How to Scrape Google Hotel Review Search Results with TalorData

Hotel search data is useful when you want to understand how properties appear in Google Hotels: their rankings, prices, ratings, review counts, locations, amenities, availability, and booking options.

For travel platforms, SEO teams, hotel groups, OTAs, and market research teams, this data can answer questions like:

The challenge is that Google Hotels is not a simple static page. Results can change by location, check-in date, check-out date, number of guests, language, device, and availability. If you try to manually copy the data, the workflow breaks quickly.

A Google Hotels API workflow gives you structured data instead of browser chaos.

TalorData’s Google Hotels API is designed to collect structured hotel search results, including hotel names, prices, ratings, reviews, locations, amenities, availability, booking options, and ranking positions. The TalorData Google Hotels page also shows key request parameters such as q, location, google_domain, check_in_date, check_out_date, adults, and no_cache.

What does “hotel review search results” mean?

In this guide, “hotel review search results” does not mean scraping every full-text guest review from every booking website.

It usually means collecting review-related signals visible in Google Hotels search results, such as:

TalorData’s Google Hotels data page lists rating and review data such as rating, review count, review score, guest rating, and visible review signals. It also lists price, location, amenities, availability, and booking option fields.

For most SEO and travel intelligence workflows, these visible review signals are enough to monitor reputation and compare hotels at scale.

Use cases

Google Hotels data can be used in several practical workflows.

TalorData highlights hotel price monitoring, travel competitor analysis, destination market research, booking channel monitoring, reputation tracking, and regional price comparison as Google Hotels data workflows.

Core Google Hotels parameters

Start with the parameters that define the search context.

A hotel search without dates is not very useful. Price and availability depend heavily on the stay dates, so check_in_date and check_out_date should be treated as core inputs.

Use future dates. A hotel API request with past check-in dates is a tiny suitcase packed for yesterday.

Basic request structure

The exact endpoint and authentication format should come from your TalorData dashboard or API documentation.

A typical JSON request looks like this:

{
  "engine": "google_hotels",
  "q": "hotels in Tokyo",
  "location": "Tokyo, Japan",
  "google_domain": "google.com",
  "check_in_date": "2026-07-15",
  "check_out_date": "2026-07-18",
  "adults": 2,
  "no_cache": true
}

The response should return structured hotel search data that you can parse into tables, dashboards, alerts, or AI workflows.

Step 1: Set up your API key

Store your TalorData API key and endpoint as environment variables.

export TALORDATA_API_KEY="your_api_key_here"
export TALORDATA_SERP_ENDPOINT="your_talordata_serp_endpoint_here"

On Windows PowerShell:

setx TALORDATA_API_KEY "your_api_key_here"
setx TALORDATA_SERP_ENDPOINT "your_talordata_serp_endpoint_here"

Install Python dependencies:

pip install requests pandas

Step 2: Create a reusable Python client

This function sends a Google Hotels request and returns JSON.

Adjust the endpoint, request body, or headers if your TalorData dashboard uses a different format.

import os
import requests
from typing import Any, Dict, Optional


TALORDATA_API_KEY = os.getenv("TALORDATA_API_KEY")
TALORDATA_SERP_ENDPOINT = os.getenv("TALORDATA_SERP_ENDPOINT")


def fetch_google_hotels(
    query: str,
    location: str,
    check_in_date: str,
    check_out_date: str,
    adults: int = 2,
    google_domain: str = "google.com",
    no_cache: bool = True,
    extra_params: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """
    Fetch Google Hotels search results with TalorData SERP API.

    Replace the endpoint or authentication format if your TalorData
    dashboard shows a different request style.
    """
    if not TALORDATA_API_KEY:
        raise RuntimeError("Missing TALORDATA_API_KEY environment variable.")

    if not TALORDATA_SERP_ENDPOINT:
        raise RuntimeError("Missing TALORDATA_SERP_ENDPOINT environment variable.")

    payload: Dict[str, Any] = {
        "engine": "google_hotels",
        "q": query,
        "location": location,
        "google_domain": google_domain,
        "check_in_date": check_in_date,
        "check_out_date": check_out_date,
        "adults": adults,
        "no_cache": no_cache
    }

    if extra_params:
        payload.update(extra_params)

    headers = {
        "Authorization": f"Bearer {TALORDATA_API_KEY}",
        "Content-Type": "application/json"
    }

    response = requests.post(
        TALORDATA_SERP_ENDPOINT,
        json=payload,
        headers=headers,
        timeout=30
    )

    response.raise_for_status()
    return response.json()

Run a basic search:

if __name__ == "__main__":
    data = fetch_google_hotels(
        query="hotels in Tokyo",
        location="Tokyo, Japan",
        check_in_date="2026-07-15",
        check_out_date="2026-07-18",
        adults=2
    )

    print(data)

Step 3: Inspect the raw response

Before writing a parser, inspect the raw JSON.

import json

print(json.dumps(data, indent=2, ensure_ascii=False)[:5000])

This matters because response fields can vary by result type, destination, availability, and hotel data returned.

