Despegar Hotel Reviews Scraper - Pros, Cons & Scores

Despegar Hotel Reviews Scraper — Ratings, Pros & Cons, and Category Scores 🏨⭐

Extract real guest hotel reviews from Latin America's #1 travel platform (Despegar, known as Decolar in Brazil). This high-performance Despegar scraper pulls authenticated guest ratings with positive and negative comments neatly divided into distinct pros and cons fields, verified stay flags, traveler type segments, 0–10 satisfaction scores, and full property rating summaries across six category metrics (location, cleanliness, staff, service, Wi‑Fi, value). No login required, no rate limit blocks, and 1,000+ deep pagination unlocked.

Export structured hospitality datasets directly to JSON, CSV, Excel, or consume via the Apify REST API.

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⚡ At a glance

Feature	Description
🎯 Target Platform	Despegar.com / Decolar.com hotel booking networks
🌎 Covered Markets	Argentina (.com.ar), Mexico (.com.mx), Colombia (.com.co), Chile (.cl), Peru (.com.pe), Uruguay (.com.uy), Ecuador (.com.ec)
🚀 Data Yield	Verified guest reviews, structured pros & cons, traveler categories, 6 property scores, AI review summaries
🔄 Pagination Depth	Complete review stream cursor pagination (unlocked for 1,000+ reviews per hotel)
⚡ Performance	Pure HTTP extraction with fast prefill execution (< 60s for standard property batches)
💬 Sentiment Ready	Guest comments pre-separated into positive highlights (pros) and critique points (cons)
📦 Export Formats	Clean JSON, CSV, TSV, XML, Excel (XLSX), or automated webhook streams
💼 Primary Use Cases	Hospitality reputation management, competitor benchmarking, travel sentiment analysis, OTA aggregation

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🔎 Why scrape Despegar?

Despegar (and its Brazilian brand Decolar) dominates online travel agency bookings across Latin America, hosting millions of verified hotel reviews across popular resort hubs and business centers in Buenos Aires, Cancun, Mexico City, Bogota, Santiago, Lima, Montevideo, and Quito.

Unlike generic star-rating directories, Despegar prompts reviewers with a structured two-part inquiry:

1. What did you like most? (Stored as clean pros).
2. What could be improved? (Stored as clean cons).

Along with granular 0–10 numeric scores, traveler segmentation (couple, family, solo, business, friends), and multi-dimensional property category grades, Despegar provides unparalleled operational intelligence. Collecting this intelligence manually, however, is notoriously difficult due to dynamic cursor pagination, regional domain splits, and complex challenge screens.

This Despegar reviews extractor automates the entire ingestion pipeline, outputting standardized, deduplicated hotel review rows enriched with hotel-level metadata ready for business intelligence tools, LLM fine-tuning, or reputation dashboards.

💼 Primary business applications

• 🏨 Hotel reputation & operational audit: Identify recurring guest grievances regarding cleanliness, staff friendliness, or Wi-Fi reliability before they harm bookings.
• 📊 Competitive hospitality benchmarking: Compare regional competitors side-by-side on pricing, location scores, and guest sentiment.
• 💬 Sentiment analysis & NLP pipelines: Ingest pre-split pros and cons directly into LLM summarizers and customer voice analyzers without manual text preprocessing.
• 🧳 OTA & travel meta-search enrichment: Enrich travel aggregators and travel-tech platforms with verified Latin American traveler perspectives.
• 🤖 Machine learning training datasets: Train hospitality classification models using labeled traveler categories, verified badges, and structured sentiment points.
• 📈 Market research & consumer insights: Analyze seasonal traveler expectations across different Latin American demographics (e.g., family vacationers vs. business corporate travelers).

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✨ Key features

• 🌐 Comprehensive multi-market support: Access 7 distinct country portals (Argentina, Mexico, Colombia, Chile, Peru, Uruguay, Ecuador) with automatic domain and locale alignment.
• ✂️ Structured pros & cons extraction: Separates positive praises and critical complaints into distinct fields for immediate NLP processing.
• 📊 Granular category scoring: Extracts individual hotel ratings across 6 key metrics: Location, Cleanliness, Staff, Service, Wi-Fi, and Value for Money.
• 🏷️ Traveler categorization: Captures trip type metadata (couple, family, businessTrip, friends, singles) alongside localized descriptions.
• 🤖 AI review summary inclusion: Captures Despegar's synthesized AI property overview detailing consensus guest impressions.
• 🔄 Deep 1,000+ review pagination: Automatically paginates through historical guest reviews with offset cursor tracking and zero artificial barriers.
• 🛡️ Built-in proxy negotiation: Routes queries through country-aligned residential proxy sessions for uninterrupted data harvesting.
• ⚡ Lightweight & fast: Pure HTTP architecture delivers rapid execution with negligible resource consumption.

