When platforms change the rules, markets respond. This project examines how Airbnb’s 2024 verification expansion reshaped Washington, D.C.’s short-term rental market in size, structure, and revenue distribution.
ℹ️ Status: The dashboard is refreshed semiannually as new data becomes available. The accompanying case study is intentionally frozen at 2025 Q3 to preserve a consistent before-and-after analysis of Airbnb’s 2024 verification expansion. Legacy full-stack versions (Flask and Django) are archived in the
/legacyfolder.
- Project Overview
- Features
- Tools & Technologies
- Usage
- Gallery
- Data & Methodology
- References
- License
- Acknowledgements
- Author
Capital Crashpad analyzes a structural break in Washington, D.C.’s Airbnb market.
In Q2 2024, after Airbnb expanded listing verification requirements, roughly 1,800 listings disappeared from the platform. This project investigates what changed — not just how many listings left, but which ones, and what the market looked like afterward.
The analysis combines:
- An interactive public-facing dashboard (GitHub Pages)
- A structured, narrative case study (nbconverted HTML)
- A PostgreSQL-backed ETL pipeline
- Multi-quarter panel data analysis in Python
Key questions explored:
- Did the market simply shrink — or reorganize?
- Were unlicensed listings disproportionately removed?
- What happened to extended-stay (31+ minimum night) rentals?
- Did revenue decline because low-performers exited — or high earners?
- Did enforcement reduce concentration among top hosts?
The findings suggest the contraction was not random. Listings clustered around the 30-day regulatory threshold declined sharply, driving much of the revenue shift. The platform became smaller, more licensed, more operational — but earnings remained highly concentrated.
This project sits at the intersection of housing policy, platform governance, and data storytelling.
Interactive Dashboard:
- Multi-metric neighborhood comparison
- Dynamic titles tied to analytical questions
- Density vs scale visualizations
- Revenue concentration analysis
- Interactive filtering by neighborhood and metric
Case Study:
- Structured narrative analysis
- Combined plots for clarity and space efficiency
- Revenue segmentation by minimum-night category
- Before/after structural comparison
- Policy-relevant interpretation without technical jargon
- Snapshot analysis covering 2023 Q2 – 2025 Q3
- Preserved as an archival narrative rather than a continuously updated report
Data Pipeline:
- PostgreSQL database with quarterly snapshots
- SQL aggregation queries for structural metrics
- Jupyter ETL workflow for reproducible updates
- nbconvert automation for HTML publication
Legacy Archive:
- Flask and Django implementations preserved in
/legacy - Earlier exploratory dashboards and Tableau version retained for reference
- Backend: Python (Pandas, NumPy), PostgreSQL, SQLAlchemy, Jupyter Notebook
- Visualization: Matplotlib, Seaborn, Plotly, Chart.js, Leaflet
- Frontend: JavaScript, HTML/CSS, Bootstrap, GitHub Pages Deployment
- Automation: Nbconvert for HTML Case Study Export
- Database: Structured SQL Views for Reusable Metrics
Visit:
https://johbry17.github.io/Capital-Crashpad/
Navigate between:
- Interactive dashboard views
- Case study analysis (HTML)
- Open the ETL notebook:
/notebooks/etl_2026.ipynb
- Run all cells to:
- Update quarterly tables
- Regenerate summary views
- Export CSVs for GitHub Pages
The case study export pipeline is archived; future updates apply only to the interactive dashboard.
Below are key visualizations from the dashboard and case study:
Dashboard:
Distribution of nightly prices and availability for Airbnb listings, highlighting the median value.
Minimum nights required for Airbnb listings, segmented by license status.
Interactive Map:
Total Airbnb listings by neighborhood, compared to the citywide average.
License status of Airbnb listings within a selected neighborhood.
Distribution of Airbnb property types in a highlighted neighborhood.
Percentage of licensed Airbnb listings in each neighborhood.
Median Airbnb price per neighborhood, relative to the citywide median.
Case Study:
Sharp decline and partial rebound in Airbnb listings following verification expansion.
Increase in the share of licensed Airbnb listings after enforcement.
Revenue decline in extended-stay Airbnb listings post-verification.
Decrease in unlicensed extended-stay listings after policy change.
Lorenz curves showing persistent concentration of Airbnb revenue among top hosts before and after verification.
Neighborhoods with higher licensing rates and greater listing availability.
Primary dataset: Inside Airbnb quarterly scrape data.
Revenue estimates are derived from price * (365 - availability_365). This serves as a directional proxy for annualized booking value. Results should be interpreted structurally rather than as exact financial totals.
Neighborhood population data used for per-1,000 density metrics.
Analysis covers multiple quarterly snapshots to identify structural inflection rather than seasonal fluctuation, intentionally concluding with 2025 Q3 in order to preserve a coherent before-and-after examination of Airbnb's 2024 verification expansion. The interactive dashboard includes more recent data releases when available.
Dataset provided by Inside AirBnB.
Neighborhood population and housing unit data from Census Reporter.
Creative Commons Attribution 4.0 International License
- Thanks to Imen Najar for early insights and support.
- Thanks to Geronimo Perez for feedback and assistance during development.
Bryan Johns, February 2026
bryan.johns@informedwanderer.com | LinkedIn | GitHub | Portfolio
— Fluent in Data. Fluent in Human.