Look for arrays with names such as:

hotel_results
properties
hotels
organic_results
results

The exact top-level field depends on the response schema. Do not guess too aggressively. First look at the JSON cave wall, then draw the map.

Step 4: Parse hotel results

Here is a defensive parser that checks several common result containers.

from typing import Any, Dict, List


def get_first_available_list(data: Dict[str, Any], keys: List[str]) -> List[Dict[str, Any]]:
    for key in keys:
        value = data.get(key)
        if isinstance(value, list):
            return value

    return []


def parse_hotel_results(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    hotel_items = get_first_available_list(
        data,
        keys=[
            "hotel_results",
            "properties",
            "hotels",
            "organic_results",
            "results"
        ]
    )

    parsed = []

    for index, item in enumerate(hotel_items, start=1):
        parsed.append({
            "position": item.get("position", index),
            "hotel_name": item.get("name") or item.get("title"),
            "hotel_id": item.get("hotel_id") or item.get("property_token") or item.get("id"),
            "link": item.get("link") or item.get("hotel_link"),
            "thumbnail": item.get("thumbnail"),
            "rating": item.get("rating"),
            "review_count": item.get("review_count") or item.get("reviews"),
            "review_score": item.get("review_score"),
            "guest_rating": item.get("guest_rating"),
            "price": item.get("price"),
            "extracted_price": item.get("extracted_price"),
            "currency": item.get("currency"),
            "address": item.get("address"),
            "latitude": item.get("latitude"),
            "longitude": item.get("longitude"),
            "hotel_class": item.get("hotel_class"),
            "amenities": item.get("amenities"),
        })

    return parsed

Use it:

data = fetch_google_hotels(
    query="hotels in Tokyo",
    location="Tokyo, Japan",
    check_in_date="2026-07-15",
    check_out_date="2026-07-18",
    adults=2
)

hotels = parse_hotel_results(data)

for hotel in hotels[:5]:
    print(hotel["position"], hotel["hotel_name"], hotel["rating"], hotel["review_count"])

Step 5: Extract review signals

For review-focused workflows, you probably want a smaller table.

def parse_hotel_review_signals(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    hotels = parse_hotel_results(data)
    rows = []

    for hotel in hotels:
        rows.append({
            "position": hotel.get("position"),
            "hotel_name": hotel.get("hotel_name"),
            "rating": hotel.get("rating"),
            "review_count": hotel.get("review_count"),
            "review_score": hotel.get("review_score"),
            "guest_rating": hotel.get("guest_rating"),
            "price": hotel.get("price"),
            "currency": hotel.get("currency"),
            "address": hotel.get("address"),
            "link": hotel.get("link")
        })

    return rows

This table is useful for:

Step 6: Save hotel review data to CSV

import pandas as pd


data = fetch_google_hotels(
    query="hotels in Tokyo",
    location="Tokyo, Japan",
    check_in_date="2026-07-15",
    check_out_date="2026-07-18",
    adults=2
)

review_rows = parse_hotel_review_signals(data)

df = pd.DataFrame(review_rows)
df.to_csv("google_hotel_review_signals.csv", index=False)

print(df.head())

A CSV output may look like this:

Step 7: Compare hotels by rating and review count

Review count and rating should be read together.

A 4.8 rating with 12 reviews does not mean the same thing as a 4.6 rating with 3,000 reviews. One is a postcard. The other is a weather pattern.

df["rating_num"] = pd.to_numeric(df["rating"], errors="coerce")
df["review_count_num"] = pd.to_numeric(df["review_count"], errors="coerce")

top_reviewed = df.sort_values(
    by=["review_count_num", "rating_num"],
    ascending=[False, False]
)

top_reviewed.to_csv("top_reviewed_hotels.csv", index=False)

print(top_reviewed[[
    "position",
    "hotel_name",
    "rating",
    "review_count",
    "price"
]].head(10))

Useful segments:

Step 8: Track hotel review signals over time

One scrape is useful. Repeated scrapes are much more useful.

Store the search context every time:

Add metadata to every row:

from datetime import datetime, timezone


def add_search_context(
    rows: List[Dict[str, Any]],
    query: str,
    location: str,
    check_in_date: str,
    check_out_date: str,
    adults: int
) -> List[Dict[str, Any]]:
    collected_at = datetime.now(timezone.utc).isoformat()

    for row in rows:
        row["query"] = query
        row["location"] = location
        row["check_in_date"] = check_in_date
        row["check_out_date"] = check_out_date
        row["adults"] = adults
        row["collected_at"] = collected_at

    return rows

Usage:

query = "hotels in Tokyo"
location = "Tokyo, Japan"
check_in_date = "2026-07-15"
check_out_date = "2026-07-18"
adults = 2

data = fetch_google_hotels(
    query=query,
    location=location,
    check_in_date=check_in_date,
    check_out_date=check_out_date,
    adults=adults
)

rows = parse_hotel_review_signals(data)
rows = add_search_context(
    rows,
    query=query,
    location=location,
    check_in_date=check_in_date,
    check_out_date=check_out_date,
    adults=adults
)

pd.DataFrame(rows).to_csv("hotel_review_snapshot.csv", index=False)

