Track India Rental Market With Square Yards Listing Data

Thirdwatch's Square Yards Scraper extracts structured rental listings from Square Yards with pay-per-result pricing. Track monthly rents, furnishing premiums, locality-level rent distributions, and gross rental yield benchmarks across 40+ Indian cities. Each listing includes rent amount, bedrooms, furnishing status, locality coordinates, and listed-by source. Built for rental market researchers, property management operations teams, relocation services, and housing-policy analysts studying urban rental dynamics in India.

Why track India's rental market from Square Yards

India's urban rental market houses over 110 million households according to Census 2021 housing data, yet rental market intelligence remains fragmented and largely anecdotal. Unlike the sale market where stamp duty registrations create a partial public record, rental transactions are almost entirely private. Listing data from platforms like Square Yards is the closest proxy for real-time rental market conditions.

The research applications are distinct from the sale market. A relocation company pricing packages for IT professionals transferring between Bangalore and Hyderabad needs locality-level rent benchmarks by bedroom count and furnishing type. A corporate housing operations team managing 500+ leases across 4 cities needs rent-renewal benchmarking data to negotiate with landlords. A housing-policy researcher studying rental affordability in Tier 1 cities needs rent-to-income distributions at the locality level. A property management startup building a dynamic pricing model needs weekly rent snapshots to calibrate its algorithm.

All of these reduce to structured rental listings with rent_monthly, bedrooms, furnishing, locality, city, area, and coordinates, refreshed on a regular schedule. The Square Yards Scraper returns all of these fields.

How does this compare to the alternatives?

NHB RESIDEX covers only 18 cities with quarterly lag and no listing-level detail. Broker networks are relationship-dependent and unstructured. The Square Yards Scraper provides listing-level granularity with structured furnishing status across a broader city set.

How to track India rentals in 5 steps

Step 1: How do I set up the environment?

pip install apify-client pandas numpy
export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull rental listings across multiple cities?

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

CITIES = ["Bangalore", "Mumbai", "Hyderabad", "Pune", "Gurgaon",
          "Chennai", "Noida", "Kolkata"]

all_rentals = []
for city in CITIES:
    run = client.actor("thirdwatch/squareyards-scraper").call(run_input={
        "queries": [city],
        "maxResults": 300,
        "propertyFor": "rent",
    })
    items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
    all_rentals.extend(items)
    print(f"{city}: {len(items)} rental listings")

print(f"Total: {len(all_rentals)} rental listings across {len(CITIES)} cities")

Setting propertyFor to "rent" filters to rental-only listings. Each listing populates the rent_monthly field instead of price.

Step 3: How do I compute locality-level rent distributions?

import pandas as pd
import numpy as np

df = pd.DataFrame(all_rentals)

# Filter valid rental amounts
df = df[df["rent_monthly"].notna() & (df["rent_monthly"] > 0)]
df = df.drop_duplicates(subset=["url"])

# Rent per sqft for cross-locality comparison
df["rent_per_sqft"] = df["rent_monthly"] / df["area"].replace(0, pd.NA)

# Locality-level distribution for 2BHK apartments (most common rental segment)
twobhk = df[(df["bedrooms"] == 2) & (df["property_type"] == "Apartment")]

locality_rents = twobhk.groupby(["city", "locality"]).agg(
    median_rent=("rent_monthly", "median"),
    p25_rent=("rent_monthly", lambda x: np.percentile(x, 25)),
    p75_rent=("rent_monthly", lambda x: np.percentile(x, 75)),
    median_rent_psf=("rent_per_sqft", "median"),
    listing_count=("url", "nunique"),
).reset_index()

# Only localities with enough data
locality_rents = locality_rents[locality_rents["listing_count"] >= 5]
locality_rents = locality_rents.sort_values("median_rent", ascending=False)

print("Top 20 localities by median 2BHK rent:")
print(locality_rents.head(20).to_string(index=False))

The interquartile range (p25 to p75) per locality gives relocation teams the negotiation band -- anything below p25 is a discount, anything above p75 is a premium.

Step 4: How do I measure the furnished vs unfurnished premium?

# Furnishing premium by city
furnishing_analysis = df.groupby(["city", "bedrooms", "furnishing"]).agg(
    median_rent=("rent_monthly", "median"),
    count=("url", "nunique"),
).reset_index()

# Pivot to compare furnished vs unfurnished
pivot = furnishing_analysis.pivot_table(
    index=["city", "bedrooms"],
    columns="furnishing",
    values="median_rent",
)

if "Furnished" in pivot.columns and "Unfurnished" in pivot.columns:
    pivot["furnished_premium_pct"] = (
        (pivot["Furnished"] / pivot["Unfurnished"] - 1) * 100
    )
    print("Furnished premium by city and BHK:")
    print(pivot[["Unfurnished", "Furnished", "furnished_premium_pct"]]
          .dropna()
          .sort_values("furnished_premium_pct", ascending=False)
          .to_string())

In Indian metros, furnished premiums typically range from 20% to 50%. Localities where the premium exceeds 40% present an opportunity for furnished rental operators; those where it is below 15% suggest the tenant pool is price-sensitive and less willing to pay for furniture.

