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How to Extract Data from is one of the largest apartment listing sites in the United States, with over 4 million active rental listings across the country. For real estate investors, property managers, and data analysts, extracting data from can provide tremendous value for understanding rental market trends, finding deals, and conducting competitive analysis.

However, does not have a public API for accessing listing data at scale. While they do have an API for direct listings by property managers, this is limited to a company‘s own inventory. For those looking to extract broad market data from the site, web scraping is currently the best approach.

In this comprehensive guide, I‘ll cover different methods and tools for extracting data from, including:

  • Scraping Listing Search Pages
  • Scraping Individual Listing Detail Pages
  • Capturing Contact Information
  • Scraping Community Pages
  • Extracting Pricing Trends
  • Avoiding Bot Detection

I‘ll share code examples using Python and Node.js, as well as recommended third-party services that can simplify the scraping process. My goal is to provide a detailed tutorial for building a custom web scraper or using APIs to extract high-value data from

Scraping Listing Search Pages

The main entry point for scraping data is through listing search pages for a given location. By extracting key fields from these pages, you can compile a database of available rentals with core attributes like address, bedrooms/bathrooms, rent price, etc.

Here are some best practices for scraping the search pages:

Use zip codes or neighborhoods for location search – Start your scraper by searching for a defined area rather than filtering by bedrooms, price, etc. This will return a more complete listing set.

Page through all results – Listings are paginated with around 25 results per page. Your scraper needs to automatically click through to extract all pages of results.

Target key data attributes – Core fields to extract include title, address, bedrooms, bathrooms, size, price, amenities, contacts, links to detail pages, etc.

Watch out for bot detection – blocks scraping bots, so use proxies, random delays, and other evasion tactics. More on this later.

Sample Python code for search page scraping:

import requests
from bs4 import BeautifulSoup

# Search for Hollywood, FL rentals
url = ""

# Page through all results   
for page in range(1, 10):

  # Construct page URL
  url_with_page = f"{url}?page={page}"

  # Fetch page HTML
  response = requests.get(url_with_page)

  # Parse HTML with BeautifulSoup  
  soup = BeautifulSoup(response.content, "html.parser")

  # Extract data from result cards
  cards = soup.find_all("div", class_="property-card")

  for card in cards:
    address = card.find("div", class_="property-address").text.strip()
    title = card.find("div", class_="property-title").text.strip() 

    bedrooms = card.find("div", class_="bed-range").text.strip()
    bathrooms = card.find("div", class_="bath-range").text.strip()

    size = card.find("div", class_="sqft").text.strip()
    price = card.find("div", class_="property-pricing").text.strip()

    amenities = [item.text for item in card.find_all("span", class_="amenity-text")]

    # Print extracted data
    print(f"Address: {address}")
    print(f"Title: {title}")
    print(f"Bedrooms: {bedrooms}") 
    print(f"Bathrooms: {bathrooms}")
    # And so on...

    # Follow link to detail page for more data
    detail_url = card.find("a")["href"]

This covers the basics of scraping the search pages – iterating through each page of results, parsing the HTML with BeautifulSoup, and extracting key attributes from each listing card. The same approach works across any location search on the site.

Scraping individual detail pages (covered next) can provide much more granular data for each listing.

Scraping Individual Listing Detail Pages

While the listing search pages provide a summary of core attributes, scraping the individual detail page for each property can uncover 100+ additional fields. This includes detailed amenities, rental terms/restrictions, school district info, comparable rentals, and more.

However, fetching and parsing thousands of detail pages requires more sophisticated logic to avoid bot detection. Here are some tips:

  • Add random delays between page requests
  • Limit requests to a few pages per minute
  • Rotate user agents and proxies to vary fingerprints
  • Maintain sessions across page requests
  • Retry failed requests across proxy/user agent combinations

This added complexity is where using a commercial web scraping API can help significantly. Services like BrightData, ScrapingBee, or ScraperAPI handle proxy rotation, browser fingerprint randomization, and retry logic automatically.

For example, here is sample Python code to scrape a detail page using the BrightData API:

import brightdata
from brightdata.utils import *

brightdata = BrightData(‘YOUR_API_KEY‘)

detail_url = "" 

scraper = brightdata.Scraper(

page = scraper.get(url=detail_url)
soup = BeautifulSoup(page.content, ‘html.parser‘)

title = soup.find("h1", class_="property-title").text.strip()
address = soup.find("div", class_="property-address").text.strip()

description = soup.find("div", class_="content-block description").text.strip() 

# And so on...


By handling proxies and browser sessions under the hood, APIs like this make it easy to scrape many listing detail pages without getting blocked.

Capturing Contact Information

One of the most valuable pieces of data on is the phone number and contact info for each listing. However, this information is only displayed in plain HTML for logged in users.

