TerraVue

Can this buyer afford this home? Returns the monthly PITI payment, front-end and back-end debt-to-income ratios (what a lender qualifies on), the income needed at a 28% front-end ratio, estimated after-tax money left over each month, the cash cushion left after closing, and a plain Comfortable / Qualifiable-tight / Stretch verdict. monthly_debts = recurring debt obligations a lender counts (car, student loan, minimum credit-card) — NOT living costs; this feeds DTI. monthly_expenses = living costs (food, utilities, childcare) used ONLY for the 'left over each month' life check, never DTI. Pass `address` to ground property tax + insurance in the ZIP's real rates. Pass `liquid_savings` to get the post-closing cash cushion (months of payment covered).

What's the most this buyer can afford? The reverse of `affordability` — 'what price can I afford?' instead of 'can I afford this specific home?'. Solves for the maximum home price under standard lender limits: a front-end DTI cap (default 28% = housing / gross income) and a back-end cap (default 36% = housing + other debts / gross income). If `liquid_savings` is given, also caps by the cash available for down payment + closing and reports which limit binds. Returns the affordable price, the PITI and DTIs at that price, and the cash to close. Pass `address` to ground property tax + insurance in the ZIP's real rates. This is a lender-limit ceiling, not a comfort recommendation — feed the result into `affordability` (or buy_vs_rent) to check monthly slack and whether buying pencils.

Look up a US address (or bare 5-digit ZIP): ZIP-level home-price appreciation forecast (FHFA data back to 1975), estimated market rent, and neighborhood signals (schools, walkability, water quality, grocery/dining/outdoors proximity). For a full street address this also returns sub-ZIP `nbhd*` fields describing the specific census tract - how its prices, incomes and build era compare to the rest of the ZIP. That is POSITION, not a neighborhood forecast; see _units. property_type: sfr | condo | townhome | multi

Should someone buy this home or keep renting? Runs the TerraVue engine: a deterministic 30-year simulation plus a 500-scenario Monte Carlo over correlated market paths. Returns the probability buying wins, the breakeven hold period, and net-worth outcomes. monthly_rent = what the person would pay to rent THE SAME HOME to live in — an owner-occupant buy-vs-rent decision, NOT rental income they'd collect as a landlord (this tool does not model rental cash flow; that lens lives on terravue.app). A rent far out of line with home_price is almost certainly a misunderstanding — confirm it before trusting the verdict. If `address` is given, the ZIP's real appreciation forecast and regional tax/insurance defaults are used (explicit parameters still win).

Analyze and RANK 2-12 ZIPs or addresses side by side on the signals TerraVue has: home-price appreciation forecast (and how each compares to the national average), school rating, walkability, education level, and water quality. Returns the correct city name per ZIP, so locations are never guessed or mislabeled. Use for "which areas have the best appreciation / schools / upside." TerraVue has no listing inventory and cannot discover ZIPs on its own — to scan a metro, pass that metro's ZIP codes (the ranking then covers exactly what you passed).

Record a question TerraVue could not answer, so it can be built. Call this whenever a user asks for something outside the current tools' coverage (rental metrics, Airbnb modeling, valuations, move-vs-stay, non-US, anything else), then tell the user their request was captured. Keep `description` to the capability needed — no names, emails, or other personal details. category: rental_metrics | str_airbnb | valuation | move_vs_stay | data_coverage | other