Scrape 99acres India Real Estate Listings (2026)

Published April 28, 2026 · 1551 words · For researchers

Thirdwatch's 99acres Scraper returns India property listings at $0.003 per record — title, property type, location, price, area, bedrooms, builder, owner-broker flag. Built for India real estate research, rental-market analysis, property-investment intelligence, and new-launch tracking.

Why scrape 99acres for India real estate research

99acres dominates Tier 1 metro India property listings. According to 99acres' 2024 report, the platform indexes 1.2M+ listings across 600+ Indian cities — alongside MagicBricks and Housing.com, it captures 80%+ of organized India real estate listings. For Tier 1 metro property research (Mumbai, Bangalore, Delhi NCR, Chennai), 99acres is canonical.

The job-to-be-done is structured. An India real estate research function maps Mumbai + Bangalore property-trend quarterly. A new-launch tracking function monitors RERA-registered projects pre-launch for first-mover investment intelligence. A rental-market platform tracks Tier 1 metro rentals weekly for investor reports. An India proptech aggregator builder ingests cross-platform listings (99acres + MagicBricks + NoBroker) for comprehensive India coverage. All reduce to city + property-type queries + per-listing aggregation.

How does this compare to the alternatives?

Three options for 99acres data:

99acres official API requires Info Edge B2B partnership. The 99acres Scraper actor page gives you raw listing data at materially lower per-record cost.

How to scrape 99acres in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a city × property-type batch?

Pass city + property-type queries.

import os, requests, pandas as pd
from itertools import product

ACTOR = "thirdwatch~acres99-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

CITIES = ["Mumbai", "Bangalore", "Delhi-NCR", "Chennai", "Hyderabad", "Pune"]
TYPES = ["apartment", "villa", "plot"]
LISTING = "rent"

queries = [{"city": c, "property_type": t, "listing": LISTING}
           for c, t in product(CITIES, TYPES)]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": queries, "maxResults": 100},
    timeout=900,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} {LISTING} listings across {df.city.nunique()} cities")

6 cities × 3 types = 18 queries × 100 = up to 1,800 records, costing $5.40.

Step 3: How do I parse INR + compute rental yields?

Compute rental-yield benchmarks per locality.

import re

def parse_inr(s):
    if not isinstance(s, str): return None
    s = s.replace("₹", "").replace(",", "").strip()
    if "Cr" in s.lower():
        return float(re.search(r"([\d.]+)", s).group(1)) * 10_000_000
    if "Lac" in s.lower():
        return float(re.search(r"([\d.]+)", s).group(1)) * 100_000
    try:
        return float(s)
    except:
        return None

df["price_inr"] = df.price.apply(parse_inr)
df["area_sqft"] = pd.to_numeric(df.area_sqft, errors="coerce")

# Annual rent / capital value = rental yield
# Approximate using locality-median capital values
yields = (
    df[df.listing == "rent"].dropna(subset=["price_inr"])
    .groupby(["city", "locality"])
    .agg(median_monthly_rent=("price_inr", "median"),
         listing_count=("title", "count"))
    .query("listing_count >= 10")
)
yields["annual_rent"] = yields.median_monthly_rent * 12
print(yields.head(15))

For full rental-yield computation, cross-reference with buy-listing capital values per locality.

Step 4: How do I track new-launch projects?

99acres new-launches publish RERA + builder data.

new_launches = df[df.listing_type == "new_launch"]
print(f"{len(new_launches)} new-launch projects")
print(new_launches[["title", "builder_name", "rera_id", "expected_possession",
                    "total_units", "price_range"]].head(15))

RERA-registered new launches enable investor-research on pre-launch + under-construction inventory.

Sample output

A single 99acres listing record looks like this. Five rows weigh ~7 KB.

{
  "listing_id": "C12345678",
  "title": "3 BHK Flat for Rent in Powai",
  "property_type": "Apartment",
  "city": "Mumbai",
  "locality": "Powai",
  "sub_locality": "Hiranandani Gardens",
  "price": "₹85,000 per month",
  "price_inr": 85000,
  "super_built_up_area_sqft": 1650,
  "carpet_area_sqft": 1100,
  "bedrooms": 3,
  "bathrooms": 3,
  "balconies": 2,
  "furnished_status": "Semi-Furnished",
  "age": "5-10 Years",
  "floor": "8th of 14",
  "parking": "1 Covered + 1 Open",
  "builder_name": "Hiranandani",
  "rera_id": "P51800015234",
  "owner_vs_broker": "owner",
  "posted_date": "2026-04-22",
  "url": "https://www.99acres.com/3-bhk-..."
}

super_built_up_area_sqft vs carpet_area_sqft enables the canonical India real-estate efficiency-ratio metric (carpet/super-built typically 65-75% in metros). rera_id enables RERA-database cross-reference for compliance verification.

Common pitfalls

Three things go wrong in 99acres pipelines. Super-built-up vs carpet area confusion — India real estate quotes super-built-up area (includes proportional common-area share); actual usable space is carpet area, typically 65-75% of super-built. For accurate rent-per-sqft analysis, normalize by carpet area. Locality-naming variance — Powai vs Powai East vs Powai Hiranandani; for clean per-locality analysis, use canonical-name mapping. Re-listing inflation — same property can be listed by multiple brokers; dedupe on (locality, area_sqft, bedrooms, builder) before treating as unique inventory.

