99acres Data Scraper for Structured Property Data

99acres Property Data Extraction Built Around Your Requirements

99acres is an Indian online real-estate marketplace within Info Edge’s real-estate business. Info Edge describes the platform as bringing together property listings and participants including developers, builders, brokers, buyers, and sellers.

For data teams, the challenge is not simply copying visible values from property pages. A useful dataset needs a defined entity model, consistent fields, normalized values, source references, timestamps, and rules for incomplete or repeated records.

NenoData’s broader real-estate workflow is designed around this type of source-specific requirement: the sources, markets, fields, update schedule, and destination are scoped before implementation, with source support and field availability confirmed rather than assumed.

A 99acres project can therefore begin with questions such as:

• Which cities or localities matter?
• Do you need sale listings, rentals, projects, resale properties, or a defined combination?
• Which fields are required for analysis?
• Should projects and individual property listings use separate record structures?
• How should lakh/crore prices and different area bases be represented?
• Is the requirement a one-time dataset or repeated observations?
• Which output format or destination will consume the data?

The answer to those questions defines the schema before broader collection is considered.

What Data May Be Available From 99acres?

Fields depend on the page type, what is publicly visible or otherwise authorized, and the final project scope.

Third-party technical research around 99acres shows that property pages and result structures can expose common real-estate attributes including listing identifiers, prices, configurations, location fields, area values, property characteristics, project context, amenities, and source URLs. These observations establish possible schema categories, not guaranteed NenoData coverage of every field or page.

This is an illustrative field map. It is not a promise that every field can be collected from every 99acres page.

Listings, Projects, and Configurations Are Different Data Entities

One of the most important schema decisions in Indian property-market data is determining what one row represents.

A portal can display an individual sale or rental listing alongside a developer project that contains multiple property configurations. Third-party 99acres research identifies this distinction as material to price, area, and supply analysis.

A practical schema may distinguish among:

Separating these concepts helps prevent analytical errors.

For example, assigning one headline project price to every possible configuration can erase differences between unit types. Similarly, combining a project-level row with individual resale listings without an entity-type field can make counts difficult to interpret.

NenoData’s existing multi-source and property-data services support custom schema definition and field mapping around agreed source structures.

The final 99acres schema should therefore be defined during scoping instead of forcing every visible property record into one generic row structure.

Normalize Indian Property Prices Without Losing Their Meaning

Indian real-estate listings frequently display prices using lakh and crore conventions. For analytics, those displayed values may need to be parsed into consistent numeric fields while the original context remains traceable.

These are normalization examples, not actual 99acres records.

The transformation rule should also preserve whether the source value is:

• an asking price
• a displayed range
• a project starting/from price
• a monthly rent
• a price-per-area value

Putting all of these into one undifferentiated price field makes downstream analysis less reliable.

NenoData’s current data-cleaning and aggregation services support defined normalization rules, field mapping, validation, and exception handling.

Keep Carpet, Built-Up, and Super-Built-Up Area Separate

Area is another field where aggressive normalization can remove important meaning.

A listing may identify an area as carpet area, built-up area, super-built-up area, or another displayed basis.

A safer normalized structure is conceptually:

This avoids pretending that two measurements with different bases are automatically interchangeable.

Conversions between different area bases should not be invented where the necessary conversion information is absent. If a comparable price-per-area metric is needed, the underlying area basis should remain available so analysts can understand what is being compared.

Standardize Property Configurations and Transaction Types

Property descriptions may express configurations and transaction types in several formats.

Where scoped, normalization rules can map consistently represented concepts such as:

• 1 BHK, 2 BHK, 3 BHK
• sale versus rent
• residential versus commercial
• apartment, villa, plot, office, or other source-stated property types
• ready-to-move or other displayed status labels

Normalization should standardize format without silently inventing missing classifications.

For example, a source-stated 3 BHK value can be preserved while also producing a numeric bedroom/configuration field if that rule is approved. An ambiguous description should instead be retained or flagged rather than forced into an unsupported category.

Handle Duplicate and Repeated Listings With Defined Rules

Duplicate handling in property data is not one problem.

1. 1

   Repeated collection of the same source listing.

   A stable source identifier can help connect observations across collection runs where the identifier remains available.

2. 2

   Exact duplicate records within one extraction.

   These can often be removed using deterministic rules.

3. 3

   Similar listings published by different brokers, owners, or sources.

   These may describe the same real-world property, but similarity does not automatically prove identity.

