Track hotel prices for any city into a spreadsheet with Python

Comparing hotel prices gets tedious when you keep repeating the same search and copying rates into a spreadsheet. Tracking how those prices change is even harder unless you save the dates, guest count and time of each search.

I built the tool used here.

This walkthrough uses GridForge's Google Hotels Scraper to collect a hotel-price snapshot and export it to CSV. You can start without code, then use Python when you want a repeatable workflow.

What you get

Each result represents one hotel from the first page of the location search. Available fields include:

Guest rating and hotel class measure different things. A four-star hotel does not necessarily have a guest rating of four. Some properties may have missing fields; keep those cells empty instead of treating a missing price as zero.

Option 1: no code

1. Open the Google Hotels Scraper and click Try for free.
2. Enter a city, neighborhood or area, such as Times Square, New York.
3. Set check-in and check-out dates. Choose the number of adults and currency.
4. Click Start.
5. Open the resulting dataset, choose Export, and download CSV or Excel.

For your first search, use one location and one stay. A neighborhood query is useful when a whole-city search gives you properties spread too far apart.

Option 2: Python

pip install apify-client 

Set your Apify token in an environment variable named APIFY_TOKEN. This script requests one stay and writes only the columns needed for a price-comparison spreadsheet:

import csv import os from datetime import datetime, timezone from decimal import Decimal from apify_client import ApifyClient client = ApifyClient(os.environ["APIFY_TOKEN"]) search = { "locations": ["Times Square, New York"], "checkIn": "2027-01-15", "checkOut": "2027-01-18", "adults": 2, "currency": "USD", "country": "us", } run = client.actor("gridforge/google-hotels-scraper").call( run_input=search, max_total_charge_usd=Decimal("0.05"), # hard spending cap for this run ) if not run or run.get("status") != "SUCCEEDED": raise RuntimeError("Hotel search did not succeed") checked_at = datetime.now(timezone.utc).isoformat() columns = ["checkedAt", "checkIn", "checkOut", "currency", "name", "pricePerNight", "pricePerNightWithTaxes", "rating", "reviewCount", "website"] with open("hotel-prices.csv", "w", newline="", encoding="utf-8") as file: writer = csv.DictWriter(file, fieldnames=columns) writer.writeheader() for hotel in client.dataset(run["defaultDatasetId"]).iterate_items(): if not hotel.get("name"): print("Check this record:", hotel) continue row = {key: hotel.get(key) for key in columns} row.update({"checkedAt": checked_at, "checkIn": search["checkIn"], "checkOut": search["checkOut"], "currency": search["currency"]}) writer.writerow(row) 

Replace the example dates with your intended stay. The script records the requested dates and currency beside each result, so every snapshot is easy to interpret later.

Use case: daily price tracking

Choosing a hotel for a three-night trip? Run the same search daily, keeping the stay dates, adult count, currency and location unchanged. Append each run to one spreadsheet and compare the tax-inclusive price for the same property across days. Once your first results look right, set up a daily Schedule in Apify so it runs on its own.

Limits

• Returns the first page Google shows, typically around 18–20 hotels per location. Search by neighborhood for more coverage.
• Displayed rates change and may differ from the final price at checkout.
• Nearby searches can overlap; deduplicate before counting unique hotels.

Cost: $1 per 1,000 hotels, plus a tiny start fee. Set a spending cap and a run can never cost more than you allow.

Not affiliated with or endorsed by Google.