LoopNet Data API: Extract Structured JSON in 2026

TL;DR To get structured LoopNet data via API, send a POST request to AlterLab's Extract API containing the target URL and a JSON schema defining your required fields. The engine handles proxy rotation, browser rendering, and anti-bot bypass, returning validated JSON instead of raw HTML.

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

Why use LoopNet data?

For data engineers building commercial real estate (CRE) applications, raw HTML is a liability. Relying on brittle CSS selectors or regex patterns to parse property listings leads to broken pipelines the moment a website updates its frontend.

Engineers typically integrate LoopNet data into their workflows for:

• AI Training & RAG: Feeding clean, structured property metadata into LLMs to power real-estate investment bots.
• Market Analytics: Aggregating price-per-square-foot trends across specific zip codes.
• Competitive Intelligence: Monitoring new listings or price drops in real-time to feed automated notification systems.

What data can you extract?

When building a real-estate data pipeline, you need specific, typed fields to ensure your downstream database remains consistent. Using a data API allows you to target specific attributes from publicly available property pages:

• Location: Full street address, city, state, and zip code.
• Financials: Listing price, price per square foot, and lease terms.
• Physical Specs: Total square footage, lot size, and building type.
• Unit Details: Number of bedrooms, bathrooms, and parking spaces.
• Metadata: Listing date, property status, and broker contact info.

The extraction approach

Traditional web scraping involves a complex stack: managing headless browsers, rotating residential proxies, solving CAPTCHAs, and writing complex XPath selectors. If the site changes a single <div> class, your scraper breaks.

A modern data API shifts the burden from the developer to the infrastructure. Instead of writing logic to "find the element with class price-val," you simply define the data you want. AlterLab handles the heavy lifting of navigating complex JavaScript-heavy sites like LoopNet and returns only the data that matches your schema.

Quick start with AlterLab Extract API

To begin, you need an API key from AlterLab. You can follow our Getting started guide to set up your environment.

Using Python

The Python SDK is the most efficient way to integrate extraction into your data pipelines. The extract method allows you to pass a schema directly, ensuring the response is ready for database insertion.

from alterlab import AlterLabSync

client = AlterLabSync("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "address": {
      "type": "string",
      "description": "The full property address"
    },
    "price": {
      "type": "string",
      "description": "The listing price"
    },
    "bedrooms": {
      "type": "string",
      "description": "Number of bedrooms"
    },
    "bathrooms": {
      "type": "string",
      "description": "Number of bathrooms"
    },
    "sqft": {
      "type": "string",
      "description": "Total square footage"
    },
    "listing_date": {
      "type": "string",
      "description": "The date the property was listed"
    }
  }
}

result = client.scrape(
    url="https://loopnet.com/example-listing-url",
    extraction_schema=schema,
)
print(result["filtered_content"])

Using cURL

For quick testing in a terminal or shell script, use the following cURL command:

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

Define your schema

The power of the Extract API lies in the JSON schema. By providing a schema, you aren't just asking for "data"; you are asking for "data in this specific format." This allows you to enforce types and descriptions, which the engine uses to guide the LLM-powered extraction process.

When the engine processes a URL, it identifies the relevant content and maps it to your keys. If a property doesn't list "bedrooms" (common in commercial listings), the engine returns null or omits the key rather than returning a broken string of HTML.

Handle pagination and scale

For high-volume real estate market analysis, you cannot rely on single, synchronous requests. You need to manage large batches of URLs and handle rate limits gracefully.

For production pipelines, we recommend using an asynchronous job pattern. This prevents your main application from blocking while waiting for the extraction to complete.

from alterlab import AlterLabSync

client = AlterLabSync(api_key="YOUR_API_KEY")
result = client.scrape(
    "https://loopnet.com/listing/1",
    extraction_schema={"type": "object", "properties": {"title": {"type": "string"}, "price": {"type": "string"}, "description": {"type": "string"}}},
)
print(result["filtered_content"])  # dict matching your schema

When scaling, keep an eye on your AlterLab pricing. We offer a transparent cost structure where you can estimate the cost of a single extraction before running it. This is useful if you are building a UI where users trigger scrapes, as you can display a cost preview using the POST /v1/extract/estimate endpoint.

Key takeaways

• Stop parsing HTML: Use a data API to transform messy web pages into clean, typed JSON.
• Schema-first extraction: Define exactly what you need (address, price, sqft) to ensure your downstream data pipelines remain stable.
• Scale with confidence: Use asynchronous jobs and structured schemas to build robust, production-grade real-estate intelligence tools.

Hit reply if you have questions.

AlterLab // Web Data, Simplified.