Platform · MagicBricks

MagicBricks Data Scraping

Verified badges are a claim the platform makes, not a fact we observed. We record the claim, and say whose it is.

MagicBricks data scraping collects Indian property listings, asking prices, status and attribution. The handling worth being explicit about: the platform displays verification and trust badges, and those are claims made by the platform rather than facts we independently established. We record them as displayed, attributed, and never as a quality field.

Trust signals are the most quietly misused field in property data. A badge in a boolean column stops looking like somebody's assertion within about one join.

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magicbricks.jsonl LIVE FEED

{"portal":"magicbricks","listing_id":"mb-44120", "transaction_type":"rent", "platform_claim_verified":true, "claim_source":"magicbricks", "asking_price":38000,"deposit_months":3, "area_basis":"carpet"} {"is_verified":"FIELD DOES NOT EXIST", "note":"named deliberately. the field says WHOSE claim it is, every read"} {"property_cluster_id":"pc-0412","cluster_confidence":0.71, "listing_count":4,"estimated_property_count":1, "broker_contact":"not_collected"}

3 of 1,884,220 listing rows · Indiabadges are CLAIMS · there is no is_verified field · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to MagicBricks or its owners. MagicBricks 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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MagicBricks at a glance

How we handle MagicBricks specifically

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

Portal: MagicBricks — India
The point: Verification badges are platform claims
Not: Independently established facts
So: Recorded as claims, with the claimant
Never: A quality or trust field
Area basis: Carpet versus super built-up. Both recorded, never converted
Duplicates: Brokers listing the same property. Clustered, not merged
Refresh: Daily; status changes are the events worth catching

Platform specifics

Claims, and why they stay claims

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

A badge is an assertion, and the field has to say so

The platform displays verification and trust indicators on some listings. They mean whatever the platform's process means — which is not published in a form anyone can reproduce.

The failure mode is specific and common:

A badge becomes a boolean.
The boolean gets used as a quality filter.
Two joins later nobody remembers it was a platform's assertion rather than a check.
And an analysis excludes unverified listings on the strength of a process it cannot describe.

So we record platform_claim_verified with claim_source naming the platform, and there is no field called is_verified. The naming is deliberate — the field says whose claim it is every time anyone reads it.

This is the observed-versus-derived distinction applied to a non-numeric field, and it matters for the same reason: a claim in a data column looks like a measurement.

What we will not do

Filter, rank or score listings on a badge, or supply a quality score derived from one. Where a client wants to weight by it, they can — on an assumption they can state.

Broker duplicates, area basis, and Indian conventions

Duplicate listings

The same property is frequently listed by several brokers, with different photographs and sometimes different prices. Clustered, never merged — property_cluster_id with confidence and basis, and both listing_count and estimated_property_count reported.

Same argument as our Fotocasa page, in a market where it bites harder because broker listing is more widespread.

Area basis

Carpet area and super built-up area, both recorded with area_basis, never converted between them — as on our 99acres page. Price per square foot only where the basis is stated.

Rent and sale

Both run on this portal and behave differently. transaction_type separates them, and rental listings carry deposit_months where published, since Indian rental deposits are substantial and are not part of the rent.

What we do not collect

A quality score derived from platform badges.
Broker or owner individuals. Agency is a commercial entity; the person is not.
Contact numbers, even where a listing displays them.
Enquirer, buyer or tenant data. Never.

Scope

What we collect on MagicBricks, 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

platform_claim_verified with claim_source naming the platform
No field called is_verified, deliberately
No filtering, ranking or scoring on a badge
property_cluster_id with confidence, listings never merged
listing_count and estimated_property_count both reported
area_basis on every area figure, never converted between bases
transaction_type separating rent from sale
deposit_months on rental listings where published
Status transitions as timestamped events

❌ What we do not, and why

A verification badge recorded as an is_verified boolean
A quality score derived from platform trust signals
Broker duplicates merged into one property record
An area converted between carpet and super built-up
Broker individuals, contact numbers or enquirer data

Core MagicBricks fields

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

Field	What it is on this platform
portal / listing_id / city / locality	The listing and where it is
platform_claim_verified / claim_source	A claim, with whose it is
property_cluster_id / cluster_confidence	A cluster, not a fact
listing_count / estimated_property_count	Both. The estimate is labelled
transaction_type	rent or sale. They behave differently
asking_price / price_is_asking	A marketing figure, flagged
deposit_months	On rentals, where published
area_value / area_basis	Carpet, super built-up, or unstated
price_per_sqft / psf_basis	Only where the basis is stated
agency_name	A commercial entity. Not individuals
status / status_changed_at	Events, not a state

Use cases

What teams do with MagicBricks data

Listing quality analysis without inherited judgement

Platform claims recorded as claims with the claimant named, so an analysis weighting by them does so on an assumption it can state rather than one it inherited.

Indian supply counting

Listings clustered rather than merged with both counts reported, in a market where broker duplication is widespread.

Rent and sale separated

Transaction type as a dimension with rental deposits recorded, since Indian deposits are substantial and are not part of the rent.

Area-basis-correct comparison

Price per square foot only where the basis is stated, since carpet and super built-up produce very different figures.

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

Send us a MagicBricks 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.

MagicBricks is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. MagicBricks 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 MagicBricks-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

MagicBricks data scraping: frequently asked questions

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

Because it is a claim the platform makes, and a boolean called is_verified stops looking like somebody's assertion within about one join.

Two joins later an analysis is excluding unverified listings on the strength of a process it cannot describe. Our field is named platform_claim_verified with a claim source, deliberately.

You can, on the field as delivered. We will not do it for you, and we do not supply a quality score derived from badges.

Where you want to weight by it, that is an assumption you can state — which is different from inheriting one we embedded.

Substantial. The same property is frequently listed by several brokers with different photographs and sometimes different prices.

We cluster with confidence and report both listing count and estimated property count, and never merge — the price difference between brokers is a finding.

Because the ratio varies by property and is not published. A conversion would be an estimate in a measurement field, and a price per square foot computed on it would be undetectably wrong.

No, even where a listing displays them. The agency is a commercial entity; the person and their number are not something we collect anywhere.

We quote individually on cities, listing volume and refresh. Clustering adds cost beyond extraction and is what makes the property count meaningful.

One scoping call, a free pilot within 24 hours including cluster confidence, then a fixed monthly quote. Request a quote.

See real MagicBricks 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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