pyhemnet 1.0.0

A Python library for accessing Swedish real estate and rental property data from Hemnet.se and Qasa.se

PyHemnet

A Python library for accessing Swedish real estate and rental property data from Hemnet.se and Qasa.se. Extract property sales information, rental listings, prices, locations, and detailed property characteristics.

Features

• 🏠 Hemnet Data Access: Access property sales data from Hemnet.se

  • Get summary statistics on properties for sale and sold properties
  • Extract detailed information including prices, location, size, broker, and more
  • Filter by location and property types
  • Support for multiple property types (villa, radhus, bostadsrätt, etc.)

• 🏘️ Qasa Rental Data: Access rental property data from Qasa.se

  • Search for available rental homes by location
  • Get detailed rental information including landlord details, fees, and requirements
  • Filter by property type, price range, furnishing, and more
  • Support for long-term rentals across Sweden

• 🚀 Easy-to-use Python API

• 💻 Object-oriented design with clean interfaces

Installation

Install from PyPI:

pip install pyhemnet

Or install from source:

git clone https://github.com/ningdp2012/pyhemnet.git
cd pyhemnet
pip install -e .

Quick Start

Hemnet - Property Sales Data

from pyhemnet import HemnetScraper, HemnetItemType

# Create a scraper instance
scraper = HemnetScraper()

# Get summary statistics
listing_count, sold_count = scraper.get_summary(location_id="17744")
print(f"Properties for sale: {listing_count}")
print(f"Sold properties: {sold_count}")

# Get detailed sold properties
homes = scraper.get_sold(
    location_id="17744",
    item_types=[HemnetItemType.VILLA, HemnetItemType.RADHUS]
)

for home in homes:
    print(f"{home['address']} - {home['final_price']} SEK")

Qasa - Rental Property Data

from pyhemnet import QasaScraper

# Create a scraper instance
scraper = QasaScraper()

# Search for rental homes
homes = scraper.get_homes(
    area_identifier="se/helsingborg",
    home_types=["apartment", "house"],
    min_monthly_cost=5000,
    max_monthly_cost=15000
)

for home in homes:
    print(f"{home['title']} - {home['rent']} {home['currency']}/month")
    print(f"Location: {home['locality']}")
    print(f"Rooms: {home['rooms']}, Size: {home['square_meters']} m²")
    print("---")

# Get detailed information for a specific home
details = scraper.get_home_details(home_id="12345")
print(f"Description: {details['description']}")
print(f"Landlord: {details['landlord']}")

Usage

Hemnet API

Initialize the Scraper

from pyhemnet import HemnetScraper, HemnetItemType

scraper = HemnetScraper()

Get Summary Statistics

Get counts of properties for sale and sold:

# Get summary for a specific location
listing_count, sold_count = scraper.get_summary(location_id="17744")
print(f"For sale: {listing_count}, Sold: {sold_count}")

# Filter by property types
listing_count, sold_count = scraper.get_summary(
    location_id="17744",
    item_types=[HemnetItemType.VILLA]
)

Get Sold Properties

Retrieve detailed information about sold properties:

homes = scraper.get_sold(
    location_id="17744",
    item_types=[HemnetItemType.VILLA, HemnetItemType.RADHUS]
)

for home in homes:
    print(f"Address: {home['address']}")
    print(f"Final price: {home['final_price']} SEK")
    print(f"Asking price: {home['asking_price']} SEK")
    print(f"Living area: {home['living_area']}")
    print(f"Sold date: {home['sold_at']}")
    print("---")

Get Current Listings

Get properties currently for sale:

listings = scraper.get_listings(
    location_id="17744",
    item_types=[HemnetItemType.BOSTADSRATT]
)

for listing in listings:
    print(f"Address: {listing['address']}")
    print(f"Price: {listing['asking_price']} SEK")
    print(f"Published: {listing['published_at']}")

Property Types

Use the HemnetItemType enum or strings:

# Using enum (recommended)
item_types = [HemnetItemType.VILLA, HemnetItemType.RADHUS]

# Using strings
item_types = ["villa", "radhus"]

Available types:

• VILLA - Detached houses
• RADHUS - Townhouses
• BOSTADSRATT - Condominiums
• FRITIDSHUS - Vacation homes
• TOMT - Land plots
• GARD - Farms
• OTHER - Other property types

