Tokyo Airbnb Pricing & Marketplace Analysis
What 25,000+ Tokyo Airbnb listings reveal about pricing behavior across submarkets, and what hosts could do about it.
Python · Tableau · Pricing Analytics
Overview
An independent analytics project examining how pricing behaves across 25,000+ Tokyo Airbnb listings: pricing distribution, submarket trends, and host performance, translated into pricing and positioning recommendations for hosts.
Problem
Airbnb hosts largely price by intuition. Across a market this size, that produces systematic gaps: comparable units priced far apart, and hosts leaving revenue on the table without knowing it. The question: where are those gaps, and what should hosts do differently?
Context
Self-directed project using public listing data, built to practice end-to-end analytics: raw data → cleaning → analysis → visualization → recommendations someone could act on.
My Role
Solo analyst: data cleaning and analysis in Python, visualization in Tableau, and a final strategy deck translating the findings into host-facing recommendations.
Approach
- Cleaned and analyzed 25,000+ listings in Python
- Evaluated pricing distribution, submarket trends, and host performance
- Quantified price variance across submarkets and listing types to identify pricing gaps versus comparable units
- Built Tableau visualizations of the pricing landscape
- Translated the analysis into revenue and positioning recommendations for hosts
Key Findings
Content in progress. This section will be updated with public-safe findings and artifacts.
Recommendations
Content in progress. This section will be updated once the findings above are published.
Artifacts
Content in progress. Tableau views and selected charts will be added here.
Reflection
Content in progress.
Status
This case study is actively being written up. The analysis is complete; the public version is being prepared so that findings, charts, and recommendations appear together rather than piecemeal.