peterpan Property Scraper — Korea Rental Data & API 🏠

🎉 Turn peterpanz.com (피터팬의 좋은방 구하기) listings into clean, structured data — one row per room, ready for analysis

For real-estate analysts, investors, relocation agents, and data teams working the Korean rental & lease market

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📋 Overview

Need Korean rental and lease listings as clean data instead of endless map browsing? This scraper turns peterpanz.com search results into structured JSON/CSV — one clean record per room, with deposit (보증금), monthly rent (월세), maintenance, size in m² and pyeong, floor, amenities, verification flags, address and photos.

Why analysts and agencies choose us:

• ✅ One clean row per room — every listing becomes a structured record, no nested mess
• ⚡ Fast & lightweight — reads peterpanz's own JSON list API directly, no slow headless browser, no proxy overhead
• 🎯 55+ data points — deposit, monthly rent, maintenance, room type, supplied & real size (m²/pyeong), floor, amenities, verification, address, coordinates and photos
• 🗺️ Map-area search — pull every listing inside a lat/lng bounding box, or by villa / apartment / officetel / store vertical
• 💴 Pay-per-result — only pay for listings you actually receive — transparent and cheap
• 💎 Two depths — fast overview for whole-area sweeps, full detail for the free-text description, full-resolution photo gallery, and listing agency
• ✨ Three ways in — by map bounding box, by pasted peterpanz API URL (keeps every filter), or by listing URL

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✨ Features

• 🏢 Listings, done right — clean per-room records from peterpanz's structured list feed
• 💴 Korean price model captured — deposit (보증금) and monthly rent (월세) as integers, so 전세 vs 월세 is obvious at a glance
• 📐 Size both ways — square metres and pyeong (평), plus a deposit-per-pyeong KPI ready out of the box
• 🧭 Coordinates included — latitude/longitude on every listing for mapping and geo-analysis
• 🛡️ Verification flags — Naver-verified, peter-verified, premium, agent vs owner — quality signals built in
• 🔎 Detail enrichment — free-text room description, full-resolution photo gallery, and the listing agency
• 🔗 Paste-a-URL mode — apply filters on peterpanz, paste the API link, every supported filter is preserved
• 📦 Clean exports — JSON, CSV, Excel straight from the dataset
• 🌐 Optional Korea proxy — off by default, available as a one-click escape hatch

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🎬 Quick Start

Pick a mode, give it a map bounding box (or a peterpanz search URL), and run. Listings stream into your dataset as clean rows. Export as JSON, CSV, or Excel.

curl -X POST https://api.apify.com/v2/acts/sian.agency~peterpan-property-scraper/runs?token=YOUR_TOKEN \
-H 'Content-Type: application/json' \
-d '{"scrapeMode": "overview", "searchMode": "byBbox", "bbox": "37.49,37.52,127.01,127.05"}'

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🚀 Getting Started (3 Simple Steps)

Step 1: Choose your input

Give a map bounding box (lat/lng), paste a peterpanz houses/area/pc API URL, or list specific listing URLs.

Step 2: Pick depth

overview for fast whole-area room cards, or detail for description, full photos and the listing agency.

Step 3: Run & export

Start the run and download your results as JSON, CSV, or Excel.

That's it! In under a minute, you'll have:

• A clean, per-room dataset
• Deposit (보증금), monthly rent (월세) and maintenance as numbers
• Room type, size (m²/pyeong), floor, amenities, address, coordinates and photos

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📥 Input Configuration

Field	Type	Required	Description
scrapeMode	string	No	overview (fast room cards) or detail (+ description, full photos, agency)
searchMode	string	No	byBbox, bySearchUrl, or byListingUrl
bbox	string	No	Map bounding box lat_min,lat_max,lng_min,lng_max (byBbox)
buildingType	string	No	villa, apt, officetel, or store (byBbox)
orderBy	string	No	price, deposit, or random
search	string	No	Optional free-text keyword
searchUrls	array	No	Pasted peterpanz houses/area/pc API URLs (filters preserved)
listingUrls	array	No	peterpanz /house/{id} URLs or bare ids (detail mode)
maxResults	integer	No	Max listings per run (FREE: 25, PAID: unlimited)
maxPages	integer	No	Max list-API pages to paginate per search

Example — overview by bounding box:

{
  "scrapeMode": "overview",
  "searchMode": "byBbox",
  "bbox": "37.49,37.52,127.01,127.05",
  "buildingType": "villa",
  "maxResults": 200
}

Example — detail by listing URL:

{
  "scrapeMode": "detail",
  "searchMode": "byListingUrl",
  "listingUrls": ["https://www.peterpanz.com/house/19439182"]
}

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📤 Output

Results are saved to the Apify dataset with 55+ fields per listing, including:

Field	Type	Description
listingId	number	peterpanz house id
url	string	Canonical /house/{id} URL
propertyTitle	string	Listing subject
contract_type	string	계약 형태 — 월세 / 전세 / 단기임대
deposit	number	보증금 in KRW
monthly_fee	number	월세 in KRW (0 ⇒ 전세)
maintenance_cost	number	관리비 in KRW
deposit_per_pyeong_krw	number	Computed deposit per 전용평
room_type	string	방 종류, e.g. 1.5룸
real_size / real_pyeong	number	전용면적 in m² / pyeong
floor	string	층, e.g. 3층/6층
address / sido / sigungu / dong	string	Address parts
latitude / longitude	number	Coordinates
naver_verification / peter_verified	boolean	Verification flags
agency_name	string	Listing agency (detail mode)
roomDescription	string	Free-text description (detail mode)
images / image_count	array / number	Photo URLs / count

