Trusted by teams turning documents into production data
Real Estate
Appraisals establish property value through comparable sales analysis. Title reports confirm ownership and identify liens. Both are critical to real estate lending. Use Sensible to get structured appraisal and title data for underwriting, due diligence, and closing.
Validated JSON
Schema-enforced output; every field matches your contract
Source coordinates
Every value links back to page + bounding box for audit
Per-document pricing
Predictable cost. No token-volatility surprises
Trusted by teams turning documents into production data
Comparable-sales grids, title exception language, and multiple valuation approaches each behave differently on the page. We extract the grids, parse the legal descriptions, and validate the figures against your underwriting schema.
01
Subject property and up to three comparables, each with adjustment lines for every attribute. Every cell in the URAR 1004 grid (sale price, adjustments, GLA, lot size) gets extracted, preserving the grid structure for automated valuation.
02
Easements, liens, deed restrictions, encumbrances: Schedule B exceptions are listed as numbered items with legal descriptions and recording references. Each exception returns as a separate structured entry.
03
Income approach, cost approach, sales comparison: commercial appraisals use all three, each with its own calculation tables. All approaches are extracted with their supporting data so your review team can evaluate the reconciled value.
Managed services
Solutions engineers handle plan, build, deploy, and adjust on your behalf. You see clean JSON in your API response. Same engine as self-serve, just with the configuration work outsourced.
What's included
01Plan.Engineers review your samples and pick the right method
02Build.SenseML configs written from your samples
03Deploy.Same engine as self-serve, ready for production
04Adjust.We update configs when formats shift or new edge cases appear
05Integrate.Help with custom integration into your downstream systems
Every appraisal and title report is different, so we build the config around your exact schema rather than a fixed list. These are the fields underwriters request most; we map whatever else your underwriting workflow needs.
01
Subject property
Address, legal description, parcel number, property type, year built, GLA, lot size, room count, condition, zoning
02
Valuation data
Appraised value, effective date, sales comparison value, cost approach value, income approach value, comparable sale prices, adjustments
03
Title information
Vesting, legal description, prior deeds, Schedule B exceptions, liens, easements, CC&Rs, insured amount, title company
config.json
SenseML
{ /* SenseML: appraisal extraction */
"fields": [
{
"method": {
"id": "queryGroup",
"queries": [
{ "id": "appraised_value", "description": "appraised value, market value, opinion of value" },
{ "id": "effective_date", "description": "effective date, date of value, as of date" },
{ "id": "gla", "description": "gross living area, GLA, total living area, square feet" },
{ "id": "property_address", "description": "subject property address, property address, subject address" }
// + more fields, mapped to your schema
]
}
}
]
}Sensible processes URAR (1004), small residential income property forms, and commercial appraisals alongside title commitments and title policies. Hybrid extraction handles the range of form types and legal language.
URAR Form 1004, 2055 exterior-only, commercial appraisals, desktop appraisals, BPOs, AVMs
Title commitments, preliminary title reports, title insurance policies (owner's and lender's), lien searches
Answers about URAR grid extraction, title exception parsing, and valuation data.
Out of the box, Sensible pulls property address, legal description, parcel number, property type, year built, GLA, lot size, room count, condition rating, and site improvements. Those fields live in configuration, so you add, drop, or rename them to match what your review process actually needs.
Yes. From title reports, Sensible extracts Schedule B exceptions, easements, liens, deed restrictions, and encumbrances. Each exception is returned as a separate item with its description and recording reference.
The default config returns appraised value, effective date, the three approach values (sales comparison, cost, income), reconciled value, and the appraiser's certification details. Since extraction is defined in configuration, you can pull additional fields off the form or reshape the output to fit your valuation pipeline.
Yes. Sensible reads the comparable sales grid, including address, sale price, sale date, GLA, lot size, and each adjustment line. URAR (1004) and summary formats both have configs today, and when a layout you work with hasn't been built yet, the extraction logic is extended to cover it.
Yes. Sensible sends extraction results to your webhook endpoint when processing completes. You can also poll the API for status.
Yes. Sensible flags extractions with low confidence for human review. You can configure review thresholds and workflows.
Sensible is SOC 2 Type II certified and HIPAA compliant. Data is encrypted in transit and at rest.
Document data is stored indefinitely by default. Custom retention policies are available and can be configured for same-day deletion if needed.
Yes. Sensible offers a 14-day free trial on the Growth plan. No credit card required to start.
Sensible uses per-document pricing for predictable costs. No token-based billing or usage surprises. Volume discounts are available for higher throughput.
Sensible provides REST APIs and SDKs for Python and Node.js. Most integrations take a few hours. Webhooks, Zapier, and direct API calls are all supported.
Sensible processes PDFs (native or scanned), Microsoft Word (DOC, DOCX), spreadsheets (XLSX, XLS, CSV), single-page images (JPEG, PNG), multi-page images (TIFF), and email bodies with attachments.
Accuracy depends on document quality and configuration. Most production deployments achieve 95%+ accuracy with proper validation rules and confidence signals.
Processing speed depends on document size, page count, OCR requirements, and which extraction methods are used. Simple single-page documents process in seconds. Larger or more complex documents that use LLM-based extraction take longer.
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