We extract furniture listings, variant finishes, pricing signals, freight estimates, and dimensional specs from Homesquare. 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 Product Listings objects from homesquare.com. All fields typed and schema-versioned.
"sku": "HSQ-847291", "product_name": "Mid-Century Modern Tufted Sofa", "brand": "Modway", "category_tree": "Living Room > Sofas & Couches", "sale_price": 899.0, "currency": "USD", "in_stock": true, "primary_image_url": "https://cdn.homesquare.com/images/HSQ-847291-main.jpg"
| # | sku | product_name | brand | category_tree | base_price | sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dimensions & Specs objects from homesquare.com. All fields typed and schema-versioned.
"sku": "HSQ-847291", "overall_height_inches": 34.5, "overall_width_inches": 84.0, "overall_depth_inches": 33.0, "weight_lbs": 112.5, "material": "Velvet", "frame_material": "Solid Wood", "assembly_required": true
| # | sku | overall_height_inches | overall_width_inches | overall_depth_inches | weight_lbs | weight_capacity_lbs |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Shipping objects from homesquare.com. All fields typed and schema-versioned.
"sku": "HSQ-847291", "retail_price": 1200.0, "sale_price": 899.0, "discount_pct": 25, "shipping_method": "LTL Freight", "white_glove_available": true, "estimated_lead_time_days": 14, "return_policy_days": 30
| # | sku | retail_price | sale_price | discount_pct | shipping_method | freight_class |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from homesquare.com. All fields typed and schema-versioned.
"review_id": "REV-992831", "sku": "HSQ-847291", "star_rating": 4.5, "reviewer_name": "Sarah J.", "review_date": "2025-11-12", "review_title": "Beautiful but firm", "helpful_votes": 12, "verified_buyer": true
| # | review_id | sku | star_rating | reviewer_name | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Variants & Options objects from homesquare.com. All fields typed and schema-versioned.
"parent_sku": "HSQ-847291", "variant_sku": "HSQ-847291-BLU", "option_type_1": "Fabric Colour", "option_value_1": "Navy Blue", "option_type_2": "Leg Finish", "option_value_2": "Walnut", "price_delta": 0.0, "stock_status": "In Stock"
| # | parent_sku | variant_sku | option_type_1 | option_value_1 | option_type_2 | option_value_2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Homesquare scraper handles complex furniture variants, dynamic freight calculations, and high-resolution imagery extraction — with JavaScript rendering and session management built in.
Extract title, description, brand, and deep category taxonomy across living room, bedroom, and outdoor collections.
Capture height, width, depth, weight, and box dimensions to feed your logistics and space-planning models.
Map complex parent-child relationships for wood finishes, fabric colours, and sizing options across all SKUs.
Track base price, sale price, and discount percentages to monitor Minimum Advertised Price (MAP) compliance.
Extract LTL freight flags, white-glove delivery availability, and estimated lead times based on product class.
Extract uncompressed base URLs for zoomable product images, swatches, and lifestyle room scenes.
Extract structured key-value pairs for frame materials, upholstery types, assembly requirements, and warranty data.
Monitor real-time inventory statuses, out-of-stock flags, and projected backorder restock dates.
Aggregate star ratings, textual feedback, and verified buyer flags for quality and sentiment analysis.
Run continuous pipelines that only emit records when prices, stock, or lead times change.
Brief in. Clean data out.
Provide target brands, categories, or SKU lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for homesquare.com.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Furniture eCommerce sites rely on heavy JS for variants and shipping logic. Here is how we maintain extraction stability.
Furniture listings often hide SKU-specific pricing and stock behind JavaScript-driven dropdowns. We use Playwright to systematically iterate through all finish and fabric combinations, capturing the exact price and stock state for every child variant.
Freight costs and delivery windows often require injecting target ZIP codes. Our pipeline can simulate location contexts to extract accurate shipping estimates and LTL freight classifications.
High-resolution imagery and swatches are heavily lazy-loaded. We intercept network requests during the crawl to capture the raw, uncompressed image URLs rather than relying on low-resolution thumbnails present in the initial DOM.
Homesquare aggregates thousands of brands, resulting in inconsistent specification tables. We use regex and NLP to normalise dimensions, materials, and assembly instructions into a unified schema across the entire catalogue.
Deep category crawls trigger rate limits. We distribute requests across a pool of US residential IPs, randomising request intervals and user-agent strings to maintain high throughput without blocks.
Furniture retailers track Homesquare pricing across shared brands (e.g., Ashley Furniture, Modway) to optimise their own pricing strategies.
Furniture manufacturers monitor Homesquare listings to ensure their products are not being sold below Minimum Advertised Price.
Design platforms ingest dimensional data, materials, and high-res imagery to populate their 3D room-planning software.
Analysts track backorder dates and estimated lead times to gauge supply chain health across different furniture categories.
Merchandising teams analyse category depth and brand representation to identify missing product lines in their own catalogues.
Private equity and market analysts track review volume and brand proliferation to evaluate trends in the home furnishings sector.
"Furniture eCommerce data is notoriously unstructured. Standardising dimensions, materials, and freight logic across thousands of brands requires purpose-built pipelines."
Extracting data from Homesquare means dealing with complex variant matrices, dynamic freight shipping calculators, and inconsistent brand-provided specification tables. DataFlirt normalises this chaos into clean, queryable schemas so your analysts can focus on pricing strategy and assortment planning rather than DOM parsing.
Everything supported by our homesquare.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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering for variant hydration and dynamic freight calculators.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent rate-limiting during deep category crawls.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About homesquare.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing, dimensions, and catalogue data is generally permissible. DataFlirt targets only public, non-authenticated listings. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for their specific commercial use cases.
We use Playwright to systematically iterate through all available dropdown options on the product page, capturing the unique SKU, price delta, and stock status for every specific combination of finish, fabric, or size.
Yes. We extract the stated shipping methods (e.g., LTL Freight, Ground) and estimated lead times. If required, we can configure the crawler to inject specific ZIP codes to capture dynamic delivery estimates.
Homesquare features thousands of brands with varying specification formats. We apply regex and parsing rules at the pipeline level to extract standard overall height, width, depth, and weight into consistent numeric fields.
We can run pipelines daily, weekly, or at custom intervals. For large catalogues, we recommend daily diff runs where we only deliver records for SKUs that have changed price or stock status since the previous day.
Our minimum engagements typically start with a defined list of target brands or categories (e.g., 10,000 SKUs) with weekly delivery. We scope pricing based on total SKU volume and extraction frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous MAP monitoring across thousands of furniture SKUs — we scope, build, and operate the pipeline. Tell us what you need.
