Automate Indian Real Estate Research with a 99acres Scraper
99acres.com is India's leading real estate marketplace, indexing millions of residential apartments, luxury villas, builder floors, plots, and commercial office spaces across major Tier-1 and Tier-2 metro hubs. For Indian PropTech companies, institutional real estate developers, co-living operators, and property valuation startups, 99acres represents an indispensable source of property deal flow and localized market intelligence.
However, extracting large-scale listing records from 99acres is technically challenging due to aggressive bot detection, IP rate limiting, and dynamic React/Next.js page rendering. WebScrapingHub eliminates these technical hurdles by offering a turnkey 99acres scraper API and managed data feeds that harvest clean, normalized datasets directly into your databases.
4 Core 99acres Property Data Pillars We Deliver
1. Residential Buy & Resale Properties
Extract secondary market resale flats, newly launched gated communities, penthouses, and builder floors.
- Total Price (₹ Lakhs / Crores) & Price per Sq Ft (₹/sqft)
- BHK Configuration (1BHK, 2BHK, 3BHK, 4BHK+)
- Super Built-up Area & Carpet Area (Sq Ft / Sq Yards)
- Possession status (Ready to move / Under construction)
2. Rental Housing & PG Accommodation
Monitor rental benchmarks, security deposits, co-living accommodations, and tenant preferences.
- Monthly rent (₹/month) & maintenance fees
- Security deposit amount & lock-in terms
- Furnishing status (Fully furnished, Semi, Unfurnished)
- Tenant preferences (Family, Bachelors, Company Lease)
3. Builder Projects & RERA Compliance
Track major builder launches from Godrej, DLF, Prestige, Sobha, Lodha, and Brigade Group.
- Builder/Developer company name & brand profile
- State RERA registration number & certificate URL
- Expected launch & possession completion year
- Total project land area (Acres), towers, & unit counts
4. Owner Phone Numbers & Dealer Leads
Harvest verified owner and property dealer contacts to power sales and cold outreach pipelines.
- Direct owner mobile phone numbers
- Dealer/Broker agency name & contact person
- Listing posting date & verified listing badge
- High-resolution property photos & floor plan URLs
Comprehensive 99acres Data Schema
Our scrapers extract and clean all available listing parameters, outputting machine-readable datasets formatted for immediate use in CRM systems, machine learning models, or analytical dashboards.
| Data Domain | Extracted Fields & Tracked Parameters | Formats |
|---|---|---|
| Listing & Pricing | Property ID, Listing URL, Title, Price (₹ Lakh/Crore), Price/Sq Ft, Maintenance Charge, Booking Amount, Price Negotiability | CSV / JSON |
| Property Specifications | Property Type (Apartment, Villa, Plot, Builder Floor, Commercial), BHK, Carpet Area (Sq Ft), Super Built-up Area, Floor Number, Total Floors, Facing (East, North), Furnishing | PostgreSQL / SQL |
| Location & Society | City, Micro-Market Locality, Society/Gated Project Name, Landmark, PIN Code, Latitude, Longitude | JSON / S3 |
| Builder & RERA | Builder Name, Project Name, State RERA ID, Possession Timeline, Construction Stage (Under Construction / Ready), Approved Banks | Snowflake / Parquet |
| Contact & Seller | Poster Type (Owner / Dealer / Builder), Contact Person Name, Phone Number, Agency Firm Name, Verified Badge Status | Excel / BigQuery |
How to Scrape 99acres with Python (Code Architecture)
99acres listings are dynamically hydrated. Below is a Python script illustrating how to structure request headers and parse listing details:
import requests
from bs4 import BeautifulSoup
import json
# Target 99acres Bangalore Search URL
url = "https://www.99acres.com/search/property/buy/residential-all/bangalore?preference=S&res_com=R"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36",
"Accept-Language": "en-US,en;q=0.9",
"Referer": "https://www.99acres.com/"
}
# Route requests through WebScrapingHub's Indian proxy pool to avoid IP blocks
response = requests.get(url, headers=headers)
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
listings = soup.find_all('div', class_='tupleNew__content')
for item in listings:
title_el = item.find('h2')
title = title_el.text.strip() if title_el else "N/A"
price_el = item.find('td', class_='tupleNew__priceVal')
price = price_el.text.strip() if price_el else "N/A"
print(f"Listing: {title} | Price: {price}")
Note: 99acres enforces strict rate limiting, CAPTCHAs, and TLS fingerprint inspection. For large-scale crawlers querying tens of thousands of records daily across Mumbai, Delhi, and Bangalore, utilize WebScrapingHub's managed proxy gateway to prevent bans.
Comprehensive Geographic Coverage Across Indian Metros
📍 Mumbai Metropolitan Region (MMR)
South Mumbai, Bandra, Andheri, Powai, Thane, Navi Mumbai, Borivali, and Goregaon.
📍 Delhi NCR & Gurgaon
Golf Course Road, Cyber City Gurgaon, Noida Expressway, Greater Noida, South Delhi, and Dwarka.
📍 Bengaluru Tech Corridors
Whitefield, Outer Ring Road (ORR), Bellandur, Electronic City, Sarjapur Road, Indiranagar, and Hebbal.
📍 Hyderabad & Pune
HITEC City, Gachibowli, Kokapet, Hinjewadi IT Park, Wakad, Baner, Kharadi, and Viman Nagar.
Strategic Enterprise Use Cases for 99acres Data
1. Dual-Portal Comps & Price Arbitrage (99acres + MagicBricks)
Combine 99acres feeds with MagicBricks scraper feeds to generate deduplicated property comps and detect price discrepancies between secondary market sellers.
2. Builder Lead Generation & Material Supply Sales
Building material manufacturers, interior design firms, and elevator suppliers harvest new project launch announcements and builder phone leads across state RERA registries.
3. Real Estate Brokerage & Dealer Lead Generation
Extract verified dealer profiles, agency names, and mobile numbers from 99acres to build targeted recruitment lists using our Real Estate Agents Database service.
4. Rental Yield & Co-Living Feasibility Studies
Analyze rental price points and PG listings near major IT parks to model occupancy and evaluate co-living or student housing investments.