STAT 3280 Final Project
Harry Li
2024-11-12
Introduction
Los Angeles has 58 billionaires and 58,000 homeless people. It is a city of stark contrasts, where immense wealth and economic hardship often coexist in close proximity. A few years of my childhood were spent in LA, and one of the most striking things I still remember to this day was how quickly your surroundings could change in the matter of a short drive. In minutes, you could go from an area of immense wealth and tourism to an area where economic challenges are written on the walls of every building. I’ve always wanted to understand this more, and this project is my attempt at doing so utilizing Airbnb listings.
Airbnb is a platform where property owners rent out their homes, apartments, or even single rooms to travelers, often as short-term rentals. Unlike traditional housing data, which focuses on long-term property values, Airbnb listings provide a unique dataset that combines tangible financial metrics like nightly prices with intangible social indicators such as neighborhood popularity, visitor reviews, or just how much a person wants to be there in general. This dual perspective allows us to capture not only a neighborhood’s economic conditions but also its cultural appeal, tourist activity, and lifestyle factors that housing prices alone cannot fully encapsulate.
In this project, I use Airbnb property listing data as of 2024 to explore how pricing and listing patterns illustrate the economic and social contrasts across LA’s diverse neighborhoods. By examining broader neighborhood groups alongside specific areas, I aim to highlight how these patterns encapsulate the vibrancy and disparity that define Los Angeles.
SECTION 1: UNDERSTANDING THE DYNAMICS OF PRICE
The histograms highlight the stark imbalances in LA’s Airbnb landscape, reflecting the broader economic contrasts across the city. The distribution of total listings reveals that most neighborhoods host only a handful of Airbnb properties, while a few areas dominate with hundreds or even over a thousand listings. Similarly, the distribution of listing prices shows a heavy concentration of affordable options under $250 per night, but with a noticeable long tail of luxury accommodations reaching upwards of $1000.
These patterns illustrate how Airbnb activity is not evenly distributed across the city but instead clusters in specific areas that cater to distinct audiences. Neighborhoods with fewer listings and lower prices likely correspond to regions with less tourist demand or economic challenges, while those with an abundance of high-priced listings reflect their status as affluent, highly desirable destinations. Together, these imbalances begin to emphasize the dichotomy of LA: a city where extreme wealth and economic hardship coexist, often just blocks apart. Here we are able to gather that the variables price and quantity provide potential to gain a deeper understanding of how Airbnb listings mirror their socioeconomic surroundings.
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The scatterplots, paired with regression lines, emphasize a crucial point: Airbnb prices are not strongly determined by the features of the property itself. The weak correlation between the number of bedrooms or guest capacity and price suggests that factors like property size or accommodation limits play a minor role. Instead, the data hints that geographical and external factors, such as neighborhood desirability, drive prices. This insight sets the stage for further exploration into how location-specific characteristics shape Airbnb pricing, which we will look into next.
## Warning: `line.width` does not currently support multiple values.The bubble plot offers an insightful look into the neighborhood-level dynamics of Airbnb listings in Los Angeles. Each circle represents a neighborhood, with its size indicating the number of listings, the x-axis showing the average price per night, and the y-axis representing the average number of bedrooms. The color gradient further adds another layer, capturing the average review scores for each neighborhood.
Neighborhoods like Beverly Hills and Venice, represented by larger circles, dominate the landscape with numerous listings and high prices, paired with decent average review scores. Interestingly, these areas often have fewer bedrooms per property, suggesting that location prestige and tourist demand outweigh property size in driving prices. For instance, Beverly Hills’ high prices are driven by its exclusivity and cultural allure rather than the size of its accommodations.
In contrast, neighborhoods like Watts and Sun Village, depicted by smaller circles in the bottom-left, reveal a different reality. These areas have lower prices, fewer listings, and relatively poorer review scores, reflecting economic challenges and limited tourist interest. Meanwhile, smaller circles in the upper-right quadrant, representing neighborhoods with high prices but fewer listings, point to the scarcity and exclusivity of certain areas, where limited availability drives up demand.
This visualization underscores the influence of neighborhood characteristics over property features in shaping Airbnb trends. It highlights how factors such as location prestige, economic conditions, and exclusivity dictate listing patterns, prices, and reviews, revealing the division in LA’s neighborhoods. With this in mind, we can take it a step further and factor in actual geographical locations on a map.
