We extract rental listings, sale properties, floor plans, station commute matrices, and historical pricing from Homes.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 Rental Properties (Chintai) objects from homes.co.jp. All fields typed and schema-versioned.
"property_id": "1002938475", "rent_jpy": 85000, "key_money_reikin": 85000, "deposit_shikikin": 85000, "layout": "1K", "area_sqm": 25.4, "floor": 3, "station_1": "Shibuya Station"
| # | property_id | title | url | rent_jpy | management_fee_jpy | key_money_reikin |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sale Properties (Baibai) objects from homes.co.jp. All fields typed and schema-versioned.
"property_id": "8847362910", "price_jpy": 45000000, "property_type": "Used Condominium", "land_area_sqm": 0, "layout": "3LDK", "age_years": 12, "station_1": "Yokohama Station", "agency_name": "Mitsui Fudosan Realty"
| # | property_id | title | price_jpy | property_type | land_area_sqm | building_area_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Building & Mansion Data objects from homes.co.jp. All fields typed and schema-versioned.
"building_id": "B993847", "name": "Roppongi Hills Residence", "address": "6-12-1 Roppongi, Minato-ku, Tokyo", "total_units": 793, "built_date": "2003-04", "developer": "Mori Building", "nearest_station": "Roppongi Station"
| # | building_id | name | address | total_units | floors_above_ground | floors_below_ground |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Transit & Location objects from homes.co.jp. All fields typed and schema-versioned.
"property_id": "1002938475", "prefecture": "Tokyo", "ward": "Shibuya-ku", "station_1_line": "JR Yamanote Line", "station_1_name": "Shibuya", "station_1_walk_min": 8, "station_2_name": "Ebisu"
| # | property_id | prefecture | city | ward | neighborhood | station_1_line |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency & Contact objects from homes.co.jp. All fields typed and schema-versioned.
"agency_id": "A44938", "agency_name": "Century 21 Tokyo", "license_number": "Tokyo Governor (4) 12345", "phone_number": "03-1234-5678", "business_hours": "10:00 - 19:00", "rating": 4.2, "active_listings": 342
| # | agency_id | agency_name | license_number | address | phone_number | business_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Homes.co.jp scraper handles every layer of the platform: property listings, transit matrices, floor plan extraction, and agency data, with Japanese residential proxies and full-width character normalisation built in.
Rent, management fee, layout, area, floor, age, and every metadata field Homes.co.jp surfaces, scraped at the individual listing level.
Extract and normalise deposit (shikikin) and key money (reikin) into exact JPY values for accurate total-cost calculations.
Capture high-resolution URLs for floor plans (madori), interior shots, exterior building photos, and surrounding area imagery.
Extract primary and secondary train lines, station names, walking minutes, and bus route dependencies for every property.
Capture broker names, license numbers, active listing counts, and contact details for the agencies managing each property.
Extract developer names, total unit counts, structure types, and management companies linked to specific condominium buildings.
Monitor when properties go offline, when rents are reduced, or when sale prices drop across specific wards.
Scrape listings across Tokyo, Osaka, Kanagawa, Hokkaido, and all 47 Japanese prefectures using a unified data schema.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide prefectures, wards, property types, or station proximities. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, Japanese proxy rotation, session management, and CAPTCHA handling.
Schema validation, null-rate checks, kanji normalisation, and yen price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Japanese real estate portals use strict rate limiting and complex DOM structures. Here is how we maintain pipeline stability.
Homes.co.jp heavily restricts non-Japanese IP addresses. Our crawlers use Japanese residential ISP proxies with realistic browser fingerprints and full cookie session management to prevent region blocks and rate limits.
Real estate data in Japan mixes full-width and half-width characters, kanji, hiragana, and katakana. We apply NFKC normalisation at the pipeline level so your database receives clean, queryable strings.
Map-based search results and asynchronous image galleries require full DOM rendering. We run Playwright browser sessions to trigger lazy-loads and capture data that basic HTTP clients miss.
Property detail pages vary wildly depending on the listing agency. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Track gross yield, rent per square meter, and vacancy trends across specific wards to inform investment strategies.
Train automated valuation models (AVMs) with historical sale prices, land area, building age, and station proximity.
Monitor rival brokerages' active listings, pricing strategies, and time-on-market metrics to gain a competitive edge.
Identify undervalued properties based on station proximity, floor plan efficiency, and yield metrics before they hit the broader market.
Analyse transit accessibility, housing density, and rent affordability across different prefectures for municipal planning.
Aggregate listings with specific parameters like pet-friendly, no key money, or English-speaking agencies for corporate relocation.
"Homes.co.jp contains the most comprehensive transit-mapped property dataset in Japan, but extracting structured data requires deep localisation and resilient infrastructure."
Most teams underestimate the investment required: reliable Japanese portal scraping requires local residential proxies, full-width character normalisation, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our homes.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and map interactions. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies specifically located in Japan. Rotation happens per-request with sticky sessions where required to prevent region-based blocking.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 homes.co.jp scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Homes.co.jp is generally permissible for non-destructive, non-PII extraction. DataFlirt targets only public property, pricing, and agency data. We do not extract personal data or circumvent authentication walls. Clients should review Homes.co.jp terms of service and consult legal counsel for specific use cases.
Homes.co.jp restricts traffic from non-Japanese IP addresses. We use Japanese residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass these restrictions.
Yes. We parse the text strings for Shikikin and Reikin and convert them into exact JPY numeric values based on the monthly rent multiplier or flat fee specified in the listing.
We extract and normalise the raw address string into structured fields: prefecture, city, ward, neighborhood (chome), and block (banchi), allowing for precise geographic querying.
Yes. We capture the high-resolution URLs for the madori (floor plan) images, as well as interior and exterior property photos, which can be downloaded directly or stored in your S3 bucket.
Full catalogue refreshes for specific wards or prefectures typically complete within a 12-24 hour window depending on scale. We configure the cadence based on your specific requirements.
Yes. Transit matrices are fully extracted, including the primary and secondary train lines, station names, and walking minutes, which are critical for Japanese real estate valuation.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off ward export or a continuous price-monitoring feed across Tokyo - we scope, build, and operate the pipeline. Tell us what you need.
