Mortgage Underwriting Data

This dataset provides a detailed record of residential mortgage applications, including applicant demographics, property appraisals, title search results, income documentation, loan-to-value calculations, and compliance with GSE guidelines. It is ideal for risk assessment, regulatory compliance analysis, and process optimization in mortgage underwriting.

  • last updated 1 Nov 2025
  • by GoMask
The brief that made it

Automated mortgage risk assessment and approval workflow

Sample rows

preview · 8 of 500 rows · all 23 columns
application_idstringapplication_statusstringloan_to_value_ratiofloattitle_search_completedbooleanunderwriter_idstringapplicant_first_namestringapplicant_last_namestringapplicant_ssnstringapplicant_date_of_birthdateapplicant_incomefloatincome_documentation_typestringproperty_address_streetstringproperty_address_citystringproperty_address_statestringproperty_address_postal_codestringproperty_address_countrystringproperty_appraisal_valuefloatproperty_appraisal_datedatetitle_search_issuesstringloan_amountfloatgse_guideline_compliancebooleangse_guideline_notesstringapplication_submission_datedate
APP-00001Approved90trueUW-101OliverMurphy294-77-38121987-04-1298300.25W-21259 Maplewood Dr.New YorkNY10024USA7200002024-03-17blank648000trueblank2024-03-14
APP-00002Approved75trueUW-105SophiaWilliams681-45-27731976-10-03142000Tax Return3001 Longfellow Ave.ChicagoIL60618USA12000002024-02-28blank900000trueblank2024-02-25
APP-00003Approved80trueUW-102JacksonHernandez546-33-97141994-08-1971055.5Pay Stub2103 Pinecrest Rd.DallasTX75214USA3650002024-03-06blank292000trueblank2024-03-03
APP-00004Approved80trueUW-104EmilyJohnson413-22-78651963-01-3154000Tax Return1998 Rosewood Ct.San JoseCA95128USA4150002024-04-03Lien discovered, resolved332000trueLien resolved prior to closing.2024-03-30
APP-00005Pending0falseblankMatthewNguyen785-98-22312003-07-2216000Pay Stub2942 Oakridge Cir.TampaFL33606USA1270002024-01-26blank0falseblank2024-01-23
APP-00006Approved80trueUW-103MiaBaker312-11-76251980-12-1582500.75W-21320 Kings Hwy.BostonMA02118USA5580002024-02-17blank446400trueblank2024-02-14
APP-00007Approved60trueUW-105AvaSmith593-24-67231951-09-0241000Tax Return8830 Willow Ln.PortlandOR97209USA2550002024-02-09Title transfer delay153000trueblank2024-02-05
APP-00008Approved90trueUW-101JamesRoberts402-59-83321985-05-28172500W-24211 Cedar St.Los AngelesCA90019USA10000002024-03-13blank900000trueblank2024-03-10

What the 500 rows show

from the 500-row sample

Approved (application status) stands out: mean loan_to_value_ratio is 78.8, against 39.6 for the rest.

  • 92%title_search_completed = true
  • 80.0median loan_to_value_ratio
  • 4income documentation types
  • 49property address states
  • 102property address cities
  • 74,750median applicant_income
Mean loan_to_value_ratio by application_status500 rows
05010043.5Pending25 rows78.8Approved377 rows38.6Rejected98 rows
loan_to_value_ratio500 rows, in bands of 10
0105210390677442510520661050100loan_to_value_ratio →

Median 80.0, from 0.0 to 91.0.

underwriter_id461 rows with a value · 39 left blank
  1. UW-10440
  2. UW-10127
  3. UW-10526
  4. UW-10225
  5. UW-31225
  6. UW-10320
  7. UW-40517
  8. UW-58316
  9. UW-30316
  10. UW-20915
23 columns by typefrom the column list below
  • string 14
  • float 4
  • date 3
  • boolean 2

Columns

23 columns in four groups
blueprint · 23 columns
columntypedescriptionexample
Text 14 columns
application_idstringUnique identifier for each mortgage applicationuniqueAPP-00001
applicant_first_namestringApplicant's first nameOliver
applicant_last_namestringApplicant's last nameMurphy
applicant_ssnstringApplicant's Social Security Numberunique294-77-3812
income_documentation_typestringType of income documentation provided (e.g., W-2, tax return, pay stub)W-2 · Tax Return · Pay Stub · OtherW-2
property_address_streetstringStreet address of the property1259 Maplewood Dr.
property_address_citystringCity where the property is locatedNew York
property_address_statestringState where the property is locatedNY
property_address_postal_codestringPostal code of the property10024
property_address_countrystringCountry where the property is locatedUSAUSA
title_search_issuesstringDescription of any issues found during the title searchoptionalTitle transfer delay
gse_guideline_notesstringNotes regarding GSE guideline compliance or exceptionsoptionalPending tax clearance.
application_statusstringCurrent status of the mortgage applicationPending · Approved · Rejected · WithdrawnApproved
underwriter_idstringIdentifier for the underwriter assigned to the applicationoptionalUW-101
Numbers 4 columns
applicant_incomefloatApplicant's annual income in USD0 or more98300.25
property_appraisal_valuefloatAppraised value of the property in USD0 or more720000
loan_amountfloatRequested loan amount in USD0 or more648000
loan_to_value_ratiofloatLoan-to-value ratio (LTV) as a percentage0 to 10090
Dates and times 3 columns
applicant_date_of_birthdateApplicant's date of birth1987-04-12
property_appraisal_datedateDate of the property appraisal2024-03-17
application_submission_datedateDate the mortgage application was submitted2024-03-14
True or false 2 columns
title_search_completedbooleanIndicates whether the title search has been completedtrue
gse_guideline_compliancebooleanIndicates if the application complies with GSE (Government-Sponsored Enterprise) guidelinestrue

Use it for

  • title search c…92%461 of 500 rowsmean loan to value ra…43.5Pending78.8Approv…38.6Reject…

    A finance dashboard

    The title_search_completed rate, loan_to_value_ratio by application_status and a breakdown of underwriter_id. Excel, Power BI or Tableau.

  • Why do the 377 Approved rows have a mean loan_to_value_ratio of 78.8?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable applications with applicant_first_name, applicant_last_name and applicant_ssn to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 23 columns, your size and your rules. See 20 rows before you pay.

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This dataset500 rows23 columns
Yours1,000 rows23 columnsproperty_address_street: UK only

blueprint · mortgage-underwriting-data-2

Behind this dataset

Same schema. As many rows as you need.

These 500 rows came out of a blueprint — 23 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Property appraisal required
  • Loan-to-value ratio maximum 80% without PMI
  • Debt-to-income ratio maximum 43% (QM rule)
  • Employment verification: 2 years history
  • Income documentation: W-2, pay stubs, tax returns
  • Title search and insurance
  • Home inspection recommended
  • Flood zone determination
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
mortgage-underwriting-data-2

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