Step 5: How do I compute gross rental yield by locality?

Cross-reference rental data with sale data from a parallel scraper run to compute gross yield.

# Pull sale listings for the same cities
all_sales = []
for city in CITIES:
    run = client.actor("thirdwatch/squareyards-scraper").call(run_input={
        "queries": [city],
        "maxResults": 300,
        "propertyFor": "sale",
    })
    items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
    all_sales.extend(items)

sale_df = pd.DataFrame(all_sales)
sale_df = sale_df[sale_df["price"].notna() & (sale_df["price"] > 0)]
sale_df = sale_df.drop_duplicates(subset=["url"])

# Median sale price per locality + BHK
sale_medians = sale_df.groupby(["city", "locality", "bedrooms"]).agg(
    median_price=("price", "median"),
).reset_index()

# Median annual rent per locality + BHK
rent_medians = df.groupby(["city", "locality", "bedrooms"]).agg(
    median_annual_rent=("rent_monthly", lambda x: x.median() * 12),
).reset_index()

# Merge and compute yield
yields = rent_medians.merge(sale_medians, on=["city", "locality", "bedrooms"])
yields["gross_yield_pct"] = (yields["median_annual_rent"] / yields["median_price"]) * 100

# Localities with attractive yields (India average is 2-4%)
attractive = yields[(yields["gross_yield_pct"] > 3.5) & (yields["gross_yield_pct"] < 10)]
attractive = attractive.sort_values("gross_yield_pct", ascending=False)
print("High-yield localities:")
print(attractive[["city", "locality", "bedrooms", "gross_yield_pct"]].head(20).to_string(index=False))

India's average residential gross yield is 2-4%, according to Knight Frank India's rental yield analysis. Localities consistently above 3.5% with stable rental demand are the value pockets for rental-focused investors.

Sample output

Two records from a Bangalore rental query:

[
  {
    "title": "2 BHK Apartment for Rent in Prestige Shantiniketan",
    "property_for": "rent",
    "property_type": "Apartment",
    "bedrooms": 2,
    "bathrooms": 2,
    "area": 1400,
    "floor": "7th of 20",
    "address": "Whitefield, Bangalore",
    "locality": "Whitefield",
    "city": "Bangalore",
    "latitude": 12.9698,
    "longitude": 77.7500,
    "developer_name": "Prestige Group",
    "project_name": "Prestige Shantiniketan",
    "name": "Prestige Shantiniketan",
    "price": null,
    "rent_monthly": 45000,
    "listed_by": "Owner",
    "available_from": "Immediately",
    "furnishing": "Semi-Furnished"
  },
  {
    "title": "3 BHK Apartment for Rent in Sobha Dream Acres",
    "property_for": "rent",
    "property_type": "Apartment",
    "bedrooms": 3,
    "bathrooms": 2,
    "area": 1580,
    "floor": "4th of 12",
    "address": "Panathur, Bangalore",
    "locality": "Panathur",
    "city": "Bangalore",
    "latitude": 12.9320,
    "longitude": 77.7180,
    "developer_name": "Sobha Limited",
    "project_name": "Sobha Dream Acres",
    "name": "Sobha Dream Acres",
    "price": null,
    "rent_monthly": 38000,
    "listed_by": "Owner",
    "available_from": "2026-06-01",
    "furnishing": "Unfurnished"
  }
]

The rent_monthly field is the structured monthly rent in rupees. The furnishing field enables premium analysis. The listed_by field (Owner vs Broker vs Developer) allows filtering for direct-landlord listings.

Common pitfalls

Three things go wrong in rental market tracking pipelines. Broker inflation -- brokers sometimes list the same unit at multiple price points or under slightly different titles to test demand; dedup on (project_name, bedrooms, floor, locality) in addition to URL to catch these near-duplicates. Deposit and maintenance exclusion -- Indian rental listings on Square Yards show only the monthly rent, not the security deposit (typically 6-10 months in Bangalore) or maintenance charges; do not mistake the listed rent_monthly for total occupancy cost. Seasonal rental cycles -- Indian metro rental markets peak during April-June (job-change season) and dip in November-January; comparing June rents to December rents without seasonal adjustment will show false appreciation or decline. Use year-over-year comparisons or at minimum 12-week rolling averages.

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