Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping. Ensure your extraction rates respect target server limits, and remember that you are responsible for maintaining compliance with relevant Terms of Service.
TL;DR
To get structured realtor.com data via API, use the AlterLab Extract endpoint. You provide the target listing URL and a JSON schema defining your required fields, such as price, bedrooms, and address. AlterLab handles the underlying infrastructure, JavaScript execution, and AI-driven data mapping, returning clean, strictly typed JSON ready for immediate integration into your data pipeline.
Why use Realtor.com data?
Engineers and data teams require reliable access to real-estate data for various programmatic use cases. When building systems dependent on housing market information, raw data velocity and accuracy define the success of the application.
Common applications for a real-estate data API include:
- Machine Learning and AI Training: Feeding localized housing market data into models to predict pricing trends, neighborhood appreciation, or rental yield forecasting.
- RAG Pipelines: Supplying real-time, ground-truth property data to Large Language Models so they can answer user queries about specific market conditions without hallucinating.
- Market Analytics: Building internal dashboards that track inventory velocity, average days on market, and price-per-square-foot variations across target zip codes.
What data can you extract?
When accessing public listing pages, you can extract a comprehensive set of property attributes. The key to building a resilient pipeline is targeting the exact fields you need rather than downloading the entire document.
Publicly available data points typically include:
- Core Property Attributes: Bedrooms, bathrooms, total square footage, lot size, and year built.
- Pricing Information: Current asking price, price per square foot, and historical price changes if listed on the public page.
- Location Data: Full address, neighborhood, city, state, and zip code.
- Listing Metadata: Days on market, listing agent or brokerage name, and property status (active, pending, sold).
- Features and Amenities: Garage capacity, heating/cooling systems, HOA fees, and architectural style.
The extraction approach
Extracting realtor.com json extraction data using traditional methods requires downloading HTML and writing CSS or XPath selectors to locate specific DOM nodes. This approach is fundamentally fragile.
Modern web applications use dynamic rendering, heavily minified JavaScript, and utility-first CSS frameworks. Class names like css-1xj2b change with every deployment. A simple A/B test changing the layout of the property gallery will silently break your parsing logic, leading to null values or, worse, misaligned data entering your database.
The modern standard is using a data API. Instead of asking "how do I parse the HTML," you define the data structure you want and let an AI extraction layer map the visual page content to your schema. AlterLab handles the proxy rotation, JavaScript rendering, and LLM-powered extraction in a single API call.
Before proceeding to the code, ensure you have set up your environment by reviewing our Getting started guide.
Quick start with AlterLab Extract API
The AlterLab Extract API requires two primary inputs: the target URL and a JSON schema. The schema dictates the shape, types, and descriptions of the data you expect.
Here is the primary implementation for a realtor.com data extraction python pipeline.
```python title="extract_realtor-com.py" {5-12,35}
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://realtor.com/example-page",
schema=schema,
)
print(result.data)
For environments where Python is not the primary language, or for testing directly from your terminal, you can interact with the API using standard HTTP tools.
```bash title="Terminal" {4-7}
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://realtor.com/example-page",
"schema": {"properties": {"address": {"type": "string"}, "price": {"type": "string"}, "bedrooms": {"type": "string"}}}
}'
Review the complete parameter list and advanced configuration options in the Extract API docs.
Define your schema
The schema is the most critical component of your request. AlterLab uses standard JSON Schema validation to ensure the LLM output exactly matches your pipeline requirements.
While the quick start example uses string types for simplicity, production pipelines should enforce strict typing. When you specify an integer or a boolean, the Extract API guarantees the output will match that type, stripping out extraneous text like currency symbols or commas.
Consider this refined schema for structured realtor.com data:
```json title="schema.json" {6,11,16}
{
"type": "object",
"properties": {
"price_usd": {
"type": "integer",
"description": "The current asking price in US Dollars. Return only the number, no symbols."
},
"bedrooms": {
"type": "integer",
"description": "Total number of bedrooms."
},
"has_garage": {
"type": "boolean",
"description": "True if the property has a garage, false otherwise."
},
"status": {
"type": "string",
"enum": ["active", "pending", "sold", "unknown"],
"description": "The current market status of the listing."
}
},
"required": ["price_usd", "bedrooms", "status"]
}
By providing detailed descriptions and utilizing JSON Schema constraints like `enum` and `required`, you explicitly instruct the extraction engine on how to handle edge cases and normalize the data before it reaches your application.
## Handle pagination and scale
Single property extraction is useful for specific lookups, but building comprehensive datasets requires processing thousands of URLs. When scaling your realtor.com api structured data pipeline, synchronous HTTP requests become a bottleneck.
For high-volume workloads, you must transition to asynchronous batch processing. This allows you to queue thousands of URLs and let AlterLab manage concurrency, rate limits, and retries.
The following example demonstrates how to dispatch a batch of extraction jobs and process them asynchronously.
```python title="batch_extract.py" {11-14,20-22}
client = alterlab.AsyncClient("YOUR_API_KEY")
async def process_listings(urls, schema):
tasks = []
# Queue all URLs concurrently
for url in urls:
task = client.extract.create(
url=url,
schema=schema
)
tasks.append(task)
# Wait for all extraction jobs to complete
results = await asyncio.gather(*tasks, return_exceptions=True)
for result in results:
if isinstance(result, Exception):
print(f"Extraction failed: {result}")
else:
print(f"Success: {result.data['price']} for {result.url}")
# List of target public URLs
urls = [
"https://realtor.com/property-1",
"https://realtor.com/property-2",
"https://realtor.com/property-3"
]
# Run the async loop
asyncio.run(process_listings(urls, target_schema))
This asynchronous pattern maximizes throughput while respecting network boundaries. It is highly recommended to combine this with webhook delivery for massive batches, allowing your server to receive pushed data asynchronously rather than holding open connections.
Cost management is critical when scaling up. AlterLab ensures you are only billed for successful payload deliveries. Evaluate your expected volume and review the AlterLab pricing to model your pipeline costs accurately.
Key takeaways
Building a robust realtor.com data api integration relies on moving away from brittle DOM parsing and adopting schema-driven extraction.
- Eliminate parsing logic: Stop writing and maintaining regex and XPath for dynamic React applications.
- Enforce data contracts: Use strict JSON Schemas to guarantee the shape and types of the data entering your database.
- Scale asynchronously: Use batching and async clients to process thousands of public listings efficiently.
- Focus on the pipeline: Offload infrastructure, proxy management, and site changes to AlterLab so your team can focus on data utilization.
Deploying an AI-powered extraction pipeline ensures your data operations remain resilient against front-end changes, delivering clean, actionable real-estate data continuously.
