Finance
37 columns
200 rows
8 formats

Finance Mortgage Application Features

This dataset provides a detailed, structured view of mortgage application features, including applicant demographics, financials, property details, and application status. It is ideal for banks and insurtech startups seeking to build AI/ML models for risk assessment, approval automation, and portfolio analytics. The schema is designed for high data integrity and regulatory compliance.

last updated 20 Aug 2025
by GoMask

The brief that made it

AI/ML-based mortgage approval and risk modeling

Sample rows

preview · 8 of 200 rows · all 37 columns

application_idstring	employment_statusstring	annual_incomefloat	bankruptcy_historyboolean	address_countrystring	applicant_first_namestring	applicant_last_namestring	applicant_date_of_birthdate	applicant_ssnstring	applicant_emailstring	applicant_phonestring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	employer_namestring	credit_scoreinteger	loan_amount_requestedfloat	property_valuefloat	property_typestring	property_address_streetstring	property_address_citystring	property_address_statestring	property_address_postal_codestring	property_address_countrystring	down_payment_amountfloat	loan_term_yearsinteger	interest_rate_typestring	co_applicant_presentboolean	co_applicant_annual_incomefloat	co_applicant_credit_scoreinteger	application_datedate	application_statusstring	existing_debts_totalfloat	monthly_liabilitiesfloat	assets_totalfloat	foreclosure_historyboolean
APP-0001	employed	128500	false	USA	Jessica	Nguyen	1990-04-12	529-34-1832	jessica.nguyen90@gmail.com	+1 415-890-2345	1264 Pine St	San Francisco	CA	94109	Salesforce Inc	787	780000	880000	single-family	3045 Market Ave	San Francisco	CA	94110	USA	100000	30	fixed	true	102000	753	2023-08-13	approved	32000	1850	420000	false
APP-0002	student	0	false	India	Ananya	Srinivas	1997-12-21	771-66-2043	ananya.srinivas97@outlook.in	+91 98765 43012	12 MG Road	Bangalore	Karnataka	560001	blank	514	34000	42000	condo	23 Residency Rd	Bangalore	Karnataka	560025	India	8000	10	adjustable	true	68000	726	2024-01-07	under_review	0	0	1200	false
APP-0003	retired	27500	true	Ireland	Michael	O'Connor	1962-06-08	412-28-9002	michael.oconnor62@eir.ie	+353 86 475 8230	17 Dame St	Dublin	Leinster	D02 F6Y4	blank	612	59000	90000	townhouse	45 St Stephen's Green	Dublin	Leinster	D02 HP90	Ireland	31000	15	adjustable	false	blank	blank	2023-09-02	rejected	5200	330	124000	false
APP-0004	employed	153000	false	UK	Oliver	Smith	1988-03-29	351-22-9840	oliver.smith88@yahoo.com	+44 20 7946 2123	22 Bishopsgate	London	Greater London	EC2N 4AJ	Barclays PLC	831	550000	670000	condo	8 Finsbury Circus	London	Greater London	EC2M 7EA	UK	120000	25	fixed	true	88000	802	2023-10-13	approved	21000	1450	335000	false
APP-0005	employed	48000	false	Nigeria	Fatima	Adebayo	1995-11-18	658-37-1202	fatima.adebayo95@mail.ng	+234 802 456 7890	17 Marina Road	Lagos	Lagos	100001	Total Nigeria	677	89000	110000	multi-family	98 Victoria Island	Lagos	Lagos	101241	Nigeria	21000	20	variable	false	blank	blank	2023-07-18	under_review	6500	430	88000	false
APP-0006	self-employed	119000	false	Mexico	Gabriel	Martinez	1973-09-23	598-19-4176	gabriel.martinez73@correo.mx	+52 55 1234 6789	101 Avenida Reforma	Mexico City	CDMX	06500	Martinez Consultores	703	120000	160000	townhouse	405 Polanco	Mexico City	CDMX	11550	Mexico	40000	18	variable	false	blank	blank	2023-06-30	approved	18000	1040	263000	false
APP-0007	student	0	false	USA	Emily	Johnson	2003-10-05	207-88-9257	emily.johnson03@icloud.com	+1 646-555-2985	521 8th Ave	New York	NY	10018	blank	335	12000	18000	other	921 Broadway	New York	NY	10003	USA	6000	5	adjustable	true	42000	651	2024-02-28	submitted	0	0	400	false
APP-0008	employed	195000	false	China	Wei	Zhang	1982-07-17	384-65-3211	wei.zhang82@163.com	+86 138 1234 5678	88 Nanjing Rd	Shanghai	Shanghai	200001	Alibaba Group	822	770000	960000	single-family	2 Xintiandi	Shanghai	Shanghai	200021	China	190000	25	fixed	false	blank	blank	2023-11-19	approved	32000	2100	580000	false
