Platform · 99acres

99acres Data Scraping

Builder projects and individual resale listings sit side by side here. They are different products, and only one of them is a property.

99acres data scraping collects Indian property listings, asking prices, project data and attribution. The separation that has to happen first: builder project listings and individual resale listings are different products on one portal — a project represents many units with configuration-based pricing, a resale listing represents one flat.

Our India property portal page treats the market at a group level. This is the one where the project-versus-resale split does the most damage if it is skipped.

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99acres.jsonl LIVE FEED

{"portal":"99acres","listing_type":"project", "builder_name":"builder-a","total_units":480, "configuration":"2BHK","config_price":8900000, "config_area":1180,"area_basis":"super_built_up", "price_per_sqft":7542,"psf_basis":"super_built_up"} {"configuration":"3BHK","config_price":12400000, "note":"a project is a price LADDER, not one price"} {"listing_type":"resale","area_basis":"unstated", "price_per_sqft":"null", "caution":"basis unstated, so no psf. carpet vs super built-up differ enormously"}

3 of 2,204,880 listing + config rows · Indiaone row per CONFIGURATION · areas never converted · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to 99acres or its owners. 99acres 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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99acres at a glance

How we handle 99acres specifically

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

Portal: 99acres — India
The split: Builder projects versus individual resale
A project is: Many units, configuration-priced
A resale listing is: One flat
So: listing_type separates them before counting
Configurations: 1BHK, 2BHK and so on, each its own price
Carpet vs super built-up: Two area bases. Both recorded
Refresh: Daily; project phases and price revisions matter

Platform specifics

Projects, resale, and the area problem

These are the reasons a 99acres dataset needs its own handling rather than a shared retail schema.

A project listing prices by configuration, not as a property

Builder project listings carry a range of unit configurations — typically expressed as bedroom counts — each with its own price and area.

A project is not one price. It is a price per configuration.
Headline project prices are from-prices, representing the smallest configuration.
Configurations release in phases, so availability shifts.
Counting a project as one listing understates supply by the number of units it contains.

So listing_type separates project from resale, and on projects we deliver one record per configuration with configuration, config_price and config_area.

price_is_from flags project headline prices, and they are excluded from average asking-price figures by default.

Unit counts

total_units where published, null otherwise. Indian project listings frequently publish it, which makes supply counting better here than in markets where they do not.

Carpet area, super built-up area, and why both matter

Indian property is quoted on more than one area basis, and the difference between them is substantial.

Carpet area is the usable internal floor area.
Super built-up area includes a share of common areas and is materially larger.
Price per square foot differs enormously depending on which basis is used.
Listings do not always state which they are quoting.

So area_basis travels with every area figure, and where the listing does not state it the basis is unstated rather than assumed.

We do not convert between bases. The ratio varies by project and is not published, so a conversion would be an estimate in a measurement field — and price per square foot computed on a converted area would be wrong in a way nobody downstream could detect.

price_per_sqft is computed only where the basis is stated, and carries the basis with it.

What we do not collect

An area converted between bases.
Units sold or booked. Not published.
Builder financials or approvals status beyond what a listing displays.
Vendor, buyer, broker individual or enquirer data. Never.

Scope

What we collect on 99acres, 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

listing_type separating project from resale
One record per configuration on project listings
price_is_from flagged, excluded from averages by default
total_units where published, null otherwise
area_basis on every area figure, unstated where not stated
price_per_sqft computed only where the basis is stated
No conversion between carpet and super built-up
Status transitions as timestamped events
price_is_asking as a constant true

❌ What we do not, and why

Projects and resale listings pooled in a supply count
A project headline price included in an average
An area converted between carpet and super built-up
A price per square foot computed on an unstated basis
Units sold, builder financials or enquirer data

Core 99acres 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
listing_type	project or resale. Separated before counting
configuration / config_price / config_area	One record per configuration
total_units	Where published. Never estimated
asking_price / price_is_from / price_is_asking	The figure, and what kind
area_value / area_basis	Carpet, super built-up, or unstated
price_per_sqft / psf_basis	Only where the basis is stated
builder_name / agency_name	Commercial entities
possession_date_stated	As published. Not a guarantee
status / status_changed_at	Events, not a state
observed_at	Timestamp

Use cases

What teams do with 99acres data

Indian supply counting done correctly

Projects and resale separated with unit counts where published, since a project listing represents many units and pooling understates supply.

Configuration-level project pricing

One record per configuration, so a project is analysed as a price ladder rather than as a single from-price.

Area-basis-correct price comparison

Price per square foot only where the basis is stated, since carpet and super built-up produce enormously different figures and the ratio is not published.

Resale market series

A clean resale-only series with project from-prices excluded by default, which is the comparable figure over time.

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

Send us a 99acres 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.

99acres is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. 99acres 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 99acres-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

99acres data scraping: frequently asked questions

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

Because they are different products. A project represents many units with configuration-based pricing; a resale listing represents one flat.

Counting a project as one listing understates supply by the number of units it contains, and its headline price is a from-price for the smallest configuration.

Because the ratio varies by project and is not published. A conversion would be an estimate sitting in a measurement field.

And a price per square foot computed on a converted area would be wrong in a way nobody downstream could detect — which is the worst kind of wrong.

The basis is unstated rather than assumed, and we do not compute a price per square foot from it.

Assuming a basis would produce a figure that looks comparable and is not.

Where it is published, and Indian project listings frequently publish it — which makes supply counting better here than in markets where they do not.

Where it is not published the field is null rather than estimated.

It is captured as published. A stated possession date is a builder's projection rather than a commitment, and we record it as a stated value rather than as a fact.

We quote individually on cities, listing volume and refresh. Configuration-level project records multiply rows, so project coverage is worth scoping deliberately.

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

See real 99acres 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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