Monitor Square Yards Listings for Real Estate Investments

Thirdwatch's Square Yards Scraper provides the listing-level data layer for monitoring India real estate investment signals across Bangalore, Hyderabad, Pune, Gurgaon, Mumbai, and other top-8 cities. Track price-per-sqft momentum, inventory velocity, developer activity, and supply composition across localities with pay-per-result pricing. Built for proptech founders, real estate investment managers, and data-driven developers looking to surface actionable micro-market signals from structured property data.

Why monitor Square Yards for investment signals

India's residential real estate investment decisions are overwhelmingly driven by anecdotal information -- broker conversations, site visits, and newspaper advertorials. According to Knight Frank's India Real Estate H1 2025 report and RBI's Housing Price Index data, residential sales volume in the top 8 Indian cities grew 11% year-on-year, but price appreciation varied by 3x across localities within the same city. The difference between a 5% annual return and a 15% return is the locality, not the city.

Structured listing data transforms this from anecdote to analysis. A proptech founder building an investment recommendation engine needs locality-level price-per-sqft trends. A family office managing a real estate portfolio across 3-4 cities needs inventory turnover signals to time entries and exits. A real estate fund running a data-driven acquisition strategy needs developer activity maps to identify early-stage supply corridors before prices adjust.

The signals are all derivable from the same underlying data: time-series snapshots of property listings with price, area, locality, developer_name, project_name, and furnishing fields. The Square Yards Scraper returns all of these per listing.

How does this compare to the alternatives?

Brokerage reports deliver macro signals 3-6 months late. Paid data platforms deliver micro-market data but limit schema control and custom signal engineering. The Square Yards Scraper gives you raw listings for building proprietary signals.

How to build an investment signal pipeline in 5 steps

Step 1: How do I set up the scraper for weekly snapshots?

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

Step 2: How do I take a multi-city snapshot?

import os
from apify_client import ApifyClient
from datetime import date

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

TARGET_CITIES = ["Bangalore", "Hyderabad", "Pune", "Gurgaon", "Noida"]

snapshot = []
snapshot_date = date.today().isoformat()

for city in TARGET_CITIES:
    run = client.actor("thirdwatch/squareyards-scraper").call(run_input={
        "queries": [city],
        "maxResults": 500,
        "propertyFor": "sale",
    })
    items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
    for item in items:
        item["snapshot_date"] = snapshot_date
    snapshot.extend(items)
    print(f"{city}: {len(items)} listings")

print(f"Total snapshot: {len(snapshot)} listings")

Run this every Sunday to build a weekly time series. Store each snapshot as a dated JSON file or push into a database.

Step 3: How do I compute price momentum by locality?

import pandas as pd
import json, glob

# Load all weekly snapshots
frames = []
for f in sorted(glob.glob("snapshots/squareyards-*.json")):
    week_data = json.loads(open(f).read())
    frames.extend(week_data)

df = pd.DataFrame(frames)
df["price_per_sqft"] = df["price"] / df["area"].replace(0, pd.NA)

# Filter outliers (below 500 or above 100K per sqft is likely data error)
df = df[(df["price_per_sqft"] > 500) & (df["price_per_sqft"] < 100000)]

# Weekly median price per sqft by locality
weekly = df.groupby(["snapshot_date", "city", "locality"]).agg(
    median_psf=("price_per_sqft", "median"),
    listing_count=("url", "nunique"),
).reset_index()

# 4-week price momentum
weekly = weekly.sort_values("snapshot_date")
momentum = weekly.groupby(["city", "locality"]).apply(
    lambda g: g.assign(
        psf_4w_ago=g["median_psf"].shift(4),
        momentum_pct=lambda x: (x["median_psf"] / x["psf_4w_ago"] - 1) * 100
    )
).reset_index(drop=True)

# Flag high-momentum localities (5%+ in 4 weeks with 20+ listings)
latest = momentum[momentum["snapshot_date"] == momentum["snapshot_date"].max()]
signals = latest[(latest["momentum_pct"].abs() > 5) & (latest["listing_count"] >= 20)]
print(signals[["city", "locality", "median_psf", "momentum_pct", "listing_count"]]
      .sort_values("momentum_pct", ascending=False))

A locality showing 5%+ price-per-sqft growth over 4 weeks with 20+ active listings is a statistically meaningful appreciation signal.

Step 4: How do I measure inventory turnover?

