Crexi Data API: Extract Structured JSON in 2026

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

To get structured Crexi data via API, use the AlterLab Extract API to send a target URL and a JSON schema definition. The API handles proxy rotation and anti-bot bypass, returning validated, typed JSON containing specific fields like price, address, and property specs without requiring manual HTML parsing.

Why use Crexi data?

Commercial and residential real-estate data is a primary signal for several high-value engineering use cases:

1. AI Training and RAG: Feeding real-time listing data into Large Language Models (LLMs) to build real-estate advisory bots or automated valuation models.
2. Market Analytics: Tracking price-per-square-foot trends across specific zip codes to identify undervalued assets.
3. Competitive Intelligence: Monitoring new listings and price drops in real-time to trigger automated alerts for investment teams.

What data can you extract?

When building a data pipeline for Crexi, you should focus on publicly available listing attributes. By defining a strict schema, you can ensure your database receives consistent types.

Common extractable fields include:

• Property Address: Full string including city, state, and zip.
• Listing Price: Numerical value or string (e.g., "$1,200,000").
• Property Specs: Square footage (sqft), bedroom count, and bathroom count.
• Listing Date: The timestamp of when the property was posted.
• Property Type: Classification (e.g., Industrial, Retail, Multi-family).

The extraction approach

Most developers start by attempting raw HTTP requests or using headless browsers like Playwright. This approach is fragile for three reasons:

1. Dynamic Content: Modern real-estate portals use heavy JavaScript rendering that simple GET requests cannot capture.
2. Bot Detection: Sophisticated anti-bot systems flag non-browser fingerprints and data-center IP ranges.
3. DOM Volatility: CSS selectors change frequently. A small update to the site's frontend breaks your entire regex or BeautifulSoup logic.

Moving to a data API shifts the burden of maintenance from your team to the infrastructure. Instead of managing a fleet of proxies and updating selectors, you define the shape of the data you want.

Quick start with AlterLab Extract API

To begin, follow the Getting started guide to configure your environment. The Extract API allows you to pass a URL and a JSON schema; the engine then uses AI to locate the data and validate it against your types.

Refer to the Extract API docs for a full list of parameters.

Python Implementation

The following example demonstrates how to extract core property details from a specific Crexi listing.

import alterlab

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://crexi.com/example-page",
    schema=schema,
)
print(result.data)

cURL Implementation

For lightweight integrations or shell scripts, use the REST endpoint directly.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://crexi.com/example-page",
    "schema": {"properties": {"address": {"type": "string"}, "price": {"type": "string"}, "bedrooms": {"type": "string"}}}
  }'

Define your schema

The power of a data API lies in the schema. Rather than hoping the scraper finds the right div, you provide a JSON Schema (Draft 7) that acts as a contract.

Example Structured Output: When the API processes the request, it returns a clean JSON object:

{
  "address": "123 Main St, Dallas, TX 75201",
  "price": "$2,500,000",
  "bedrooms": "0",
  "bathrooms": "2",
  "sqft": "12,000",
  "listing_date": "2026-01-15"
}

If the AI cannot find a field, it returns null rather than guessing, ensuring your downstream pipeline doesn't ingest "hallucinated" data.

Handle pagination and scale

When scaling from a single page to thousands of listings, synchronous requests become a bottleneck. For high-volume Crexi data extraction, use asynchronous batch jobs.

Async Batch Example

Instead of waiting for each response, dispatch multiple URLs and poll for the results.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

urls = ["https://crexi.com/listing-1", "https://crexi.com/listing-2", "https://crexi.com/listing-3"]
schema = {"properties": {"price": {"type": "string"}, "address": {"type": "string"}}}

# Dispatch async jobs
job_ids = []
for url in urls:
    job = client.extract_async(url=url, schema=schema)
    job_ids.append(job.id)

# Poll for results
for j_id in job_ids:
    res = client.get_job_result(j_id)
    print(f"Result for {j_id}: {res.data}")

Cost and Optimization

To manage spend at scale, use the cost estimation endpoint before committing to a large batch. This is critical when using complex schemas that require more LLM orchestration.

Costs are based on a pay-as-you-go balance. You can review the AlterLab pricing page to see how balance is deducted per request.

Key takeaways

• Avoid raw parsing: HTML structures on real-estate sites change too often.
• Use Schemas: Define your data requirements in JSON Schema to ensure type safety.
• Scale Asynchronously: Use extract_async for large datasets to avoid timeout issues.
• Stay Compliant: Always target public data and respect robots.txt.

AlterLab // Web Data, Simplified.