Capital Crashpad

When 1,800 Listings Vanished

Short-term rentals reshape housing markets, neighborhoods, and regulation. Capital Crashpad's dashboard and case study chart the sharp drop in Washington, DC Airbnbs after a nationwide rollout of identity checks and quality removals, showing how platform governance can shift local markets beyond city law.


Project Links

Live Website

Case Study

GitHub Repository


This project is, quite literally, close to home for me; I live in DC. I stumbled on a sharp drop in Airbnb listings while digging through the data, and it caught my attention. Some internet sleuthing revealed it coincided with changes in company policy, not local laws. Routine EDA turned into a deeper investigation: the data showed a structural break, with lucrative extended-stay rentals disappearing. I built this tool for anyone in DC to explore these shifts, whether you’re curious about your block or the big picture.

Project Overview

The guiding question was not how many Airbnbs exist, but how platform policies reshaped the structure of supply.

Using quarterly listing data stored in PostgreSQL and analyzed in Python, I built a full pipeline to track changes in pricing, licensing status, minimum-night distributions, and projected revenue before and after enforcement. The dashboard integrates Leaflet and JavaScript to visualize neighborhood patterns and regulatory thresholds, while the written case study interprets those shifts in plain language.

The result is both a tool and an argument. The dashboard enables exploration; the case study explains what changed and why it matters. Together, they show how platform governance can alter local housing markets almost overnight.

Technically, the project demonstrates database design, SQL querying, Python-based analysis, and geospatial visualization. More importantly, it reflects my focus as a data communicator: translating structural market changes into insights that policymakers and residents can actually understand and use.


Gallery



Line chart showing drop and rebound in Airbnb listings after verification expansion Listings Over Time (2023–2025): Active listings fall sharply between Q1 and Q2 2024, marking a structural break coinciding with Airbnb’s expanded verification and quality removals.

Line chart showing increase in licensed Airbnb listings Licensing Share: Following the Q2 2024 contraction, the share of licensed listings rises as unlicensed supply exits the platform.

Bar plot of minimum nights required for Airbnb listings, colored by license status Minimum Nights (Pre vs Post): Unlicensed extended-stay (31+ night) listings decline disproportionately, accounting for a large share of the overall market drop.

Bar plot of minimum nights required for Airbnb listings, colored by license status Minimum Nights (Dashboard View): Post-enforcement distribution shows a reduced extended-stay segment and a higher proportion of licensed listings.

Violin plot of nightly prices and availability for the upcoming year, with median annotated Price Distribution: Nightly prices remain widely dispersed after enforcement, with median levels relatively stable despite reduced supply.

Choropleth map showing total Airbnb listings by neighborhood in Washington, D.C., relative to the average Listings by Neighborhood: Supply remains concentrated in central DC, though listing density varies significantly across neighborhoods.

Choropleth map of license compliance percentage per neighborhood License Compliance by Neighborhood: Licensing rates vary geographically, as uneven compliance patterns continue across the city despite the increased proportion of licensed listings.

Lorenz curve of Airbnb host revenue concentration before and after verification, showing no change Revenue Concentration: Host revenue remains highly concentrated before and after enforcement, indicating persistent structural inequality within the market.

Bubble chart showing neighborhoods with higher licensing and availability Neighborhood Stability: Licensing rates and listing availability move together across many neighborhoods, illustrating broad platform-level effects rather than isolated geographic shifts.

References

Dataset sourced from the Inside Airbnb Project. Additional data on neighborhood population and housing units came from Census Reporter.