Platform · NoBroker

NoBroker Data Scraping

Listings from owners rather than agents. That changes duplication, price freshness and data quality all at once.

NoBroker data scraping collects Indian property listings, rents and asking prices from a platform built around owner-direct listings rather than agent mandates. That single structural difference changes three things at once: duplication drops sharply, price updates are slower, and attribute completeness is more variable — because the person writing the listing is not a professional.

Our Idealista page argues that deduplication is the product in agent-mandate markets. This is the mirror case, and it is worth understanding what changes when the agent is removed.

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nobroker.jsonl LIVE FEED

{"listing_id":"nb-44120","city":"Bengaluru", "locality":"Example locality", "listing_source":"owner_direct", "rent_amount":32000,"deposit_amount":200000, "price_type":"asking", "bhk":2,"furnishing":"semi", "last_price_change":"2026-03-02", "days_since_price_change":176, "caution":"unchanged for 176 days. looks like a live offer. owners do not reprice"} {"area_value":1150,"area_basis":"unstated", "area_basis_unstated_rate":0.412, "note":"carpet vs built-up vs super built-up. 41% have no stated basis"} {"owner_name":"not_collected", "owner_contact":"not_collected", "note":"no agency layer here. the listing IS an individual advertising their own home"}

3 of 604,880 listing rows · Indiaowner-direct · area basis unstated on 41.2% · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to NoBroker or its owners. NoBroker and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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NoBroker at a glance

How we handle NoBroker specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Portal: NoBroker — India, owner-direct model
The structural difference: Owners list, not agents
Effect one: Duplication drops sharply. One owner, one listing
Effect two: Price updates are slower. Owners do not reprice like agents
Effect three: Attribute completeness is variable — a non-professional wrote it
Comparison: Against agent portals, the same market looks different
Locality: Indian rental markets are locality-level, not city-level
Refresh: Daily. Listing lifecycle is longer than on agent portals

Platform specifics

What removing the agent changes

These are the reasons a NoBroker dataset needs its own handling rather than a shared retail schema.

Duplication drops, and that is the easy part

On agent-mandate portals the same property appears several times under different agencies, which is why our Idealista page argues that deduplication is the product.

Owner-direct listings mostly do not have that problem. One owner, one property, one listing.

But the consequence people miss is what it does to a comparison:

A supply count from an owner-direct portal and one from an agent portal are not the same measure, even before deduplication.
Comparing raw counts across the two produces a difference that is mostly about listing model.
Deduplicated counts are comparable — which is the argument for doing the work on the agent side rather than avoiding the comparison.

We still run clustering here, because owners do occasionally list on several portals and because a small duplicate rate is not a zero rate. cluster_confidence and cluster_basis travel as they do everywhere.

Price freshness and attribute quality both change

Owners do not reprice like agents

A letting agent adjusts an asking rent in response to viewing volume and market feedback. An owner frequently sets a number and leaves it.

A stale listing looks like a live offer.
Time-on-market runs longer, partly for that reason.
An index built on asking rents from owner-direct listings lags an agent-portal index in a moving market.

We record last_price_change and days_since_price_change so staleness is visible rather than invisible. A listing unchanged for months is a different signal from one repriced last week.

Attributes are written by non-professionals

Agent listings follow a template. Owner listings do not, so completeness varies and conventions are inconsistent — carpet area versus built-up area versus super built-up area is stated inconsistently or not at all.

area_basis is recorded where stated and flagged unstated where not, which on this portal is a larger share than on agent platforms. We report the unstated rate per batch, because a per-square-foot analysis needs to know how much of its input had no basis.

Locality-level, and what Indian rental data cannot tell you

Locality is the unit

Indian rental markets vary sharply within a city — by locality, by proximity to employment clusters, and by building age. A city-level rent figure describes no actual tenant.

locality is on every record and we design locality panels the same way we design pincode panels on our India coverage page, with the same stated representativeness.