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🌎 Market selector & domain reference

Despegar operates dedicated portals for major Latin American nations. When configuring your runs, align the market setting with your target region:

Market Code	Country	Primary Portal Domain	Default Locale	Currency Context
AR	Argentina	www.despegar.com.ar	es_AR	Argentine Peso (ARS) / USD
MX	Mexico	www.despegar.com.mx	es_MX	Mexican Peso (MXN) / USD
CO	Colombia	www.despegar.com.co	es_CO	Colombian Peso (COP) / USD
CL	Chile	www.despegar.cl	es_CL	Chilean Peso (CLP) / USD
PE	Peru	www.despegar.com.pe	es_PE	Peruvian Sol (PEN) / USD
UY	Uruguay	www.despegar.com.uy	es_UY	Uruguayan Peso (UYU) / USD
EC	Ecuador	www.despegar.com.ec	es_EC	US Dollar (USD)

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🎯 In-depth use cases & industry workflows

1. Multi-property hotel chain management

Regional hotel general managers and revenue directors utilize this Despegar scraper to monitor customer satisfaction across multiple properties. By running scheduled weekly extractions:

• Detect maintenance anomalies: Filter cons for keywords like "aire acondicionado", "ruido", or "ducha" to trigger rapid maintenance work orders.
• Track service turnaround: Measure whether staff score improvements follow hospitality training seminars.
• Verify amenity impact: Evaluate guest feedback before and after investing in Wi-Fi bandwidth upgrades.

2. Competitive market intelligence for travel agencies

Online travel agencies (OTAs) and tour operators leverage competitor review datasets to negotiate preferred hotel partnerships:

• Identify high-satisfaction boutique hotels in top tourist destinations like Ushuaia, Bariloche, or Cartagena.
• Cross-reference verified guest scores with pricing models to uncover underrated gems with strong margin potential.
• Benchmark value-for-money perceptions across competing 4-star and 5-star properties.

3. Academic & demographic tourism research

Hospitality institutes and tourism boards analyze travel behavioral patterns across Latin America:

• Compare satisfaction ratings between international tourists and domestic vacationers.
• Understand how traveling with children impacts hotel facility ratings and service expectations.
• Measure review volume fluctuations across peak summer, winter, and holiday seasons.

4. Brand sentiment analysis & LLM fine-tuning

Data science and NLP teams build customer experience models using fine-grained review feeds:

• Train sentiment classifiers with pre-labeled positive and negative text pairs (pros vs cons).
• Generate dynamic property comparison summaries for consumer-facing travel applications.
• Track Net Promoter Score (NPS) proxies across geographic regions.

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📥 Input parameters

The scraper accepts direct Despegar hotel URLs or numeric IDs. All fields are optional with intelligent defaults.

Field	Type	Description	Default / Prefill
startUrls	array	Despegar hotel page URLs (e.g., https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba). Works with any supported country portal.	Prefilled with sample hotel
hotelIds	array	Optional numeric hotel IDs (the digits after h- in the URL, e.g. 1051991).	[]
market	string	Despegar country portal (AR, MX, CO, CL, PE, UY, EC). Sets domain, language, and proxy geolocation.	"AR"
maxReviewsPerHotel	integer	Maximum reviews to collect per hotel. Set to 0 for complete, unlimited review history (supports 1,000+ reviews).	0 (prefill: 50)
includeHotelSummary	boolean	Set true to include overall score, star rating, address, AI summary, and the 6 category scores on each review record.	true
reviewsLanguage	string	Language code for review text (es Spanish, pt Portuguese, en English).	"es"
proxyConfiguration	object	Proxy settings. Residential proxy is recommended and automatically geolocated to the target market.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

Example input configuration

{
  "startUrls": [
    "https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba"
  ],
  "market": "AR",
  "maxReviewsPerHotel": 0,
  "includeHotelSummary": true,
  "reviewsLanguage": "es",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

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🚀 Step-by-step extraction guide

1. Locate your target property: Navigate to Despegar (e.g. despegar.com.ar) and search for your hotel of interest.
2. Copy the hotel URL: Copy the browser URL from the address bar (e.g. https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba).
3. Configure the Actor:
   • Paste the hotel URL into the startUrls input field.
   • Confirm the market setting matches the top-level domain (e.g. AR for .com.ar).
   • Choose your desired limit in maxReviewsPerHotel (set 0 for all reviews).
4. Execute the run: Click the Start button in the Apify Console.
5. Download your data: Once status reaches SUCCEEDED, export your dataset in JSON, CSV, or Excel format.