Qasa API

Initialize the Scraper

from pyhemnet import QasaScraper

scraper = QasaScraper()

Search for Rental Homes

Search for available rental properties with various filters:

homes = scraper.get_homes(
    area_identifier="se/helsingborg",  # Single location
    home_types=["apartment", "house", "terrace_house"],
    shared=False,  # Non-shared only
    furnished=None,  # Both furnished and unfurnished
    min_monthly_cost=5000,
    max_monthly_cost=20000,
    pets_allowed=True,
    smoking_allowed=False
)

for home in homes:
    print(f"ID: {home['id']}")
    print(f"Title: {home['title']}")
    print(f"Rent: {home['rent']} {home['currency']}")
    print(f"Location: {home['locality']}, {home['route']}")
    print(f"Rooms: {home['rooms']}, Size: {home['square_meters']} m²")
    print(f"Available from: {home['start_date']}")
    print("---")

Search Multiple Locations

# Search in multiple areas
homes = scraper.get_homes(
    area_identifier=["se/stockholm", "se/gothenburg", "se/malmo"],
    min_monthly_cost=8000,
    max_monthly_cost=15000
)

Get Detailed Home Information

Get comprehensive details for a specific rental property:

details = scraper.get_home_details(home_id="12345")

# Basic information
print(f"Title: {details['title']}")
print(f"Rent: {details['rent']} {details['currency']}")
print(f"Rooms: {details['roomCount']}")
print(f"Size: {details['squareMeters']} m²")
print(f"Description: {details['description']}")

# Location details
location = details['location']
print(f"Address: {location['route']} {location['streetNumber']}")
print(f"City: {location['locality']}")
print(f"Coordinates: {location['latitude']}, {location['longitude']}")

# Landlord information
landlord = details['landlord']
print(f"Landlord: {landlord.get('firstName', landlord.get('companyName'))}")
print(f"Response rate: {landlord.get('landlordApplicationResponseRate')}%")

# Rental period
duration = details['duration']
print(f"Start date: {duration['startOptimal']}")
print(f"End date: {duration['endOptimal']}")
print(f"Extension possible: {duration['possibilityOfExtension']}")

# Additional costs
electricity = details['electricityFee']
heating = details['heatingFee']
water = details['waterFee']
print(f"Electricity: {electricity.get('monthlyFee')} SEK/month ({electricity.get('paymentPlan')})")

Available Home Types

Qasa supports various home types:

• apartment - Apartments
• house - Houses
• terrace_house - Terrace houses
• duplex - Duplex apartments
• studio - Studio apartments
• room - Single rooms

Category Filters

Filter by specific categories:

• firsthand - First-hand contracts
• studentHome - Student housing
• seniorHome - Senior housing
• corporateHome - Corporate housing

# Example: Search for student housing
homes = scraper.get_homes(
    area_identifier="se/lund",
    category="studentHome",
    max_monthly_cost=8000
)

Data Structure

Hemnet Data

Sold Property Data

Each sold property dictionary contains:

{
    'id': str,              # Hemnet ID
    'listing_id': str,      # Listing identifier
    'address': str,         # Street address
    'location': str,        # Location description
    'housing_type': str,    # Type of housing (Villa, Radhus, etc.)
    'rooms': int,           # Number of rooms
    'living_area': str,     # Living area with units
    'land_area': str,       # Land area with units
    'asking_price': int,    # Initial asking price in SEK
    'final_price': int,     # Final sold price in SEK
    'price_change': str,    # Price change information
    'sold_at': str,         # Sale date (YYYY-MM-DD format)
    'broker': str,          # Broker agency name
    'labels': list,         # List of property labels/tags
}

Current Listing Data

Each listing dictionary contains:

{
    'id': str,                      # Hemnet ID
    'address': str,                 # Street address
    'location': str,                # Location description
    'housing_type': str,            # Type of housing
    'rooms': int,                   # Number of rooms
    'living_area': str,             # Living area with units
    'land_area': str,               # Land area with units
    'asking_price': int,            # Asking price in SEK
    'published_at': str,            # Publication date (YYYY-MM-DD)
    'removed_before_showing': bool, # Removed before showing
    'new_construction': bool,       # New construction flag
    'broker_name': str,             # Broker name
    'broker_agent': str,            # Broker agency name
    'labels': list,                 # List of property labels/tags
    'description': str,             # Property description
}

Qasa Data

Rental Home List Data

Each rental home in the list contains:

