Pet-Friendly Stays Directory
SEO-first directory of pet-friendly accommodations across the USA. Features PostGIS spatial queries, NPS API integration, and Schema.org structured data.
TL;DR: TL;DR: I built a comprehensive directory of pet-friendly accommodations across the USA. Features PostGIS for geographic search, confidence scoring for data quality, and structured pet policy information for 10,000+ properties.
The Problem
Traveling with pets is frustrating because:
- "Pet-friendly" is vague: Does it mean dogs only? Any size? Extra fees?
- Scattered information: Check each hotel's site individually
- No reliable search: Google results are often outdated
- Policy details hidden: Breed restrictions, weight limits buried in fine print
I wanted a single source where pet owners could find detailed, trustworthy accommodation info.
My Approach
I built a data collection and serving pipeline:
- Multi-query scraping: SerpAPI searches with different query strategies
- Pet policy extraction: Structured parsing of fees, restrictions, amenities
- Geographic indexing: PostGIS for "within X miles" queries
- Confidence scoring: Rate properties based on data completeness
The system focuses on data quality over quantity—each property has a confidence score based on how much we actually verified.
Architecture
Pet-Friendly Stays Directory - Architecture Diagram
Key Features
- Property Categories: Hotels, vacation rentals, B&Bs, cabins, motels, hostels
- Pet Policy Details: Dogs/cats allowed, fees, deposits, weight limits, breed restrictions
- Pet Amenities: Dog parks, pet beds, treats, walking services
- Geographic Search: PostGIS enables "within 10 miles of downtown"
- Confidence Scoring: 0.0-1.0 based on data completeness
- Verification Status: Tracks when data was last confirmed
- Multi-tier Collection: Different query strategies for different needs
Results & Metrics
| Metric | Value |
|---|---|
| Target Markets | 40 (20 Tier 1 + 20 Tier 2) |
| Properties per City | 200-600 (depends on strategy) |
| Collection Cost | $0.40-1.00 per city |
| Fields per Property | 25+ |
| Pet Policy Fields | 10+ |
| Database Size | ~100MB per 10,000 properties |
What I Learned
The hardest part was pet policy extraction. Hotels describe their policies inconsistently:
- "Pets welcome" (no details)
- "Dogs under 25 lbs, $75/stay"
- "Two pets max, no aggressive breeds, $50/night fee"
I built a multi-pattern parser that handles variations:
# Weight limits
weight_patterns = [
r"under (\d+)\s*(lbs?|pounds?)",
r"up to (\d+)\s*(lbs?|pounds?)",
r"(\d+)\s*(lbs?|pounds?) max"
]
# Fee extraction
fee_patterns = [
r"\$(\d+)\s*per (night|stay|pet)",
r"(\d+)\s*dollar.*fee",
r"fee:?\s*\$(\d+)"
]
The confidence scoring was valuable for frontend display. We show properties with high confidence (rating, reviews, verified policies) more prominently than those with minimal data.
Frequently Asked Questions
What problem does Pet-Friendly Stays solve?
It eliminates the guesswork when traveling with pets. Instead of checking each hotel individually, you can search for properties with specific pet policies: weight limits, fees, amenities, breed restrictions.
What technologies power this project?
Python for data collection, SerpAPI for Google Local results, Supabase PostgreSQL with PostGIS for geographic storage and queries, and Pydantic for data validation.
How accurate is the pet policy data?
Each property has a confidence score based on data completeness and recency. High-confidence properties (0.8+) have verified policies from official sources. Lower-confidence entries show what we found but may need verification.
Frequently Asked Questions
More Projects
View allBuilt by Abhinav Sinha
AI-First Product Manager who builds production-grade tools. Passionate about turning complex problems into elegant solutions using AI, automation, and modern web technologies.