StaySTRA is a short-term rental analytics platform I built for investors who want to make decisions based on data, not vibes. It covers 200+ US markets with revenue projections, regulation tracking, property-level analysis, and market comparisons — the kind of tool that would normally require a funded startup with a data team. I built it with Claude.
It has paying subscribers. My co-founder is an AI.
What it does
StaySTRA provides real-time market analytics across 200+ US metro areas: occupancy modeling, average daily rate benchmarking, revenue projections, and seasonality analysis. Each market gets continuous data processing, not a one-time snapshot.
Property-level revenue projections let you model returns on a specific property before you buy. Comp-based analysis uses actual STR performance data, not Zillow estimates with a multiplier.
Regulation tracking monitors ordinances, permit requirements, zoning restrictions, and enforcement patterns across every covered market. STR laws change constantly — this is the thing that saves investors from buying a property they can’t legally rent.
Market comparisons let you stack markets side by side on occupancy, ADR, revenue potential, regulatory risk, and saturation metrics. The question isn’t “is this a good Airbnb market?” — it’s “is it better than these other three?”
In-depth market guides with real numbers get updated automatically as new data flows through the pipeline.
How it’s built
The stack is WordPress with a custom theme and purpose-built plugins handling subscriptions, gated content, market data display, analytics, SEO, and integrations. Stripe handles payments. Brevo handles transactional email. Everything runs in containerized behind a reverse proxy, with Cloudflare CDN in front.
The data layer runs on a database layer with pre-computed aggregations for fast market lookups. When you pull up a market page, you’re not waiting on a live query across hundreds of thousands of data points — the aggregations are pre-computed, and the application layer reads from them. New data triggers a refresh pipeline, not a full recompute.
The content pipeline is where the AI architecture gets interesting. Generating and maintaining market analysis across 200+ markets isn’t a task you do by hand. I built multi-model AI workflows where Claude handles content generation, quality assurance, fact verification, and SEO optimization in sequence. Each market page goes through a generation, review, fact-check, and publish pipeline. When market data updates, the content pipeline regenerates affected sections without touching the rest of the page.
The whole platform was pair-programmed with Claude — not just the content, but the WordPress plugins, the data pipelines, the subscription logic, the SEO layer. Every custom plugin started as a conversation about what I needed, iterated through implementation, and shipped to production. The AI-first development approach is what makes a one-person operation compete with funded startups that have engineering teams.
The analytical approach extends beyond StaySTRA — the same data-driven philosophy drives the Valuation Engine, a self-improving AI that predicts home sale prices nightly.
Why I built it
I’ve been in Austin real estate since 2009, and I watched the STR investment space get flooded with bad data and worse advice. Most “analytics” tools were either too expensive for individual investors or just repackaging publicly available data behind a paywall with a nicer chart.
I wanted something that actually helped investors make decisions — honest numbers, current regulations, real market context. The AI-first approach meant I could build what would normally require a team of engineers and data analysts, solo. The result is a real SaaS product with real subscribers, built and operated by one person and an AI.