Despegar Reviews Scraper [Only $1.5/1K💰] | Hotels | Scores

Despegar Reviews Scraper

Extract complete hotel review sets from any Despegar country site — Mexico, Argentina, Colombia, Chile, Peru, the US and more — with reviewer, traveller type, month, score and separated liked/disliked comments, plus six category scores and Despegar's AI review summary. No browser, no API keys: plain HTTP against the same endpoints Despegar's pages serve to anonymous visitors, at $0.0015 per review — the cheapest per-review price verified on the Apify Store.

Built for hospitality marketers, hotel owners, OTAs benchmarking competitors, travel market researchers and voice-of-customer analysts.

Main Use Cases

• Hotel reputation monitoring — track score, category scores and review sentiment per property over time with scheduled runs
• Guest reviews dataset — build a structured review database of Latin American hotels for analysis or model training
• Hotel competitor benchmarking — compare aggregate and category scores (service, staff, location, internet, cleaning, price/quality) across a competitive set
• Voice-of-customer analysis — positive and negative comments arrive already separated, so sentiment/feedback models don't have to infer polarity from one blob of text
• Hospitality market research — review volume, country of origin, traveller type and language mix per market
• Travel review data for OTAs — enrich partner feeds or power review widgets with structured guest feedback

How It Works — No Browser, No API Keys

The actor sends plain HTTP requests to the same review endpoints Despegar's own pages call when an anonymous visitor opens a hotel. Each hotel page URL yields its review stream plus a summary record — no browser, no cookies, no login. A 625-review hotel costs about 13 HTTP requests and finishes in seconds — roughly 100× cheaper than a headless-browser approach.

The one trick that matters: complete coverage. Despegar's own unfiltered review query silently omits roughly a third of a hotel's reviews. The actor therefore runs every traveller-type query (all, couple, family, friends, singles) and merges the results — turning a 399-review hotel into 625, a 97-review hotel into 201. No other store actor does this merge.

Residential proxies are required and pinned to the market. Despegar returns HTTP 403 to every datacenter IP. The actor defaults to RESIDENTIAL proxy, derives the proxy country from each start URL's domain (AR for despegar.com.ar, MX for despegar.com.mx, …) and pins it for the whole run — a request that reaches a market through a foreign exit IP gets rejected. Split mixed-country inputs into one run per country, or set proxyCountry yourself.

Build a Latin American Hotel Review Database Without a Reviews API

Despegar offers no public reviews API. The three viable routes for guest review data in LatAm are: (1) this actor's HTTP approach — cheapest and fastest; (2) browser-based scraping — 100× the compute; (3) manual collection — not scalable. The merge behaviour above is what makes the dataset complete rather than a sample: the unfiltered query alone is missing roughly one review in three, and per-review pricing means the merge costs the same per review as an incomplete run.

The Output tab splits the run into two datasets — reviews (one row per guest review, de-duplicated by Despegar review id) and hotels (one row per hotel with aggregate + category scores and the AI summary) — so absence of a review list never hides the reputation summary, and vice versa.

Input

Field	Type	Required	Default	Notes
startUrls	array	Yes	—	Despegar hotel page URLs; the country domain picks language/country (mix .com.ar, .com.mx, us.despegar.com, … freely)
maxHotels	integer	No	20	Maximum hotels to scrape; extra URLs dropped once reached
maxReviewsPerHotel	integer	No	0	Cap per hotel; 0 = every review the endpoint returns. Small caps bias the sample (see FAQ)
cutoffDate	string	No	""	Keep only reviews from this month onwards, YYYY-MM. Despegar publishes month granularity only
locale	string	No	""	Force the request locale, e.g. es-MX, pt-BR. Empty = derive from each URL's domain (recommended)
travelerType	enum	No	all	all = run every traveller-type query and merge (complete coverage). Single type = cheaper, targeted segment
reviewPageSize	integer	No	100	Reviews per request; the endpoint caps at 100
includeHotelSummary	boolean	No	true	Emit one hotelSummary item per hotel (score, six category scores, review count, AI summary)
fetchHotelPages	boolean	No	true	Fetch each hotel page to capture the hotel name. false saves one request per hotel, hotelName stays empty
maxRequestsPerCrawl	integer	No	0	Safety cap on total requests; 0 = no limit
maxConcurrency	integer	No	10	Parallel requests, 1–50
proxyConfiguration	object	No	RESIDENTIAL	Required in the cloud — datacenter IPs get 403
proxyCountry	string	No	from URL	Pin the proxy to one ISO country (AR, MX, …); empty derives it per hotel

Output — Guest Review and Hotel Summary Records

reviews dataset (one row per review):

