Remax Urls Scraper extracts detailed real estate property information from Remax listing URLs and delivers it in clean, structured JSON. It helps professionals turn raw property pages into actionable real estate data for analysis, research, and decision-making.
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Remax Urls Scraper collects structured property data from individual Remax listing URLs and normalizes it into developer-friendly JSON. It solves the problem of manual property research by automating data collection at scale. This project is built for real estate investors, analysts, brokers, data teams, and developers who need reliable listing data.
- Works directly with individual property URLs for precise extraction
- Focuses on structured, analytics-ready output rather than raw HTML
- Designed for scalable data collection across many listings
- Suitable for research, reporting, and system integrations
| Feature | Description |
|---|---|
| Structured JSON Output | Returns normalized property data ready for analysis or storage. |
| Property Detail Extraction | Captures price, address, beds, baths, square footage, and year built. |
| Agent & Office Data | Extracts agent names, office details, and contact numbers. |
| Media Collection | Gathers property image URLs for visual analysis or marketing. |
| School & Area Info | Includes school and neighborhood-level details when available. |
| Multi-URL Support | Processes multiple property URLs in a single run. |
| Field Name | Field Description |
|---|---|
| listingId | Unique identifier of the property listing. |
| listingStatus | Current status such as for sale or pending. |
| address | Full formatted property address. |
| price | Listed property price. |
| beds | Number of bedrooms. |
| baths | Number of bathrooms. |
| sqft | Interior area in square feet. |
| images | Array of property image URLs. |
| agent | Listing agent name. |
| office | Brokerage or office name. |
| Contact_Number | Public contact phone number. |
| school_info | Object containing nearby school and district details. |
| remarks | Property description or listing remarks. |
| tax | Annual property tax information. |
| year_built | Year the property was constructed. |
| url | Source listing URL. |
[
{
"listingId": "1893550",
"listingStatus": "For Sale",
"address": "5931 WOODRIDGE ROCK, SAN ANTONIO, TX 78249",
"price": 320000,
"beds": "3",
"baths": "2",
"sqft": "1,675",
"Contact_Number": "(210) 848-4715",
"images": [
"https://images.remax.com/rets-properties-sabor/0a759249a15351e8281c63d00edf31970898ee3d-1-medium.jpeg"
],
"agent": "Fiona Downing",
"office": "LPT Realty, LLC",
"school_info": {
"City": "San Antonio",
"Elementary School": "Boone",
"Middle Or Junior School": "Rudder",
"High School": "Marshall",
"School District": "Northside"
},
"remarks": "This 3-bedroom, 2-story home features a flexible floor plan.",
"tax": 7076,
"year_built": "1984",
"url": "https://www.remax.com/tx/san-antonio/home-details/5931-woodridge-rock-san-antonio-tx-78249/10285353997149320632/M00000616/1893550"
}
]
Remax Urls/
├── src/
│ ├── main.py
│ ├── extractors/
│ │ ├── listing_parser.py
│ │ ├── agent_parser.py
│ │ └── school_parser.py
│ ├── utils/
│ │ ├── text_cleaner.py
│ │ └── validators.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── outputs.sample.json
├── requirements.txt
└── README.md
- Real estate investors use it to evaluate properties quickly, so they can make faster investment decisions.
- Market analysts use it to collect listing data at scale, so they can identify pricing trends.
- Brokers and agents use it to monitor competing listings, so they can adjust strategies.
- Data teams use it to feed structured property data into dashboards, so they can automate reporting.
- Researchers use it to study housing characteristics, so they can support urban or economic analysis.
Can this handle multiple property URLs at once? Yes, it is designed to process multiple listing URLs in a single run, making it suitable for batch data collection.
What kind of output format does it produce? The scraper produces clean, structured JSON that can be easily stored, analyzed, or integrated into other systems.
Does it include agent and school information? Yes, agent, office, and available school or neighborhood details are included as structured objects.
Is the data suitable for analytics and machine learning? Absolutely. The normalized structure makes it well-suited for analytics pipelines and downstream modeling.
Primary Metric: Processes dozens of property URLs per minute depending on page complexity.
Reliability Metric: Maintains a high success rate when valid listing URLs are provided.
Efficiency Metric: Optimized parsing minimizes unnecessary requests and resource usage.
Quality Metric: Extracted fields are consistently populated with high completeness across listings.
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