To access contact info at scale, you need to:

  1. Programmatically create accounts – Unique accounts for each scraper instance

  2. Log in before scraping – Maintain logged in sessions across page requests

  3. Parse contact fields – Extract info within locked attributes

Again, services like BrightData offer built-in support for creating and logging into accounts programmatically. The API handles cookies, sessions, captchas, etc. behind the scenes so you can focus on data extraction.

Here‘s an example using the Puppeteer API in Node.js:

const { PuppeteerHandler } = require(‘brightdata‘);

const handler = new PuppeteerHandler({
  launchOptions: {
    headless: true,

const page = await handler.newPage();

// Create and log into account
await page.goto(‘‘);
// ...register form submit logic

// Now logged in, scrape contact info 
await page.goto(‘‘);

const title = await page.$eval(‘h1‘, el => el.innerText); 
const phone = await page.$eval(‘.phone-number‘, el => el.innerText);

console.log({ title, phone });

await handler.close();

Having the phone number and other contact details can enable outreach to property managers directly.

Scraping Community Pages

Beyond individual listings, has detailed "community" pages for each multi-tenant property. These contain additional info like:

  • Property manager name
  • Number of total units
  • Year built
  • Forms of payment accepted
  • School district
  • Reviews from residents
  • Demographic targeting
  • Historical availability %

This data provides useful market context around each rental community. To extract it, you need to:

  1. Capture the community URL from each listing page
  2. Iterate through the community URLs to fetch each page
  3. Use a robust scraper configuration to avoid blocks
  4. Parse page sections like "Facts and Features", "Resident Experience" etc.

For example:

# After scraping all listing detail pages

community_urls = [] 

for listing in all_listings:
  community_url = listing[‘community_url‘]

community_urls = list(set(community_urls)) # dedup 

for url in community_urls:

  page = brightdata_scraper.get(url) # proxy rotation, retries, etc

  soup = BeautifulSoup(page.content, ‘html.parser‘)

  facts = soup.find("div", {"data-name": "Facts and Features"})

  property_manager = facts.find("div", class_="manager")
  total_units = facts.find("div", class_="totalUnits") 

  # And so on...

Compiling community data alongside your listing inventory provides a richer market analysis.

A major value of an scraper is unlocking rental rate trends over time. By repeatedly extracting listing data on a daily or weekly basis, you can assemble dynamic pricing history for each unit.

This involves:

  • Storing snapshots of listing data over time
  • Deduplicating based on address
  • Analyzing price changes for each listing ID

For example, you could produce daily average price reports by market:

Date         | Atlanta Avg | Dallas Avg | Phoenix Avg
2022-01-01   | $1800       | $2200      | $2100
2022-01-02   | $1850       | $2300      | $2000  
2022-01-03   | $1875       | $2400      | $1975

When scoped to a specific city or neighborhood, pricing trends can signal opportunities for properties below market rate. They also help guide lease renewal negotiations.

Avoiding Bot Detection

The biggest challenge when scraping at scale is avoiding bot detection. The site actively tries to block scraping bots through:

  • IP blacklists
  • Browser fingerprinting
  • Human behavior analysis
  • CAPTCHA challenges

Here are some best practices to maximize scraping uptime:

  • Use dedicated residential proxies – Avoid shared IPs flagged for scraping
  • Limit requests to a few pages per minute
  • Randomize user agents on each request
  • Actual browser rendering – API or headless browsers may be detected
  • Inject human-like delays between actions
  • Rotate proxies and browsers frequently – Vary fingerprints

Top-tier proxy services like BrightData, SmartProxy, and GeoSurf (for residential IPs) are purpose-built for difficult sites like They make evasion easy through autoscaling, self-healing browsers, and real mobile IPs.

With the right tools and precautions, it‘s possible to extract large volumes of data from successfully. The combination of detail page attributes, contact info, community data, and pricing trends can add up to powerful rental market intelligence.


In this guide, I‘ve covered a variety of techniques for extracting data from at scale, including:

  • Scraping search listings and detail pages
  • Capturing contact information
  • Compiling community page data
  • Analyzing pricing trends over time
  • Avoiding bot detection with proxies and headless browsers

The lack of a public API makes challenging to scrape. But with robust tools and strategies, it‘s possible to build powerful datasets reflective of the broader rental market.

For anyone looking to tap into data, I recommend considering a commercial web scraping or proxy service. They handle the heavy lifting of managing proxies, browsers, and evasion tactics programmatically. This allows you to focus on extracting and structuring the data.

With some technical skill and the right resources, you can leverage data to gain valuable intel for real estate investing, property management, financial analysis, and more. Let me know if you have any other questions!

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