Thirdwatch's actor uses HTTP + structured data extraction at $0.10/1K, ~88% margin. Pair 99acres with MagicBricks Scraper and NoBroker Scraper for comprehensive India real estate coverage. A fourth subtle issue worth flagging: 99acres' new-launch tier publishes "Expected Possession" dates that often slip 6-18 months past announced — for investment-research, treat possession dates as upper-bound estimates rather than committed timelines. A fifth pattern unique to India real estate: per-locality price-per-sqft variance is much higher than per-city average suggests — Mumbai's average ₹25K/sqft hides ranges from ₹8K (suburbs) to ₹85K (South Mumbai). For accurate market analysis, segment by sub-locality rather than aggregating to city level. A sixth and final pitfall: 99acres listings often quote prices in negotiable ranges ("₹85K (Negotiable)") which inflate listed-vs-transaction price gap; for accurate transaction-price research, supplement with RERA-registered transaction records for high-stakes deals.

Operational best practices for production pipelines

Tier the cadence to match signal half-life. India real estate listings change slowly — weekly polling on top localities + monthly on long-tail covers most use cases. 60-80% cost reduction with negligible signal loss when watchlist is properly tiered.

Snapshot raw payloads. Pipeline cost is dominated by scrape volume, not storage. Persisting raw JSON snapshots lets you re-derive metrics — particularly useful as locality-name normalization tables evolve. Compress with gzip at write-time.

Schema validation. Run a daily validation suite asserting expected core fields with non-null rates above 80% (required) and 50% (optional). 99acres schema occasionally changes during platform UI revisions — catch drift early. Cross-snapshot diff alerts on RERA-status changes + builder-name updates catch structural shifts that pure aggregate-trend monitoring misses. A seventh and final operational pattern at production scale: cross-snapshot diff alerts. Beyond detecting individual changes, build alerts on cross-snapshot field-level diffs — name changes, category re-classifications, status changes. These structural changes precede or follow material events and are leading indicators of organization-level disruption. Persist a structured-diff log alongside aggregate snapshots: for each entity, persist (field, old_value, new_value) tuples per scrape. Surface high-leverage diffs to human reviewers; low-leverage diffs stay in the audit log.

An eighth pattern worth flagging for cost-controlled teams: implement an incremental-diff pipeline that only re-processes records whose hash changed since the previous snapshot. For watchlists where 90%+ of records are unchanged between snapshots, hash-comparison-driven incremental processing reduces downstream-compute by 80-90% while preserving full data fidelity. Combine with snapshot-storage compression and tiered-cadence polling for end-to-end pipeline-cost reductions of 70%+ at scale. This pattern is particularly important when scaling beyond 100K records per snapshot — the difference between manageable monthly costs and runaway compute spend.

Related use cases

• Monitor 99acres rental trends by city
• Build India property research pipeline with 99acres
• Scrape MagicBricks for India property research
• All Thirdwatch use-case guides

Frequently asked questions

Why scrape 99acres for India real estate research?

99acres (Info Edge) is one of India's three dominant property platforms alongside MagicBricks and Housing.com. According to 99acres' 2024 report, the platform indexes 1.2M+ listings across 600+ Indian cities with strong Tier 1 metro coverage (Mumbai, Bangalore, Delhi NCR, Chennai). For India real estate research, investment intelligence, and rental-market analysis, 99acres is essential alongside MagicBricks.

What data does the actor return?

Per listing: title, property type (Apartment/Villa/Plot/Independent House/Commercial), city + locality + sub-locality, price (₹), price-per-sqft, super-built-up area, carpet area, bedrooms, bathrooms, balconies, furnished status, age, floor, total floors, parking, builder name, owner-vs-broker flag, posted date, image URLs. About 90%+ of 99acres listings have comprehensive metadata.

How does 99acres compare to MagicBricks?

Both platforms have ~50% market share each in India's organized property-listing market. MagicBricks (Times Group) skews toward Tier 2/3 cities + new-launch projects. 99acres (Info Edge) has stronger Tier 1 metro coverage with deeper rental + buy split. For comprehensive India real estate research, run both — typically 30-40% non-overlap. Listings on both platforms have ~70% overlap; the unique 30% per platform is what cross-source dedup misses.

Can I track new-launch projects?

Yes. 99acres maintains a dedicated "New Projects" tier with structured data (RERA registration, expected possession date, builder, total units, price-per-sqft launch range). For investment-research workflows tracking pre-launch + RERA-approved projects, this surface is materially deeper than buy-listing surface.

How fresh do property snapshots need to be?

For active property-research, weekly cadence catches new listings within 7 days. For investment-research benchmarking, monthly cadence suffices. For new-launch-monitoring (RERA-registered projects), daily cadence catches builder marketing cycles. Most 99acres listings stay active 30-90 days.

How does this compare to 99acres' official API?

99acres official API requires Info Edge B2B partnership ($25K+/year enterprise contract). The actor delivers similar coverage at $0.003/record without partnership gatekeeping. For active brokerage operations using full ATS integration, the API is required. For research-only use cases, the actor is materially cheaper.

Run the 99acres Scraper on Apify Store — pay-per-record, free to try, no credit card to test.