# Compare consecutive snapshots for listing appearance/disappearance
snapshot_dates = sorted(df["snapshot_date"].unique())

if len(snapshot_dates) >= 2:
    prev = set(df[df["snapshot_date"] == snapshot_dates[-2]]["url"])
    curr = set(df[df["snapshot_date"] == snapshot_dates[-1]]["url"])

    new_listings = curr - prev
    removed_listings = prev - curr
    persisted = curr & prev

    turnover_rate = len(removed_listings) / max(len(prev), 1)

    print(f"Previous week: {len(prev)} listings")
    print(f"Current week: {len(curr)} listings")
    print(f"New: {len(new_listings)}, Removed: {len(removed_listings)}")
    print(f"Turnover rate: {turnover_rate:.1%}")

    # Per-locality turnover
    for city in TARGET_CITIES:
        city_prev = set(df[(df["snapshot_date"] == snapshot_dates[-2]) &
                           (df["city"] == city)]["url"])
        city_curr = set(df[(df["snapshot_date"] == snapshot_dates[-1]) &
                           (df["city"] == city)]["url"])
        city_removed = city_prev - city_curr
        city_turnover = len(city_removed) / max(len(city_prev), 1)
        print(f"  {city}: {city_turnover:.1%} turnover")

High turnover (listings disappearing quickly) indicates strong absorption -- units are selling. Low turnover with rising inventory indicates oversupply. Combined with price momentum, these two signals triangulate market health.

Step 5: How do I track developer activity as a leading indicator?

# New developers or projects appearing per locality
prev_projects = set(
    df[df["snapshot_date"] == snapshot_dates[-2]]
    .apply(lambda r: f"{r['city']}|{r['locality']}|{r['project_name']}", axis=1)
)
curr_projects = set(
    df[df["snapshot_date"] == snapshot_dates[-1]]
    .apply(lambda r: f"{r['city']}|{r['locality']}|{r['project_name']}", axis=1)
)

new_projects = curr_projects - prev_projects
for p in sorted(new_projects):
    city, locality, project = p.split("|")
    dev = df[(df["project_name"] == project) & (df["city"] == city)]["developer_name"].iloc[0]
    print(f"New project: {project} by {dev} in {locality}, {city}")

Established developers (Prestige, Brigade, Godrej, Lodha) entering a previously mid-tier locality is one of the strongest leading indicators of price appreciation. Track new project_name entries from these developers across snapshots.

Sample output

Two records from a Gurgaon investment monitoring snapshot:

[
  {
    "title": "4 BHK Apartment in DLF The Camellias",
    "property_for": "sale",
    "property_type": "Apartment",
    "bedrooms": 4,
    "bathrooms": 5,
    "area": 5800,
    "floor": "18th of 42",
    "locality": "Golf Course Road",
    "city": "Gurgaon",
    "latitude": 28.4440,
    "longitude": 77.1000,
    "developer_name": "DLF Limited",
    "project_name": "DLF The Camellias",
    "price": 180000000,
    "listed_by": "Developer",
    "furnishing": "Furnished"
  },
  {
    "title": "3 BHK Apartment in Sobha City",
    "property_for": "sale",
    "property_type": "Apartment",
    "bedrooms": 3,
    "bathrooms": 3,
    "area": 1850,
    "floor": "6th of 22",
    "locality": "Sector 108",
    "city": "Gurgaon",
    "latitude": 28.4100,
    "longitude": 76.9450,
    "developer_name": "Sobha Limited",
    "project_name": "Sobha City",
    "price": 21500000,
    "listed_by": "Developer",
    "furnishing": "Semi-Furnished"
  }
]

The developer_name, project_name, and locality fields power the developer activity tracking signal. The price and area fields power the price momentum signal. Together they feed the complete investment signal pipeline.

Common pitfalls

Three things derail investment signal pipelines. Listing mix bias -- if Square Yards features more luxury listings one week and more affordable listings the next, median price-per-sqft will swing without any real market movement. Always segment by (property_type, bedrooms) before computing price aggregates. New construction skew -- localities with active new launches will show a surge of high-priced listings from a single developer, inflating the median. Filter by listed_by (Developer vs Broker vs Owner) to separate primary market from resale. Snapshot timing drift -- if your weekly snapshot runs on different days or at different times, you introduce day-of-week effects. Pin snapshots to the same day and approximate time each week.

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