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Document Intelligence mortgage document models

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This content applies to: checkmark v4.0 (preview) checkmark

The Document Intelligence Mortgage models use powerful Optical Character Recognition (OCR) capabilities and deep learning models to analyze and extract key fields from mortgage documents. Mortgage documents can be of various formats and quality. The API analyzes mortgage documents and returns a structured JSON data representation. The models currently support English-language documents only.

Supported document types:

  • Uniform Residential Loan Application (Form 1003)
  • Uniform Underwriting and Transmittal Summary (Form 1008)
  • Closing Disclosure form

Development options

Document Intelligence v4.0 (2024-02-29-preview) supports the following tools, applications, and libraries:

Feature Resources Model ID
Mortgage model • Document Intelligence Studio
• REST API
• C# SDK
• Python SDK
• Java SDK
• JavaScript SDK		 • prebuilt-mortgage.us.1003
• prebuilt-mortgage.us.1008
• prebuilt-mortgage.us.closingDisclosure

Input requirements

  • For best results, provide one clear photo or high-quality scan per document.

  • Supported file formats:

    Model PDF Image:
    JPEG/JPG, PNG, BMP, TIFF, HEIF    		 Microsoft Office:
    Word (DOCX), Excel (XLSX), PowerPoint (PPTX), and HTML
    Read ✔ ✔ ✔
    Layout ✔ ✔ ✔ (2024-02-29-preview, 2023-10-31-preview)
    General Document ✔ ✔
    Prebuilt ✔ ✔
    Custom extraction ✔ ✔
    Custom classification ✔ ✔ ✔ (2024-02-29-preview)

  • For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).

  • The file size for analyzing documents is 500 MB for paid (S0) tier and 4 MB for free (F0) tier.

  • Image dimensions must be between 50 x 50 pixels and 10,000 px x 10,000 pixels.

  • If your PDFs are password-locked, you must remove the lock before submission.

  • The minimum height of the text to be extracted is 12 pixels for a 1024 x 768 pixel image. This dimension corresponds to about 8-point text at 150 dots per inch (DPI).

  • For custom model training, the maximum number of pages for training data is 500 for the custom template model and 50,000 for the custom neural model.

    • For custom extraction model training, the total size of training data is 50 MB for template model and 1G-MB for the neural model.

    • For custom classification model training, the total size of training data is 1GB with a maximum of 10,000 pages.

Try mortgage documents data extraction

To see how data extraction works for the mortgage documents service, you need the following resources:

  • An Azure subscription—you can create one for free.

  • A Document Intelligence instance in the Azure portal. You can use the free pricing tier (F0) to try the service. After your resource deploys, select Go to resource to get your key and endpoint.

Screenshot of keys and endpoint location in the Azure portal.

Document Intelligence Studio

  1. On the Document Intelligence Studio home page, select mortgage.

  2. You can analyze the sample mortgage documents or upload your own files.

  3. Select the Run analysis button and, if necessary, configure the Analyze options:

    Screenshot of Run analysis and Analyze options buttons in the Document Intelligence Studio.

Try Document Intelligence Studio

Supported languages and locales

See our Language Support—prebuilt models page for a complete list of supported languages.

Field extraction 1003 Uniform Residential Loan Application (URLA)

The following are the fields extracted from a 1003 URLA form in the JSON output response.

Name Type Description Example output
LenderLoanNumber String Lender loan number or universal loan identifier 10Bx939c5543TqA1144M999143X38
AgencyCaseNumber String Agency case number 115894
Borrower Object An object that contains the borrower's identity markers such as name, SSN, birth date.
Co-Borrower Object An object that contains the Co-Borrower's names, and signed date.
CurrentEmployment Object An Object that contains information about the current employment including: Employer name, Employer Phone number, Employer address.
Loan Object An object that contains loan information including: amount, purpose type, refinance type.
Property object An object that contains information about the property including: address, number of units, value.

The 1003 URLA key-value pairs and line items extracted are in the documentResults section of the JSON output.

Field extraction 1008 Uniform Underwriting and Transmittal Summary

The following are the fields extracted from a 1008 form in the JSON output response.

Name Type Description Example output
Borrower Object An object that contains information about the borrower including: name, and number of borrowers.
Property Object An object that contains information about the property including: address, occupancy status, sales price.
Mortgage Object An object that contains information about the mortgage including: Loan type, amortization type, loan purpose type.
Underwriting Object An object that contains information about the underwriting information including: underwriter name, appraiser name, borrower income.
Seller Object An object that contains information about the seller including: Name, address, number.

The form 1008 key-value pairs and line items extracted are in the documentResults section of the JSON output.

Field extraction mortgage closing disclosure

The following are the fields extracted from a mortgage closing disclosure form in the JSON output response.

Name Type Description Example output
Closing Object An object that contains information about the closing information including: Issue date, Closing date, Disbursement date.
Transaction Object An object that contains information about the transaction information including: Borrowers name, Borrowers address, Seller name.
Loan Object An object that contains loan information including: term, purpose, product.

The mortgage closing disclosure key-value pairs and line items extracted are in the documentResults section of the JSON output.

Next steps