Field	Description
hotelName, hotelId, hotelUrl	The hotel the review belongs to (name empty when fetchHotelPages: false)
reviewerName, reviewerCountry, reviewerCountryCode	Reviewer identity — a first name on ~70% of reviews, a country on ~60%; null otherwise
travelerType, travelerTypeLabel	Stable type (couple, family, friends, singles, …) + localized label
language, locale	Language of the text and the locale used for the request
date, dateMonth, dateIso	Review date: raw value (Marzo 2024), sortable YYYY-MM, and first day of month for charting
rating, ratingLabel	0–10 score, numeric and as displayed
positiveComment, negativeComment	What the guest liked / disliked, already separated
usefulCount	How many readers found it helpful
provider, providerLabel	DESPEGAR / guest-verified label
scrapedAt	ISO timestamp of the run

hotels dataset (one row per hotel): hotelName, hotelUrl, score, generalRating, reviewCount, categories (six category scores), generatedReview (Despegar's AI summary).

Example Input

{
    "startUrls": [
        { "url": "https://www.us.despegar.com/hotels/h-352691/bluebay-grand-esmeralda-all-inclusive-riviera+maya" },
        { "url": "https://www.despegar.com.ar/hotels/h-2867398/nickelodeon-hotels-resorts-riviera-maya-by-karisma" }
    ],
    "maxHotels": 2,
    "cutoffDate": "2025-01",
    "travelerType": "all"
}

startUrls expects objects with a url field (what the Console form writes for you). Mix country domains in one input — each URL decides its own language and market.

Example Output — Review Record (trimmed)

{
    "type": "review",
    "hotelId": "2867398",
    "hotelName": "Nickelodeon Hotels & Resorts Riviera Maya , By Karisma",
    "hotelUrl": "https://www.despegar.com.ar/hotels/h-2867398/...",
    "locale": "es-AR",
    "reviewerName": "Ana",
    "reviewerCountry": "Indonesia",
    "travelerType": "family",
    "language": "es",
    "dateMonth": "2024-03",
    "rating": 7.1,
    "positiveComment": "Las instalaciones son increíbles, los juegos para los niños son muy buenos.",
    "negativeComment": "Es un hotel \"todo incluido\" horrible, la comida es malísima.",
    "usefulCount": 18,
    "scrapedAt": "2026-09-26T18:24:02.546Z"
}

Hotel Review Monitoring at $0.0015 Per Review

One price per review, one price per hotel summary: the actor bills via Apify's pay-per-event model — $0.0015 per review, $0.00009 per hotel summary, plus Apify's standard $0.00005 actor start. No subscriptions, no per-GB charges, no browser minutes. travelerType: all (the default) costs the same per review as an unfiltered run — completeness is free. The cheapest competitor verified on the Store charges $0.00433 per review, so a 1,000-review monitoring batch costs $1.50 here vs ~$4.33 there. Free Apify accounts see a preview; paid plans run to your caps.

Integrations & Automation

• API — run and fetch via the Apify REST API, or call it from AI agents through the Apify MCP server
• Scheduling — recurring runs keep a hotel watchlist fresh; compare dateMonth across runs for month-over-month tracking
• Webhooks — push new reviews or score changes to your app or dashboard
• Export — JSON, CSV, Excel, XML, HTML

Recommended schedule: monthly for long-term reputation trend lines (review volumes are month-granular); weekly during peak seasons or after promotional pushes.

Related Actors

• Despegar Reviews Scraper — knagymate/despegar-reviews-scraper — $0.002 start + $0.00433/review; Despegar + Decolar reviews
• Despegar & Decolar Reviews Scraper — AI Summary & Filters — korado_labs/despegar-reviews-scraper — review extraction with AI summary
• Despegar Reviews Scraper — haketa/despegar-reviews-scraper — Despegar reviews
• Hotel Reputation Checker — kestrel/hotel-reputation-checker — free multi-source hotel reputation data

This actor differentiates on price per review and on complete review sets: the traveller-type merge recovers the ~1/3 of reviews that unfiltered queries omit, at the same per-review price.

FAQ

Why use this actor instead of the official Despegar API?

Despegar does not offer a public reviews API. The actor reads the same review endpoints Despegar's own hotel pages serve to anonymous visitors — the only machine-readable surface that exists. No OAuth, no partner program, no login.

What are alternatives to this actor for hotel review data?

On the Store: knagymate/despegar-reviews-scraper ($0.00433/review), korado_labs/despegar-reviews-scraper, haketa/despegar-reviews-scraper, and free kestrel/hotel-reputation-checker. Off-Store: browser-based scrapers (100× the compute). Ours is the only one that merges traveller-type queries into a complete set.