What we do not produce

Transaction rents. These are asking rents. Actual agreed rents are not published.
Occupancy or vacancy rates. A listing disappearing can mean let, withdrawn or expired.
Owner identity, contact details or photographs. None of it, in any form.

The last point needs emphasis on this platform specifically. An owner-direct listing is an individual's property advertised by that individual. The commercial content — rent, attributes, locality, availability — is what we collect. The person is not, and on a platform with no agency layer that distinction matters more than it does elsewhere.

Scope

What we collect on NoBroker, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Owner-direct listings with locality on every record
Clustering still applied, since a low duplicate rate is not zero
last_price_change and days_since_price_change, so staleness is visible
area_basis recorded where stated, flagged unstated where not
Unstated area-basis rate reported per batch
Rent and deposit as separate fields
Furnishing state and amenities as published
price_type constant asking
Listing lifecycle, with disappearance reason unknown by default

❌ What we do not, and why

A raw listing count compared against an agent portal's raw count
A per-square-foot figure from an unstated area basis
An agreed rent, which is not published
Occupancy or vacancy inferred from listing disappearance
Owner names, contact details or photographs, in any form

Core NoBroker fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
listing_id / city / locality	The listing and the unit that matters
listing_source	owner_direct, and where the platform distinguishes otherwise
rent_amount / deposit_amount / price_type	Asking, with the type constant
last_price_change / days_since_price_change	So staleness is visible
area_value / area_basis / area_basis_unstated_rate	With the basis, or flagged, and the batch rate
bhk / furnishing / floor	As published
property_cluster_id / cluster_confidence	Low duplicate rate is not zero
amenities	As published
available_from	Where stated
first_seen / last_seen / disappearance_reason	Lifecycle, reason unknown by default
observed_at	Timestamp

Use cases

What teams do with NoBroker data

Owner-direct versus agent market comparison

Deduplicated counts on both sides, since raw counts across the two listing models differ for reasons that are mostly about the model rather than about supply.

Asking-rent staleness analysis

Days since price change recorded, so a listing unchanged for months is distinguishable from one repriced last week — which matters more here than on agent portals.

Locality-level rental benchmarking

Locality as the unit with panel representativeness stated, since Indian rents vary sharply within a city and a city figure describes no actual tenant.

Data-quality-aware per-square-foot analysis

Area basis flagged unstated with the rate reported, so an analysis knows how much of its input had no stated basis before it computes anything.

The 24-hour sample — run on your sources, not ours

Send us a NoBroker item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

NoBroker is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. NoBroker data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what real estate data covers, and a NoBroker-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

NoBroker data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Three things change at once. Duplication drops sharply, because one owner lists one property once. Price updates are slower, because owners do not reprice the way agents do. And attribute completeness is more variable, because a non-professional wrote the listing.

Each of those changes what an analysis can conclude.

Not raw counts. A supply figure from an owner-direct portal and one from an agent-mandate portal are not the same measure, and the difference is mostly about listing model.

Deduplicated counts are comparable — which is the argument for doing the clustering work on the agent side rather than avoiding the comparison.

Because a stale listing looks like a live offer. Owners frequently set a number and leave it, so an index built on owner-direct asking rents lags an agent-portal index in a moving market.

Recording it makes staleness visible rather than invisible.

Because agent listings follow a template and owner listings do not. Carpet area, built-up area and super built-up area are stated inconsistently or not at all.

We flag it unstated where it is, and report the unstated rate per batch — a per-square-foot analysis needs to know how much of its input had no basis before it computes anything.

No, in any form. This needs emphasis on this platform specifically: an owner-direct listing is an individual's property advertised by that individual.

We collect the commercial content — rent, attributes, locality, availability. The person is not collected, and on a platform with no agency layer that distinction matters more than it does elsewhere.

We quote individually on localities in scope, listing volume and refresh. Listing lifecycle is longer here than on agent portals, so daily is usually sufficient.

One scoping call, a free pilot within 24 hours including the area-basis unstated rate, then a fixed monthly quote. Request a quote.

See real NoBroker data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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