Finance
32 columns
200 rows
8 formats

Mortgage Default Prediction Data

This dataset provides detailed mortgage loan records, including borrower profiles, property information, loan terms, payment history, and default/foreclosure status. It is designed to help lenders assess and predict mortgage default risk, optimize portfolio management, and support regulatory compliance and risk modeling initiatives.

last updated 20 Aug 2025
by GoMask

The brief that made it

Predicting mortgage default risk using machine learning models

Sample rows

preview · 8 of 200 rows · all 32 columns

loan_idstring	borrower_employment_statusstring	borrower_credit_scoreinteger	default_flagboolean	property_typestring	borrower_idstring	borrower_first_namestring	borrower_last_namestring	borrower_date_of_birthdate	borrower_annual_incomefloat	property_street_addressstring	property_citystring	property_statestring	property_postal_codestring	property_countrystring	loan_origination_datedate	loan_amountfloat	loan_term_monthsinteger	loan_interest_ratefloat	loan_typestring	down_payment_amountfloat	loan_to_value_ratiofloat	payment_due_datedate	last_payment_datedate	last_payment_amountfloat	payment_statusstring	number_of_missed_paymentsinteger	default_datedate	foreclosure_flagboolean	foreclosure_datedate	loan_servicerstring	portfolio_idstring
LOANXG2JQ9	employed	780	false	condo	B001	Emily	Smith	1986-07-13	72000	348 Maple Ave	Chicago	IL	60616	USA	2021-06-20	350000	360	3.9	fixed	35000	80	2024-07-01	2024-06-29	1670	current	0	blank	false	blank	Midwest Home Lending	PF1001
AAA12D4PLO	self-employed	819	false	single-family	B002	Marcus	Evans	1971-11-02	255000	19 Queen St	Toronto	ON	M5H2N2	Canada	2022-02-15	850000	300	2.6	fixed	170000	80	2024-07-05	2024-07-03	4100	current	0	blank	false	blank	Canadian Mortgage Corp	PF1002
LOANZTA7K4	employed	705	false	townhouse	B003	Priya	Sharma	1994-04-27	67000	2128 Market Rd	San Francisco	CA	94114	USA	2023-01-16	420000	240	3.7	fixed	42000	80	2024-06-30	2024-06-28	2325	current	0	blank	false	blank	Bay Area Finance	PF1003
LOANPUXJ92	retired	592	false	single-family	B004	Linda	Brown	1957-03-19	32000	77 Old Mill Rd	Manchester	ENG	M23 2AF	UK	2019-08-04	120000	180	7.2	FHA	6000	95	2024-07-10	2024-06-11	820	delinquent	3	blank	false	blank	Northern Trust UK	PF1004
LOANWQ1V8S	employed	690	false	multi-family	B005	Ahmed	Khan	1978-07-21	119000	435 Sheikh Zayed Rd	Dubai	DU	00000	UAE	2020-03-25	1200000	360	2.3	fixed	240000	80	2024-07-01	2024-06-30	5900	current	0	blank	false	blank	Dubai Residential Finance	PF1005
LOANB2K9M5	retired	484	false	other	B006	Olga	Ivanova	1964-10-11	18500	15 Prospekt Mira	Moscow	MOW	129010	Russia	2018-12-20	80000	120	7.5	VA	4000	98	2024-07-14	2024-06-13	900	delinquent	6	blank	false	blank	Moscow Capital Bank	PF1006
AAAV3G8JLN	employed	752	false	single-family	B007	Sarah	Johnson	1989-01-05	53000	501 Main St	Dallas	TX	75201	USA	2021-08-09	280000	360	3.2	fixed	28000	80	2024-07-04	2024-07-02	1200	current	0	blank	false	blank	Texas Mortgage Group	PF1007
