We extract lottery schemes, winner lists, waitlists, project specifications, and pricing from mhada.gov.in. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Lottery Schemes objects from mhada.gov.in. All fields typed and schema-versioned.
"scheme_code": "281", "board_name": "Mumbai Board", "scheme_name": "Pahari Goregaon", "income_group": "MIG", "total_tenements": 227, "carpet_area_sqm": 65.4, "base_cost": 6850000.0, "emd_amount": 50000.0
| # | scheme_code | board_name | scheme_name | income_group | total_tenements | carpet_area_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Winner Lists objects from mhada.gov.in. All fields typed and schema-versioned.
"applicant_number": "1180054321", "applicant_name": "Ramesh Kumar Patil", "scheme_code": "281", "category": "General", "priority_number": 14, "flat_number": "B-402", "status": "Winner", "draw_date": "2025-08-14"
| # | applicant_number | applicant_name | scheme_code | category | priority_number | flat_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Waitlists objects from mhada.gov.in. All fields typed and schema-versioned.
"applicant_number": "1180058992", "applicant_name": "Sneha Desai", "scheme_code": "281", "category": "SC", "waitlist_number": 3, "status": "Waitlisted", "draw_date": "2025-08-14", "board_name": "Mumbai Board"
| # | applicant_number | applicant_name | scheme_code | category | waitlist_number | status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Project Details objects from mhada.gov.in. All fields typed and schema-versioned.
"project_id": "PRJ-9921", "board": "Pune Board", "location": "Pimpri Chinchwad", "pincode": "411018", "total_flats": 1200, "available_flats": 450, "rera_number": "P52100004512", "completion_date": "2026-12-31"
| # | project_id | board | location | pincode | total_flats | available_flats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Board Announcements objects from mhada.gov.in. All fields typed and schema-versioned.
"circular_id": "CIR-2025-042", "board_name": "Konkan Board", "title": "Extension of EMD Payment Deadline", "date_published": "2025-07-10", "pdf_url": "https://mhada.gov.in/documents/cir-042.pdf", "category": "Notification", "language": "Marathi", "is_active": true
| # | circular_id | board_name | title | date_published | pdf_url | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our MHADA pipeline converts unstructured PDFs, nested ASP.NET tables, and bilingual announcements into clean, queryable databases. We handle the server timeouts so you do not have to.
MHADA publishes winner and waitlist results as unstructured PDFs. We use OCR and spatial parsing to convert these documents into structured tabular data.
Extract base costs, EMD amounts, carpet areas, and total tenements for every active and historical housing scheme across all regional boards.
Capture applicant names, application numbers, categories, and priority statuses from published accepted and rejected lists.
Track schemes across Mumbai, Pune, Konkan, Nashik, Aurangabad, Amravati, and Nagpur boards from a single normalised schema.
Categorise properties by Economically Weaker Section (EWS), Low Income Group (LIG), Middle Income Group (MIG), and High Income Group (HIG).
Extract MahaRERA registration numbers from project brochures to cross-reference with official state real estate regulatory data.
Access past lottery results and pricing data to build time-series models of affordable housing demand in Maharashtra.
Handle bilingual content gracefully. We map Marathi location names and categories to standardized English equivalents for easier querying.
Run daily diffs during active lottery periods to capture new circulars, deadline extensions, and updated applicant lists immediately.
Brief in. Clean data out.
Specify the regional boards, lottery years, or specific data points (e.g. winner lists vs project specs) you require.
We configure Scrapy crawlers, PDF extraction modules, and retry logic to handle mhada.gov.in server instability.
Schema validation, null-rate checks, and PDF parsing accuracy verification before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Government websites present unique extraction challenges. Here is how we ensure reliable data delivery from mhada.gov.in.
MHADA publishes lottery results as massive PDF files, often with misaligned columns or scanned pages. We utilize spatial OCR and machine learning models to reconstruct these PDFs into clean, relational tables mapping applicants to flat numbers.
During lottery announcements, mhada.gov.in frequently crashes or throttles connections. Our infrastructure employs exponential backoff, distributed request queuing, and low-concurrency polling to ensure we capture the data without overwhelming the target servers.
The portal relies heavily on complex ASP.NET ViewStates and session cookies for pagination and form submissions. We maintain persistent Playwright sessions that correctly pass these hidden tokens, preventing session drops mid-extraction.
Circulars and project details are often published in Marathi, English, or a mix of both. We capture the raw text and apply normalisation dictionaries to standardize locations, categories, and statuses across the dataset.
We maintain a hash index of all previously scraped circulars and lists. Subsequent runs only extract and deliver new documents, saving you from processing duplicate PDFs and reducing downstream compute costs.
Analysts track base costs and total applications across income groups to gauge localized demand for affordable housing in specific micro-markets.
Banks and NBFCs ingest winner lists to identify pre-qualified leads for home loans and mortgage products immediately after a lottery draw.
Urban development researchers analyze historical lottery data to study housing supply deficits and demographic shifts across Maharashtra.
Private real estate developers monitor MHADA project specifications and pricing to competitively position their own upcoming affordable housing projects.
Real estate brokers track waitlists and winners to facilitate secondary market transactions once lock-in periods expire.
Property listing websites integrate MHADA scheme details via our API to provide comprehensive new-launch coverage to their users.
"MHADA holds the definitive dataset for affordable housing demand in Maharashtra, but the data is locked in unstructured PDFs and slow government portals."
Extracting data from mhada.gov.in requires navigating unpredictable server uptimes, complex ASP.NET session states, and thousands of unstructured PDF result sheets. DataFlirt handles the OCR, table extraction, and retry logic so your team gets clean, queryable real estate data without the operational overhead.
Everything supported by our mhada.gov.in scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We integrate AWS Textract and custom vision models to parse highly irregular PDF tables, converting visual columns into structured JSON arrays.
Scrapy combined with Playwright handles the complex ASP.NET navigation, while custom middleware manages the frequent connection drops typical of government infrastructure.
Pipelines run on Kubernetes. Airflow orchestrates the sequential extraction, PDF downloading, and OCR processing steps, ensuring strict dependency management.
Data delivered to where your team already works — no new tooling required.
About mhada.gov.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information, such as published lottery results and scheme details, is generally permissible. DataFlirt targets only public, non-authenticated data. We do not bypass applicant login walls or extract private KYC documents. Clients should consult legal counsel regarding their specific data usage.
We download the raw PDFs and process them through our OCR and spatial parsing pipeline. This converts the visual tables into structured rows and columns, mapping applicant names to flat numbers and waitlist positions.
Live draws often cause severe server instability on mhada.gov.in. We queue requests and employ exponential backoff. While we extract data as fast as the server allows, absolute real-time delivery during peak crashes is limited by the source hardware.
Yes. Alongside the structured JSON or CSV data, we can deliver the raw source PDFs to your S3 bucket for compliance and verification purposes.
We extract data across all MHADA regional boards, including Mumbai, Pune, Konkan, Nashik, Nagpur, Amravati, and Aurangabad.
We capture the original Marathi text and apply a normalisation layer to map standard terms, locations, and categories to English, ensuring your database remains consistent and queryable.
Yes. We can run one-off historical extraction pipelines to pull past schemes, pricing, and result lists that are still accessible in the portal archives.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually downloading PDFs and fighting server timeouts. Let DataFlirt deliver clean, structured Maharashtra housing data directly to your warehouse.
