Verify a Property with a Local Human

Property Verification is useful when software knows what information or action it needs but the final step requires a person at a physical location. On HumanTask API, the request is framed as a bounded job: confirm observable facts about a property by sending a human to the address with a defined verification checklist. The requester defines the target, timing and proof before anyone accepts the work.

When to Use Property Verification

The best property verification jobs are concrete enough that two independent workers would understand the same objective. Examples include confirm that a building exists at the stated address; document the current exterior condition; check visible signage, access points or occupancy indicators; or verify a renovation milestone using a predetermined shot list. These are not broad consulting assignments. They are observable field actions that can be accepted, completed and checked.

Define a Property Verification Request

A requester should provide full address, property reference or unit information, and questions that can be answered by observation. It should also state access instructions, required photos, and deadline. The instruction should separate facts the worker can observe from decisions the AI or business will make later. That distinction prevents a simple field task from quietly turning into specialist advice.

Required inputs

• Full address
• Property reference or unit information
• Questions that can be answered by observation
• Access instructions
• Required photos
• Deadline

Evidence That Makes the Result Useful

For property verification, a useful completion package may contain property photos, address confirmation, structured checklist responses, timestamp, and notes describing access limitations or conflicting visible information. The evidence schema should be selected before dispatch. A task that merely says “send proof” is weaker than one that specifies which files, fields and observations are mandatory. The receiving agent can then test completeness without interpreting a chat message.

Suggested result fields

• Property photos
• Address confirmation
• Structured checklist responses
• Timestamp
• Notes describing access limitations or conflicting visible information

Failure Modes to Plan For

Real locations create exceptions that software APIs rarely face. In this capability, common examples are: the address is ambiguous, access requires authorization not supplied, the property cannot be identified from public areas, and the requested observation would require professional judgment rather than simple verification. Those outcomes should be returned as explicit exception states rather than hidden inside a free-text note. An AI agent can then retry with new instructions, choose another location, widen the deadline or escalate to a specialist.

Exception examples

• The address is ambiguous
• Access requires authorization not supplied
• The property cannot be identified from public areas
• The requested observation would require professional judgment rather than simple verification

Example Workflow for an AI Agent

Consider this workflow: A property acquisition workflow can verify a street address, collect the same exterior angles for every candidate asset, and attach the evidence to the deal record. The agent first decides that a physical check is necessary, then creates a property_verification task with the address, deadline and evidence fields. The worker accepts the job, completes only the permitted actions and submits the requested proof. After the result arrives, the software can validate required fields, store the media references and continue its original plan.

Who Uses Property Verification

Property Verification can support real-estate platforms, property managers, lenders, remote investors and AI property workflows. These users have different business goals, but they share the same bottleneck: the missing fact or action exists offline. A reusable task definition lets them solve that bottleneck without maintaining a field team in every city.

API Shape for Property Verification

The machine-facing representation should be narrow. A property_verification request can carry a normalized location, human-readable instructions, a deadline, budget, required evidence and a client reference. The response should return a task identifier and lifecycle state. Follow-up operations should expose status and evidence without forcing the caller to scrape a dashboard. MCP can present the same operation as an agent tool; REST and OpenAPI can serve conventional application integrations.

Example task object

{
  "capability": "property_verification",
  "location": {"address": "TARGET_ADDRESS"},
  "deadline": "ISO_8601",
  "instructions": "TASK-SPECIFIC_INSTRUCTIONS",
  "evidence_required": ["TASK_SPECIFIC_FIELDS"]
}

Launch a Property Verification Task

Start with one property verification request that has an unambiguous outcome. Define the location, deadline and proof first; then create the task through the web flow or the available developer interface. If the workflow repeats, promote the same evidence schema into a reusable integration.

What makes property verification different from a generic gig

The value is not simply that a person is available. The value is that the request is standardized enough for software to understand the expected result. For property verification, the schema should reflect the actual decision being supported: the agent needs evidence about confirm observable facts about a property by sending a human to the address with a defined verification checklist. That makes the task easier to price, route, compare and audit than an open-ended message to a freelancer.