We extract rental listings, sales properties, pricing histories, station distances, and building specs from Suumo.jp. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Rental Listings objects from suumo.jp. All fields typed and schema-versioned.
"property_id": "1003429811", "rent_jpy": 125000, "management_fee_jpy": 8000, "layout": "1LDK", "area_sqm": 42.5, "station_1": "Shinjuku Station", "station_1_walk_min": 8, "key_money_jpy": 125000
| # | property_id | title | rent_jpy | management_fee_jpy | deposit_jpy | key_money_jpy |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Sales (Buy) objects from suumo.jp. All fields typed and schema-versioned.
"property_id": "88910234", "price_jpy": 45000000, "layout": "3LDK", "area_sqm": 75.2, "building_age": 12, "land_rights": "Freehold", "station_1": "Meguro Station", "station_1_walk_min": 12
| # | property_id | title | price_jpy | layout | area_sqm | balcony_area_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Building Intelligence objects from suumo.jp. All fields typed and schema-versioned.
"building_id": "B998123", "building_name": "Park Tower Shinjuku", "address": "Nishi-Shinjuku 6-chome", "structure": "RC", "year_built": 2015, "total_units": 342, "nearest_station": "Nishi-Shinjuku Station"
| # | building_id | building_name | address | prefecture | city | ward |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency Data objects from suumo.jp. All fields typed and schema-versioned.
"agency_id": "A77612", "agency_name": "Tokyo Real Estate Co.", "license_number": "Tokyo (3) 12345", "phone_number": "03-1234-5678", "business_hours": "10:00 - 19:00", "active_listings_count": 412, "holidays": "Wednesday"
| # | agency_id | agency_name | license_number | address | phone_number | business_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from suumo.jp. All fields typed and schema-versioned.
"search_url": "https://suumo.jp/chintai/tokyo/sc_shinjuku/", "total_results": 14502, "position": 1, "property_id": "1003429811", "rent_jpy": 125000, "layout": "1LDK", "scraped_at": "2026-05-12T09:14:33Z"
| # | search_url | prefecture | ward | total_results | page_number | position |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Suumo scraper handles the complexity of Japanese real estate portals: Zenkaku/Hankaku character normalisation, strict pagination limits, and dynamic geographic searches, bypassing bot protection to deliver clean property data.
Extract rent, key money (reikin), deposit (shikikin), management fees, and layout types (1K, 2LDK) cleanly separated into numeric and categorical fields.
Parse multiple transit lines, nearest stations, and walking minutes. Normalised to support geospatial analysis and transit-oriented valuation.
Monitor rent fluctuations and sale price adjustments over time. Track days on market and identify stale listings before they are delisted.
Capture building age, structural materials (RC, SRC, wooden), total floors, and seismic standard compliance flags.
Convert text-heavy amenity lists into boolean flags for auto-lock, washlet, delivery box, pet-friendly, and internet-included features.
Standardise addresses into prefecture, city, ward, and chome levels. Resolve Zenkaku (full-width) and Hankaku (half-width) character inconsistencies.
Extract broker license numbers, active listing counts, and contact details to monitor competitor agency performance.
Execute bounding box coordinate searches to extract properties within specific geographic polygons rather than relying solely on ward boundaries.
Run daily delta extractions. We maintain state and only push new, updated, or delisted properties to reduce your processing overhead.
Brief in. Clean data out.
Provide target prefectures, wards, station lines, or specific property types. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, Japan-based proxy rotation, and text normalisation logic for suumo.jp.
Schema validation, null-rate checks, price-outlier detection, and address normalisation verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Japanese real estate portals heavily restrict automated access and use complex DOM structures. Here is how we build resilient pipelines.
Suumo.jp blocks datacenter IPs and non-Japanese traffic aggressively. We route requests through residential ISP proxies physically located in Japan, paired with realistic browser fingerprints and automated session management.
Japanese web data often mixes full-width (Zenkaku) and half-width (Hankaku) characters, complicating downstream joins. Our pipeline automatically normalises character widths, standardises kanji variants, and handles legacy encoding issues natively.
Suumo truncates search results after a set number of pages. We programmatically subdivide large geographic searches by transit line, walking distance, or specific chome coordinates to ensure 100% listing extraction without hitting pagination walls.
Certain property clusters are only visible via map interactions. We use Playwright to simulate viewport panning and zooming, triggering the underlying XHR requests to capture coordinates and listings hidden from standard list views.
For massive metropolitan areas like Tokyo, we maintain a hash index of last-seen values per property. Subsequent runs only push diffs, reducing compute cost and giving you a clean changelog of rent drops or delistings.
Funds calculate precise yield models by correlating historical sale prices with current market rent data across specific Tokyo wards.
Automated Valuation Models (AVMs) train on millions of Suumo records to predict property values based on age, layout, and station proximity.
Brokerages track competitor listing volumes, days on market, and exclusive inventory to adjust their own acquisition strategies.
Researchers map housing density, average rent indices, and layout trends to understand demographic shifts along major transit corridors.
Corporate relocation firms use real-time webhooks to match incoming expatriates with properties meeting strict corporate housing criteria.
Consultancies aggregate ward-level rent indices and key money trends to publish authoritative quarterly real estate reports.
"Suumo.jp holds the definitive ground truth for Japanese real estate, but extracting structured data from its complex, heavily-paginated DOM requires highly localised infrastructure."
Most teams fail at Japanese real estate scraping due to strict bot protection, complex address hierarchies, and Zenkaku/Hankaku character encoding issues. DataFlirt manages the proxy rotation, JavaScript hydration, and data normalisation so your quants can focus on yield models, not DOM parsing.
Everything supported by our suumo.jp scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and map interaction flows.
We maintain dedicated pools of residential ISP proxies specifically located in Japan. Rotation happens per-request to avoid geo-blocking and rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About suumo.jp scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law, targeting only public, non-authenticated property and pricing data. We do not extract personal data, circumvent authentication walls, or access the REINS backend. Clients should review Suumo's Terms of Service and consult legal counsel for specific use cases.
Our pipelines natively handle Shift-JIS to UTF-8 conversion. We apply normalisation functions to convert Zenkaku (full-width) characters to Hankaku (half-width) where appropriate, and parse address strings into structured prefecture, city, ward, and chome fields.
Yes. Every pipeline run produces timestamped snapshots. We maintain state and can deliver diffs showing exact rent adjustments, changes in key money, or when a property is removed from the market.
Yes. We can configure pipelines to execute searches based on specific bounding box coordinates, allowing you to extract listings independent of standard ward or station boundaries.
We use Japan-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Our smallest packages start at a defined geographic scope (e.g., specific Tokyo wards) with weekly delivery. For nationwide catalogues or daily cadences, we price based on volume and compute requirements. Contact us for a scoped quote.
Yes. We provide a sample run of up to 500 properties for a specific ward or station as part of the pre-engagement scoping process. This allows you to validate schema fit, character normalisation, and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off Tokyo property dump or a continuous price-monitoring feed across Japan, we scope, build, and operate the pipeline. Tell us what you need.