APP-0009	self-employed	92000	false	USA	Sarah	Lee	1985-11-28	684-74-2547	sarah.lee85@outlook.com	+1 312-381-2298	1353 N Wells St	Chicago	IL	60610	Lee Architecture	701	210000	290000	condo	715 S Wabash Ave	Chicago	IL	60605	USA	80000	22	variable	false	blank	blank	2023-12-08	under_review	5400	600	116000	false
APP-0010	employed	105000	false	Russia	Svetlana	Petrova	1977-02-13	485-19-6033	svetlana.petrova77@yandex.ru	+7 921 345 6789	66 Nevsky Prospekt	Saint Petersburg	Leningrad Oblast	191025	Gazprom	718	52000	85000	manufactured	34 Primorsky	Saint Petersburg	Leningrad Oblast	197198	Russia	33000	15	variable	false	blank	blank	2023-08-23	submitted	2100	120	97000	false
APP-0011	retired	26000	true	USA	Ava	Stewart	1949-05-15	190-56-7821	ava.stewart49@aol.com	+1 212-934-2840	212 E 72nd St	New York	NY	10021	blank	489	15000	38000	manufactured	41 Park Ave	New York	NY	10016	USA	23000	7	adjustable	false	blank	blank	2023-10-01	rejected	4200	220	47000	false
APP-0012	employed	117000	false	USA	Lucas	Miller	1984-08-18	433-54-1109	lucas.miller84@gmail.com	+1 617-555-9001	123 Beacon St	Boston	MA	02108	Liberty Mutual	781	430000	540000	single-family	84 Commonwealth Ave	Boston	MA	02116	USA	110000	28	fixed	true	89000	763	2023-09-11	approved	38000	2100	315000	false
APP-0013	student	0	false	India	Priya	Gupta	1999-03-10	804-21-5623	priya.gupta99@rediffmail.com	+91 98304 11472	44 Park Street	Kolkata	West Bengal	700016	blank	529	18000	25000	condo	80 Ballygunge	Kolkata	West Bengal	700019	India	7000	12	variable	true	50000	660	2024-03-03	submitted	0	0	900	false
APP-0014	employed	132000	false	Japan	Hiroshi	Tanaka	1972-05-02	304-22-7845	hiroshi.tanaka72@docomo.jp	+81 90 1234 5678	5 Chome Ginza	Tokyo	Tokyo	104-0061	Hitachi	746	250000	320000	condo	2 Shibuya	Tokyo	Tokyo	150-0002	Japan	70000	20	fixed	false	blank	blank	2023-07-27	under_review	15000	1100	197000	false
APP-0015	employed	96000	false	UAE	Ahmed	Al-Farsi	1986-02-14	553-47-6923	ahmed.alfarsi86@emirates.ae	+971 50 123 4567	22 Sheikh Zayed Rd	Dubai	Dubai	00000	Emirates Airlines	681	78000	120000	multi-family	15 Marina Walk	Dubai	Dubai	00001	UAE	42000	16	variable	false	blank	blank	2024-04-01	under_review	9000	550	56000	false
APP-0016	employed	71000	false	Spain	Sofia	Garcia	1992-09-07	275-58-2119	sofia.garcia92@yahoo.com	+34 627 123 456	32 Gran Via	Madrid	Madrid	28013	Telefonica	723	220000	270000	single-family	49 Calle Serrano	Madrid	Madrid	28001	Spain	50000	24	fixed	true	76000	728	2023-12-16	submitted	23000	1800	128000	false
APP-0017	retired	33000	false	Poland	Victor	Kowalski	1968-11-22	309-95-2045	victor.kowalski68@wp.pl	+48 601 234 567	11 Nowy Swiat	Warsaw	Mazowieckie	00-001	blank	545	43000	63000	manufactured	22 Saska	Warsaw	Mazowieckie	03-948	Poland	20000	11	adjustable	false	blank	blank	2023-08-17	rejected	3900	280	35000	true
APP-0018	employed	101000	false	USA	Chloe	Robinson	1986-10-24	893-88-7652	chloe.robinson86@comcast.net	+1 303-555-1298	875 Broadway	Denver	CO	80203	UCHealth	764	320000	400000	condo	392 Lincoln St	Denver	CO	80203	USA	80000	21	fixed	true	95000	791	2023-10-25	approved	18000	1200	191000	false
APP-0019	self-employed	108000	false	Switzerland	Isabella	Lopez	1994-01-29	419-23-7865	isabella.lopez94@protonmail.ch	+41 78 123 4567	14 Bahnhofstrasse	Zurich	Zurich	8001	Lopez Designs	718	185000	235000	condo	99 Limmatquai	Zurich	Zurich	8001	Switzerland	50000	20	variable	false	blank	blank	2023-09-09	under_review	13000	1050	143000	false
APP-0020	retired	17400	true	USA	Jacob	Williams	1959-12-12	267-21-8120	jacob.williams59@verizon.net	+1 646-555-8392	217 W 84th St	New York	NY	10024	blank	401	12000	17000	other	77 Riverside Dr	New York	NY	10024	USA	5000	6	adjustable	false	blank	blank	2024-01-03	rejected	800	100	17200	true

What the 200 rows show

from the 200-row sample

Retired (employment status) stands out: 16 of its 23 rows have bankruptcy_history = true, against 5 of 177 for the rest.