Now you can compare:

Step 9: Monitor multiple destinations

You can run the same workflow across many cities.

destinations = [
    {"query": "hotels in Tokyo", "location": "Tokyo, Japan"},
    {"query": "hotels in Seoul", "location": "Seoul, South Korea"},
    {"query": "hotels in Singapore", "location": "Singapore"},
]

all_rows = []

for destination in destinations:
    data = fetch_google_hotels(
        query=destination["query"],
        location=destination["location"],
        check_in_date="2026-07-15",
        check_out_date="2026-07-18",
        adults=2
    )

    rows = parse_hotel_review_signals(data)
    rows = add_search_context(
        rows,
        query=destination["query"],
        location=destination["location"],
        check_in_date="2026-07-15",
        check_out_date="2026-07-18",
        adults=2
    )

    all_rows.extend(rows)

pd.DataFrame(all_rows).to_csv("multi_destination_hotel_reviews.csv", index=False)

This is useful for:

Step 10: Handle errors and retries

Travel data can be sensitive to dates, availability, and query context. Add retry logic for production.

import time
from requests import RequestException


def fetch_with_retries(
    query: str,
    location: str,
    check_in_date: str,
    check_out_date: str,
    adults: int = 2,
    retries: int = 3,
    delay_seconds: int = 2
) -> Dict[str, Any]:
    last_error = None

    for attempt in range(1, retries + 1):
        try:
            return fetch_google_hotels(
                query=query,
                location=location,
                check_in_date=check_in_date,
                check_out_date=check_out_date,
                adults=adults
            )
        except RequestException as error:
            last_error = error
            print(f"Attempt {attempt} failed: {error}")
            time.sleep(delay_seconds)

    raise RuntimeError(f"Failed after {retries} attempts: {last_error}")

For production, also log:

What fields should you store?

For hotel review search monitoring, start with this schema:

{
  "query": "hotels in Tokyo",
  "location": "Tokyo, Japan",
  "check_in_date": "2026-07-15",
  "check_out_date": "2026-07-18",
  "adults": 2,
  "collected_at": "2026-06-30T09:00:00Z",
  "hotel": {
    "position": 1,
    "hotel_name": "Example Hotel Tokyo",
    "hotel_id": "example_hotel_id",
    "rating": 4.6,
    "review_count": 1820,
    "review_score": 9.1,
    "guest_rating": "Excellent",
    "price": "$210",
    "currency": "USD",
    "address": "Example address, Tokyo",
    "latitude": 35.6762,
    "longitude": 139.6503,
    "amenities": ["Free Wi-Fi", "Breakfast"],
    "link": "https://..."
  }
}

For more advanced workflows, add:

TalorData lists price fields such as price, extracted price, currency, nightly rate, total price, taxes, and fees, as well as booking provider, booking link, offer price, booking source, hotel website, and deal details.

Common mistakes

Mistake 1: Not setting check-in and check-out dates

Hotel search data depends on dates. Always store and control them.

Mistake 2: Comparing different guest counts

A search for one adult and a search for two adults may return different prices or availability.

Mistake 3: Treating rating alone as reputation

Rating without review count can be misleading. Track both.

Mistake 4: Ignoring position

A hotel with a strong rating but low visibility may still lose demand to higher-ranked competitors.

Mistake 5: Not saving raw responses

Always save raw JSON while building the parser. It helps when fields vary by destination or result type.

Mistake 6: Forgetting localization

Hotel results can change by market, language, domain, and search location. Store the full search context.

Final thoughts

Scraping Google Hotel review search results with TalorData is mainly about turning Google Hotels into a structured monitoring workflow. Start free testing of Google Hotel API

Start with the core request fields:

Then parse the fields that matter: hotel name, position, rating, review count, price, address, amenities, availability, and booking options.

Once the data is in JSON or CSV, you can build reputation tracking, hotel price monitoring, OTA analysis, destination research, AI travel tools, and local SEO dashboards.

Google Hotels data is a crowded lobby. A good API workflow turns it into a clean guest list.

FAQ

Can I scrape Google Hotels data with TalorData?

Yes. TalorData provides a Google Hotels API for collecting structured hotel search data such as hotel names, prices, ratings, reviews, locations, amenities, availability, booking options, and ranking positions.

Can I collect full hotel review text?

This guide focuses on review signals visible in hotel search results, such as rating, review count, review score, and guest rating. If you need full review text, check whether your endpoint or product plan supports detailed review data.

What are the most important parameters?

Start with q, location, check_in_date, check_out_date, adults, google_domain, and no_cache.

Why do hotel results change between requests?

Hotel results can change because of dates, guest count, location, availability, price updates, booking providers, and localization settings.