How to Scrape HotPads: A Complete Guide to Extracting Rental Data
Learn how to scrape HotPads.com to extract rental prices, property details, and location data. Master anti-bot bypasses for Zillow Group's map-based platform.
Anti-Bot Protection Detected
- Akamai Bot Manager
- Advanced bot detection using device fingerprinting, behavior analysis, and machine learning. One of the most sophisticated anti-bot systems.
- DataDome
- Real-time bot detection with ML models. Analyzes device fingerprint, network signals, and behavioral patterns. Common on e-commerce sites.
- Google reCAPTCHA
- Google's CAPTCHA system. v2 requires user interaction, v3 runs silently with risk scoring. Can be solved with CAPTCHA services.
- Rate Limiting
- Limits requests per IP/session over time. Can be bypassed with rotating proxies, request delays, and distributed scraping.
- IP Blocking
- Blocks known datacenter IPs and flagged addresses. Requires residential or mobile proxies to circumvent effectively.
About HotPads
Learn what HotPads offers and what valuable data can be extracted from it.
The Urban Rental Powerhouse
HotPads is a map-based rental search engine that specializes in urban areas, providing listings for apartments, houses, and rooms for rent. As part of the Zillow Group (which includes Zillow and Trulia), it leverages a massive database of real estate information, making it a primary destination for renters in the US.
Comprehensive Rental Data
The data on HotPads is exceptionally valuable for market analysis, as it often contains 'for rent by owner' (FRBO) listings and boutique apartment data that larger portals might miss. For scrapers, it represents a high-quality source of real-time rental inventory and pricing trends, allowing for granular tracking of urban housing shifts.
Why it Matters
Accessing HotPads data allows real estate professionals and researchers to analyze rental markets with high spatial precision. Whether you are monitoring property management performance or identifying emerging real estate hotspots, the platform's focus on high-density living makes it an indispensable resource for urban real estate intelligence.
Why Scrape HotPads?
Discover the business value and use cases for extracting data from HotPads.
Real-time rental market monitoring
Competitive pricing analysis for landlords
Lead generation for real estate agents
Investment research for property acquisition
Urban housing density and availability studies
Scraping Challenges
Technical challenges you may encounter when scraping HotPads.
Aggressive Akamai 'Press & Hold' challenges
Map-based dynamic loading (AJAX)
Frequent changes to CSS class names (obfuscation)
Strict rate limiting on IP addresses
Data truncation in search results requiring deep links
Scrape HotPads with AI
No coding required. Extract data in minutes with AI-powered automation.
How It Works
Describe What You Need
Tell the AI what data you want to extract from HotPads. Just type it in plain language — no coding or selectors needed.
AI Extracts the Data
Our artificial intelligence navigates HotPads, handles dynamic content, and extracts exactly what you asked for.
Get Your Data
Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why Use AI for Scraping
AI makes it easy to scrape HotPads without writing any code. Our AI-powered platform uses artificial intelligence to understand what data you want — just describe it in plain language and the AI extracts it automatically.
How to scrape with AI:
- Describe What You Need: Tell the AI what data you want to extract from HotPads. Just type it in plain language — no coding or selectors needed.
- AI Extracts the Data: Our artificial intelligence navigates HotPads, handles dynamic content, and extracts exactly what you asked for.
- Get Your Data: Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.
Why use AI for scraping:
- Bypasses Akamai and DataDome automatically
- Handles JavaScript rendering without custom setup
- Schedules runs to track price drops
- Exports directly to structured formats like CSV or JSON
No-Code Web Scrapers for HotPads
Point-and-click alternatives to AI-powered scraping
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape HotPads. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
Common Challenges
Learning curve
Understanding selectors and extraction logic takes time
Selectors break
Website changes can break your entire workflow
Dynamic content issues
JavaScript-heavy sites often require complex workarounds
CAPTCHA limitations
Most tools require manual intervention for CAPTCHAs
IP blocking
Aggressive scraping can get your IP banned
No-Code Web Scrapers for HotPads
Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape HotPads. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.
Typical Workflow with No-Code Tools
- Install browser extension or sign up for the platform
- Navigate to the target website and open the tool
- Point-and-click to select data elements you want to extract
- Configure CSS selectors for each data field
- Set up pagination rules to scrape multiple pages
- Handle CAPTCHAs (often requires manual solving)
- Configure scheduling for automated runs
- Export data to CSV, JSON, or connect via API
Common Challenges
- Learning curve: Understanding selectors and extraction logic takes time
- Selectors break: Website changes can break your entire workflow
- Dynamic content issues: JavaScript-heavy sites often require complex workarounds
- CAPTCHA limitations: Most tools require manual intervention for CAPTCHAs
- IP blocking: Aggressive scraping can get your IP banned
Code Examples
import requests
from bs4 import BeautifulSoup
# Note: This will likely be blocked by Akamai without high-quality proxies
url = "https://hotpads.com/san-francisco-ca/apartments-for-rent"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
soup = BeautifulSoup(response.content, 'html.parser')
# Representative selectors (subject to change)
listings = soup.select('.ListingCard-sc-1')
for item in listings:
price = item.select_one('.Price-sc-16o2x1v-0').text
address = item.select_one('.Address-sc-16o2x1v-1').text
print(f"Price: {price}, Address: {address}")
else:
print(f"Blocked or Error: {response.status_code}")
except Exception as e:
print(f"Request failed: {e}")When to Use
Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.
