TerraVue — US Property Analysis

Buying a home is often the largest financial decision a person makes, yet most advice relies on vague estimates or calculators that apply a national average to a local decision. This skill replaces guesswork with a 500-scenario Monte Carlo simulation grounded in FHFA house-price records at ZIP granularity back to 1975, plus Zillow ZORI rents and Census ACS data.

What it does

• Buy vs. Rent Analysis returns the probability that buying outperforms renting over a specific horizon, modeling the renter investing the down payment and any monthly difference so both sides are compared on the same money over the same clock.
• Affordability Modeling determines whether a specific property fits a buyer's profile, or solves for the maximum purchase price under lender DTI limits and cash on hand — and reports which limit actually binds.
• Location Benchmarking retrieves ZIP-level appreciation estimates, school ratings, tax and insurance rates, walkability, water quality and market rents.
• Comparative Analysis ranks multiple ZIPs side by side on appreciation, schools, education level and water quality.
• Breakeven Estimation reports the year buying overtakes renting — or states plainly that it does not within the horizon, which is frequently the honest answer.

How it works

1. Gather context by identifying intent — evaluating a specific address, or determining a general budget.
2. Retrieve local data from the address or ZIP to pull regional tax, insurance, rent and appreciation history.
3. Run simulations across 500 scenarios, sampling from each ZIP's own historical volatility rather than a national figure.
4. Report probabilities rather than verdicts, along with the assumptions driving them.

Frameworks & tools

A remote MCP server at https://api.terravue.app/mcp — no install, no runtime, no API key. It draws on FHFA HPI, Zillow ZORI and Census ACS.

Why this beats prompting it yourself

Language models reliably hallucinate mortgage math and have no access to ZIP-specific appreciation history. This skill forces the agent to call a dedicated engine and verified datasets instead of producing plausible-looking but incorrect figures — and, just as importantly, to refuse the questions the data cannot answer.

Use cases

• Determine whether an $800k home in Austin beats renting over a 5-year hold.
• Identify whether a buyer is limited by monthly income (DTI) or by liquid cash.
• Rank several ZIP codes against each other on appreciation and school ratings.
• See how sensitive a decision is to the assumed investment return, by running it at 9.5% and again at 4.5%.

Known limitations

US residential properties only. No individual property valuations, no listing lookups, and no sub-ZIP neighborhood forecasting — sub-ZIP appreciation edges mean-revert, so census-tract data describes where a home sits within its ZIP today and never predicts that pocket's future.