We extract classified ads, seller profiles, pricing signals, and verified contact details from Jiji.ng. 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 Vehicles & Cars objects from jiji.ng. All fields typed and schema-versioned.
"ad_id": "3A9X2B1", "title": "Toyota Camry 2018 Silver", "price": 8500000, "condition": "Foreign Used", "make": "Toyota", "model": "Camry", "year_of_manufacture": 2018, "location": "Lagos, Ikeja"
| # | ad_id | title | price | condition | make | model |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Real Estate objects from jiji.ng. All fields typed and schema-versioned.
"ad_id": "9B4Y1C3", "title": "4 Bedroom Duplex in Lekki", "price": 120000000, "property_type": "House", "bedrooms": 4, "bathrooms": 5, "location": "Lagos, Lekki"
| # | ad_id | title | price | property_type | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Electronics objects from jiji.ng. All fields typed and schema-versioned.
"ad_id": "7C2Z8D4", "title": "Apple iPhone 13 Pro Max", "price": 650000, "brand": "Apple", "model": "iPhone 13 Pro Max", "condition": "Used", "promoted": true
| # | ad_id | title | price | brand | model | condition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from jiji.ng. All fields typed and schema-versioned.
"seller_id": "S_94821", "name": "AutoWorld NG", "joined_date": "2021-03-14", "verification_status": "Verified", "active_ads_count": 42, "response_time": "Within hours", "phone_number": "+2348012345678"
| # | seller_id | name | joined_date | verification_status | active_ads_count | last_seen |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from jiji.ng. All fields typed and schema-versioned.
"keyword": "generator", "category": "Home, Furniture & Appliances", "region": "Abuja", "position": 1, "ad_id": "5E1W9F2", "promoted_badge": true, "price": 150000
| # | keyword | category | region | position | ad_id | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Jiji.ng scraper navigates category hierarchies, handles JavaScript-rendered contact details, and bypasses aggressive rate limits to deliver structured market intelligence.
Automated execution of the 'Show Contact' JavaScript flow to extract verified seller phone numbers across thousands of listings.
Extract deep category-specific attributes like mileage, transmission, bedrooms, and square footage directly from the listing DOM.
Track seller verification status, join dates, active ad counts, and average response times to score lead quality.
Filter and extract data by specific Nigerian states and cities, maintaining geographical accuracy for local market analysis.
Identify and track VIP/Premium listings to understand competitor marketing spend and category saturation.
Capture high-resolution image URLs for every listing, useful for condition verification and machine learning models.
Monitor price drops, status changes, and sold listings over time using hash-based diffing on historical records.
Navigate deep category pagination walls without triggering rate limits, ensuring maximum coverage of active inventory.
Extract entire macro-categories (e.g., all mobile phones in Lagos) to build complete market pricing models.
Brief in. Clean data out.
Provide target categories, search keywords, or specific regions. We map the required fields and extraction logic.
We configure Scrapy crawlers with Playwright for JS execution, routing traffic through African residential proxy pools.
Schema validation, null-rate checks, and phone number format verification before full production launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Classifieds platforms rely on masking and rate-limiting to protect their inventory. Here is how our infrastructure maintains constant access.
Jiji hides seller phone numbers behind a JavaScript interaction to prevent basic scraping. We deploy Playwright sessions to render the DOM, simulate the click event, and extract the unmasked number without triggering bot protections.
Datacenter IPs are quickly flagged by Jiji's security layer. We route requests through high-quality African residential proxies, rotating IPs per request and managing cookie sessions to mimic genuine local user behaviour.
User-generated classifieds are notoriously messy. Our extraction pipelines use regex and NLP heuristics to normalise unstructured descriptions into clean categorical data, ensuring consistent schema delivery.
Standard pagination often cuts off after a certain depth. We use advanced search filters, price bracketing, and date-range queries to chunk large categories, ensuring we extract the entire inventory.
Marketplaces update their front-end frequently. We employ multi-layer fallback selectors (CSS, XPath, and JSON-LD where available) to ensure pipeline stability when Jiji alters its layout.
Car dealerships and valuation platforms track average listing prices for specific makes, models, and years across Nigeria.
Sales teams extract verified phone numbers and seller profiles from specific business categories to build targeted outreach lists.
Proptech companies aggregate property listings to map rental yields and sales prices per square meter across Lagos and Abuja.
Large retailers monitor the electronics and appliance categories to understand grey-market pricing and inventory volume.
Financial institutions cross-reference seller profiles, join dates, and phone numbers with internal databases to flag suspicious entities.
Analysts use classifieds volume and pricing trends as high-frequency indicators of consumer spending and inflation.
"Jiji.ng contains the ground truth of the Nigerian informal and classifieds economy. Querying it systematically requires solving JavaScript masking and aggressive rate limits at scale."
Extracting clean data from user-generated classifieds is an infrastructure challenge. Masked phone numbers, unstructured text, and strict bot mitigation require a dedicated extraction stack. DataFlirt manages the proxies, the renderers, and the parsing logic, delivering structured market intelligence directly to your warehouse.
Everything supported by our jiji.ng 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 category traversal, while Playwright is selectively invoked for DOM interactions like phone number unmasking.
We utilize specific residential proxy pools optimized for African domains, minimizing block rates and ensuring consistent access to regional inventory.
Pipelines run on Kubernetes clusters with Airflow managing scheduling, retry logic, and SLA alerting. State and deduplication are handled via Redis.
Data delivered to where your team already works — no new tooling required.
About jiji.ng scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available classifieds data is generally permissible for non-personal, analytical use cases. DataFlirt extracts only public listings and seller profiles. We do not bypass authentication walls to access private messages. Clients must review Jiji's ToS and consult legal counsel regarding their specific data usage.
We use headless browsers (Playwright) to load the listing, execute the necessary JavaScript to trigger the 'Show Contact' button, and extract the resulting unmasked number. This process is rate-limited and routed through residential proxies to mimic human interaction.
Yes. We can configure the pipeline to extract data exclusively from specific states (e.g., Lagos, Abuja, Rivers) or even specific neighborhoods, reducing extraction volume and focusing on your target market.
Pipelines can be configured to run daily or intra-day. For high-velocity categories like mobile phones or cars in Lagos, we can configure sub-category sweeps every few hours to capture new listings.
While users write arbitrary text, we extract the structured metadata fields Jiji enforces (make, model, year, condition). For the raw description text, we deliver it as a clean string field, allowing you to run your own NLP or regex parsing downstream.
We typically engage for continuous daily or weekly extraction pipelines targeting specific macro-categories (e.g., all Vehicles or Real Estate). Contact us with your target categories and frequency for a precise infrastructure quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily real estate pricing updates or a continuous feed of automotive leads — we scope, build, and operate the extraction infrastructure. Tell us your requirements.