11%bankruptcy_history = true
88,000median annual_income
3interest rate types
5application statuses
6property types
22property address countries

Bankruptcy history rate by employment_statusbankruptcy_history = true

annual_income200 rows, in bands of 20k

Median 88,000, from 0.0 to 195,000.

address_country200 rows · top 10 of 22 values

USA79
Mexico14
Spain12
Russia11
UAE11
India10
Poland9
Ireland8
UK8
China8

37 columns by typefrom the column list below

string 21
integer 3
float 8
date 2
boolean 3

Columns

37 columns in four groups

blueprint · 37 columns
column	type	description	example
Text 21 columns
`application_id`	string	Unique identifier for the mortgage applicationunique	APP-0001
`applicant_first_name`	string	Applicant's first name	Jessica
`applicant_last_name`	string	Applicant's last name	Nguyen
`applicant_ssn`	string	Applicant's Social Security Number (or equivalent national ID)unique	529-34-1832
`applicant_email`	string	Applicant's email addressunique	michael.oconnor62@eir.ie
`applicant_phone`	string	Applicant's phone number	+1 415-890-2345
`address_street`	string	Applicant's current street address	1264 Pine St
`address_city`	string	Applicant's current city	San Francisco
`address_state`	string	Applicant's current state or province	CA
`address_postal_code`	string	Applicant's postal or ZIP code	94109
`address_country`	string	Applicant's country of residence	USA
`employment_status`	string	Applicant's current employment statusemployed · self-employed · unemployed · retired · student	employed
`employer_name`	string	Name of applicant's employer (if employed or self-employed)optional	Salesforce Inc
`property_type`	string	Type of property being mortgaged6 values	single-family
`property_address_street`	string	Street address of the property to be mortgaged	3045 Market Ave
`property_address_city`	string	City of the property to be mortgaged	San Francisco
`property_address_state`	string	State or province of the property to be mortgaged	CA
`property_address_postal_code`	string	Postal or ZIP code of the property to be mortgaged	94110
`property_address_country`	string	Country of the property to be mortgaged	USA
`interest_rate_type`	string	Type of interest rate requestedfixed · variable · adjustable	fixed
`application_status`	string	Current status of the mortgage applicationsubmitted · under_review · approved · rejected · withdrawn	approved
Numbers 11 columns
`annual_income`	float	Applicant's annual income in local currency0 or more	128500
`credit_score`	integer	Applicant's credit score as reported by a credit bureau300 to 850	787
`loan_amount_requested`	float	Total mortgage loan amount requested0 or more	780000
`property_value`	float	Appraised value of the property to be mortgaged0 or more	880000
`down_payment_amount`	float	Amount of down payment provided by the applicant0 or more	100000
`loan_term_years`	integer	Requested loan term in years1 to 40	30
`co_applicant_annual_income`	float	Annual income of the co-applicant (if present)0 or more · optional	102000
`co_applicant_credit_score`	integer	Credit score of the co-applicant (if present)300 to 850 · optional	753
`existing_debts_total`	float	Total outstanding debts of the applicant0 or more	32000
`monthly_liabilities`	float	Total monthly liabilities (e.g., credit card, auto loans)0 or more	1850
`assets_total`	float	Total assets held by the applicant0 or more	420000
Dates and times 2 columns
`applicant_date_of_birth`	date	Applicant's date of birth	1990-04-12
`application_date`	date	Date the mortgage application was submitted	2023-08-13
True or false 3 columns
`co_applicant_present`	boolean	Indicates if a co-applicant is present on the application	true
`bankruptcy_history`	boolean	Indicates if the applicant has a history of bankruptcy	false
`foreclosure_history`	boolean	Indicates if the applicant has a history of foreclosure	false

Use it for

A finance dashboard
The bankruptcy_history rate, annual_income by employment_status and a breakdown of address_country. Excel, Power BI or Tableau.
Why do 21 of 200 rows have bankruptcy_history = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP-0001128500employed
APP-000327500retired
APP-001126000retired
A software demo
Believable applications with applicant_first_name, applicant_last_name and applicant_date_of_birth to fill a screen in front of a buyer.

This dataset200 rows37 columns

Yours1,000 rows37 columnsaddress_street: UK only

blueprint · finance-mortgage-application-features

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 37 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

Include property value and location
Segment applicants by income bracket
Track approval status
Flag applications with missing documents

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: finance-mortgage-application-features

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?