We extract furniture listings, pricing signals, material specifications, and stock availability from Hometown. 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 Furniture Listings objects from hometown.in. All fields typed and schema-versioned.
"sku": "HT-BED-0042", "title": "Winston Engineered Wood Queen Bed", "category": "Furniture", "price": 18499.0, "mrp": 25999.0, "discount_pct": 28
| # | sku | title | category | sub_category | price | mrp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from hometown.in. All fields typed and schema-versioned.
"sku": "HT-BED-0042", "current_price": 18499.0, "mrp": 25999.0, "discount_abs": 7500.0, "discount_pct": 28, "stock_status": "In Stock", "emi_starting_price": 894.0
| # | sku | current_price | mrp | discount_abs | discount_pct | bank_offers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Specifications objects from hometown.in. All fields typed and schema-versioned.
"sku": "HT-BED-0042", "primary_material": "Engineered Wood", "colour": "Walnut", "style": "Contemporary", "warranty_summary": "12 Months Manufacturer Warranty", "country_of_origin": "India"
| # | sku | primary_material | secondary_material | colour | style | warranty_summary |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pincode Delivery objects from hometown.in. All fields typed and schema-versioned.
"sku": "HT-BED-0042", "pincode": "560034", "delivery_available": true, "estimated_days": 5, "delivery_charge": 0.0, "cod_available": false
| # | sku | pincode | delivery_available | estimated_days | delivery_charge | installation_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Media & Assets objects from hometown.in. All fields typed and schema-versioned.
"sku": "HT-BED-0042", "primary_image_url": "https://hometown.in/images/ht-bed-0042-main.jpg", "gallery_urls": "['https://hometown.in/images/ht-bed-0042-side.jpg', 'https://hometown.in/images/ht-bed-0042-detail.jpg']", "360_view_available": false, "manual_pdf_url": "https://hometown.in/docs/ht-bed-0042-assembly.pdf", "swatch_urls": "[]"
| # | sku | primary_image_url | gallery_urls | video_url | 360_view_available | manual_pdf_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Hometown scraper captures all product metadata, pricing changes, and stock availability across categories. We manage the proxy rotation and session states required to extract accurate data at scale.
Extract dimensions, primary material, finish, weight, and assembly requirements for every piece of furniture.
Capture current price, MRP, absolute discount, percentage drop, and EMI starting prices.
Simulate delivery checks across multiple Indian pincodes to map stock availability and estimated delivery timelines.
Link parent products to child variants based on colour, fabric, or size selections.
Capture primary images, gallery URLs, lifestyle room shots, and assembly manual PDFs.
Map the full category tree from top-level departments down to specific sub-categories and filters.
Extract warranty summaries, care instructions, and brand details directly from the product description.
Track out-of-stock flags and low-stock warnings to monitor competitor inventory levels.
Receive only updated records. We hash previous runs and emit only rows where pricing or stock has changed.
Brief in. Clean data out.
Provide categories, search terms, or specific SKUs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and pincode simulation for hometown.in.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Furniture retail sites rely on heavy JavaScript for variant selection and pincode validation. We handle the technical overhead so you receive clean tables.
Stock and delivery times on Hometown depend on the user's pincode. We inject specific pincodes into the session state via Playwright to extract accurate availability data for your target regions.
Furniture often has multiple variants for wood finish or fabric colour. We map the JSON state behind the frontend to extract all variant combinations without executing unnecessary page loads.
Dimensions and materials are often buried in HTML tables or bullet points. Our parsers clean and normalise this data into strict typed columns like length_cm, width_cm, and primary_material.
We route requests through Indian residential proxies to prevent IP blocking during full catalogue crawls, ensuring consistent extraction without triggering firewall rules.
For daily price monitoring, we maintain a hash of the catalogue and only export records where pricing, discounts, or stock status have changed.
Furniture retailers monitor Hometown's pricing, discount strategies, and EMI offers to adjust their own pricing models.
D2C brands analyse category depth, material trends, and popular finishes to inform product development and sourcing.
Marketplaces compare Hometown's inventory against their own to identify missing brands, styles, or price segments.
Analysts track out-of-stock rates across different pincodes to map supply chain efficiency and regional demand.
Design platforms ingest furniture catalogues to populate 3D planning tools and client recommendation engines.
Economic researchers track price changes in consumer durables and furniture over time to measure retail inflation.
"Furniture retail requires precise dimensional and material data. Scraping Hometown means standardising unstructured text into queryable specifications."
Extracting furniture catalogues involves handling multi-dimensional variants, pincode-dependent stock states, and deeply nested taxonomy trees. DataFlirt manages the residential proxy rotation and JavaScript execution required to capture this data reliably, delivering clean schemas directly to your data warehouse.
Everything supported by our hometown.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.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering, cookie sessions, and pincode injection flows.
We maintain pools of residential ISP proxies across India. Rotation happens per-request to prevent IP blocks during large catalogue crawls.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About hometown.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Hometown is generally permissible. DataFlirt targets only public product, pricing, and specification data. We do not extract personal data or circumvent authentication walls.
Yes. We can simulate delivery availability and estimated timelines for a predefined list of Indian pincodes during the crawl.
We extract all available variants (e.g., different wood finishes or fabric colours) for a product and map them to the parent SKU, capturing price differences between variants.
Full catalogue refreshes typically run daily or weekly depending on your requirements. Delta runs targeting specific high-velocity categories can be configured for higher frequency.
Yes. We extract URLs for primary images, gallery shots, lifestyle images, and assembly manuals (PDFs). We deliver the URLs; you can download the assets directly.
Our smallest packages start at a defined category list with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can validate schema fit and field completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across categories, we build and operate the pipeline. Tell us what you need.
