Hotel rate shopping tool for revenue teams: your comp set's prices as data
Nightly and total prices for 20 hotels per city search, and one hotel's rate on every booking site Google lists. Runs on a daily schedule and lands as rows in a table, in Excel or through the API. Hotel price monitoring with a comp set you choose.
- Booking.com
- Expedia.com
- Hotels.com
- Agoda
- Trip.com
- KAYAK.com
- Priceline
- Tripadvisor.com
- Travelocity.com
- Orbitz.com
- momondo.com
- Vio.com
Where hotel rate shopping falls apart
Three gaps a revenue or distribution team hits sooner or later.
- FixedThe comp set in many rate shoppers
A rate shopper with a comp set you cannot change
Many rate shoppers work from a set list of properties. Adding the new hotel two streets away can mean a sales call. You pay for the list, not for the rates you need.
- 17 to 36Booking sites per hotel in our Paris test
Parity breaks on the sites nobody checks
A hotel is listed on far more booking sites than a team checks by hand. In our Paris test of 16 September 2026, two smaller sites listed one hotel at about $27 a night under the hotel's own price.
- No historyWhat a view of today's rates gives you
Today's rates without history hide the pattern
Hotel rates move all the time. A view of today's rates can't tell you when a competitor started undercutting you or how long it lasted.
What Scrapewise does for hotel revenue teams
Three things: a city search, one hotel on every booking site, and your own table with history.
City search: 20 hotels with prices for your dates
The Google Hotels API returns 20 hotels per call for a city and a pair of dates, each with the nightly rate, the stay total, the hotel class and the guest rating. One call costs €0.0015.
One hotel on every booking site
The hotel rate parity call returns one hotel's rate on every booking site Google lists for your dates, the hotel's own website included, with the nightly rate, the stay total and a flag on paid listings.
Your own table, with history
Every run is saved in your account, so each hotel and date builds a rate history. Read it in the table, export CSV or Excel, or pull it through the REST API or the MCP server.
How a revenue team gets to a ready rate comparison
Four steps from a list of hotels to one table you can compare.
- 01
Scrape: save your hotels and city searches
Paste the Google Hotels link for your own hotel and for each competitor, add the city searches you care about, and pick the check-in and check-out dates. Put them on a schedule. Daily is the shortest.
- 02
Clean: one currency, prices as numbers
Pick the currency on Google Hotels before you copy the link and every price in that call uses it. The nightly rate and the stay total arrive as numbers, next to the text Google shows.
- 03
Match: the same hotel across booking sites
The rate parity call is about one hotel, so its rates on the different booking sites come back already lined up by Google Hotels. Your comp set is the list of hotels you chose.
- 04
Ready comparison: one row per hotel, site and date
You end with one row per hotel per booking site per date, with every earlier run kept. Compare the lowest site with your own rate in the table or in Excel.
From a rate question to the right page
Each need of a revenue team maps to one data source or use case.
- Industry needCheck one hotel's rate on every booking siteUse caseHotel rate parity callRead more →
- Industry needSee your comp set's prices for a city and datesUse caseGoogle Hotels searchRead more →
- Industry needTrack fares on your routesUse caseGoogle Flights searchRead more →
- Industry needSee what rate parity data looks likeUse caseHotel rate parity dataRead more →
- Industry needFollow prices on other public sitesUse caseCompetitor price trackingRead more →
Hotel Rate Shopping Data Built for Revenue Teams
Daily Rate Runs
Hotel price monitoring on a daily schedule, with the rate history kept for every hotel and date you save.
Every Booking Site Google Lists
One call returns one hotel on every booking site Google shows for the dates, the hotel's own website included. In our Paris test that was 17 to 36 per hotel.
Your Comp Set, Your Choice
You choose the hotels. Add a competitor by pasting its Google Hotels link. You pay €0.0015 per call, and only for the hotels you run.
Rate Data You Own, Without the Black-Box Tool
Scrapewise is the data layer under a rate shopper. You choose the comp set, the rates land in your own table, and you load them into the systems you already use.
- ✕ Fixed comp-set list, hard to expand
- ✕ Vendor-defined refresh schedule
- ✕ Black-box dashboard, no raw data export
- ✕ Per-property pricing that scales painfully
- ✓ You choose the comp set: add a hotel by pasting its link
- ✓ Daily runs with a dated rate history
- ✓ One hotel on every booking site Google lists
- ✓ Rows in a table, CSV, Excel or the REST API, paid per call
Plugs into the tools travel teams already run
Pipe Scrapewise rate data into your RMS, BI, and revenue management stack.
Power BI
BI / dashboards
Tableau
BI / analytics
Looker
BI / analytics
MCP server
Claude Code and Claude Desktop
Excel / CSV
Direct export
Snowflake
Warehouse, via REST API
BigQuery
Warehouse, via REST API
REST API
RMS / custom
If you can call a REST API or open a CSV, you can integrate Scrapewise.
From scraped pages to a ready comparison
Scrape, clean, match, ready comparison: the four steps behind every hotel rate table. Start from the result you need.
Hotel and flight data
Compare alternatives
Your Comp Set Just Moved Its Rates. Did You See It?
Scrapewise gives revenue teams the rate data itself: your comp set's prices for the nights you sell, saved on every daily run.
Frequently Asked Questions
Common questions from hotel revenue and distribution teams.
A hotel rate shopping tool shows you what your competitors charge for the same nights you are selling, so you can set your own rate with the market in view. Scrapewise gives you that as data: one city search returns 20 hotels with the nightly rate and the stay total, and you save the searches and dates you care about. Hotel price monitoring then means running them every day and keeping the history.