This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.
TL;DR
Use AlterLab's Extract API to get structured Redfin data as validated JSON. Pass a URL and JSON schema defining fields like address and price. Receive typed output ready for data pipelines—no HTML parsing or anti-bot handling needed.
Why use Redfin data?
Real-estate data powers AI training for price prediction models, market analytics dashboards, and competitive intelligence feeds. Engineers use it to build automated valuation tools or monitor neighborhood trends. The data's value comes from its timeliness and granularity for specific use cases like investment analysis.
What data can you extract?
Redfin's public listing pages contain structured real-estate data fields including:
- address: Full property address (street, city, state, ZIP)
- price: Current listing price as displayed
- bedrooms: Number of bedrooms (integer or string)
- bathrooms: Number of bathrooms (may include halves)
- sqft: Finished square footage
- listing_date: When the property was listed
- property_type: Single family, condo, townhome, etc. All fields are publicly visible on Redfin property pages without authentication.
The extraction approach
Raw HTTP requests combined with HTML parsing fail frequently on Redfin due to dynamic content, anti-bot measures, and frequent frontend changes. Maintaining selectors for price or sqft fields becomes a constant burden. A data API approach shifts the complexity: AlterLab handles page rendering, anti-bot bypass, and structured extraction using AI, letting you focus on the data schema instead of parsing logic.
Quick start with AlterLab Extract API
Begin by installing the AlterLab SDK (Getting started guide). The Extract API takes a URL and JSON schema, returning validated data. Here's a Python example:
```python title="extract_redfin-com.py" {5-12}
client = alterlab.Client("YOUR_API_KEY")
schema = {
"type": "object",
"properties": {
"address": {
"type": "string",
"description": "The address field"
},
"price": {
"type": "string",
"description": "The price field"
},
"bedrooms": {
"type": "string",
"description": "The bedrooms field"
},
"bathrooms": {
"type": "string",
"description": "The bathrooms field"
},
"sqft": {
"type": "string",
"description": "The sqft field"
},
"listing_date": {
"type": "string",
"description": "The listing date field"
}
}
}
result = client.extract(
url="https://www.redfin.com/CA/San-Francisco/123-main-st/home/12345678",
schema=schema,
)
print(result.data)
The equivalent cURL request:
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://www.redfin.com/CA/San-Francisco/123-main-st/home/12345678",
"schema": {
"properties": {
"address": {"type": "string"},
"price": {"type": "string"},
"bedrooms": {"type": "string"},
"bathrooms": {"type": "string"},
"sqft": {"type": "string"},
"listing_date": {"type": "string"}
}
}
}'
Both examples return structured JSON like:
{
"address": "123 Main St, San Francisco, CA 94105",
"price": "$1,250,000",
"bedrooms": "3",
"bathrooms": "2.5",
"sqft": "1,800",
"listing_date": "2026-03-15"
}
Define your schema
The JSON schema parameter drives AlterLab's extraction accuracy. Specify each field's type (string, number, boolean) and description to guide the AI. AlterLab validates the output against your schema, ensuring type safety and reducing post-processing. For numeric fields like sqft, use "type": "number" and AlterLab will attempt to parse commas and currency symbols. The schema acts as a contract between your pipeline and the extraction service—change it to get different fields without altering your core logic.
Handle pagination and scale
For bulk extraction (e.g., all listings in a ZIP code), use AlterLab's async job system. Submit hundreds of URLs via the batch endpoint, then poll for results. This respects rate limits while maximizing throughput. Adjust concurrency based on your plan—see AlterLab pricing for tier-specific limits. Implement exponential backoff for retry logic, and use webhooks for real-time results when scaling to thousands of daily requests.
```python title="batch_redfin.py" {8-15}
client = alterlab.Client("YOUR_API_KEY")
urls = [
"https://www.redfin.com/CA/San-Francisco/123-main-st/home/12345678",
"https://www.redfin.com/CA/San-Francisco/456-oakave/home/12345679",
# ... hundreds more
]
Submit batch job
batch_job = client.extract_batch(
urls=urls,
schema={"properties": {"address": {"type": "string"}, "price": {"type": "string"}}}
)
Poll for completion
while batch_job.status in ["pending", "processing"]:
time.sleep(5)
batch_job = client.get_batch_job(batch_job.id)
Download results
results = batch_job.results()
for result in results:
if result.status == "completed":
print(result.data)
## Key takeaways
- AlterLab's Extract API converts public Redfin pages into typed JSON via schema-driven AI extraction
- Eliminates fragile HTML parsing and anti-bot maintenance overhead
- Focus on defining your data contract (schema) rather than page structure
- Always verify compliance with Redfin's robots.txt and Terms of Service
- Scale efficiently with batch jobs and async processing for pipeline integration
<div data-infographic="steps">
<div data-step data-number="1" data-title="Define Schema" data-description="Specify the fields you want as a JSON schema"></div>
<div data-step data-number="2" data-title="Call Extract API" data-description="POST the URL + schema to AlterLab"></div>
<div data-step data-number="3" data-title="Receive Typed JSON" data-description="Get back validated, structured data — no parsing needed"></div>
</div>
<div data-infographic="try-it" data-url="https://redfin.com" data-description="Extract structured real-estate data from Redfin"></div>
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