We extract cost of living indices, rent prices, crime statistics, and quality of life metrics across thousands of cities on Numbeo. Delivered as clean JSON, CSV, or Parquet to your warehouse.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Cost of Living objects from numbeo.com. All fields typed and schema-versioned.
"city": "London", "country": "United Kingdom", "currency": "GBP", "meal_inexpensive": 20.0, "milk_1l": 1.25, "eggs_12": 3.4, "data_contributors": 1428, "last_update_date": "2026-05-10"
| # | city | country | currency | meal_inexpensive | meal_midrange | milk_1l |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Rent & Utilities objects from numbeo.com. All fields typed and schema-versioned.
"city": "Berlin", "country": "Germany", "rent_1bed_centre": 1250.0, "rent_1bed_outside": 900.0, "basic_utilities_85m2": 285.4, "internet_60mbps": 42.5, "currency": "EUR", "price_range_min": 1000.0
| # | city | country | rent_1bed_centre | rent_1bed_outside | rent_3bed_centre | rent_3bed_outside |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Prices objects from numbeo.com. All fields typed and schema-versioned.
"city": "Singapore", "country": "Singapore", "price_sqm_centre": 28500.0, "price_sqm_outside": 14200.0, "average_monthly_salary": 6100.0, "mortgage_interest_rate": 4.2, "price_to_income_ratio": 18.5, "currency": "SGD"
| # | city | country | price_sqm_centre | price_sqm_outside | average_monthly_salary | mortgage_interest_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Crime & Safety objects from numbeo.com. All fields typed and schema-versioned.
"city": "Tokyo", "country": "Japan", "crime_index": 24.3, "safety_index": 75.7, "level_of_crime": "Low", "safe_walking_night": "Very High", "worried_mugged": "Very Low", "corruption_bribery": "Low"
| # | city | country | crime_index | safety_index | level_of_crime | crime_increasing |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Quality of Life objects from numbeo.com. All fields typed and schema-versioned.
"city": "Zurich", "country": "Switzerland", "qol_index": 198.4, "purchasing_power_index": 118.2, "healthcare_index": 74.3, "climate_index": 81.2, "cost_of_living_index": 128.5, "pollution_index": 18.9
| # | city | country | qol_index | purchasing_power_index | healthcare_index | climate_index |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Numbeo scraper parses complex HTML tables, normalises crowd-sourced data, handles currency conversions, and tracks historical index shifts without triggering rate limits.
Extract data across 11,000+ cities globally. We maintain a master index of valid city URLs to ensure comprehensive coverage without missing secondary municipalities.
Numbeo defaults to local currencies. We capture the base local currency and can apply real-time exchange rates to normalise datasets into USD, EUR, or GBP.
Extract historical index data from archive pages to build time-series models for inflation, rent increases, and purchasing power degradation.
Capture the exact price ranges (min, max, mean) for 50+ individual items per city, from a litre of milk to monthly fitness club fees.
Scrape qualitative indices for healthcare system satisfaction, air quality, water pollution, and green space accessibility.
Extract commute time indices, CO2 emission estimates, and traffic inefficiency scores for urban mobility analysis.
Capture the number of contributors and the last update timestamp for every city metric to filter out low-confidence, stale data points.
Extract the baseline reference indices (e.g., New York = 100) and the underlying formulas used to generate the aggregate scores.
Run pipelines monthly or quarterly to capture fresh crowd-sourced submissions and track macroeconomic shifts over time.
Brief in. Clean data out.
Provide a list of target cities, countries, or regions. We configure the extraction schema for the specific indices required.
We deploy Scrapy crawlers with proxy rotation to navigate Numbeo's geographic hierarchies and parse unstructured HTML tables.
We run schema validation, check for null rates in low-contribution cities, and verify currency normalisation accuracy.
Clean JSON, CSV, or Parquet files pushed to your S3 bucket, BigQuery dataset, or API webhook on your defined schedule.
Extracting data from Numbeo requires parsing heavily nested tables, managing crowd-sourced data inconsistencies, and respecting rate limits. Here is how we build resilience.
Numbeo presents data in complex, variable-length HTML tables. Our parsers map table rows to structured schema fields dynamically, ensuring that missing items in a specific city do not misalign the entire dataset.
Because Numbeo is crowd-sourced, smaller cities often have statistically insignificant data. We capture the 'contributors' count and can filter out records below your defined confidence threshold.
Prices are displayed in local currency by default. We extract the raw local value, the currency code, and apply consistent FX conversion logic to provide unified pricing across global datasets.
Numbeo employs rate limiting for aggressive scraping. We distribute requests across rotating proxy pools and introduce randomised delays to maintain continuous extraction without IP bans.
Numbeo occasionally adds new items to their cost of living basket. Our pipeline detects unexpected table rows and alerts our engineers to map new variables into the schema.
HR platforms and remote-first companies use cost of living indices to calculate geographic pay bands and localise salaries.
Global mobility firms build cost-comparison calculators for expats moving between major financial centres.
Economists and hedge funds track real-time crowd-sourced inflation indicators ahead of official government CPI releases.
Property funds analyse price-to-income ratios and gross rental yields across secondary cities to identify undervalued markets.
Travel aggregators display local restaurant and transport costs to help users budget for international trips.
Logistics companies evaluate traffic inefficiency and infrastructure quality indices when planning regional distribution hubs.
"Numbeo holds the most granular, hyper-local cost of living data available globally, but extracting it consistently requires handling thousands of unstructured HTML tables and crowd-sourced anomalies."
Building a reliable pipeline for Numbeo means normalising fragmented crowd-sourced data, standardising local currencies to base rates, and handling constant DOM shifts. DataFlirt manages the extraction layer so your data science team can focus on econometric modelling rather than writing table parsers.
Everything supported by our numbeo.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 orchestrates high-throughput extraction across Numbeo's geographic directory structure, handling retries and proxy rotation automatically.
Custom Python middleware cleans crowd-sourced text inputs, strips currency symbols, and casts price ranges into typed numeric fields.
Airflow schedules monthly extraction runs, validates data completeness against historical baselines, and pushes Parquet files directly to S3 or BigQuery.
Data delivered to where your team already works — no new tooling required.
About numbeo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available, factual data (like prices and indices) is generally permissible. DataFlirt extracts only public aggregated statistics. We do not bypass authentication or extract personal data. Clients should review Numbeo's Terms of Service regarding commercial use of their aggregated data.
Numbeo relies on user contributions, so smaller cities often lack complete data. Our pipeline emits null values for missing fields rather than breaking, and we extract the 'contributors' count so you can filter out statistically insignificant records.
Yes. While Numbeo displays local currency, we extract the base value and currency code. We can apply standard exchange rates during the pipeline run to normalise all outputs to USD, EUR, or any target currency.
Because the data is crowd-sourced and aggregated over time, daily scraping yields minimal changes. We recommend monthly or quarterly pipeline runs to capture meaningful shifts in cost of living indices and property prices.
Yes. We can target Numbeo's historical archive pages to extract past indices, allowing you to build time-series models comparing current costs to previous years.
Yes. We provide a sample extraction of up to 50 cities during the scoping phase so you can validate the schema, null rates, and currency formatting before committing to a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of global property yields or a quarterly feed of cost of living indices across 10,000 cities — we build and operate the pipeline.
