Rental Payment Timeliness Dataset
This dataset provides detailed records of tenant rental payments, including payment dates, amounts, late fees, and payment status, enabling analysis of payment timeliness and identification of late payment trends. It links each payment to tenants, properties, and property managers, supporting comprehensive property management analytics and operational improvements. The data is ideal for optimizing rent collection processes, reducing delinquencies, and enhancing tenant screening.
Sample rows
preview · 8 of 75 rows · all 16 columns| payment_idstring | payment_statusstring | amount_paidfloat | late_fee_appliedboolean | property_idstring | tenant_idstring | unit_numberstring | lease_idstring | rent_due_datedate | payment_datedate | amount_duefloat | days_lateinteger | late_fee_amountfloat | payment_methodstring | manager_idstring | payment_notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PAY0001 | paid | 1620 | false | PROP0006 | TEN0032 | A203 | LEA0045 | 2024-03-01 | 2024-03-01 | 1620 | 0 | 0 | online_portal | MAN0002 | blank |
| PAY0002 | late | 1200 | true | PROP0002 | TEN0011 | 101B | LEA0020 | 2024-02-01 | 2024-02-05 | 1200 | 4 | 60 | bank_transfer | MAN0001 | Tenant was traveling for work. |
| PAY0003 | late | 2000 | true | PROP0010 | TEN0005 | 3D | LEA0067 | 2024-03-01 | 2024-03-03 | 2000 | 2 | 100 | credit_card | MAN0005 | Delayed due to payroll issue. |
| PAY0004 | paid | 1100 | false | PROP0003 | TEN0022 | 12C | LEA0012 | 2024-03-01 | 2024-03-01 | 1100 | 0 | 0 | check | MAN0003 | blank |
| PAY0005 | partial | 1700 | true | PROP0009 | TEN0007 | G01 | LEA0061 | 2024-02-01 | 2024-02-10 | 1850 | 9 | 90 | cash | MAN0004 | Partial payment, balance due next month. |
| PAY0006 | paid | 950 | false | PROP0001 | TEN0014 | C5 | LEA0007 | 2024-03-01 | 2024-03-01 | 950 | 0 | 0 | online_portal | MAN0001 | blank |
| PAY0007 | missed | 0 | true | PROP0004 | TEN0029 | B12 | LEA0016 | 2024-02-01 | 2024-02-12 | 1350 | 11 | 67.5 | other | MAN0002 | No payment received; tenant notified. |
| PAY0008 | paid | 1750 | false | PROP0011 | TEN0026 | 4A | LEA0082 | 2024-03-01 | 2024-03-01 | 1750 | 0 | 0 | bank_transfer | MAN0006 | blank |
| PAY0009 | late | 1600 | true | PROP0007 | TEN0017 | E8 | LEA0049 | 2024-03-01 | 2024-03-05 | 1600 | 4 | 80 | credit_card | MAN0003 | Forgot to schedule auto-payment. |
| PAY0010 | paid | 1400 | false | PROP0005 | TEN0010 | 2B | LEA0033 | 2024-02-01 | 2024-01-31 | 1400 | 0 | 0 | online_portal | MAN0002 | blank |
| PAY0011 | late | 1200 | true | PROP0003 | TEN0002 | 15A | LEA0014 | 2024-03-01 | 2024-03-03 | 1200 | 2 | 60 | check | MAN0004 | Bank holiday caused delay. |
| PAY0012 | late | 900 | true | PROP0012 | TEN0031 | F3 | LEA0090 | 2024-03-01 | 2024-03-02 | 900 | 1 | 30 | bank_transfer | MAN0005 | blank |
| PAY0013 | paid | 1620 | false | PROP0006 | TEN0016 | A101 | LEA0044 | 2024-02-01 | 2024-02-01 | 1620 | 0 | 0 | online_portal | MAN0002 | blank |
| PAY0014 | partial | 1500 | true | PROP0007 | TEN0009 | E7 | LEA0048 | 2024-02-01 | 2024-02-09 | 1600 | 8 | 80 | cash | MAN0003 | Partial payment; remaining due next cycle. |
| PAY0015 | paid | 1850 | false | PROP0013 | TEN0035 | H2 | LEA0101 | 2024-03-01 | 2024-03-01 | 1850 | 0 | 0 | online_portal | MAN0006 | blank |
| PAY0016 | paid | 1300 | false | PROP0008 | TEN0023 | C11 | LEA0051 | 2024-03-01 | 2024-03-01 | 1300 | 0 | 0 | check | MAN0005 | blank |
| PAY0017 | missed | 0 | true | PROP0002 | TEN0001 | 102A | LEA0021 | 2024-03-01 | 2024-03-10 | 1200 | 9 | 60 | other | MAN0001 | No payment received for March. |