Advantages
- ●Fastest execution (no browser overhead)
- ●Lowest resource consumption
- ●Easy to parallelize with asyncio
- ●Great for APIs and static pages
Limitations
- ●Cannot execute JavaScript
- ●Fails on SPAs and dynamic content
- ●May struggle with complex anti-bot systems
How to Scrape HotPads with Code
Python + Requests
import requests
from bs4 import BeautifulSoup
# Note: This will likely be blocked by Akamai without high-quality proxies
url = "https://hotpads.com/san-francisco-ca/apartments-for-rent"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
soup = BeautifulSoup(response.content, 'html.parser')
# Representative selectors (subject to change)
listings = soup.select('.ListingCard-sc-1')
for item in listings:
price = item.select_one('.Price-sc-16o2x1v-0').text
address = item.select_one('.Address-sc-16o2x1v-1').text
print(f"Price: {price}, Address: {address}")
else:
print(f"Blocked or Error: {response.status_code}")
except Exception as e:
print(f"Request failed: {e}")Python + Playwright
from playwright.sync_api import sync_playwright
def scrape_hotpads():
with sync_playwright() as p:
# Using stealth to avoid Akamai detection
browser = p.chromium.launch(headless=True)
context = browser.new_context(user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36")
page = context.new_page()
page.goto("https://hotpads.com/chicago-il/apartments-for-rent")
# Wait for listings to load dynamically
page.wait_for_selector(".styles__ListingCardContainer-sc-1")
listings = page.query_selector_all(".styles__ListingCardContainer-sc-1")
for listing in listings:
price_el = listing.query_selector(".Price-sc-1")
if price_el:
print(f"Found Listing: {price_el.inner_text()}")
browser.close()
scrape_hotpads()Python + Scrapy
import scrapy
class HotpadsSpider(scrapy.Spider):
name = "hotpads"
start_urls = ["https://hotpads.com/sitemap-rentals-index.xml"]
def parse(self, response):
# Hotpads uses XML sitemaps for easier URL discovery
for url in response.xpath('//loc/text()').getall():
yield scrapy.Request(url, callback=self.parse_listing)
def parse_listing(self, response):
yield {
'price': response.css('.Price-sc-16o2x1v-0::text').get(),
'address': response.css('.Address-sc-16o2x1v-1::text').get(),
'description': response.css('.Description-sc-1::text').get(),
}Node.js + Puppeteer
const puppeteer = require('puppeteer-extra');
const StealthPlugin = require('puppeteer-extra-plugin-stealth');
puppeteer.use(StealthPlugin());
async function scrape() {
const browser = await puppeteer.launch({ headless: true });
const page = await browser.newPage();
await page.goto('https://hotpads.com/los-angeles-ca/apartments-for-rent');
await page.waitForSelector('.ListingCard');
const data = await page.evaluate(() => {
return Array.from(document.querySelectorAll('.ListingCard')).map(el => ({
price: el.querySelector('.Price')?.innerText,
address: el.querySelector('.Address')?.innerText
}));
});
console.log(data);
await browser.close();
}
scrape();What You Can Do With HotPads Data
Explore practical applications and insights from HotPads data.
Rental Price Indexing
Create a local rental price index to identify undervalued neighborhoods for potential renters or investors.
How to implement:
- 1Scrape daily pricing data for specific zip codes
- 2Calculate the average price per square foot
- 3Visualize trends over time using a dashboard
Use Automatio to extract data from HotPads and build these applications without writing code.
What You Can Do With HotPads Data
- Rental Price Indexing
Create a local rental price index to identify undervalued neighborhoods for potential renters or investors.
- Scrape daily pricing data for specific zip codes
- Calculate the average price per square foot
- Visualize trends over time using a dashboard
- Lead Generation for Managers
Scrape 'For Rent by Owner' (FRBO) listings to offer property management or maintenance services.
- Filter listings by property type and ownership status
- Extract property manager or owner contact information
- Outreach to newly posted listings with service proposals
- Investment Alert System
Automate alerts for real estate investors when listings meet specific return-on-investment criteria.
- Define target metrics like maximum price and minimum bedrooms
- Run the scraper at hourly intervals
- Push notifications to Slack or email when matches are found
- Market Availability Reporting
Analyze housing inventory shifts to provide insights for urban planning or real estate media.
- Collect volume data on active vs. deactivated listings
- Categorize availability by urban density zones
- Report monthly growth or decline in specific rental sectors
- Competitor Analysis for Landlords
Property owners can monitor nearby listing prices to ensure their own rates remain competitive.
- Select a radius around a target property
- Scrape all active listings within that radius
- Analyze amenities vs. price points to optimize rental income
Supercharge your workflow with AI Automation
Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.
Pro Tips for Scraping HotPads
Expert advice for successfully extracting data from HotPads.
Use Residential Proxies
Akamai easily flags datacenter IPs; residential proxies are mandatory for scale.
Sitemap Crawling
Use the sitemaps found in robots.txt to discover listing URLs instead of scraping the map search to avoid rate limits.
Handle Stealth
Use stealth plugins to mimic real browser fingerprinting and bypass JavaScript challenges.
Coordinate Extraction
Latitude and longitude are often embedded in the page's JSON state for mapping purposes.
Randomize Delay
Implement jitter (random delays) between requests to mimic human browsing behavior and avoid triggered rate limiting.
Target Off-Peak Hours
Scrape during low-traffic periods for the US to reduce the likelihood of aggressive server-side bot mitigation.
Testimonials
What Our Users Say
Join thousands of satisfied users who have transformed their workflow
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
Jonathan Kogan
Co-Founder/CEO, rpatools.io
Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.
Mohammed Ibrahim
CEO, qannas.pro
I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!
Ben Bressington
CTO, AiChatSolutions
Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!
Sarah Chen
Head of Growth, ScaleUp Labs
We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.
David Park
Founder, DataDriven.io
The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!
Emily Rodriguez
Marketing Director, GrowthMetrics
Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.
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Frequently Asked Questions About HotPads
Find answers to common questions about HotPads