What sets prices in Tokyo’s Airbnb market?

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?

My role

Solo analyst on a self-directed project using public listing data: data cleaning and analysis in Python, visualization in Tableau, and a final strategy deck translating the findings into host-facing recommendations.

I minored in Japanese at Seattle University and spent spring and summer 2024 on exchange at 上智大学 (Sophia University) in Tokyo, with coursework spanning Japanese language, Anthropology of Japan, and management. This project points that Japan connection at a business question in the same city.

What the analysis looked at

• Pricing distribution across 25,000+ listings
• Price variance across submarkets and listing types, to identify pricing gaps versus comparable units
• Host performance
• Tableau visualizations of the pricing landscape

Findings and recommendations

The pricing gaps, the submarket comparison, and the host-performance split publish here together with the charts behind them, followed by the host-facing recommendations they rest on. Findings that arrive without the visual evidence are harder to check, and this analysis is worth checking.

Method and limits

Cleaned and analyzed in Python, visualized in Tableau, then translated into revenue and positioning recommendations for hosts. The analysis is complete; the public version is being prepared so that findings, charts, and recommendations appear together rather than piecemeal.

What I took from the work

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.