Apify Hotel Price Monitoring

Hotel Price Monitoring: Rate Snapshots From Google Hotels

Turn Google Hotels into a hotel price monitor. Each poll takes a destination and stay dates and returns every listed property with numeric nightly and total rates. Snapshot the run, diff it against the last one on property_token, and you have rate-drop alerts, competitor rate watching, or a price history for one market.

When to use this skill

• The user wants hotel price monitoring for a destination, a set of stay dates, or specific properties.
• They want to watch competitor hotel rates (a hotelier checking the market around their property).
• They want alerts when a rate drops below a threshold before a trip or an event.
• They ask "do hotel prices go down closer to the date": monitor the same stay across days and answer with data.

Not for: one-off exports of a single search (use the scrape-hotel-prices skill), booking or reservations (the Actor is read-only), or flight fare tracking (use the Google Flights Data Scraper).

What each snapshot returns (one object per results page)

Each dataset item is a page of results with search_metadata (total_results, properties_count, pages_processed, pagination_limit_reached) and a properties array. Per property: name, type, rate_per_night and total_rate (display lowest plus numeric extracted_lowest and before-tax variants), overall_rating, reviews count, amenities, gps_coordinates, check_in_time, check_out_time, and property_token, the stable key you diff on. When present: hotel_class, location_rating, deal plus deal_description, a per-vendor prices array with cancellation flags, and a reviews_breakdown by topic. Sponsored listings arrive separately in ads.

Prerequisites

• Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=skillrepo).
• Authentication via apify login, or an APIFY_TOKEN environment variable (Apify Console, Settings, Integrations).

The Actor

• Store page: https://apify.com/johnvc/google-hotels-search-scraper?fpr=9n7kx3&fp_sid=skillrepo
• Actor ID: johnvc/google-hotels-search-scraper
• Pricing: pay per page of results processed plus a small per-run setup fee (see references/gotchas.md).

Run it with the Apify CLI

One monitoring poll, one page, fixed stay dates:

apify actors call "johnvc/google-hotels-search-scraper" -i '{"search_type":"search","q":"hotels near Moscone Center San Francisco","check_in_date":"2026-10-05","check_out_date":"2026-10-08","currency":"USD","max_pages":1}' \
  --json \
  --user-agent apify-awesome-skills/apify-hotel-price-monitoring \
  2>/dev/null

Read a finished run's snapshot later, for example from a scheduled run:

apify datasets get-items <DATASET_ID> --format json --user-agent apify-awesome-skills/apify-hotel-price-monitoring 2>/dev/null

Every call carries the three flags this repo expects: --json (or --format json), --user-agent apify-awesome-skills/apify-hotel-price-monitoring, and 2>/dev/null.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/google-hotels-search-scraper

Then ask, for example: "Check hotel prices near the Louvre for November 12 to 14 and tell me which of yesterday's hotels got cheaper." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

1. Fix the monitor definition. One Actor input per monitor: q (destination or market), check_in_date and check_out_date, party size, and an explicit currency. Keep every field identical across polls; only then are two snapshots comparable.
2. Bound each poll. max_pages: 1 covers about 20 properties and is enough for most competitor sets. Narrow the market with server-side filters (hotel_class, guest_rating, max_price) instead of adding pages.
3. Poll on a schedule. Wrap the same input in an Apify Schedule or a cron job. Daily is the usual cadence; hourly only for short event windows.
4. Store and diff. Flatten each page object's properties into rows keyed on property_token, keep extracted_lowest from rate_per_night and total_rate plus the run timestamp, then compare with the previous snapshot: new lows, threshold crossings, and week-over-week drift.
5. Alert and report. Send drops past the user's threshold, and chart a property's rate across snapshot dates to answer "when should I book".

Inputs

• q (string): destination, market, or landmark query
• check_in_date / check_out_date (YYYY-MM-DD): the monitored stay, required with a query
• adults, children, children_ages: party, keep constant across polls
• currency (ISO 4217): set it explicitly so snapshots compare cleanly
• hotel_class, guest_rating, stars, min_price, max_price: narrow to the competitor set
• gl, hl: country and language codes
• max_pages (default 1): pages per poll, the cost bound per snapshot

Cost

Billing is pay per event: a per-run setup fee plus a per-page fee, about $0.04 for a one-page poll at the time of writing. A daily one-page monitor is roughly $1.20 a month; three markets daily is under $4 a month. Estimate with the live prices in references/gotchas.md before scheduling.

Honest limits

• This is a polled monitor, not a push feed: freshness equals your poll interval.
• Rates apply to the exact stay dates and party in the input; a monitor with rolling dates (always 30 days out) needs the dates recomputed before each poll.
• Listing sets shift between polls: hotels enter and leave page one, so a missing property_token does not always mean sold out.
• The Actor returns current prices only. Price history exists only in the snapshots you keep.
• Read-only data: it cannot hold, book, or confirm any rate.

Troubleshooting

• A tracked property vanished from the snapshot: it may have dropped off the fetched pages; raise max_pages by one or query the hotel name directly.
• Rates jump wildly between polls: confirm currency, party fields, and dates are identical across polls before trusting the diff.
• Empty properties: dates in the past or check_out_date not after check_in_date are the usual cause in long-running monitors.
• pagination_limit_reached true: expected on one-page polls; only raise max_pages if your competitor set is bigger than one page.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related travel-data Actors

• Google Flights Data Scraper: https://apify.com/johnvc/Google-Flights-Data-Scraper-Flight-and-Price-Search?fpr=9n7kx3&fp_sid=skillrepo
• Google Travel Explore API: https://apify.com/johnvc/google-travel-explore-api?fpr=9n7kx3&fp_sid=skillrepo
• Google Local API: https://apify.com/johnvc/google-local-api?fpr=9n7kx3&fp_sid=skillrepo