Skip to main content

Python wrapper for Wasatch Front Regional MLS API

Project description

WFRMLS Python Client

A comprehensive Python wrapper for the Wasatch Front Regional MLS (WFRMLS) API, providing easy access to all RESO-certified endpoints.

⚠️ Important Notice

Media, History, and Green Verification endpoints are currently unavailable due to server-side issues (504 Gateway Timeouts and missing entity types). These features have been temporarily disabled until the server issues are resolved.

🚀 Quick Start

from wfrmls import WFRMLSClient

# Initialize client with bearer token
client = WFRMLSClient(bearer_token="your_bearer_token")

# Or use environment variable WFRMLS_BEARER_TOKEN
client = WFRMLSClient()

# Get active properties
properties = client.property.get_properties(
    top=10,
    filter_query="StandardStatus eq 'Active'"
)

# Get property details
property_detail = client.property.get_property("12345678")

# Get member information
members = client.member.get_active_members(top=10)

# Get office information
offices = client.office.get_active_offices(top=10)

📦 Installation

pip install wfrmls

🔧 Setup

Environment Variables

Create a .env file in your project root:

WFRMLS_BEARER_TOKEN=your_bearer_token_here

Getting Your Bearer Token

  1. Visit the Vendor Dashboard
  2. Login to your account
  3. Navigate to Service Details to retrieve your bearer token

📚 API Reference

Core Resources

  • Property - Real estate listings and property data
  • Member - Real estate agent information
  • Office - Brokerage and office details
  • OpenHouse - Open house schedules and events

Service Clients

# Property operations
client.property.get_properties()
client.property.get_property(listing_id)
client.property.search_properties_by_radius(lat, lng, radius)

# Member (agent) operations  
client.member.get_members()
client.member.get_member(member_id)

# Office operations
client.office.get_offices()
client.office.get_office(office_id)

# Open house operations
client.openhouse.get_open_houses()
client.openhouse.get_open_house(openhouse_id)

🔍 Advanced Features

OData Query Support

# Field selection
properties = client.property.get_properties(
    select=["ListingId", "ListPrice", "StandardStatus"],
    top=50
)

# Complex filtering
properties = client.property.get_properties(
    filter_query="ListPrice ge 200000 and ListPrice le 500000 and StandardStatus eq 'Active'",
    orderby="ListPrice desc"
)

# Include related data
properties = client.property.get_properties(
    expand=["Media", "Member"],
    top=25
)

Geolocation Search

# Search within radius (miles)
properties = client.property.search_properties_by_radius(
    latitude=40.7608,  # Salt Lake City
    longitude=-111.8910,
    radius_miles=10,
    additional_filters="StandardStatus eq 'Active'"
)

# Search within polygon area
polygon = [
    {"lat": 40.7608, "lng": -111.8910},
    {"lat": 40.7708, "lng": -111.8810},
    {"lat": 40.7508, "lng": -111.8710},
    {"lat": 40.7608, "lng": -111.8910}  # Close polygon
]

properties = client.property.search_properties_by_polygon(
    polygon_coordinates=polygon,
    additional_filters="PropertyType eq 'Residential'"
)

Data Synchronization

from datetime import datetime, timedelta

# Get incremental updates (recommended every 15 minutes)
cutoff_time = datetime.utcnow() - timedelta(minutes=15)
updates = client.property.get_properties(
    filter_query=f"ModificationTimestamp gt {cutoff_time.isoformat()}Z"
)

# Track deletions for data integrity
deleted_records = client.deleted.get_deleted(
    filter_query="ResourceName eq 'Property'"
)

🏗️ Architecture

The client follows a modular architecture with service separation:

WFRMLSClient
├── property          # Property listings
├── member           # Real estate agents  
├── office           # Brokerages/offices
├── openhouse        # Open house events
├── lookup           # Lookup tables
├── adu              # Accessory Dwelling Units
├── deleted          # Deletion tracking
└── data_system      # API metadata

Note: Media, History, and Green Verification clients are currently disabled due to server-side issues.

⚠️ Error Handling

from wfrmls.exceptions import (
    WFRMLSError, 
    AuthenticationError, 
    NotFoundError, 
    RateLimitError
)

try:
    property = client.property.get_property("12345678")
except NotFoundError:
    print("Property not found")
except RateLimitError:
    print("Rate limit exceeded - wait before retrying")  
except AuthenticationError:
    print("Invalid bearer token")
except WFRMLSError as e:
    print(f"API error: {e}")

📊 Utah Grid Address System

The API supports Utah's unique grid address system:

# Standard address: "123 Main Street"
# Grid address: "1300 E 9400 S"

# Grid addresses are automatically detected and handled
properties = client.property.get_properties(
    filter_query="StreetName eq '9400 S'"
)

🚦 Rate Limits

  • 200 records per request maximum
  • 15-minute recommended update frequency for data sync
  • Use NextLink pagination for large datasets (more efficient than $skip)

🧪 Development

Setup Development Environment

# Clone repository
git clone https://github.com/theperrygroup/wfrmls.git
cd wfrmls

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# Install development dependencies
pip install -e .[dev]

Running Tests

# Run tests with coverage
pytest --cov=wfrmls --cov-report=html

# Run specific test file
pytest tests/test_property.py

# Run with verbose output
pytest -v

Code Quality

# Format code
black wfrmls tests
isort wfrmls tests

# Lint code
flake8 wfrmls tests
pylint wfrmls

# Type checking
mypy wfrmls

📝 Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Follow the style guide in STYLE_GUIDE.md
  4. Ensure 100% test coverage
  5. Commit changes (git commit -m 'Add amazing feature')
  6. Push to branch (git push origin feature/amazing-feature)
  7. Open a Pull Request

📄 License

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

🔗 Links

🆘 Support

For API access issues, contact UtahRealEstate.com support. For library issues, open an issue in this repository.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wfrmls-1.3.7.tar.gz (71.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wfrmls-1.3.7-py3-none-any.whl (63.5 kB view details)

Uploaded Python 3

File details

Details for the file wfrmls-1.3.7.tar.gz.

File metadata

  • Download URL: wfrmls-1.3.7.tar.gz
  • Upload date:
  • Size: 71.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for wfrmls-1.3.7.tar.gz
Algorithm Hash digest
SHA256 1f385bcc7a01deaf851e8b8f1fb1342802d52835132efa854c0d885575422a5f
MD5 568f89a23797d02886795dd790319fe7
BLAKE2b-256 8a3b574c3c37bc330fff1e886ed600ca4dbea2295d0b32b3743c752be845c62f

See more details on using hashes here.

File details

Details for the file wfrmls-1.3.7-py3-none-any.whl.

File metadata

  • Download URL: wfrmls-1.3.7-py3-none-any.whl
  • Upload date:
  • Size: 63.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.3

File hashes

Hashes for wfrmls-1.3.7-py3-none-any.whl
Algorithm Hash digest
SHA256 b7b069648740fa832b692e3831b635dded3477a32b5fa961505f6dd657b34714
MD5 81e0c06c9dee6fe266ee4dcbec84846f
BLAKE2b-256 a01aab7d73f469239d83a2c3f85292eff8520d15e0611b4d873e8dd1eec2c449

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page