4. 4

   Project and resale records referring to related property inventory.

   These are different entity types and should not be merged simply because names or locations overlap.

NenoData’s current aggregation services describe exact-match and agreed business-rule deduplication, while its entity-resolution service keeps uncertain matches reviewable instead of silently merging ambiguous records.

For a 99acres requirement, duplicate rules can therefore be defined during scoping. The page should not promise perfect real-world property identity resolution.

Scope 99acres Data by Market and Property Requirement

A useful extraction requirement is usually narrower than “all 99acres data.”

Info Edge describes 99acres as an India-focused real-estate marketplace, but that does not establish that every location, listing type, or page is equally accessible for a NenoData engagement.

Final geographic and page coverage should be confirmed against representative target URLs during scoping.

One-Time 99acres Dataset or Recurring Property Monitoring

Different business questions need different collection patterns.

NenoData’s real-estate service supports one-time and scheduled property-data workflows, with frequency confirmed during scoping.

No fixed 99acres refresh frequency is promised by this page.

Delivery Built for the System That Will Use the Data

A scraped page is not necessarily a usable dataset.

The project should define both the data schema and the destination before production delivery.

• CSV for analysis or bulk transfer
• Excel for review-oriented business workflows
• JSON for structured engineering use
• API-ready output for downstream applications
• database or warehouse delivery where confirmed as part of a pipeline requirement
• scheduled files or recurring feeds where technically feasible

“API-ready” describes a delivery method. It does not mean NenoData is claiming access to an official public 99acres developer API.

How a Scoped 99acres Data Project Works

1. 1

   Scope the sources and schema

   Share representative 99acres URLs or search criteria, required locations, property types, fields, intended use, expected frequency, and preferred output. NenoData reviews the source requirement, access boundaries, page types, and expected schema before broader collection is proposed.

2. 2

   Extract agreed fields

   Where the source and collection method are approved and technically feasible, agreed publicly visible or otherwise authorized fields are collected from the scoped pages. Field availability is confirmed against actual source pages rather than assumed from a generic property template.

3. 3

   Normalize and validate

   Records can be mapped into the approved schema with rules for: price representation; area value and area basis; BHK/configuration; property and transaction types; null and missing fields; duplicate handling; source identifiers; timestamps; validation exceptions.

4. 4

   Deliver the approved output

   The resulting dataset or feed is delivered in the format and destination agreed for the engagement. Recurring delivery and workflow maintenance apply where they are explicitly included in scope.

Fully managed workflow handled by NenoData’s web scraping service, scoped before production delivery.

Access, Terms, and Data Boundaries

A 99acres data project should begin with source feasibility, not an assumption of unrestricted access.

For 99acres specifically:

• Public visibility does not automatically establish an unrestricted right to collect or commercially reuse information.
• Applicable source terms, rights, privacy obligations, and the customer’s intended use should be considered.
• Login-protected or private data should not be assumed available.
• Personal or contact information requires separate necessity, privacy, and authorization review.
• Field availability can vary by page type and can change as a platform changes.
• No universal access or India-wide completeness guarantee is made.
• This page does not claim an official public 99acres API.
• NenoData should not be represented as affiliated with or endorsed by 99acres or Info Edge unless a documented relationship exists.

The output should also be understood as structured information derived from the scoped source—not as a substitute for licensed MLS feeds, government property records, title records, completed transaction data, or other authoritative property sources where those are required.

What Teams Can Use Structured 99acres Data For

Why Use NenoData for a Scoped Property-Data Workflow?

NenoData is a managed data-extraction company that scopes agreed public sources, required fields, validation rules, and delivery destinations before collection. Its current real-estate offering specifically covers custom property-data extraction, source and field scoping, normalization, structured output, scheduled delivery, and custom pipelines subject to source feasibility.

For a 99acres requirement, that approach matters because the difficult questions are often data-design questions rather than simply page-fetching questions:

• What should one row represent?
• Which displayed prices are genuinely comparable?
• Which area basis belongs to each measurement?
• Should project configurations be nested or stored as separate records?
• What constitutes a duplicate?
• Which fields are required versus optional?
• How should missing or changed values be represented?
• Which delivery shape will the downstream system accept?

Defining those rules before scaling the collection gives the resulting dataset a clearer structure and reduces ambiguity downstream.

See also: Zillow Data Scraping · Trulia Scraper · Real Estate Data Scraping Services

Frequently Asked Questions