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
The pricing gaps, the submarket comparison, and the host-performance split publish here together with the charts behind them. Findings that arrive without the visual evidence are harder to check, and this analysis is worth checking.
Recommendations
The host-facing recommendations publish with the findings they rest on, so the reasoning stays legible end to end rather than arriving as a list of assertions.
Artifacts
Selected Tableau views from the pricing landscape publish alongside the findings.
Reflection
The useful part of running this one alone was owning every step, including the unglamorous middle. Cleaning 25,000 listings is most of the work and none of the story, and it is the part that decides whether anything downstream can be trusted.
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.