How to Scrape Numbeo in 2026 (API & Python)

The fastest way to scrape Numbeo in 2026 is to call a structured API that returns normalized JSON — itemized cost-of-living prices and composite quality-of-life indices, per city or country — instead of parsing Numbeo's rendered pages yourself. Numbeo is the web's default source for city cost-of-living comparisons, and it does have an official API, but it's a paid commercial subscription rather than a quick free signup. This guide covers DIY, no-code, and a structured API, plus what Numbeo's terms actually allow.

Why scrape Numbeo?

Numbeo's crowdsourced price and index data anchors a specific slice of location research:

• Cost-of-living research — compare rent, groceries, restaurants, and utilities across cities for relocation planning or market analysis.
• Remote-work and digital-nomad tools — rank candidate cities by affordability, safety, and quality of life for a "where should I live" product.
• Real-estate affordability analysis — pair rent and property-price indices with local income data to score housing affordability by market.
• Salary and compensation research — adjust nominal salary offers by a city's cost-of-living index to compare real purchasing power across locations.
• Country- and region-level benchmarking — track how a country's cost-of-living or crime index compares to its neighbors, or how it moves period over period.

Is it legal to scrape Numbeo?

Option 1: DIY in Python (and why it breaks)

A DIY scraper requests a Numbeo city or index page and parses the rendered tables:

import requests
from bs4 import BeautifulSoup

resp = requests.get(
    "https://www.numbeo.com/cost-of-living/in/Los-Angeles",
    headers={"User-Agent": "Mozilla/5.0 (compatible; research-bot/1.0)"},
)
soup = BeautifulSoup(resp.text, "html.parser")
# Prices live in nested tables split across "Restaurants", "Markets",
# "Transportation", "Utilities", "Rent Per Month" sections — each needs
# its own row/column parsing logic

It demos and then breaks:

• The ToS explicitly restricts this. Numbeo's Terms of Use name automated scraping and crawling directly and require prior written permission — real risk, not a theoretical one.
• Nested, multi-section tables. Cost-of-living pages split prices across a dozen category tables (restaurants, markets, transportation, utilities, rent, and more); index pages split into their own titled sub-sections — each needs separate parsing logic that breaks on markup changes.
• No city discovery. Numbeo has no public city search or autocomplete — you have to already know (or scrape from a country/rankings page) the exact slug Numbeo expects.
• Index families multiply the surface. Quality of life, crime, health care, pollution, traffic, and property investment are each separate pages with different section layouts, so "get Numbeo data" is really six-plus different parsers.

Option 2: No-code tools

Marketplace scraper actors exist for one-off Numbeo pulls (cost-of-living tables exported to CSV), and they're a reasonable way to grab a snapshot for a spreadsheet. They don't hold up in a recurring pipeline — no scheduling, no normalized schema across cities, and the same Terms of Use exposure as DIY since Numbeo requires permission for automated collection regardless of the tool doing it.

Option 3: A structured Numbeo API

For a repeatable, permission-scoped workflow, a Numbeo scraping API returns normalized JSON with no page parsing to maintain. Pull a city's cost of living:

curl "https://api.crawlora.net/api/v1/numbeo/cost-of-living/city/Los-Angeles" \
  -H "x-api-key: $CRAWLORA_API_KEY"

{
  "code": 200,
  "msg": "OK",
  "data": {
    "city": "Los Angeles, CA",
    "city_slug": "Los-Angeles",
    "country": "United States",
    "categories": [
      {
        "category": "Restaurants",
        "items": [
          { "name": "Meal at an Inexpensive Restaurant", "currency_symbol": "$", "price": 25, "range_low": 16, "range_high": 45 }
        ]
      },
      {
        "category": "Rent Per Month",
        "items": [
          { "name": "1 Bedroom Apartment in City Centre", "currency_symbol": "$", "price": 2563.18, "range_low": 2000, "range_high": 3400 }
        ]
      }
    ],
    "contributors_12mo": 178,
    "last_update": "13 July 2026",
    "source_url": "https://www.numbeo.com/cost-of-living/in/Los-Angeles"
  }
}

