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Daily price tracking for competitor hotels, with Python and SQLite

#python #webscraping #googlehotels #pricetracking

If you run a hotel, a travel site, or just a spreadsheet of competitors you want to keep an eye on, the question that comes up every morning is simple: are my competitors cheaper than me today, and by how much?

Google Hotels is the easiest place to answer it. For every hotel it shows the lowest price from 20-40 booking sites plus the hotel's own website, for the exact dates you pick, in any currency. There is no official API, but you can get the same data with a one-page Python script and a daily schedule.

This post shows the full setup. Code is in github.com/huynhtanan/google-hotels-api (MIT). Disclosure: I built the Apify scraper this uses; it's paid per result, new Apify accounts get free monthly credit that covers a few thousand hotels.

The script: one SQLite table, one function per concern

Give it a list of hotel names (or hotel URLs from Google Hotels) and a check-in date. Each run saves one row per hotel to a local SQLite file and prints how prices moved versus the previous observation.

# track_prices.py — see github.com/huynhtanan/google-hotels-api/blob/main/python/track_prices.py
import os, sqlite3
from datetime import date, timedelta
from pathlib import Path
from apify_client import ApifyClient

HOTELS   = ["Hilton Paris Opera", "Pullman Paris Tour Eiffel", "Le Meurice Paris"]
CHECK_IN = date.today() + timedelta(days=30)
DB       = Path("prices.sqlite3")

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("zet46/google-hotels-scraper").call(run_input={
    "queries": HOTELS, "maxHotelsPerQuery": 1,
    "checkIn": CHECK_IN.isoformat(),
    "checkOut": (CHECK_IN + timedelta(days=1)).isoformat(),
    "currency": "EUR",
})
items = list(client.dataset(run.default_dataset_id).iterate_items())

with sqlite3.connect(DB) as con:
    con.execute("""CREATE TABLE IF NOT EXISTS prices (
        run_at TEXT, hotel TEXT, check_in TEXT, nightly REAL,
        PRIMARY KEY (run_at, hotel, check_in))""")
    for h in items:
        con.execute("INSERT OR REPLACE INTO prices VALUES (?,?,?,?)",
                    (str(run.started_at), h["name"], CHECK_IN.isoformat(), h.get("pricePerNight")))
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Full version (with the "DOWN 5.4% (was 280.00 on 2026-10-15)" comparison printing) is in the repo.

First run, then the next day 

$ python track_prices.py
...Status: SUCCEEDED, Message: Done: 3 hotels from 3 queries

Prices on 2026-10-15 for check-in 2026-11-14 (EUR):

  Hilton Paris Opera                               280.00   (first observation)
  Le Meurice                                      1535.00   (first observation)
  Pullman Paris Tour Eiffel                        320.00   (first observation)
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The next time the script runs it compares against the previous row for the same (hotel, check_in) and prints the direction and the diff:

  Hilton Paris Opera                               265.00   DOWN  5.4%  (was 280.00 on 2026-10-15)
  Le Meurice                                      1535.00   flat  0.0%  (was 1535.00 on 2026-10-15)
  Pullman Paris Tour Eiffel                        340.00     UP  6.3%  (was 320.00 on 2026-10-15)
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Because the data lives in SQLite, you can plot the whole history or alert yourself with one SQL line:

-- competitors who undercut their own 7-day average by 10% or more
SELECT hotel, nightly, avg7
FROM (
  SELECT hotel, nightly, run_at,
         AVG(nightly) OVER (PARTITION BY hotel, check_in
                            ORDER BY run_at ROWS BETWEEN 7 PRECEDING AND 1 PRECEDING) AS avg7
  FROM prices)
WHERE avg7 IS NOT NULL AND nightly < avg7 * 0.9
ORDER BY run_at DESC;
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Running it every day

Two options, both free:

  • Apify Scheduler (zero infrastructure). On the actor page click Schedule actor, pick a cron (e.g. daily 09:00), save your input (hotel list + dates). Then from any machine run the script above with client.actor(...).last_run().get() to pull the latest completed dataset. No local cron, no server.
  • Local cron / Windows Task Scheduler. Call python track_prices.py daily. Each run also triggers a fresh scrape, so this is enough on its own.

Cost

The scraper charges $2 per 1,000 hotels. Ten competitors tracked daily is ~300 hotels/month, so about $0.60/month. Turn on includeProviderPrices: true only if you also want the per-site breakdown (Booking.com vs Expedia vs the hotel's website); it doubles the per-hotel cost but lets you catch rate-parity breaks on specific channels.

A couple of things that bit me

  • pricePerNight is the nightly price including taxes that Google displays. totalPrice is the whole stay including taxes and fees. If you compare across sources, pick one and stick with it.
  • Dates and currency are not URL query parameters on Google Hotels; they are encoded in the ts protobuf blob. The scraper handles that for you, which is why calling it directly is a lot less work than parsing Google's HTML yourself.
  • Hotel names are matched by Google's own search, so "Hotel Lutetia Paris" may silently return 0 results if Google's suggest doesn't lock on; pass the hotel's Google Hotels URL (/travel/hotels/entity/<token>) via hotelUrls when you need to be exact.

Feedback welcome

If you'd like a specific feature (price by room type, SQLite -> Google Sheets sync, a Discord alert webhook), the Apify actor repo issues are open and I ship fixes within a day. Code and more examples: github.com/huynhtanan/google-hotels-api.

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