Holiday Rental Market Intelligence
An autonomous pricing advisor for a 10-guest villa in Akrotiri, Chania. Every week it crawls hundreds of comparable Airbnb listings, cross-references the owner's live rates, and emails a plain-language recommendation — priced in Greek, reasoned in full, delivered before the weekend.
The Challenge
Running a 10-guest villa in a competitive market like Crete is a pricing minefield. The owner was competing against hundreds of villas that adjust rates weekly based on demand, events, and pure guesswork — and the only way to understand the market was to open dozens of Airbnb tabs every Monday morning.
Every week the owner needed answers to the same questions:
- What are comparable villas charging for July this year?
- How full is the market for the first week of August?
- Are my rates leaving money on the table, or am I priced out?
- Did a major competitor just drop their September rates?
Checking even fifty listings by hand across seven months of the season cost a full working day and still missed most of the signal. The owner was not looking for another dashboard — he wanted a recommendation. "Price July at €970. Hold August. Drop October by 8%." That was the product we set out to build.
The Solution
We designed a closed-loop pricing engine. A Python pipeline crawls Airbnb and Booking.com for comparable 5+ bedroom villas, aggregates prices and availability into a clean competitive picture, cross-references the owner's live rates from his Smoobu channel manager, asks an LLM to derive optimal prices from scratch, and emails the reasoned recommendation straight to the owner's inbox in Greek.
Every step runs unattended. If the crawler breaks, the system says so by email before the failure matters — via a 02:00 healthcheck, a four-day staleness watchdog, and a failure alert that triggers if the first five properties of a crawl all return zero prices.
Built with:
Key Features
48-Pass Discovery Crawl
Airbnb search results are randomized per session, so we crawl 4 sub-regions × 3 off-season months × 4 stay lengths × 3 sort orders every Monday — surfacing listings the broad "Crete, Greece" search literally never returns.
Dual-Mode Scheduling
A full Monday crawl (~4 hours) refreshes the complete market. Lightweight watchlist runs on Wed/Fri (~55 minutes) skip discovery and refresh only peak months for the known comp set.
Anti-Anchoring AI Prompt
The AI derives prices from scratch using median + premium formulas before being shown the current rate, then compares its derived number and shows the math. Recommendations stopped looking like timid nudges.
Self-Healing Healthcheck
A 02:00 probe hits Airbnb's GraphQL with the same fragments as the crawler and detects shape changes in the response — giving us hours to react before the main Monday pipeline fires.
CSV-as-Cache
On re-crawl the previous results file is loaded as a property cache, skipping expensive detail-page fetches for listings we already know. Cut runtime by more than half.
Interactive HTML Report
A final deploy step ships the generated report to a Hetzner VPS so the owner can open marketintelligence.villapenelope.gr anywhere and see the latest picture.
Results
By the latest run, the system tracks 553 unique 5+ bedroom villas in Crete with monthly price coverage of 100-400 competitors depending on the month. The owner gets one email, one decision, fully reasoned — before every weekend.
What We Took Away
Scraping is a maintenance problem, not a build problem
The crawler took a couple of weeks to write. Keeping it alive took four separate API patches in the following month. Plan for ongoing maintenance from day one — or don't start.
Stability beats completeness
We gave up per-listing pricing coverage the moment it started causing weekly crashes. The owner cares that the email arrives and the recommendation is defensible. "Good enough and bulletproof" beats "perfect and fragile" every time.
The AI should derive, not react
Anchoring is the default failure mode for any LLM given a "current value" in the prompt. Force the model to compute a number before it sees the one you want it to critique, and its recommendations become noticeably sharper.
Who It's For
Vacation Rental Owners
Single-property owners who compete against hundreds of listings and need market intelligence without hiring a revenue manager.
Boutique Property Managers
Small agencies managing 5-30 properties who want data-backed pricing without committing to enterprise RMS subscriptions.
Villa Brands
Luxury villa operators who need defensible, explainable pricing recommendations — not a black-box algorithm.
Any Single-Business Market Tracker
The same architecture works for any competitor-price monitoring problem where you own one product and need to watch many.
Want Market Intelligence for Your Business?
If you own a single business and need to watch many competitors, the same architecture works. Let's talk about what your version would look like.