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📤 Output dataset & field coverage

Each extracted guest review is output as an enriched individual record containing the review feedback, reviewer profile, and associated hotel property metrics.

💬 Review & author fields

Field	Type	Coverage	Description
reviewId	String	🟢 High	Unique Despegar review identifier
hotelId	String	🟢 High	Numeric hotel identification code
hotelName	String	🟢 High	Hotel establishment name
authorName	String	🟢 High	Public name of the reviewer
authorCountry	String	🟡 Conditional	Country of origin of the guest
ratingPoints	String	🟢 High	Guest rating score (0–10 scale)
pros	String	🟡 Conditional	Positive highlights submitted by the guest (comment.good)
cons	String	🟡 Conditional	Negative feedback or criticism (comment.bad)
travelerType	String	🟢 High	Traveler trip segment (couple, family, businessTrip, etc.)
travelerTypeLabel	String	🟢 High	Localized trip label (e.g., "En pareja", "En familia")
isVerified	String	🟢 High	Verified stay indicator ("true" / "false")
reviewDate	String	🟢 High	Month and year of stay or review publication
reviewLanguage	String	🟢 High	Language code of the review text
usefulVotes	String	🟢 High	Number of users who found this review helpful

🏨 Hotel property & category metrics

Field	Type	Coverage	Description
hotelScore	String	🟢 High	Hotel overall score (0–10 scale)
hotelScoreText	String	🟢 High	Qualitative label (e.g., "Excelente", "Muy bueno")
totalReviews	String	🟢 High	Total reviews registered on Despegar for this property
scoreLocation	String	🟢 High	Location category score (0–10)
scoreCleanliness	String	🟢 High	Cleanliness category score (0–10)
scoreStaff	String	🟢 High	Staff & personal service score (0–10)
scoreService	String	🟢 High	General hotel service score (0–10)
scoreWifi	String	🟢 High	Wi-Fi and internet access score (0–10)
scoreValue	String	🟢 High	Price-to-quality value score (0–10)
aiSummary	String	🟡 Conditional	Despegar AI-generated consensus review overview
city	String	🟢 High	Hotel city location
address	String	🟢 High	Full street address of the hotel
stars	String	🟡 Conditional	Official star category (e.g., "3", "4", "5")
hotelUrl	String	🟢 High	Canonical Despegar hotel page URL
market	String	🟢 High	Despegar country portal used (AR, MX, etc.)
scrapedAt	String	🟢 High	ISO 8601 data extraction timestamp

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📋 Sample output record

{
  "reviewId": "7849201",
  "hotelId": "1051991",
  "hotelName": "Amerian Cordoba Park Hotel",
  "authorName": "Carlos Mendoza",
  "authorCountry": "Argentina",
  "ratingPoints": "9.2",
  "pros": "La ubicación es inmejorable, a pasos de todo el centro histórico. El desayuno buffet muy completo y el personal de recepción súper atento.",
  "cons": "El ascensor es un poco lento en horarios pico de la mañana.",
  "travelerType": "couple",
  "travelerTypeLabel": "En pareja",
  "isVerified": "true",
  "reviewDate": "Enero de 2026",
  "reviewLanguage": "es",
  "usefulVotes": "4",
  "hotelScore": "8.8",
  "hotelScoreText": "Excelente",
  "totalReviews": "1420",
  "scoreLocation": "9.6",
  "scoreCleanliness": "8.9",
  "scoreStaff": "9.1",
  "scoreService": "8.7",
  "scoreWifi": "8.4",
  "scoreValue": "8.5",
  "aiSummary": "Los huéspedes destacan la excelente ubicación céntrica, la variedad del desayuno y la amabilidad del personal de recepción.",
  "city": "Córdoba",
  "address": "Bv. San Juan 165, Córdoba, Argentina",
  "stars": "4",
  "hotelUrl": "https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba",
  "market": "AR",
  "scrapedAt": "2026-11-10T14:15:20.120Z"
}

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💻 Developer integration & API examples

Trigger runs and retrieve review streams programmatically using Python, Node.js, or cURL.

Python integration

from apify_client import ApifyClient

# Initialize client with your Apify API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Run the Despegar Reviews Scraper actor
run = client.actor("haketa/despegar-reviews-scraper").call(
    run_input={
        "startUrls": ["https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba"],
        "market": "AR",
        "maxReviewsPerHotel": 100,
        "includeHotelSummary": True,
        "reviewsLanguage": "es"
    }
)

# Fetch dataset items
reviews = client.dataset(run["defaultDatasetId"]).list_items().items
for rev in reviews:
    print(f"[{rev['ratingPoints']}/10] {rev['authorName']}: {rev['pros'][:60]}...")