SECTION 2: GEOGRAPHICAL CONTEXT
I’ve grouped the >200 neighborhoods into six groups based on geographical location. The bar plots reveal a significant pattern: the same group dominate both in pricing and number of listings. Westside & Coastal Wealthy Areas is consistently the most expensive and has the most Airbnbs. This dual dominance suggests a feedback loop, where affluent areas attract more visitors and investment, further cementing their desirability. On the other end, South LA and Eastside & Northeast LA rank at the bottom for both price and quantity. This consistency underscores the entrenched nature of economic disparities in LA, where wealthier areas benefit from visibility and tourist appeal, while less affluent areas struggle to attract interest or investment. The fact that these groups consistently are at the top and bottom of both plots point towards the fact that quantity and price are reflecting the same thing, the quality of the neighborhood it resides in.
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The density heatmap provides a spatial visualization of Airbnb listing concentrations across Los Angeles, offering insight into how listings reflect the characteristics of their surroundings. High-density clusters emerge in affluent areas such as Hollywood Hills and the Westside, which are known for their prestige, tourist appeal, and economic vibrancy. In contrast, regions like South LA display sparse density, mirroring the economic challenges and lower tourism activity associated with these neighborhoods.
This visualization highlights the stark geographical divide in LA’s Airbnb landscape, where areas of wealth and popularity are marked by dense activity, while other regions remain quieter and less prominent. The distribution of Airbnb listings mirrors the city’s broader contrasts, with clear lines between neighborhoods thriving on visibility and affluence and those where such activity is minimal.
The Shiny app adds a layer of interactivity, allowing users to explore specific regions. When filtering by Westside & Coastal Wealthy Areas, the data consistently shows listings with high prices, aligning with their affluence and desirability. Conversely, filtering by South LA or Eastside & Northeast LA reveals consistently sparse, lower prices, reinforcing their status as less desirable locations. This interactivity allows users to directly observe the geographic disparities, offering concrete evidence that Airbnb pricing and desirability reflect the socioeconomic conditions of the neighborhoods they occupy.
SECTION 3: GROWTH OVER TIME
Now that we’ve explored the current state of Airbnb listings and how they reflect LA’s stark socioeconomic contrasts, it’s essential to examine how these patterns developed over time. A historical perspective can provide deeper insight into whether these divides are improving or, as the data suggests, remaining stubbornly persistent.
The line plot reveals an enduring pattern: affluent neighborhoods, such as the Westside & Coastal Wealthy Areas, consistently maintain higher Airbnb prices over time, while less affluent regions remain consistently lower. While occasional fluctuations occur, these are often the result of a single, exceptionally expensive listing being added and do not represent broader trends. This reinforces the idea that the economic divides reflected in Airbnb pricing are stable and deeply entrenched. Even as new listings emerge, the relative pricing differences between neighborhoods persist, highlighting the ongoing and unchanging nature of wealth disparities in LA.
The animation of Airbnb listings over time highlights where growth has occurred. It is clear to see that over the past two decades, affluent areas like Hollywood Hills and the Westside consistently attract new listings while in contrast, neighborhoods like South LA show limited growth. The lack of significant shifts over the years suggests that these patterns are deeply rooted, with wealthier neighborhoods continuously benefiting from investment and visibility while less affluent areas struggle to break the cycle.
Conclusion
Los Angeles, a city where immense wealth and deep economic challenges coexist, is a prime example of socioeconomic disparity. Through the lens of Airbnb data, this project shines light the stark contrasts that define its neighborhoods. Affluent areas like Beverly Hills and Venice dominate in pricing, density, and growth, reinforcing their desirability and status as cultural and tourist hubs. These regions thrive on visibility and investment, creating a feedback loop that perpetuates their prosperity. On the other hand, neighborhoods such as Watts and South LA struggle to attract listings and visitors, reflecting entrenched economic challenges and a lack of tourist interest.
The spatial and time-series analysis further highlights the deeply rooted nature of these divides. Affluent areas show consistent growth in Airbnb listings, while less affluent neighborhoods see minimal changes over time, emphasizing the persistence of these disparities. The data suggests that factors beyond property size or features, such as neighborhood prestige, desirability, and social dynamics, are the primary drivers of Airbnb pricing and activity.
This analysis paints a vivid picture of LA’s dual nature. Airbnb data, with its unique blend of economic and social indicators, provides a powerful tool for exploring these dynamics, offering insights that extend beyond traditional housing metrics. Ultimately, this project demonstrates how even within the same city, the lived experiences of residents and visitors vary drastically, shaped by the economic and cultural forces that define each neighborhood.