{
    'id': str,              # Qasa home ID
    'title': str,           # Property title
    'rent': int,            # Monthly rent
    'currency': str,        # Currency (e.g., 'SEK')
    'rooms': int,           # Number of rooms
    'square_meters': int,   # Size in square meters
    'start_date': date,     # Available from date (date object or None)
    'locality': str,        # City/locality
    'route': str,           # Street name
    'street_number': str,   # Street number
    'country_code': str,    # Country code (e.g., 'SE')
}

Detailed Rental Home Data

Detailed home information includes:

{
    # Basic information
    'id': str,
    'title': str,
    'rent': int,
    'roomCount': int,
    'squareMeters': int,
    'currency': str,
    'description': str,
    'shared': bool,
    'firsthand': bool,
    'studentHome': bool,
    'seniorHome': bool,
    'corporateHome': bool,

    # Property details
    'floor': int,
    'buildingFloors': int,
    'bedCount': int,
    'bedroomCount': int,
    'hasKitchen': bool,
    'toiletCount': int,
    'houseRules': str,
    'housingAssociation': str,
    'buildYear': int,
    'energyClass': str,
    'kitchenRenovationYear': int,
    'bathroomRenovationYear': int,

    # Location (nested dict)
    'location': {
        'locality': str,
        'latitude': float,
        'longitude': float,
        'route': str,
        'streetNumber': str,
        'countryCode': str,
        'postalCode': str,
        'pointsOfInterest': {
            'nodes': [
                {
                    'category': str,
                    'distance': int,
                    'name': str,
                    'latitude': float,
                    'longitude': float
                }
            ]
        }
    },

    # Landlord (nested dict)
    'landlord': {
        'uid': str,
        'firstName': str,
        'companyName': str,
        'premium': bool,
        'professional': bool,
        'landlordApplicationResponseRate': int,
        'landlordApplicationResponseTimeHours': int,
        'bio': {'intro': str},
        'createdAt': str,
        'seenAt': str
    },

    # Duration (nested dict)
    'duration': {
        'startOptimal': str,
        'endOptimal': str,
        'startAsap': bool,
        'endUfn': bool,
        'possibilityOfExtension': bool
    },

    # Fees (nested dicts)
    'electricityFee': {
        'paymentPlan': str,
        'monthlyFee': int
    },
    'heatingFee': {
        'paymentPlan': str,
        'monthlyFee': int
    },
    'waterFee': {
        'paymentPlan': str,
        'monthlyFee': int
    },
    'tenantBaseFee': int,

    # Requirements (nested dict)
    'rentalRequirement': {
        'approvedCreditCheck': bool,
        'verifiedIncome': bool,
        'rentMultiplier': int,
        'verifiedIdNumber': bool
    },

    # Additional info
    'rentalType': str,
    'status': str,
    'publishedAt': str,
    'tenantCount': int,
    'minTenantCount': int,
    'maxTenantCount': int,
    'tenureType': str
}

Finding Location IDs

To find Hemnet location IDs:

1. Go to Hemnet.se
2. Search for your desired location
3. Look at the URL - it contains location_ids[]=XXXXX
4. Use that ID in your code

Example: For Stockholm https://www.hemnet.se/bostader?location_ids[]=17744, use location_id="17744"

Finding Qasa Area Identifiers

To find Qasa area identifiers:

1. Go to Qasa.se
2. Search for your desired location
3. Look at the URL - it contains the area identifier like se/city-name
4. Use that identifier in your code

Example: For Helsingborg https://www.qasa.se/rent/se/helsingborg, use area_identifier="se/helsingborg"

Common area identifiers:

• Stockholm: "se/stockholm"
• Gothenburg: "se/gothenburg"
• Malmö: "se/malmo"
• Uppsala: "se/uppsala"
• Lund: "se/lund"
• Helsingborg: "se/helsingborg"

Requirements

• Python 3.10+
• cloudscraper >= 1.2.71
• beautifulsoup4 >= 4.12.0
• requests >= 2.31.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Disclaimer

This package is created for exploring python and web technologies and learning purposes only. It is not intended for production use or commercial applications.

• This is an unofficial package and is not affiliated with or endorsed by Hemnet AB or Qasa AB
• Always respect website terms of service and robots.txt directives
• Web scraping may be subject to legal restrictions in your jurisdiction
• Use at your own risk and responsibility