A residential neighborhood in Suginami City, with about 400 cherry trees along the Zenpukuji River, where you can still spot kingfishers
The closest neighborhoods in the same city.
Zenpukuji River curves from the west around to the south, and the entire bank along it is the Tokyo metropolitan Zenpukuji River Green Space. The green space has about 400 cherry trees planted, plus a children's plaza and tennis courts.
Under Ome Kaido, the northern border, is Minami-asagaya Station on the Marunouchi Line - a direct train to Shinjuku with no transfer, 6 stops away. Along the street are shops, but walking south into the neighborhood it's basically all residential.
The station's address is actually in Asagaya-minami 1-chome next door to the north - this neighborhood isn't actually within the "Asagaya" neighborhood name. Real estate listings still mostly label it as Minami-asagaya Station - 48 of 52 buildings. This neighborhood covers Narita-higashi 1 to 5-chome and Narita-nishi 3 and 4-chome; in 2020 about 27,000 people lived here, about 166 per hectare.
From our street photos, greenery ranks 63rd out of 395 Tokyo neighborhoods - the highest-ranking category for this neighborhood. Street cleanliness ranks 87th, also in the upper range.
Kingfishers have been recorded in the green space, and several kinds of wild ducks in winter. To take the JR, walk from Minami-asagaya Station along Nakasugi-dori to Asagaya Station outside the neighborhood, about 10 minutes.
Building appearance ranks 371st, the weakest category for this neighborhood. Public transit also ranks 336th, in the lower range. The southern end of the neighborhood is farthest from the station - the straight-line distance from the southwest corner of Narita-nishi 3-chome is about 1.7 km, and walking to the station takes more than 20 minutes.
In the torrential rain of September 2005, about 1,700 buildings along Zenpukuji River flooded, with both Narita-higashi and Narita-nishi on the list of affected areas. Based on our own calculation using points taken every 40 meters within the neighborhood on the Geospatial Information Authority of Japan's flood map, about 40% fall within the expected flood zone, more than half of which is 0.5 to 3 meters deep.
For the flood depth at your own home, Suginami City Office asks residents to check using the Tokyo Metropolitan Disaster Prevention App.
In 1958, the Japan Housing Corporation built Asagaya Jutaku in Narita-higashi; 232 of the 350 units were row houses designed by Maekawa Kunio. It later came to be called the "miracle housing complex," and was demolished for redevelopment in 2013.
In 2016, a new building with 575 units was completed on the same site; trees from the old housing complex were transplanted and preserved wherever possible.
Tokyo's Zenpukuji River upstream underground regulating reservoir will be excavated starting from the Zenpukuji River Green Space in Narita-nishi 3 and 4-chome within this neighborhood, with construction scheduled for fiscal years 2025 to 2041. The construction zone in Narita-nishi 3-chome will be enclosed with a 15-meter-high soundproof wall from fiscal years 2027 to 2038, though the green space's central plaza will remain usable.
Compared with the 395 Tokyo neighborhoods in this guide (scores 0–10)
Hospitals with inpatient beds (2020 data), public elementary and junior high schools whose catchment covers this area, high schools and universities within 1 km, stations, parks of 1 ha or more, and large shopping malls and department stores. Many Tokyo junior high students attend private schools, so junior high catchments are only a guide.
Maps show the shops and the sights. What a street is like, you only know by walking it. TidyMap uses AI street imagery analysis and public facility data to turn each area's street environment and everyday convenience into something you can compare. Search an address and look at the street outside first.
How to read it: yellow = poor, blue = good; grey = not enough data
TidyMap rates the street environment and everyday convenience. It does not assess the home itself, safety, noise or actual living experience. Street environment scores use AI assessments of imagery; convenience scores use public facility data.
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