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Tokyo 23 wards · 764 districts · ML analysis

Tokyo real estate:fair prices, decided by data.

764 districts evaluated by machine learning. Overpriced or undervalued, safety, future potential — all in one view.

Find undervalued areas freeSee Pricing

What you weren't shown

Three things you'd never ask the agent.

And if you did, they probably wouldn't know. But these are exactly the things that matter when you decide.

Is this price really fair?

ML-estimated fair price gap (±%), for every one of 764 districts.

Is this area really safe?

Flood, earthquake, liquefaction, crime — combined into one 100-point score.

Will it still hold value?

Official land price trends and population outlook through 2050.

So — how wrong is the market, exactly?

What the data found

Below the fair price.

Re-evaluate 764 districts and the median market price runs about 8% below the ML fair estimate. "Popular" doesn't always mean "priced right."

Impression"It's popular, so it'll rise"
→
Real data+8.2% & Safety 100

Compare areas by the numbers

Independent analysis of government data.
Choose with confidence — data, not sales pitches

764 districts

5-layer heatmap to visualize undervalued areas

Switch between price gap, safety, land price change, population growth, and value score layers with a single tap. Visually narrow down investment candidates.

30 APIs to analyze why an area is cheap

Beyond the integrated hazard score — drill into school districts, medical facilities, and welfare data to understand each area's true potential.

8-axis cross analysis for multi-dimensional comparison

Pick any 2 axes (price gap, safety, land price change, population, elderly rate, facilities, etc.) on a scatter plot to compare wards visually.

All 23 wards.

ChiyodaChuoMinatoShinjukuBunkyoTaitoSumidaKotoShinagawaMeguroOtaSetagayaShibuyaNakanoSuginamiToshimaKitaArakawaItabashiNerimaAdachiKatsushikaEdogawa
ChiyodaChuoMinatoShinjukuBunkyoTaitoSumidaKotoShinagawaMeguroOtaSetagayaShibuyaNakanoSuginamiToshimaKitaArakawaItabashiNerimaAdachiKatsushikaEdogawa

How it works

Three steps. Data speaks for itself.

1

Gather public data

30 sources from the MLIT — transactions, official land prices, hazards.

Public, official data
2

ML estimates the fair price

Trained on 50,000+ real transactions, the model returns a fair price per district.

50,000+ real deals
3

Five angles, one view

Gap, safety, value, land trend, and population — all in one comparable score.

Five angles

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Experience the actual analysis tools

Price Gap

Visualize the gap between ML fair price and market price. Green areas are undervalued — spot investment opportunities at a glance.

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Evidence

Here's what the data actually said.

"Expensive" doesn't always mean "safe." Three patterns from the 764-district analysis.

Discovery
Setagaya
+8.2%

8% under ML's fair price, safety score 100

Setagaya. Both price and safety align — value you only see in the data.

Caution
Chuo
44/100

Cheap on price, but safety score just 44

Chuo. Hazard risk may not be reflected in the price — proceed carefully.

ML discovery
ML
35.5%

Building age explains 35.5% of price

Across 18 features, building age dominates. The first quantification of the new-build premium.

And across the rankings —

Top 5 Underpriced

Market price below ML estimate = investment opportunity

+8.1%Setagaya
  1. 01Setagaya+8.1%
  2. 02Chuo+7.5%
  3. 03Minato+5.9%

Top 5 Safety

Crime, flood & earthquake risk combined into a 100-point score

  1. 01Taito100 pts
  2. 02Meguro100 pts
  3. 03Setagaya100 pts

Top 5 Value

Composite rating factoring price, commute & safety

  1. 01Itabashi132.1
  2. 02Nerima125.0
  3. 03Suginami105.6

Land Price TOP 5

MLIT
  1. 01Chuo462.9万/㎡
  2. 02Shibuya413.6万/㎡
  3. 03Minato375.9万/㎡
  4. 04Chiyoda336.5万/㎡
  5. 05Shinjuku199.8万/㎡

Price Growth TOP 5

MLIT
  1. 01Chiyoda+10.0%
  2. 02Taito+9.5%
  3. 03Shibuya+8.8%
  4. 04Shinjuku+8.8%
  5. 05Chuo+8.7%

Use case

So — what would you find?

Renter · 30s

Move to a neighborhood that'll grow.

Your current rent might get you somewhere better. Spot land-price trends and population inflow early.

+3.2%/yrLand-price uptrend
First buyer · 40s

Double-check the agent.

"Is this really fair?" Cross ML's fair-price gap with safety score and finally have the answer.

¥760K/㎡Itabashi ¥760K · safety 100
Investor · 40s

A basis, not a hunch.

Need data-backed reasons. Across all 23 wards: gap × safety × value, your own decision frame.

+8.2%Gap × safety 100

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Frequently Asked Questions

Where does the data come from?
All data is sourced exclusively from MLIT (Ministry of Land, Infrastructure, Transport and Tourism) public APIs and official land price data. Our ML model (R²=0.736) estimates fair prices per district.
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