Numbeo has no public city search endpoint, so discover a valid city_slug from the country endpoint first, then pull an index — quality of life, crime, health care, pollution, traffic, or property investment — for the same city:

import requests

h = {"x-api-key": "YOUR_API_KEY"}
base = "https://api.crawlora.net/api/v1/numbeo"

# Discover city slugs for a country
country = requests.get(f"{base}/cost-of-living/country", headers=h, params={"country": "United States"}).json()["data"]
slug = country["cities"][0]["city_slug"]

# Cost-of-living detail for that city
col = requests.get(f"{base}/cost-of-living/city/{slug}", headers=h).json()["data"]

# Crime index for that city
crime = requests.get(f"{base}/indices/city/{slug}", headers=h, params={"index": "crime"}).json()["data"]

The indices endpoints return a shared envelope regardless of index — headline values plus (for most families) titled sub-sections of individual metrics:

{
  "code": 200,
  "msg": "OK",
  "data": {
    "index": "crime",
    "city": "Los Angeles, CA",
    "city_slug": "Los-Angeles",
    "country": "United States",
    "headline_indices": [
      { "name": "Crime Index", "value": 53.83 },
      { "name": "Safety Index", "value": 46.17 }
    ],
    "sections": [
      {
        "title": "Crime rates in Los Angeles, CA, United States",
        "rows": [{ "name": "Level of crime", "value": 61.08, "qualifier": "High" }]
      }
    ],
    "contributors_note": "Contributors: 486",
    "last_update": "13 June 2026",
    "source_url": "https://www.numbeo.com/crime/in/Los-Angeles"
  }
}

Ranking endpoints (cost-of-living/rankings, cost-of-living/rankings-by-country, indices/rankings, indices/rankings-by-country) work the same way and accept scope/period on the current-scoped endpoints for historical snapshots where Numbeo publishes them. Store one row per city or country and re-run on a schedule.

What you can collect

Per-city and per-country cost-of-living data: itemized prices by category (restaurants, markets, transportation, utilities, rent, and more) with price, range_low, range_high, and currency symbol, plus the six headline cost indices (cost_of_living_index, rent_index, cost_of_living_plus_rent_index, groceries_index, restaurant_price_index, local_purchasing_power_index). Per-city and per-country index data for six families — quality of life, crime, health care, pollution, traffic, and property investment — as headline values plus titled sub-index breakdowns. Ranked city and country lists for both families, with contributors_12mo/contributors_note and last_update metadata on every response. Public data only.

Limitations and common challenges

• Crowdsourced, not audited. Numbeo's prices come from site visitors submitting what they pay — contributors_12mo and contributors_note tell you the sample size behind a number, and a city with a few hundred contributors (Los Angeles, Zurich) is far more reliable than one with a handful. Treat low-contributor cities as directional, not precise.
• No city search endpoint. There's no autocomplete — resolve a city_slug from a country or rankings response before requesting city detail.
• Six separate index families. Quality of life, crime, health care, pollution, traffic, and property investment each return a different response shape (some have sections, property-investment has price_categories instead) — plan your schema per family, not one generic shape.
• ToS-gated for scale. Numbeo's own terms require written permission for automated collection and a paid plan for commercial use — scope any project to what's genuinely public reference use, and go through Numbeo's own API or license for heavy commercial redistribution.
• Public data only. This collects what Numbeo already displays publicly — never a way to build a competing cost-of-living database at Numbeo's scale.

Where this gets used

• Relocation and remote-work tools — rank cities by affordability and quality of life for a location-decision product.
• Compensation research — adjust salary figures by a city's cost-of-living index to compare offers across markets.
• Real-estate affordability models — pair rent and property indices with income data to score housing markets.
• Market and country research — benchmark a country's cost, safety, and health-care indices against peers.

Sources

Start collecting

Try it first, free: run any public URL through the Free Web Scraper, or check whether a site blocks bots with the Anti-Bot Checker — no signup.

Test the cost-of-living and indices endpoints in the Playground, check the schema in the API docs, and review pricing. Cost of living tells you what things cost; Yahoo Finance tells you what people earn from markets and salaries in that same economy — pair the two for real purchasing-power research. If housing affordability is the angle, how to scrape real estate listings pairs directly with Numbeo's rent and property indices for a full cost-of-living-plus-housing picture.

Part of our how-to-scrape guide series — every platform we cover, in one index.