We extract residential and commercial listings, pricing signals, floor plans, station proximity metrics, and agency details from athome.co.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 Residential Rentals objects from athome.co.jp. All fields typed and schema-versioned.
"property_id": "1049283746", "rent_price": 85000, "management_fee": 5000, "deposit_shikikin": 85000, "key_money_reikin": 85000, "floor_plan": "1K", "area_sqm": 25.4, "built_year": 2018, "walk_minutes": 5
| # | property_id | title | property_type | rent_price | management_fee | deposit_shikikin |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Property Sales objects from athome.co.jp. All fields typed and schema-versioned.
"property_id": "5938271640", "sale_price": 45000000, "land_area": 120.5, "built_year": 2010, "land_rights": "Freehold", "nearest_station": "Shinjuku", "walk_minutes": 12
| # | property_id | title | property_type | sale_price | land_area | building_area |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building Details objects from athome.co.jp. All fields typed and schema-versioned.
"building_id": "B938472", "name": "Grand Palace Shibuya", "total_floors": 15, "built_year": 2021, "construction_type": "RC", "auto_lock": true, "delivery_box": true
| # | building_id | name | address | total_floors | built_year | construction_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Agency Intelligence objects from athome.co.jp. All fields typed and schema-versioned.
"agency_id": "A48291", "agency_name": "Tokyo Real Estate Co.", "license_number": "Tokyo Governor (3) 12345", "business_hours": "10:00 - 19:00", "holidays": "Wednesday", "active_listings_count": 342, "rating": 4.2
| # | agency_id | agency_name | address | phone_number | license_number | business_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from athome.co.jp. All fields typed and schema-versioned.
"keyword": "Shibuya 1LDK", "prefecture": "Tokyo", "position": 1, "property_id": "1049283746", "price": 150000, "nearest_station": "Shibuya", "floor_plan": "1LDK", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | prefecture | city | ward | search_type | position |
|---|---|---|---|---|---|---|
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Our At Home scraper handles the complexities of Japanese real estate data: prefectural geographic hierarchies, specific fee structures, transit line mappings, and property feature normalisation.
Extract residential rentals, sales, land, and commercial properties. Capture all metadata surfaced on the listing page.
Capture rent, management fees, shikikin (deposit), reikin (key money), brokerage fees, and renewal fees accurately.
Extract nearest stations, train lines, walk minutes, and bus routes. Normalise prefectures, cities, and wards.
Extract building structure (RC, SRC, wooden), built year, total floors, floor level, and total units.
Capture URLs for floor plan images, interior photos, exterior shots, and panoramic views.
Extract agency names, license numbers, contact information, and business hours for every listing.
Traverse complex search interfaces by train line, station, municipality, or custom keyword criteria.
Navigate deep search result pages reliably to ensure full extraction of the available property catalogue.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences.
Monitor listings for price drops, availability changes, or updated photos using hash-based diffing.
Brief in. Clean data out.
Provide target prefectures, train lines, or specific property types. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for athome.co.jp.
Schema validation, null-rate checks, and address normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting Japanese real estate data requires specific localisation and infrastructure. Here is how we build resilient pipelines.
Accessing Japanese portals from foreign data centres triggers immediate blocks. Our crawlers use Japanese residential ISP proxies with realistic browser fingerprints to maintain high success rates.
Real estate data often contains mixed full-width and half-width characters. We normalise all text output to standard UTF-8, ensuring clean data entry into your warehouse.
We map and validate the complex Japanese addressing system, separating prefectures, wards, cities, and local districts into structured columns for easier downstream analysis.
Portal layouts change based on property type and agency. Our selector strategy uses multiple fallback chains per field so a layout variation does not break the extraction.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Investors build yield models by comparing sale prices against rental rates in specific wards and municipalities.
Aggregators and valuation models ingest listing data to train automated valuation models (AVMs) for the Japanese market.
Analysts track rental indices, average key money trends, and inventory levels across major metropolitan areas.
Real estate agencies monitor competitor listing volumes, pricing strategies, and time-on-market metrics.
Researchers correlate property values with station proximity, train line access, and building age.
Corporate housing providers track available inventory meeting specific size and location criteria for expat placements.
"athome.co.jp contains the most granular transit and fee structures in Japanese real estate, but the data is trapped in complex DOM structures and rigid search interfaces."
Extracting data from Japanese real estate portals requires handling specific geographic hierarchies, complex fee structures like shikikin and reikin, and strict anti-bot measures. DataFlirt manages the proxies, normalises the address strings, and delivers structured records so your analysts can focus on property valuation.
Everything supported by our athome.co.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 and deduplication. Playwright handles JavaScript rendering and interaction flows. Combined via middleware.
We maintain pools of Japanese residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent geo-blocking.
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 athome.co.jp scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property and agency data. We do not extract personal user data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use Japanese residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for block rates in real time and trigger pool rotation automatically.
Yes. Our pipelines can traverse the entire geographic hierarchy from Hokkaido to Okinawa, capturing listings across all prefectures, cities, and wards.
Full catalogue refreshes at daily or weekly cadences complete within defined windows depending on the target volume. Change detection ensures you receive updates on price drops and availability promptly.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per property ID for rent, sale price, and availability from the date your pipeline starts.
Our smallest packages start at a defined geographic scope, such as specific Tokyo wards, with weekly delivery. For national catalogues, we price based on volume and delivery frequency.
Yes. We extract the high-resolution image URLs for floor plans, exterior shots, and interior photos, delivering them as part of the structured record.
Yes. We provide a sample run of up to 500 properties as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off Tokyo ward export or a continuous national property feed - we scope, build, and operate the pipeline. Tell us what you need.