Example:

{
  "listingId": 19439182,
  "url": "https://www.peterpanz.com/house/19439182",
  "propertyTitle": "주말 정상영업 신논현역 도보 3분 인기많은 리모델링 빌라",
  "contract_type": "단기임대",
  "deposit": 1000000,
  "monthly_fee": 1000000,
  "maintenance_cost": 100000,
  "room_type": "1.5룸",
  "real_size": 28,
  "real_pyeong": 8.46,
  "floor": "3층/6층",
  "address": "강남구 논현동",
  "sigungu": "강남구",
  "dong": "논현동",
  "latitude": 37.5061376,
  "longitude": 127.0271979,
  "agency_name": "노블공인중개사사무소",
  "image_count": 7
}

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💼 Use Cases & Examples

1. Rental Market Research

Analysts mapping deposit & rent by neighbourhood. Input: a map bounding box · Output: per-room dataset with 보증금 & 월세 · Use: build a rent heatmap by gu and dong.

2. Property Investment Analysis

Investors comparing 전세/월세 across buildings. Input: a filtered peterpanz search · Output: deposit-per-pyeong on every room · Use: rank candidate areas by value.

3. Relocation & Tenant Search

Relocation agents shortlisting rooms for clients. Input: bbox + building-type filter · Output: clean rows with deposit, rent, maintenance, amenities · Use: hand clients a tidy true-cost comparison sheet.

4. Real-Estate Lead Generation

Agencies building prospect lists of active listings. Input: broad bbox sweep · Output: address, agency, verification records · Use: feed CRM pipelines.

5. Price & Trend Monitoring

Data teams tracking deposit/rent movements over time. Input: scheduled runs on the same area · Output: snapshots to diff week-over-week · Use: detect price changes and new inventory.

6. Academic & Policy Research

Researchers studying Korean housing markets. Input: multiple areas · Output: structured, reproducible datasets · Use: quantitative housing studies.

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🔗 Integration Examples

JavaScript/Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('sian.agency/peterpan-property-scraper').call({
  scrapeMode: 'overview', searchMode: 'byBbox',
  bbox: '37.49,37.52,127.01,127.05'
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

run = client.actor('sian.agency/peterpan-property-scraper').call(
    run_input={'scrapeMode': 'overview', 'searchMode': 'byBbox',
               'bbox': '37.49,37.52,127.01,127.05'}
)

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

cURL

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~peterpan-property-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"scrapeMode": "overview", "searchMode": "byBbox", "bbox": "37.49,37.52,127.01,127.05"}'

Automation Workflows (N8N / Zapier / Make)

1. Trigger: Schedule or webhook
2. HTTP Request: Call the actor API
3. Process: Handle the JSON results
4. Action: Save to a sheet, notify, or sync to CRM

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📊 Performance & Pricing

FREE Tier (Try It Now)

• 25 listings per run — full feature access, same quality
• No credit card required
• Perfect for testing and small projects

PAID Tier (Production Ready)

• Unlimited listings per run
• Pay-per-result: only charged for listings you actually receive

💴 Cheap by design — reading peterpanz's own JSON list feed with no proxy overhead keeps the per-listing price among the lowest for Korean real-estate data.

🔗 View current pricing

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❓ Frequently Asked Questions

Q: How many listings can I scrape? A: FREE tier: 25 per run. PAID tier: unlimited.

Q: Do I need an account or API key? A: No. Just provide a bounding box, a search URL, or listing URLs.

Q: What output formats are available? A: JSON, CSV, and Excel — export directly from the Apify dataset.

Q: Are GPS coordinates included? A: Yes — every overview row carries latitude/longitude plus the structured address.

Q: What's the difference between overview and detail? A: Overview is the fast list-API data (already ~90% of fields). Detail fetches each listing's full page for the free-text description, full-resolution photo gallery and listing agency, and merges them in.

Q: How do 전세 (jeonse) and 월세 (wolse) appear? A: Each row has both deposit (보증금) and monthly_fee (월세). A monthly_fee of 0 with a large deposit indicates a 전세 listing.

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🐛 Troubleshooting

No results returned

• Check that the bounding box is lat_min,lat_max,lng_min,lng_max and covers a populated area, or that the pasted URL is a peterpanz houses/area/pc API link.

Fewer rows than expected

• FREE tier caps at 25 listings per run — upgrade for unlimited.
• A small bounding box or a narrow building-type filter may simply return fewer listings.

A specific listing failed in detail mode

• The listing may have expired or been delisted; the run continues and skips it.

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⚖️ Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Disclaimer: This is an independent tool and is not affiliated with, endorsed by, or sponsored by peterpan, peterpanz.com, or 피터팬의 좋은방 구하기 / its operator. "peterpan" / "피터팬의 좋은방 구하기" is a trademark of its respective owner. Use this actor in compliance with the site's terms of service and all applicable laws.

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🤝 Support

Join our active support community

• For issues or questions, open an issue in the actor's repository
• Check the SIÁN Agency Store for more automation tools
• 📧 apify@sian-agency.online

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