🏠 MLIT Japan Real Estate Transaction Prices Scraper

🚀 Export official Japan real estate transaction prices from the MLIT WebLand public API in seconds. Prefecture, city, type, price, area, structure.

🕒 Last updated: 2026-05-29 · 📊 10 fields per record · MLIT WebLand API · All 47 prefectures · Quarterly periods 2005 to present

The MLIT Japan Real Estate Transaction Prices Scraper turns the official Ministry of Land WebLand API into a clean dataset of residential and commercial transactions across all 47 Japanese prefectures.

🎯 Target Audience	💡 Primary Use Cases
📊 Analysts	Build clean datasets from the source
🏢 Business teams	Plug official data into internal tools
🤖 ML engineers	Training data ready for pandas and BigQuery
📰 Journalists	Verify numbers in seconds
🎓 Researchers	Reproducible historical pulls
👩‍💻 Developers	Skip writing a client library

📋 What the MLIT Japan Real Estate Transaction Prices Scraper does

• Calls the public MLIT WebLand endpoint.
• Parses the response and flattens each record into a clean row.
• Casts numeric fields and normalizes timestamps for downstream tools.
• Surfaces upstream errors as a clean error record instead of crashing.
• Exports as CSV, Excel, JSON, JSONL, XML, RSS, or HTML.

💡 Why it matters: the source is public but its format is not analyst friendly. This actor returns a flat, tool-ready dataset.

🎬 Full Demo

🚧 Coming soon.

⚙️ Input

Field	Type	Required	Description
prefectureCode	enum	No	Two-digit prefecture code (13 Tokyo, 27 Osaka, etc.).
cityCode	string	No	Optional 5-digit city code.
from	string	No	Start period in YYYYN format.
to	string	No	End period in YYYYN format.
maxItems	integer	No	Free 10, paid up to 1,000,000.

Example 1:

{ "prefectureCode": "13", "from": "20231", "to": "20234", "maxItems": 100 }

Example 2:

{ "prefectureCode": "27", "from": "20241", "to": "20242" }

⚠️ Good to Know: the upstream source enforces its own rate limits. Heavy pulls should be spread across runs.

📊 Output

Each record is a flat object. error is always last.

Field	Type	Description
🗺️ prefecture	string	Prefecture name
🏙️ city	string	Municipality
🏘️ district	string	District name
🏷️ type	string	Property type
💴 price	number	Trade price in JPY
📐 area	number	Floor or land area in sqm
🏗️ year_built	string	Year of construction
📅 transaction_date	string	Period of transaction
🏠 structure	string	Building structure
🕒 scrapedAt	string	When this row was fetched
❌ error	string	Set if upstream returned an error

Sample record:

{
  "prefecture": "Tokyo",
  "city": "Minato Ku",
  "district": "Roppongi",
  "type": "Pre-owned Condominiums, etc.",
  "price": 95000000,
  "area": 65,
  "year_built": "2015",
  "transaction_date": "2023年第3四半期",
  "structure": "RC",
  "scrapedAt": "2026-05-29T10:00:00.000Z",
  "error": null
}

✨ Why choose this Actor

| 🆓 | Calls the fully public MLIT API, no key. | | 🧹 | Romanized field names ready for analysis. | | 🔢 | Prices and areas cast to numbers. | | 🗺️ | All 47 prefectures supported. | | 🛟 | Errors surfaced as clean records. | | 💾 | Push to dataset, instant download. |

📈 How it compares to alternatives

Approach	Setup time	Clean keys	Numeric casting	Error handling
Roll your own fetch	30 min plus	❌	❌	❌
Manual download	hours	❌	❌	❌
This Actor	5 sec, no install	✅	✅	✅

🚀 How to use

1. Click Try for free.
2. Fill in the input fields (defaults work for a first run).
3. Click Start. Your dataset is ready in seconds.

💼 Business use cases

📊 Analytics dashboards. Pipe to a BI tool, refresh on a schedule.

💼 Internal data products. Mirror the source into your own warehouse.

📰 Newsroom fact-checking. Verify any number before publishing.

🤖 ML feature engineering. Use the clean dataset as model input.

🔌 Automating MLIT Japan Real Estate Transaction Prices Scraper

• Make and Zapier: trigger on a schedule, push to Airtable or Slack.
• Cron schedule: native Apify scheduler.
• Webhooks: receive a POST when a run finishes.
• Pipe to BigQuery, Snowflake, Postgres: native Apify integrations.

🌟 Beyond business use cases

🎓 Education. Bring real public data into classrooms.

🧪 Personal research. Track indicators or markets over time.

🤝 Non-profit and open data. Build public dashboards.

🧰 Tinkering. Prototype quickly with structured data.

🤖 Ask an AI assistant about this scraper

Paste this README into ChatGPT or Claude and ask it to map your workflow to the actor inputs. The schema, examples and field list above contain everything an LLM needs to design a working pipeline.

❓ Frequently Asked Questions

❓ Do I need an API key? No, the source is fully public.

❓ Is there a rate limit? The upstream enforces a soft limit. Avoid hammering.

❓ Can I schedule runs? Yes, via Apify scheduler, Make, Zapier or cron.

❓ Are values cast to numbers? Yes where applicable.

❓ How are errors handled? Pushed as a single record with error populated.

❓ Is this scraping or API? Public API or feed.

❓ Will the schema change? Core fields are stable.

❓ Can I filter the results? Yes, see the input schema above.

❓ What if the upstream changes? We update the actor.

❓ What format can I download? CSV, Excel, JSON, JSONL, XML, RSS, HTML straight from the Apify dataset UI.

🔌 Integrate with any app

Apify ships native integrations with Make, Zapier, Slack, Discord, Google Drive, Google Sheets, Gmail, Airbyte, Keboola, Telegram, GitHub, and any REST API or webhook endpoint.

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