LOANM9X4F2	self-employed	820	false	condo	B008	Wei	Chen	1992-09-30	132000	66 Orchard Blvd	Singapore	SG	238801	Singapore	2022-06-01	480000	240	2	fixed	96000	80	2024-07-07	2024-07-04	2000	current	0	blank	false	blank	Singapore National Bank	PF1008
LOANJ6YZQ8	employed	665	false	townhouse	B009	James	Miller	1981-04-18	64000	910 Cedar Ct	Seattle	WA	98109	USA	2021-12-13	345000	240	4.1	fixed	34500	80	2024-07-03	2024-07-01	2150	current	0	blank	false	blank	Pacific Home Loans	PF1009
LOANRB5L8K	employed	712	false	condo	B010	Elena	Garcia	1975-02-23	98000	73 Avenida Diagonal	Barcelona	CT	08019	Spain	2022-03-10	295000	180	3.8	fixed	29500	80	2024-07-13	2024-07-10	1900	current	0	blank	false	blank	Iberia Mortgage Services	PF1010
LOANF3MH2R	retired	350	false	single-family	B011	Robert	Taylor	1960-06-09	21000	2 Pine Hill Dr	Boston	MA	02110	USA	2017-11-22	115000	240	6.8	FHA	11500	97	2024-07-05	2024-06-04	980	delinquent	5	blank	false	blank	East Coast Lending	PF1011
LOANQ6R2V1	employed	735	false	single-family	B012	Tom	White	1990-02-16	57000	222 Kings Rd	London	ENG	SW3 5XP	UK	2020-09-17	190000	180	3.5	fixed	19000	80	2024-07-02	2024-07-01	1250	current	0	blank	false	blank	London Home Finance	PF1012
LOANV8B5SQ	employed	799	false	single-family	B013	Anna	Lee	1983-11-26	86000	88 Harbor St	Sydney	NSW	2000	Australia	2021-05-17	570000	360	3.3	fixed	57000	80	2024-07-11	2024-07-09	2450	current	0	blank	false	blank	Southern Cross Mortgages	PF1013
LOANH2CX9E	employed	775	false	single-family	B014	David	Nguyen	1988-08-05	95000	302 Willow Dr	Houston	TX	77002	USA	2022-10-21	410000	360	4	fixed	41000	80	2024-07-12	2024-07-09	1860	current	0	blank	false	blank	Texas Home Servicing	PF1014
LOANNT3J7C	retired	398	true	other	B015	Mohammed	Ali	1955-05-08	12000	12 Nile Ave	Cairo	C	11211	Egypt	2016-04-12	54000	144	8.5	FHA	0	100	2024-07-05	blank	blank	defaulted	12	2024-04-18	false	blank	Egyptian Home Loans	PF1015
LOANKE6G1R	employed	785	false	single-family	B016	John	Williams	1976-12-18	97000	99 Sunset Blvd	Los Angeles	CA	90012	USA	2021-02-14	485000	360	2.7	fixed	48500	80	2024-07-06	2024-07-04	1825	current	0	blank	false	blank	LA Mortgage Services	PF1016
LOANP0Q8F6	employed	710	false	condo	B017	Maria	Rodriguez	1997-05-21	58000	17 Calle Mayor	Madrid	MD	28013	Spain	2023-06-18	210000	180	3.6	fixed	21000	80	2024-07-05	2024-07-02	1230	current	0	blank	false	blank	Madrid Finance Group	PF1017
LOANJ1B3Q7	employed	769	false	condo	B018	Julia	Peterson	1984-03-02	53000	56 Lakeview Rd	Miami	FL	33131	USA	2022-08-12	295000	240	3.1	fixed	29500	80	2024-07-14	2024-07-11	1280	current	0	blank	false	blank	South Florida Lending	PF1018
LOANZ4D9K2	retired	316	true	other	B019	Hiroshi	Tanaka	1952-08-25	28000	11 Ginza St	Tokyo	TK	104-0061	Japan	2015-09-08	65000	120	8	FHA	0	100	2024-07-02	blank	blank	defaulted	14	2024-03-19	true	2024-05-10	Tokyo Financial Partners	PF1019
LOANF7Y2N8	employed	691	false	townhouse	B020	Nia	Williams	2000-11-15	54000	1225 College St	Ann Arbor	MI	48104	USA	2023-06-12	210000	180	3.3	fixed	21000	80	2024-07-06	2024-07-05	1230	late	1	blank	false	blank	Great Lakes Lending	PF1020

What the 200 rows show

from the 200-row sample

Retired (borrower employment status) stands out: mean borrower_credit_score is 371.2, against 734.9 for the rest.

