We extract e-auction listings, SARFAESI notices, property details, and bank branch data from Foreclosureindia. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Auction Listings objects from foreclosureindia.com. All fields typed and schema-versioned.
"auction_id": "FI-849201", "bank_name": "State Bank of India", "branch_name": "Koramangala", "borrower_name": "Ramesh Kumar", "property_type": "Residential Flat", "city": "Bengaluru", "state": "Karnataka", "reserve_price": 4500000.0, "emd_amount": 450000.0
| # | auction_id | bank_name | branch_name | borrower_name | property_type | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dates & Deadlines objects from foreclosureindia.com. All fields typed and schema-versioned.
"auction_id": "FI-849201", "auction_date": "2024-11-15", "auction_time": "11:00 AM to 01:00 PM", "emd_submission_date": "2024-11-14", "inspection_date": "2024-11-10", "possession_type": "Symbolic", "notice_type": "E-Auction"
| # | auction_id | auction_date | auction_time | emd_submission_date | inspection_date | tender_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Details objects from foreclosureindia.com. All fields typed and schema-versioned.
"auction_id": "FI-849201", "property_category": "Residential", "area_size": 1200.0, "area_unit": "Sq.ft", "address": "Flat 402, Tower B, Green View Apartments", "pincode": "560034", "ownership_type": "Freehold"
| # | auction_id | property_category | area_size | area_unit | address | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bank & Branch Data objects from foreclosureindia.com. All fields typed and schema-versioned.
"bank_id": "SBI-00192", "bank_name": "State Bank of India", "branch_name": "Koramangala", "nodal_officer": "Suresh Reddy", "contact_number": "+91-9876543210", "email": "sbi.0432@sbi.co.in", "ifsc_code": "SBIN0000432"
| # | bank_id | bank_name | branch_name | nodal_officer | contact_number | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Legal & Notice Info objects from foreclosureindia.com. All fields typed and schema-versioned.
"auction_id": "FI-849201", "notice_date": "2024-10-10", "act_name": "SARFAESI Act 2002", "authorized_officer": "Chief Manager", "publication_name": "Times of India", "document_url": "https://foreclosureindia.com/docs/12345.pdf", "status": "Active"
| # | auction_id | notice_date | act_name | guarantor_name | authorized_officer | publication_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Foreclosureindia aggregates thousands of bank notices daily. Our pipeline standardises varying formats, cleans address strings, and aligns dates into predictable data models.
Extract and normalise property types across residential, commercial, industrial, and agricultural categories.
Capture Reserve Price, Earnest Money Deposit (EMD), and Tender Document fees as strict numerical types.
Standardise auction dates, EMD submission deadlines, and property inspection windows into ISO 8601 formats.
Identify the specific lending institution, branch address, and nodal officer handling the recovery.
Track SARFAESI Act invocations, borrower names, guarantor details, and possession status (Physical vs Symbolic).
Clean and structure raw address strings into distinct city, state, and pincode fields.
Harvest direct URLs to original auction notices, newspaper clippings, and tender documents.
Monitor listings for cancellations, postponements, or successful auction completions.
Detect net-new notices and modifications to existing listings via daily delta extractions.
Brief in. Clean data out.
Specify target states, property types, or specific banks. We configure the extraction schema to match your requirements.
We deploy Scrapy spiders with proxy rotation and custom parsing logic to handle Foreclosureindia's specific DOM structures.
Automated checks ensure numerical consistency between Reserve Price and EMD, and validate date formatting.
Clean, structured records pushed to your preferred endpoint via S3, BigQuery, Snowflake, or Webhook.
Bank auction data is notoriously inconsistent. We build parsing engines that handle the variations so your database stays clean.
Different banks upload notices using varying formats. We apply regex patterns and NLP tokenizers to extract clean numerical values from text-heavy strings like 'Rs. 45.50 Lakhs'.
Auction dates appear in formats ranging from '15-Nov-2024' to '15/11/24'. Our pipeline standardises all temporal data into UTC ISO 8601 timestamps for reliable database querying.
We iterate through every state, city, and bank category, handling stateful pagination and session tokens to ensure complete coverage of the active database.
Auctions are frequently delayed or cancelled. We hash listing content and emit delta updates when a previously scraped auction changes its deadline or reserve price.
We route requests through Indian residential proxies to distribute load and prevent IP blocking during full-site historical extractions.
Identify high-value commercial and industrial NPAs at steep discounts to market value.
Enrich real estate portals with bank auction inventory to provide complete market visibility.
Monitor specific pin codes for residential bank auctions matching target yield profiles.
Track NPA volumes across different banks and states to gauge regional economic stress.
Cross-reference borrower names and property details against existing litigation databases.
Source discounted inventory to present to high-net-worth clients seeking value acquisitions.
"Foreclosureindia aggregates the nation's distressed assets, but extracting structured, queryable data from fragmented bank notices requires dedicated infrastructure."
Bank auction portals suffer from inconsistent data entry, varying date formats, and unstructured address fields. DataFlirt normalises this chaos into predictable schemas, handling proxy rotation and stateful extraction so your analysts can evaluate assets immediately.
Everything supported by our foreclosureindia.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles high-throughput crawling of directory pages, while Playwright resolves dynamic content and complex search forms.
We route traffic through distributed Indian residential IPs to maintain high success rates without triggering security challenges.
Airflow schedules daily extractions, pushing data through AWS Lambda processing nodes for immediate normalisation and delivery.
Data delivered to where your team already works — no new tooling required.
About foreclosureindia.com scraping, legality, and pipeline operations.
Ask us directly →Yes. The data listed on Foreclosureindia consists of public notices mandated by the SARFAESI Act. DataFlirt extracts this publicly available information without bypassing authentication walls or extracting proprietary premium content.
We run daily extraction pipelines. New notices and updates to existing listings are typically processed and delivered within 24 hours of appearing on the site.
Yes. We can configure the pipeline to target specific geographic regions, property types, or lending institutions based on your investment criteria.
Bank notices often omit data. Our schema enforces strict typing for present fields and returns explicit nulls for missing data, ensuring your downstream ingestion processes do not break.
The base pipeline extracts the direct URLs to the PDF documents. We can configure custom download and OCR pipelines to extract text from these PDFs for an additional infrastructure cost.
Yes. We convert text representations like 'Rs. 1.5 Crores' into standard integer values (15000000) for immediate database querying.
We build managed pipelines starting with daily updates for defined geographic regions or bank sets. Contact us for a precise quote based on your required data volume.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually checking bank portals. We deliver structured, normalised auction data directly to your warehouse. Tell us your target criteria.
