Nepremicnine.net Spider Scraper

Nepremicnine.net Spider Scraper collects structured real estate listings from one of Slovenia’s leading property portals. It helps professionals efficiently gather property data for analysis, monitoring, and decision-making using filtered browse pages.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project extracts real estate offers from Nepremicnine.net based on predefined browse filters. It solves the problem of manually tracking and comparing large volumes of property listings. It is built for investors, analysts, agencies, and data-driven real estate platforms.

Real Estate Listings Extraction

• Collects property offers from filtered browse pages
• Supports multiple locations and property types
• Captures pricing and descriptive metadata
• Designed for consistent, repeatable data collection

Features

Feature	Description
Multi-URL Support	Scrape multiple filtered browse pages in one run.
Detailed Listings	Extract titles, prices, links, and property subtitles.
Structured Output	Returns clean, analysis-ready property data.
Stealth Operation	Minimizes blocking during data collection.

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What Data This Scraper Extracts

Field Name	Field Description
title	Property listing title or headline.
link	Direct URL to the property detail page.
price	Listed property price.
subtitle	Short description such as property type or offer mode.
baseUrl	Source browse page URL used for extraction.

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Example Output

[
  {
    "title": "GOLO",
    "link": "https://www.nepremicnine.net/oglasi-prodaja/golo-hisa_6828156/",
    "price": "390.000,00 €",
    "subtitle": "Prodaja: Hiša, Samostojna",
    "baseUrl": "https://www.nepremicnine.net/oglasi-prodaja/ljubljana-okolica/hisa/samostojna/cena-do-450000-eur,velikost-od-150-m2/"
  }
]

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Directory Structure Tree

Nepremicnine.net - Spider/
├── src/
│   ├── runner.py
│   ├── crawler/
│   │   ├── listings_parser.py
│   │   └── pagination.py
│   ├── utils/
│   │   ├── request_handler.py
│   │   └── data_cleaner.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── inputs.sample.json
│   └── output.sample.json
├── requirements.txt
└── README.md

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Use Cases

• Real estate investors use it to monitor new listings, so they can identify opportunities early.
• Property analysts use it to aggregate market data, so they can track pricing trends.
• Agencies use it to collect competitor listings, so they can benchmark offerings.
• Data teams use it to feed dashboards, so they can automate reporting workflows.

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FAQs

What type of pages can be scraped? Any browse page with applied filters such as location, price range, or property type can be used.

Can multiple locations be tracked at once? Yes, multiple browse URLs can be provided to collect listings from different regions.

Is the output suitable for analytics tools? The structured JSON output is designed to integrate easily with databases and BI tools.

What if no data is returned? Verify that the browse URLs are active and contain available listings.

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Performance Benchmarks and Results

Primary Metric: Processes dozens of listings per browse page within seconds.

Reliability Metric: Consistently achieves high success rates on active listing pages.

Efficiency Metric: Optimized requests reduce unnecessary page loads and overhead.

Quality Metric: Extracted records maintain strong completeness across core listing fields.

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