| PAY0018 | late | 1400 | true | PROP0005 | TEN0025 | 2C | LEA0035 | 2024-02-01 | 2024-02-02 | 1400 | 1 | 50 | bank_transfer | MAN0002 | blank |
| PAY0019 | paid | 2000 | false | PROP0010 | TEN0012 | 3A | LEA0065 | 2024-03-01 | 2024-03-01 | 2000 | 0 | 0 | credit_card | MAN0005 | blank |
| PAY0020 | missed | 0 | true | PROP0008 | TEN0030 | C12 | LEA0052 | 2024-02-01 | 2024-02-15 | 1300 | 14 | 65 | cash | MAN0005 | Repeated missed payments this quarter. |
What the 75 rows show
from the 75-row sampleMissed (payment status) stands out: mean amount_
- 55%late_
fee_ applied = true - 1,295median amount_
paid - 6payment methods
- 1,390median amount_
due - 1median days_
late - 25.0median late_
fee_ amount
Median 1,295, from 0 to 2,200.
- string 9
- integer 1
- float 3
- date 2
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
payment_id | string | Unique identifier for each rental payment recordunique | PAY0001 |
tenant_id | string | Unique identifier for the tenant making the payment | TEN0032 |
property_id | string | Unique identifier for the property being rented | PROP0006 |
unit_number | string | Specific unit or apartment number within the propertyoptional | A203 |
lease_id | string | Unique identifier for the lease agreement associated with the paymentoptional | LEA0045 |
payment_status | string | Status of the payment (e.g., paid, late, partial, missed)paid · late · partial · missed | paid |
payment_method | string | Method used for payment (e.g., bank_transfer, credit_card, cash, check, online_portal)6 values · optional | online_portal |
manager_id | string | Unique identifier for the property manager responsible for the unitoptional | MAN0002 |
payment_notes | string | Additional notes or comments regarding the payment (e.g., reason for lateness, partial payment explanation)optional | Account in collections. |
| Numbers 4 columns | |||
amount_due | float | Total rent amount due for the period0 or more | 1620 |
amount_paid | float | Actual amount paid by the tenant0 or more | 1620 |
days_late | integer | Number of days the payment was late (0 if on time or early)0 or more | 0 |
late_fee_amount | float | Amount of late fee charged (0 if none applied)0 or more | 0 |
| Dates and times 2 columns | |||
rent_due_date | date | Date when the rent payment was due according to the lease | 2024-03-01 |
payment_date | date | Date when the rent payment was actually made | 2024-03-01 |
| True or false 1 column | |||
late_fee_applied | boolean | Indicates if a late fee was applied to this payment | false |
Use it for
A real estate dashboard
The late_
fee_ applied rate, amount_ paid by payment_ status and a breakdown of property_ id. Excel, Power BI or Tableau. Why do the 11 missed rows have a mean amount_
paid of 0.0? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Payments75PAY00011620paidPAY00021200latePAY00032000late
A software demo
Believable payments with tenant_
id, property_ id and unit_ number to fill a screen in front of a buyer.
blueprint · rental-payment-timeliness-dataset
Behind this dataset
Same schema. As many rows as you need.
These 75 rows came out of a blueprint — 16 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.
- Include only active rental agreements
- Flag payments made after due date
- Calculate average delay per tenant
- Highlight tenants consistently late >3 times
- Record payment method for each instance
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
- rental-payment-timeliness-dataset