.9%. This visualization overhauls our current understanding of impact by directing attention away from volume and toward an accurate statistical overview of communities where Airbnb reconstructs local housing conditions. The spatial patterns revealed by these visualizations establish the premise of the latter portion of our inquiry. The question that follows focuses on what kinds of communities lie beneath these clusters, and how do the characteristics of residents in high-concentration areas differ from those where the platform is hardly present?

Viz 2. Percentage of housing allocated to Airbnb across all CCDs.

( I I I ) And third, what patterns present themselves when we overlay census data onto these Airbnb-dense areas? What is the social and temporal context for who lives in the neighborhoods most transformed by short-term rentals and how do their characteristics differ from communities where the platform has barely penetrated?

The third and most complex visualization provides a complete demographic profile of each CCD in alphabetical order. From the DP05 file, we extracted the median age and most prevalent race by comparing eight mutually exclusive racial and ethnic groups per CCD then selecting the one with the highest population. From the S1901 income file, we included median household income and also selected the income bracket with the largest share of residents in each CCD. The S1501 file on educational attainment presented only data on the share of CCD residents 25 years and older. The poverty rate presented another layer of nuance. The S1501 table doesn’t present a single overall poverty figure and, instead, breaks poverty down by educational attainment. Thus we created two columns with one averaging the poverty rates for residents with “some college or less” and another showing the poverty rate for those with a “bachelor’s degree or higher”.

Viz 3. CCD Demographics by Airbnb Concentration.

A Closer Look at the Santa Monica CCD :

Take the Santa Monica CCD as a case study in how humanistic inquiry deepens our interpretation of data. This region encompasses not only the city of Santa Monica itself but also surrounding neighborhoods like Venice, Marina del Rey, and parts of West Los Angeles. Historically, this coastal strip developed as a tourist destination with its beaches, pier, and promenade being attractions since the 1920s (Santa Monica Conservancy). At the same time, the postwar era also established it as a residential community comprised of mid-century apartment buildings and single-family homes that housed a mix of artists, families, and residents drawn by Los Angeles living and ocean proximity. That residential character has been under pressure for decades, as rising property values and coastal desirability gradually priced out long-term residents.

Viz 4. Airbnb listings filtered for Santa Monica CCD.

Our data shows that Airbnb has accelerated this transformation. With 9.5% of housing units now effectively removed from the long-term market and allocated to short-term rental use, the Santa Monica CCD exemplifies what Cocola-Gant and Russo term tourism gentrification, defined as a process by which the expansion of tourist accommodation reshapes neighborhoods, displacing residents not through industrial closures or urban renewal, but through the slow erosion of housing availability (Cocola-Gant and Russo 5). This is not exemplary of tourist demand meeting host entrepreneurship at a sustainable middle ground, but about who can afford to remain in a community when nearly one in ten homes exist primarily for visitors.

Viz 5. Percentage of housing allocated to Airbnb exclusively in Santa Monica CCD.

Wachsmuth and Weisler’s work on Airbnb and the rent gap provides the framework for understanding this dynamic. They argue that the platform enables hosts to capture ground rent that would otherwise remain unrealized, incentivizing the conversion of long-term housing for short-term use in gentrifying neighborhoods (Wachsmuth and Weisler 1150). The rent gap, or the disparity between actual and potential rent, is most pronounced in neighborhoods undergoing transition where rising desirability has not been fully reflected in property values. Airbnb allows property owners to take advantage of this gap, capturing tourist-level rents before the long-term market catches up. In Santa Monica, where coastal desirability has been established, the platform accelerates the complete conversion of residential neighborhoods at an unprecedented rate.

Conclusions :

On your last morning in Los Angeles, you pack your bags, leave the key in the lockbox, and board your ride back to LAX. As the car turns through Santa Monica streets toward the freeway, you pass apartment buildings and bungalows, some with key lockboxes visible. You didn’t notice them on the ride here, but after exploring this project, they’re impossible to ignore.

Our point map of over 45,000 listings, color-coded by room type and plotted across the county, makes these patterns visible at a glance. Entire homes appear in blue, private rooms in red, and shared rooms in green. Clusters emerge prominently along the coast, in Hollywood, and in pockets of Alhambra and Pasadena. Scatter is apparent across the San Fernando Valley, South Los Angeles, and the southeastern cities. This is the geography of Airbnb in Los Angeles County determined by tourism, desirability, and the existing contours of inequality. By connecting each dot to the demographic reality of its CCD — the most prevalent race, the median income, the poverty rate split by educational attainment — we can begin to see not just where the platform operates, but what it means for the people already living there.

Safransky’s work on “geographies of algorithmic violence” reminds us that platforms like Airbnb do not operate on neutrality. She argues that digital platforms and their algorithms redline urban space in ways that perpetuate historical practices of discriminatory lending, concentrating opportunity in some neighborhoods while systematically devaluing others (Safransky 202). When we examine where Airbnb listings concentrate and where they are sparse, we see patterns that amplify existing inequalities in an unconventional twist of events. As opposed to the expectations of commodification that would be presumed in lower socioeconomic communities, we observe the opposite. Coastal, predominantly white, higher-income areas absorb the platform’s commercial energy, while South Los Angeles and the southeastern cities, home to predominantly Black and Latino populations with lower median incomes and educational attainment, remain largely untouched by the short-term rental economy. This is not a reflection of consumer preference but of geography shaped by decades of uneven development, transportation investment, and cultural representation.

This project does not argue that Airbnb is inherently good or bad. It argues that the platform’s impacts are unevenly distributed, wherein understanding this distribution requires looking beyond raw counts to proportional impact and beyond listing locations to the communities hosting them. We hope this website serves as a starting point for conversations that matter to the future of Los Angeles and cities like it: conversations about housing, community, and who gets to call a place home. The next time you visit a city and slide a key from a lockbox, we hope you’ll wonder about the neighborhood around you as a place where people live alongside your temporary presence there.