How do I get every review for a hotel?

Keep travelerType at all (default). Switching to general cuts coverage by roughly a third for the same hotel. The merge runs one query per traveller type and de-duplicates by Despegar review id.

Why is my run returning nothing / HTTP 403?

Despegar rejects all datacenter IPs — including Apify's own servers and Apify Datacenter proxies — with 403. The default proxyConfiguration (Apify Proxy, RESIDENTIAL group) is required. Also check that the proxy country matches the market: a random-country pool can get a despegar.com.ar hotel rejected while the same hotel succeeds pinned to AR.

Why does my small, capped run return only one traveller type?

maxReviewsPerHotel stops the crawl as soon as the budget is met, and the traveller-type queries run concurrently — so a small cap is filled by whichever stream answers first. For a representative mix leave it at 0, or set travelerType to one type on purpose.

Can I scrape reviews by date or only new ones?

Set cutoffDate to a YYYY-MM month. Reviews are filtered as they stream in, so the run fetches only the pages it needs and stays cheap.

Does it work for Brazil?

No — www.despegar.com.br fails DNS resolution (confirmed locally and from Apify cloud infrastructure). Use a different country domain for Brazilian properties.

How much does the hotel summary cost?

One hotelSummary item per hotel at $0.00009 — roughly 16× cheaper than a single review row. It carries the aggregate score, six category scores, review count and Despegar's AI summary.

For AI Agents & LLM Apps

• Purpose: given Despegar hotel page URLs, returns one row per guest review (score, month, traveller type, separated liked/disliked comments) plus one summary row per hotel with category scores — complete sets via traveller-type merge.
• Minimal working input:

{ "startUrls": [{ "url": "https://www.us.despegar.com/hotels/h-352691/bluebay-grand-esmeralda-all-inclusive-riviera+maya" }], "maxReviewsPerHotel": 0, "travelerType": "all" }

• Variant inputs — date window: add "cutoffDate": "2025-01"; targeted audience: "travelerType": "family"; multi-market: add "url" entries with different country domains; skip hotel pages: "fetchHotelPages": false.
• Output fields: hotelName, hotelId, hotelUrl, reviewerName, reviewerCountry, reviewerCountryCode, travelerType, travelerTypeLabel, language, locale, date, dateMonth, dateIso, rating, ratingLabel, positiveComment, negativeComment, usefulCount, provider, providerLabel, scrapedAt (reviews dataset) and hotelName, hotelUrl, score, generalRating, reviewCount, categories, generatedReview (hotels dataset).
• Behaviors an agent should know: travelerType: "all" (default) is required for a complete review set — the unfiltered query alone omits ~1/3 of reviews; a single-type value is deliberately cheaper but partial. cutoffDate takes YYYY-MM only (Despegar has month granularity — never pass a day). startUrls must be objects with a url key, not bare strings. fetchHotelPages: false leaves hotelName empty but saves one request per hotel. A small maxReviewsPerHotel biases the sample to whichever traveller-type stream answers first — leave it 0 for a representative mix. Reviews are not sorted regardless of any order parameter. Residential proxy is mandatory in the cloud: keep apifyProxyGroups: ["RESIDENTIAL"]; for a single country pin proxyCountry to the ISO code. Output splits across two datasets: reviews and default/hotels.
• Billing: pay-per-event — $0.0015 per review + $0.00009 per hotel summary + standard $0.00005 actor start; residential proxy traffic is billed separately by Apify only when used.

Legal & Compliance Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Despegar.com or Despegar Group. It accesses only review data Despegar's own website serves to anonymous visitors — it does not log in, bypass access controls, solve CAPTCHAs or use any private API. Review records include personal data published by Despegar (reviewer first name, country): treat it accordingly — do not use it for profiling or unsolicited outreach, and check whether GDPR, CCPA or local equivalents apply before you store or redistribute it. Users are responsible for their own compliance with Despegar's Terms of Service, robots.txt and applicable law. Data is intended for legitimate purposes such as reputation monitoring, market research and competitive analysis.

SEO Keywords

despegar scraper, despegar hotel reviews, hotel review scraping, hotel reputation monitoring, guest reviews dataset, travel review data, hotel rating analysis, voice of customer hotels, latam hotel reviews, despegar reviews api alternative, hotel category scores, hospitality market research, hotel competitor benchmarking, reseñas de hoteles despegar, hotel feedback analysis, review monitoring dashboard, hotel guest satisfaction data, mexico hotel reviews, argentina hotel reviews, colombia hotel reviews