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Living in Suwa 1-chōme, Tama-shi: rent, safety & local area

Overall score
79
Top 13% in Greater Tokyo
🧺 Convenience top 5%

Suwa 1-chōme is a neighborhood in Tama-shi, Japan, with 69 everyday amenities within walking distance. Nearest station: Keio-Nagayama (5 min walk). Rent for one or two people runs ¥49K–¥116K. On sonar you can check whether it fits your commute and budget.

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savings

Rent

¥49K–¥116K
Single / couple (Studio–1LDK)

Family (2LDK–3LDK) ¥80K–¥175K

train

Getting around

5 min walk
Keio-Nagayama Sta. · Keio Sagamihara Line

Add your commute destination to see the commute score for this area

people

Total population

4,124
Foreign residents 52 (1.3%)
Resident age groupsAvg. age 47.9
16%
10%
11%
15%
14%
12%
23%
~1940s70+

Census 2020 · age groups are for Tama-shi

terrain

Terrain (slopes & elevation)

87m
Hilly
FlatSteep

Notable elevation change, about 27 m. The walk from the station has real ups and downs, so check the route itself when you view a place.

storefront

Local amenities

Top 5%
Greater Tokyo · 96 pts
🛒
Supermarket3
🏪
Convenience9
💊
Pharmacy13
🌳
Park8
🏥
Hospital6
Café4
🍴
Dining13

Within walking distance

child_care

Childcare

Top 71%
Greater Tokyo · 46 pts
🧒Nurseries1
🏫Schools4
🌳Park8

For families: 4 schools, 8 parks nearby.

volume_off

Quiet

Top 74%
Greater Tokyo · 77 pts
Road noiseNone
Railway noiseTracks nearby
Night noiseIzakaya ×7

No major road or railway runs nearby, so it stays quiet.

shield

Safety (crime data)

Top 28%
Greater Tokyo · 95 pts · 0.12 per 100 people/yr · 警視庁 2024年
CautionArea avgSafe
Snatching / Bike theft5

Violent crime (robbery, arson, etc.) was 0 in the latest year; most reports are theft (bicycle theft, shoplifting and the like). Annual counts per neighborhood from National Police Agency open data. Counts run higher where daytime foot traffic is heavy, so for a per-resident view see sonar's safety score (population-normalized).

warning

Disaster risk (hazard map)

2
floodFlood〜0.5m
tsunamiTsunamiNone
landslideLandslideWarning zone
wavesStorm surgeNone
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Scored from police, municipal and OpenStreetMap open data, using the same formula for every neighborhood.

Sources: OpenStreetMap, National Police Agency open data, MLIT hazard maps, and the Real Estate Information Library, aggregated per neighborhood. May differ from current conditions.