Rental Application Screening

This dataset provides comprehensive information on rental applications, including applicant demographics, employment and income details, rental and criminal history, and automated screening outcomes. Designed for real estate agencies, it enables automated tenant screening, risk assessment, and process optimization for rental placements. The dataset supports compliance, fraud detection, and predictive analytics for improved rental decisions.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Automated tenant screening and risk scoring

Sample rows

preview · 8 of 50 rows · all 32 columns
application_idstringapplication_statusstringcredit_scoreintegerhas_evictionsbooleancurrent_address_citystringapplicant_first_namestringapplicant_last_namestringapplicant_emailstringapplicant_phonestringdate_of_birthdatessn_last4stringcurrent_address_streetstringcurrent_address_statestringcurrent_address_postal_codestringcurrent_address_countrystringemployment_statusstringemployer_namestringposition_titlestringmonthly_incomefloatrental_history_yearsfloatprevious_landlord_namestringprevious_landlord_contactstringcriminal_recordbooleanpetsbooleannumber_of_petsintegersmokerbooleanrequested_move_in_datedatedesired_lease_term_monthsintegerproperty_idstringapplication_datedatetimescreening_scorefloatagency_idstring
APP001pending752falseSan FranciscoOliviaMartinezolivia.martinez1@email.com+1 415-555-12781991-07-163241450 King StCA94107USAemployedSalesforceSoftware Engineer87004.5Janet Lee+1 415-555-9090falsetrue2false2024-07-0112PROP1012024-06-01T13:45:2188.5AGC01
APP002approved800falsePalo AltoEthanNguyenethan.nguyen@email.com+1 650-555-22111985-03-1189122310 University AveCA94303USAself-employedNguyen ConsultingFounder113008Michael Choi+1 408-555-8765falsefalse0false2024-08-0124PROP1022024-06-02T10:30:0093.2AGC02
APP003pending680falseLos AngelesJasminePatelj.patel@email.org+1 213-555-31311997-10-0511821126 W 39th PlCA90037USAstudentblankblank21002Tariq Hassan+1 213-555-5172falsefalse0false2024-09-159PROP1032024-06-03T16:19:1377.1AGC01
APP004approved723falseEncinoBenjaminKimben.kim@email.com+1 818-555-90901972-02-1920991901 Ventura BlvdCA91316USAemployedUCLA HealthNurse Manager640015Susan Lee+1 818-555-1212falsetrue1false2024-07-1012PROP1042024-06-04T09:12:3484.9AGC03
APP005pending780falseChicagoSophiaWrightsophiawright@email.net+1 312-555-24571960-09-0284201410 N Clark StIL60610USAretiredblankblank300025Harold Grant+1 312-555-1234falsefalse0false2024-07-1518PROP1052024-06-05T11:48:5691AGC04
APP006approved765falseNew YorkMasonJohnsonmasonj@email.com+1 917-555-44561994-12-225531251 E 30th StNY10016USAemployedMorgan StanleyAnalyst69005.5Gregory Lee+1 212-555-2345falsefalse0true2024-07-0512PROP1062024-06-06T14:37:2087.3AGC01
APP007pending655falseAustinLucasGarcialucas.garcia@email.com+1 512-555-88332000-05-107429800 E 6th StTX78702USAstudentblankblank15001Carmen Diaz+1 512-555-4433falsetrue1false2024-08-1010PROP1072024-06-07T08:12:4568.9AGC05
APP008approved789falseNew YorkAvaBakerava.baker@email.com+1 646-555-12121989-06-25664223 W 18th StNY10011USAemployedGoogleUX Designer83007.5Sam Gold+1 646-555-5555falsefalse0false2024-07-2012PROP1082024-06-08T10:41:1190.8AGC02

What the 50 rows show

from the 50-row sample

Approved (application status) stands out: mean credit_score is 782.7, against 694.9 for the rest.

