Market structure

Why Station-Level Data Is Redefining Real Estate Valuation in Tokyo

Ward-level averages blur the micro-market reality of Tokyo. Investors need the station as their unit of analysis — where connectivity, amenity density and walkability shape pricing, rent and yield.

Updated Dec 2025•6 min

1) The problem with ward averages

Averages hide composition effects: a shift in layout mix or vintage can move the mean without any real price change. Neighborhood borders are porous; commuter time and line access dominate more than ward lines.

2) The station as a micro-market

Each station’s reach defines a natural catchment. We normalize comps by walking minutes andbuilding age to compare assets fairly across the network.

Core idea: Price per m² = P₀ × e^(−β × WalkMinutes), adjusted for vintage and layout.

3) What you can do with station-level models

  • Spot mispricings vs distance-decay fair values.
  • Quantify “age drag” and time-to-refurbishment.
  • Segment yield bands by layout (1K vs 1LDK vs 2LDK).
  • Underwrite exit scenarios with sensitivity to ±50 bps yield.

4) From data to decisions

Start with station-tagged sold comps, normalize, and fit decay/age coefficients. Use the fits to compare current listings, and stress-test NOI under realistic rent assumptions. The result: cleaner pricing, better risk control, and repeatable decision-making.

Continue reading: Distance-to-Station: the price premium

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