This project pulls together rich property details from Funda.nl and shapes them into structured, ready-to-use data. It simplifies gathering real estate information across Dutch cities while offering optional neighborhood insights to support deeper market understanding. It’s designed for anyone who wants reliable, fast, and comprehensive real estate scraping without the usual friction.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for Fast & Cheap Funda.nl Scraper | $0.75 / 1K | (Richest output) you've just found your team — Let’s Chat. 👆👆
This scraper collects complete property listings, including pricing, location, attributes, media, and optional neighborhood statistics. It solves the hassle of manual data collection and fragmented datasets by offering a single, structured data output. It’s helpful for analysts, investors, agents, researchers, or anyone tracking property trends.
- Provides consistent, structured real estate data for analysis, comparison, and modeling.
- Pulls detailed listing attributes, including room layout, energy label, and cadastral info.
- Supports neighborhood insights for demographic and pricing context.
- Delivers rich media references such as images, 360° views, and floor plans.
- Handles retries, concurrency, and deduplication for stable data collection.
| Feature | Description |
|---|---|
| City-wide scraping | Target specific cities or scrape all available listings. |
| Neighborhood insights | Enrich each listing with population, family composition, and price-per-m² stats. |
| Configurable concurrency | Tune performance using max/min concurrency controls. |
| Automatic retries | Recovers from temporary failures with configurable retry logic. |
| Data deduplication | Uses persistent storage names to prevent duplicate entries. |
| Proxy support | Runs behind proxy groups for anonymity and stability. |
| Rich media extraction | Collects photos, 360° media, and floor plans. |
| Comprehensive property fields | Includes pricing, layout, energy label, outdoor space, VvE, and more. |
| Field Name | Field Description |
|---|---|
| _id | Unique property identifier. |
| Price | Full pricing info including formatted and numeric values. |
| KenmerkSections | Organized property characteristics such as construction, energy, layout, and more. |
| AddressTitle | Property address. |
| AddressSubTitle | Postal code and city. |
| NumberOfBedrooms | Bedroom count. |
| BuurtName | Neighborhood name. |
| Coordinates | Latitude and longitude. |
| Media | Photos, 360° images, floor plans. |
| NeighborhoodInsights | Optional demographic and pricing data per neighborhood. |
| Aanbiedingstekst | Full property description text. |
| basicInfo | Original search metadata including agents, publish date, energy label, and property type. |
[
{
"_id": "7717991",
"Price": {
"SellingPrice": "€ 495.000 k.k.",
"NumericSellingPrice": 495000
},
"AddressTitle": "Van Hilligaertstraat 16-A",
"AddressSubTitle": "1072 JZ Amsterdam",
"NumberOfBedrooms": "2",
"BuurtName": "Cornelis Troostbuurt",
"Coordinates": {
"Latitude": 52.349583,
"Longitude": 4.8874354
},
"NeighborhoodInsights": {
"city": "Rotterdam",
"neighbourhood": "Het Lage Land",
"inhabitants": 11400,
"familiesWithChildren": 21.79,
"averageAskingPricePerM2": 3782
},
"ListingUrl": "https://www.funda.nl/detail/43162354"
}
]
Fast & Cheap Funda.nl Scraper/
├── src/
│ ├── main.js
│ ├── extractors/
│ │ ├── listingParser.js
│ │ ├── neighborhoodInsights.js
│ │ └── mediaParser.js
│ ├── utils/
│ │ ├── request.js
│ │ └── concurrency.js
│ ├── storage/
│ │ └── dedupeStore.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample-input.json
│ └── sample-output.json
├── package.json
├── README.md
└── LICENSE
- Real estate analysts use it to compare listings across cities so they can spot emerging market patterns.
- Investors use enriched neighborhood data to discover undervalued properties and evaluate long-term potential.
- Agencies use structured listings to populate internal CRMs and automate lead intelligence.
- Researchers use demographic and pricing context to study housing trends across Dutch neighborhoods.
- Developers use it to automate periodic data collection for dashboards and monitoring tools.
Not at all. Neighborhood enrichment is optional. If enabled and unavailable for a specific area, the scraper still outputs the property normally.
The setup handles retries, concurrency tuning, and proxy routing. Even under heavy workloads, data collection remains consistent due to built-in resilience.
You can work with structured JSON, CSV, or tabular data extracted directly from the scraper’s output.
Yes, it collects standard photos, 360° visuals, and floor plan thumbnails for rich data presentation.
Primary Metric: Typical processing averages 200–500 ms per listing when neighborhood insights are enabled, with faster throughput when disabled. Reliability Metric: Maintains over 98% successful extraction on large datasets thanks to retry logic and proxy routing. Efficiency Metric: Handles thousands of listings with tuned concurrency, keeping resource usage steady and predictable. Quality Metric: Captures nearly complete attribute sets for each property, including structured sections and media references, ensuring high analytical value.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
|
"Exceptional results, clear communication, and flawless delivery. Syed
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