AI-derived property condition score from listing photos

GET /api/properties/property-condition/

2 credits

What it returns

An AI condition assessment for a US residential property — overall condition category (Excellent/Good/Fair/Poor), confidence score, per-room breakdown when photos are available, and any flags (water damage, deferred maintenance signals). Built from a computer-vision pipeline on the listing's photo set.

When to use it

• Filter MLS feeds to move-in-ready vs. fixer candidates.
• Adjust AVM expectations based on actual condition.
• Pre-screen photos to skip a manual review pass.

Pricing

2 credits.

FAQ

What model is used?

A multimodal AI vision pipeline; specific models aren't disclosed. Outputs are described in domain terms (Excellent, Good, …) so the contract is stable across model swaps.

Does it work on listings without photos?

No — returns null overall and a low confidence score. Use property-condition-custom-photos to upload your own (NB: that endpoint is currently broken; see [DEVELOPER_API_REPORT.md]).

Parameters

• property_address (query, required)

Responses

• 200
• 400 — Bad request — a required parameter is missing or failed validation. Costs 0 credits.
• 401 — Authentication failed — missing or invalid API key. Costs 0 credits.
• 402 — Payment required — no active subscription or insufficient credit balance. Costs 0 credits.
• 404 — Not found — the address could not be confidently matched to a single property. The body includes a did_you_mean array of suggested addresses (each with an mpr_id) to show the user and retry with; an empty array means no close match. Costs 0 credits.

More in AI-Powered Analysis

• Composite full property report — everything aggregated — GET /api/properties/full-report/
  The most comprehensive property analysis endpoint — aggregates 11 internal sources into a single response with 292 data fields.
• Property condition analysis with your own photos — POST /api/properties/property-condition-custom-photos/
  An overall property-condition rating and confidence score, combining a database lookup with AI vision analysis of property photos you supply.
• AI-powered square-footage breakdown by floor — GET /api/properties/sqft-per-floor/
  Per-floor square-footage distribution for a US residential property, derived from a multi-stage AI analysis (photo/room detection, architectural-style inference, and multi-method validation).
• Comparable sold properties (comps) for a subject property — GET /api/properties/comps/
  A ranked list of comparable sold properties within a search radius of the subject — each comp scored on similarity (size, beds/baths, year built, distance, condition delta) with the sale price and date.
• LLM-powered room-by-room renovation cost breakdown — POST /api/properties/renovation-cost-breakdown/
  An itemized room-by-room renovation cost breakdown powered by a multimodal LLM analyzing the property's images plus its size/feature metadata.
• Local market outlook (buyer's/balanced/seller's) for a ZIP — GET /api/properties/market-outlook/
  A composite Market Outlook score (0-100) classifying a ZIP code as a buyer's market (<40), balanced (40-60), or seller's market (>60).
• Solar potential + AI roof condition for a property — GET /api/solar/analysis/
  Two coupled analyses in one call: