AI vision analyzes street-level imagery, satellite views, and property records to rank addresses by visible neglect and distress signals. Upload a list, get a scored CSV. Work the high-potential properties first.
Screen lists of addresses before pulling comps, skip tracing, or driving for dollars. Focus your outreach budget on properties that actually show signs of distress.
Attach a distress priority column to your inbound leads. Route the highest-scoring addresses to your best negotiators instead of working the list blind.
Identify properties where the exterior already signals renovation need. A high distress score often correlates with the kind of deferred maintenance that creates deal opportunity.
You can pull a county list, run absentee filters, even skip trace the owners. But none of that tells you whether the property looks neglected, the roof is deteriorating, or the yard has been abandoned. That information costs you a drive-by or a satellite tab you check one address at a time.
The gold standard for visual assessment, but you cover 30 to 50 properties an hour and still need to record what you see. It does not scale to thousands.
Absentee owner, tax delinquency, and code violations narrow a list, but they miss properties that are visually distressed without a matching public record.
You can open Google Maps and look at every address yourself. At two minutes each, a 500-address list takes over 16 hours of screen time.
Score a single property directly in the app, or upload a CSV of addresses for batch processing. The system validates and deduplicates before scoring begins.
The pipeline fetches street-level imagery, satellite views, and property record data. AI vision models evaluate each source independently, producing distress scores, confidence levels, and lists of specific signals detected.
Get a composite distress score for every address, plus sub-scores and signal breakdowns. For batch jobs, download a scored CSV ready for your CRM or outbound workflow.
Each address is analyzed from multiple angles. Sub-scores are weighted and fused into a single composite, with satellite confidence automatically reduced when tree cover or obstructions limit visibility. You get the score and the reasoning behind it.
AI analyzes the street-level image for visible distress: peeling paint, roof damage, overgrown vegetation, boarded windows, structural deterioration, and more. Returns a 1–10 score with confidence and a list of specific signals observed.
Aerial imagery is evaluated for roof condition, yard neglect, debris, and property footprint anomalies. The model explicitly flags when heavy tree canopy limits its view and reduces its own confidence accordingly.
Structured data from public records: year built, estimated value, square footage, lot size, roof type, wall type, owner occupancy status, and other indicators that add context to the visual scoring.
Every scored address returns a composite distress score, individual sub-scores from each data source, confidence levels, and a list of the specific signals the AI detected. You get the number and the evidence behind it.
In batch mode, every field is included in your downloadable CSV. In single-score mode, the app renders the full breakdown interactively.
Most lead scoring tools rely on one data type. This pipeline triangulates across street imagery, aerial views, and public records. If one source is weak (tree cover on satellite, no Street View available), the composite adjusts automatically.
Every score comes with the signals that drove it. You see what the AI noticed: "peeling paint," "overgrown yard," "roof wear." Your team can evaluate whether the signals match what matters in your market.
Upload a CSV, run a batch job, download a scored file. The system handles deduplication, tracks job status, and stores history. Designed for the workflow you already have, not a new one you need to learn.
Confidence scores are first-class outputs. When the satellite view is blocked by tree canopy, the model says so and reduces its own confidence. You always know how much weight to give each score.
The score reflects what the AI observes in imagery and records. It is a screening tool, not an inspection report. Confidence levels are included precisely because models are imperfect. Properties should always be verified before making acquisition decisions.
The model explicitly detects canopy obstruction. When it cannot see the roof or yard, it reduces its own confidence score. The composite formula then gives less weight to that satellite sub-score, so tree-covered properties are not falsely rated as low-distress.
US addresses with available Street View and satellite imagery. Coverage depends on Google's imagery database. Rural properties or very new construction may have limited imagery. If an image source is unavailable, the system scores using whichever sources succeed.
One credit covers one scored address in a batch job. Credits are purchased through the app via Stripe. If an address fails to resolve during scoring, the credit is released back to your balance. Unused credits do not expire.
Images are used internally during scoring but are not displayed to end users. The value delivered is the scores, signals, and property data. This keeps the service focused on actionable output rather than image browsing.
No. This is a screening and prioritization tool. It does not replace property inspections, appraisals, title searches, or professional investment advice. Scores indicate relative visible distress, not property value or deal viability.
Sign up, buy address credits, and upload your first CSV. Results come back ranked with scores and signals for every address.
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