4%default_flag = true
706median borrower_credit_score
5loan types
5payment statuses
48property countries
77,500median borrower_annual_income

Mean borrower_credit_score by borrower_employment_status200 rows

borrower_credit_score200 rows, in bands of 100

Median 706, from 301 to 872.

property_type200 rows · 5 values

single-family63
condo47
other33
multi-family32
townhouse25

32 columns by typefrom the column list below

string 15
integer 3
float 6
date 6
boolean 2

Columns

32 columns in four groups

blueprint · 32 columns
column	type	description	example
Text 15 columns
`loan_id`	string	Unique identifier for each mortgage loanunique	LOANXG2JQ9
`borrower_id`	string	Unique identifier for each borrower	B001
`borrower_first_name`	string	Borrower's first name	Emily
`borrower_last_name`	string	Borrower's last name	Smith
`borrower_employment_status`	string	Borrower's employment status (e.g., employed, self-employed, unemployed, retired)employed · self-employed · unemployed · retired	employed
`property_street_address`	string	Street address of the mortgaged property	348 Maple Ave
`property_city`	string	City where the mortgaged property is located	Chicago
`property_state`	string	State or province of the mortgaged property	IL
`property_postal_code`	string	Postal code of the mortgaged property	60616
`property_country`	string	Country of the mortgaged property	USA
`property_type`	string	Type of property (e.g., single-family, condo, townhouse, multi-family)single-family · condo · townhouse · multi-family · other	condo
`loan_type`	string	Type of mortgage loan (e.g., fixed, adjustable, FHA, VA)fixed · adjustable · FHA · VA · other	fixed
`payment_status`	string	Current payment status (e.g., current, late, delinquent, defaulted)current · late · delinquent · defaulted · paid-off	current
`loan_servicer`	string	Name of the institution servicing the loan	Midwest Home Lending
`portfolio_id`	string	Identifier for the lender's portfolio containing this loanoptional	PF1001
Numbers 9 columns
`borrower_annual_income`	float	Borrower's annual income in local currency0 or more	72000
`borrower_credit_score`	integer	Borrower's credit score at time of loan origination300 to 900	780
`loan_amount`	float	Original principal amount of the mortgage loan0 or more	350000
`loan_term_months`	integer	Total number of months for the loan term1 or more	360
`loan_interest_rate`	float	Annual interest rate of the mortgage loan (percentage)0 to 100	3.9
`down_payment_amount`	float	Amount paid upfront as down payment0 or more	35000
`loan_to_value_ratio`	float	Loan-to-value ratio at origination (percentage)0 to 100	80
`last_payment_amount`	float	Amount of the most recent payment made0 or more · optional	1670
`number_of_missed_payments`	integer	Total number of missed payments to date0 or more · optional	0
Dates and times 6 columns
`borrower_date_of_birth`	date	Borrower's date of birth	1986-07-13
`loan_origination_date`	date	Date when the loan was originated	2021-06-20
`payment_due_date`	date	Date when the next payment is dueoptional	2024-07-01
`last_payment_date`	date	Date of the most recent payment madeoptional	2024-06-29
`default_date`	date	Date when the loan was officially classified as defaulted (if applicable)optional	2024-04-18
`foreclosure_date`	date	Date when foreclosure proceedings began (if applicable)optional	2024-05-10
True or false 2 columns
`default_flag`	boolean	Indicates whether the loan has defaulted (true/false)	false
`foreclosure_flag`	boolean	Indicates whether the property has entered foreclosure (true/false)optional	false

Use it for

A finance dashboard
The default_flag rate, borrower_credit_score by borrower_employment_status and a breakdown of property_type. Excel, Power BI or Tableau.
Why do the 49 retired rows have a mean borrower_credit_score of 371.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Loans200
LOANXG2JQ9780employed
LOANNT3J7C398retired
LOANZ4D9K2316retired
A software demo
Believable loans with borrower_id, borrower_first_name and borrower_last_name to fill a screen in front of a buyer.

This dataset200 rows32 columns

Yours1,000 rows32 columnsproperty_street_address: UK only

blueprint · mortgage-default-prediction-data

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 32 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 borrower demographics
Record monthly payment status
Label defaults and foreclosures
Exclude early loan payoffs

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

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: mortgage-default-prediction-data

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?