  • 4%has_evictions = true
  • 737.5median credit_score
  • 5employment statuses
  • 7agencies
  • 15current address states
  • 5,650median monthly_income
Mean credit_score by application_status50 rows
0400800696.0pending30 rows782.7approved18 rows678.0rejected2 rows
credit_score50 rows, in bands of 25
061253336117561600725850credit_score →

Median 737.5, from 601 to 838.

current_address_city50 rows · top 10 of 22 values
  1. Chicago6
  2. New York5
  3. San Francisco4
  4. Los Angeles4
  5. Las Vegas4
  6. Brooklyn3
  7. Austin2
  8. Washington2
  9. Denver2
  10. Boston2
32 columns by typefrom the column list below
  • string 19
  • integer 3
  • float 3
  • date 2
  • datetime 1
  • boolean 4

Columns

32 columns in four groups
blueprint · 32 columns
columntypedescriptionexample
Text 19 columns
application_idstringUnique identifier for each rental applicationuniqueAPP001
applicant_first_namestringApplicant's first nameOlivia
applicant_last_namestringApplicant's last nameMartinez
applicant_emailstringApplicant's email addressuniqueethan.nguyen@email.com
applicant_phonestringApplicant's primary phone number+1 415-555-1278
ssn_last4stringLast 4 digits of applicant's Social Security Number (for verification)3241
current_address_streetstringApplicant's current street address450 King St
current_address_citystringApplicant's current city of residenceSan Francisco
current_address_statestringApplicant's current state of residenceCA
current_address_postal_codestringApplicant's current postal code94107
current_address_countrystringApplicant's current country of residenceUSA
employment_statusstringApplicant's current employment statusemployed · self-employed · unemployed · student · retiredemployed
employer_namestringName of applicant's current employer (if applicable)optionalSalesforce
position_titlestringApplicant's job title or position (if employed)optionalSoftware Engineer
previous_landlord_namestringName of previous landlord (if applicable)optionalJanet Lee
previous_landlord_contactstringContact information for previous landlordoptional+1 415-555-9090
property_idstringIdentifier for the property being applied toPROP101
application_statusstringCurrent status of the applicationpending · approved · rejected · withdrawnpending
agency_idstringIdentifier for the real estate agency processing the application7 agenciesAGC01
Numbers 6 columns
monthly_incomefloatApplicant's gross monthly income in USD0 or more8700
credit_scoreintegerApplicant's credit score (if available)300 to 850 · optional752
rental_history_yearsfloatNumber of years of prior rental history0 or more · optional4.5
number_of_petsintegerNumber of pets owned by the applicant0 or more · optional2
desired_lease_term_monthsintegerDesired lease term in months1 or more12
screening_scorefloatAutomated screening score assigned to the application0 to 100 · optional88.5
Dates and times 3 columns
date_of_birthdateApplicant's date of birth1991-07-16
requested_move_in_datedateDate applicant wishes to move in2024-07-01
application_datedatetimeDate and time the application was submitted2024-06-01T13:45:21
True or false 4 columns
has_evictionsbooleanIndicates if applicant has any prior evictionsfalse
criminal_recordbooleanIndicates if applicant has a criminal recordfalse
petsbooleanIndicates if applicant has petstrue
smokerbooleanIndicates if applicant is a smokerfalse

Use it for

  • has evictions4%2 of 50 rowsmean credit score by …696.0pending782.7approv…678.0reject…

    A real estate dashboard

    The has_evictions rate, credit_score by application_status and a breakdown of current_address_city. Excel, Power BI or Tableau.

  • Why do the 18 approved rows have a mean credit_score of 782.7?

    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_email to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 32 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

10,000 rows of yours: $12.99One-time. No subscription. All prices

This dataset50 rows32 columns
Yours10,000 rows32 columnscurrent_address_street: UK only

blueprint · rental-application-screening

Behind this dataset

Same schema. As many rows as you need.

These 50 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
  • Applicants must provide proof of income
  • Credit check mandatory for all adults
  • Rental history over 2 years required
  • Applications missing references are rejected
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
rental-application-screening

More in Real Estate

Browse all
Also in Real EstateProperty Climate Resilience ScorecardOpen House Visitor Feedback DatasetReal Estate Price ValuationAll 1,947 datasets

What should your data show?

Preview 20 rows free
No signup. No card.