How to scrape real estate data from QuintoAndar using the Minexa API

QuintoAndar publishes thousands of property listings across Brazilian cities, updated continuously. If you are building a dataset of house prices, neighborhood trends, or listing volumes in Rio de Janeiro, pulling that data manually is not realistic at any meaningful scale.

This guide walks through how to extract structured listing data from QuintoAndar using the Minexa API — a two-phase workflow where you train a scraper once visually, then call it programmatically as many times as you need.

Watch the full tutorial first

The video below covers the complete workflow from opening the Minexa Chrome extension to generating your API request.

https://www.youtube.com/watch?v=-tRWlWJfE2E

Phase 1: Training the scraper on QuintoAndar

Open the Minexa Chrome extension from the home page at minexa.ai. You will see the starting screen before navigating anywhere.

Navigate to the QuintoAndar house listings page for Rio de Janeiro. Once the page loads, the extension detects the listing structure automatically.

Click the 'I'm on the right page' button in the extension popup to confirm the target page.

The extension then shows pagination detection results. When using the API, you will handle pagination yourself via a JS code scenario — the extension detection here is for reference during training only.

Choose whether to scrape the listing page only, or also follow each property link to extract detail page data. For most pipeline use cases, the list view contains enough structured fields to start.

Select simple scraping mode, then confirm the data container. Minexa highlights the repeating listing block automatically.

After confirming, all extracted data points become visible. Use the next/prev navigation to review every column the scraper found.

Phase 2: Calling the Minexa API

Once training is complete, click 'API request' to get your scraper ID and a ready-to-run Python snippet.

The API call targets https://api.minexa.ai/data and accepts your scraper ID, the target URLs, column selection, and thread count. Here is a working example:

import requests

response = requests.post(
  'https://api.minexa.ai/data',
  headers={'Authorization': 'Bearer YOUR_API_KEY'},
  json={
    'scraper_id': 4817,
    'urls': ['https://www.quintoandar.com.br/comprar/imovel/rio-de-janeiro-rj-brasil/casa'],
    'columns': 'top_40',
    'threads': 5
  }
)
print(response.json())

Set up your own cron job to call this endpoint on a schedule if you need recurring extractions across multiple URLs.

What the extracted data looks like

Below is a sample of fields returned per listing:

[
  {
    "price": "R$ 850.000",
    "address": "Tijuca, Rio de Janeiro",
    "bedrooms": "3 quartos",
    "area": "120 m²",
    "listing_url": "https://www.quintoandar.com.br/imovel/..."
  },
  {
    "price": "R$ 1.200.000",
    "address": "Botafogo, Rio de Janeiro",
    "bedrooms": "4 quartos",
    "area": "180 m²",
    "listing_url": "https://www.quintoandar.com.br/imovel/..."
  }
]

Each row maps to one property card. Fields include sale price, neighborhood address, bedroom count, total area in square meters, and the direct listing URL for deeper extraction if needed.

To get started, install the Minexa Chrome extension and train your first QuintoAndar scraper: Install Minexa. Full API documentation is available at minexa.stoplight.io.

For a related example covering another property market, see: How to scrape real estate data from OLX India using the Minexa API.