Node.js integration

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('haketa/despegar-reviews-scraper').call({
    startUrls: ['https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba'],
    market: 'AR',
    maxReviewsPerHotel: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((r) => {
    console.log(`${r.authorName} (${r.travelerTypeLabel}) rated ${r.ratingPoints}/10`);
});

cURL API execution

curl "https://api.apify.com/v2/acts/haketa~despegar-reviews-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "startUrls": ["https://www.despegar.com.ar/hoteles/h-1051991/amerian-cordoba-cordoba"],
    "market": "AR",
    "maxReviewsPerHotel": 50
  }'

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💡 Best practices for optimal results

• Align market with URL: Ensure the market selector matches the country domain of your hotel URL (e.g. AR for .com.ar, MX for .com.mx).
• Deep pagination extraction: Set maxReviewsPerHotel: 0 to scrape complete historical reviews. For large properties with thousands of reviews, the offset cursor automatically traverses all pages.
• Fast prefill testing: When setting up monitoring workflows, keep maxReviewsPerHotel: 50 to verify data structures in under 30 seconds.
• Preserve authentic wording: Set reviewsLanguage: "es" (or "pt" for Brazilian properties) to receive reviews in their original submitted phrasing.
• Empty comments are normal: Many travelers submit numeric ratings without typing textual comments; these records will retain full scores, timestamps, and author metadata while leaving pros and cons blank.
• Batch multiple properties: Combine competing hotel URLs into a single run to assemble comprehensive destination-level comparison datasets.

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❓ Frequently Asked Questions

Do I need a Despegar account or login credentials?

No. The scraper accesses publicly available guest reviews without requiring account authentication or developer keys.

Can I scrape hotels from multiple countries in one run?

Yes. You can supply multiple hotel URLs from different Despegar domains. When doing so, set market to the primary country or submit hotel IDs with market configuration.

How many reviews can be scraped per hotel?

The pagination engine supports 1,000+ reviews per property without hard caps. Setting maxReviewsPerHotel: 0 instructs the scraper to paginate until the earliest submitted review.

What happens if a guest leaves only a score without text?

Many travelers choose to provide a numeric rating without typing comments. In these cases, ratingPoints, authorName, travelerType, and date are recorded accurately while pros and cons remain empty.

Can I export reviews in Portuguese or English?

Yes. Despegar translates review comments dynamically. Set reviewsLanguage to "pt" or "en" in the input schema to retrieve translations.

How can I feed this data into automated BI dashboards?

Export your dataset directly to CSV or Excel from the Apify console, or utilize Apify integrations with Zapier, Make, Google Sheets, or webhooks to stream new records continuously.

What is the difference between travelerType and travelerTypeLabel?

travelerType provides normalized machine-readable keys (e.g., family, couple, businessTrip), whereas travelerTypeLabel provides localized display text (e.g., "En familia", "En pareja").

Are reviews de-duplicated?

Yes. The actor maintains an in-memory session de-duplication registry based on reviewId and review content signatures, ensuring zero duplicate records in your output dataset.

Can I schedule recurring extractions to track new reviews?

Yes. Using Apify's built-in scheduler, you can run the scraper automatically on a weekly or monthly interval. Since all rows contain timestamps (reviewDate and scrapedAt), filtering for new submissions is straightforward in data warehouses or spreadsheets.

Does this actor extract room rates or booking availability?

No. This actor specializes purely in hotel guest reviews, satisfaction scores, category breakdowns, and AI review summaries. For live hotel room pricing and availability, use dedicated OTA price tracking actors.

How does proxy rotation work for Despegar?

Despegar enforces regional request verification. The actor automatically routes requests through Apify's residential proxy pool with country matching corresponding to the chosen market parameter, ensuring zero blockages.

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📋 Changelog

Version 1.2 — November 2026

• Unlocked 1,000+ deep pagination: Removed restrictive review page ceilings to enable complete historical review archive downloads.
• Optimized prefill defaults: Lowered prefill to 50 items for rapid platform test runs (< 60s).
• Default property fallback: Added intelligent fallback so empty input runs {} execute successfully with verified sample data.
• Enhanced schema compliance: Standardized all output field types to strings with validated template mappings.

Version 1.1 — October 2026

• Added Despegar AI review summary extraction (aiSummary).
• Added 6 granular category score metrics (scoreLocation, scoreCleanliness, scoreStaff, scoreService, scoreWifi, scoreValue).
• Hardened residential proxy routing across all 7 regional markets.

Version 1.0 — August 2026

• Initial release with support for hotel URLs, numeric hotel IDs, structured pros/cons splitting, and verified guest detection.