Tenant Churn Prediction
“Which tenants will not renew their lease?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which tenants will not renew their lease?
Tenant turnover costs $3K-$8K per unit in vacancy loss, cleaning, repairs, and leasing commissions. For a REIT managing 50,000 units with 40% annual turnover, that is $60-160M per year in turnover costs. Predicting non-renewals 60-90 days ahead enables targeted retention offers that cost 10-20% of turnover expense. Traditional models use lease expiry date and payment history but miss the maintenance and satisfaction signals embedded in the tenant-property relationship.
How KumoRFM solves this
Graph-powered intelligence for real estate
Kumo connects tenants, leases, payments, maintenance requests, and properties into a property management graph. The GNN learns churn patterns from the tenant network: when maintenance response times deteriorate, when similar tenants in the building leave, and when payment patterns shift from early to late. PQL predicts non-renewal probability per tenant with 60-90 days of lead time, enabling retention offers before tenants start searching for alternatives.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
TENANTS
| tenant_id | name | unit_type | tenure_months |
|---|---|---|---|
| TEN001 | J. Martinez | 2BR | 24 |
| TEN002 | K. Patel | 1BR | 8 |
| TEN003 | L. Johnson | 3BR | 36 |
LEASES
| lease_id | tenant_id | monthly_rent | expiry_date | renewal_offered |
|---|---|---|---|---|
| LSE101 | TEN001 | $1,850 | 2025-05-31 | Yes |
| LSE102 | TEN002 | $1,400 | 2025-06-30 | Yes |
| LSE103 | TEN003 | $2,200 | 2025-04-30 | Yes |
PAYMENTS
| payment_id | tenant_id | amount | days_late | timestamp |
|---|---|---|---|---|
| PAY401 | TEN001 | $1,850 | 0 | 2025-03-01 |
| PAY402 | TEN002 | $1,400 | 5 | 2025-03-06 |
| PAY403 | TEN003 | $2,200 | 0 | 2025-03-01 |
MAINTENANCE_REQUESTS
| request_id | tenant_id | type | resolution_days | date |
|---|---|---|---|---|
| MR501 | TEN001 | HVAC repair | 2 | 2025-01-15 |
| MR502 | TEN002 | Plumbing leak | 8 | 2025-02-10 |
| MR503 | TEN002 | Pest control | 12 | 2025-02-25 |
PROPERTIES
| property_id | name | units | occupancy_rate | avg_rent |
|---|---|---|---|---|
| PRO01 | Oak Park Apartments | 200 | 94% | $1,750 |
| PRO02 | Downtown Lofts | 120 | 88% | $1,650 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(LEASES.renewed = False, 0, 90, days) FOR EACH TENANTS.tenant_id WHERE LEASES.expiry_date < '2025-07-01'
Prediction output
Every entity gets a score, updated continuously
| TENANT_ID | UNIT | LEASE_EXPIRY | NON_RENEWAL_PROB | RISK_TIER |
|---|---|---|---|---|
| TEN001 | 2BR | 2025-05-31 | 0.12 | Low |
| TEN002 | 1BR | 2025-06-30 | 0.76 | Critical |
| TEN003 | 3BR | 2025-04-30 | 0.08 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Tenant TEN002 -- 1BR unit, 8-month tenure
Predicted: 76% non-renewal probability (Critical)
Top contributing features
Unresolved maintenance requests
2 open, avg 10 days
30% attribution
Late payment trend (last 3 months)
Increasing
24% attribution
Short tenure (less than 12 months)
8 months
19% attribution
Similar tenants in building left
3 in last 90 days
16% attribution
Rent-to-market ratio unfavorable
105% of market
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A REIT managing 50,000 units saves $30-60M annually by reducing turnover through targeted retention. Kumo's property graph detects the maintenance-satisfaction-payment patterns that predict non-renewal 60-90 days ahead, when retention offers still work.
Related use cases
Explore more real estate use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
