Vacancy Duration Prediction
“How long will this unit sit vacant?”
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A real-world example
How long will this unit sit vacant?
Every vacant day costs 3.3% of monthly rent. Property managers price units based on gut feel and stale comps, leading to either pricing too high (extended vacancy costing $1K-$3K per unit) or pricing too low (leaving $500-$2K per year on the table). For a REIT with 20,000 units and 35% annual turnover, optimizing listing price and timing based on predicted vacancy duration saves $8-15M annually.
How KumoRFM solves this
Graph-powered intelligence for real estate
Kumo connects units, listings, applications, market data, and seasonal trends into a leasing graph. The GNN learns vacancy duration patterns from the property network: how pricing relative to comparable listed units affects days-on-market, how seasonal demand patterns vary by unit type and neighborhood, and how listing quality (photos, description) accelerates leasing. PQL predicts expected vacancy days per unit at different price points, enabling revenue-maximizing pricing.
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
UNITS
| unit_id | property_id | type | sqft | floor | renovated |
|---|---|---|---|---|---|
| UNIT01 | PRO01 | 2BR/2BA | 1,050 | 3 | Yes |
| UNIT02 | PRO01 | 1BR/1BA | 680 | 1 | No |
| UNIT03 | PRO02 | Studio | 450 | 5 | Yes |
LISTINGS
| listing_id | unit_id | asking_rent | photos | listed_date |
|---|---|---|---|---|
| LST201 | UNIT01 | $1,950 | 12 | 2025-03-01 |
| LST202 | UNIT02 | $1,350 | 6 | 2025-03-01 |
| LST203 | UNIT03 | $1,200 | 15 | 2025-03-01 |
APPLICATIONS
| app_id | listing_id | applicant_count | qualified_pct |
|---|---|---|---|
| APP101 | LST201 | 4 | 75% |
| APP102 | LST202 | 1 | 100% |
MARKET_DATA
| neighborhood | avg_rent_1br | avg_rent_2br | vacancy_rate | absorption |
|---|---|---|---|---|
| Oak Park | $1,300 | $1,800 | 5.2% | Positive |
| Downtown | $1,450 | $2,100 | 8.5% | Negative |
SEASONAL_TRENDS
| month | demand_index | avg_dom_1br | avg_dom_2br |
|---|---|---|---|
| March | 85 | 28 | 22 |
| June | 100 | 18 | 14 |
| December | 62 | 42 | 35 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT FIRST(LISTINGS.leased_date, 0, 90, days) - LISTINGS.listed_date FOR EACH UNITS.unit_id
Prediction output
Every entity gets a score, updated continuously
| UNIT_ID | TYPE | ASKING_RENT | PREDICTED_DOM | REVENUE_OPTIMAL_RENT |
|---|---|---|---|---|
| UNIT01 | 2BR/2BA | $1,950 | 16 days | $1,920 |
| UNIT02 | 1BR/1BA | $1,350 | 35 days | $1,280 |
| UNIT03 | Studio | $1,200 | 12 days | $1,250 |
Understand why
Every prediction includes feature attributions — no black boxes
Unit UNIT02 -- 1BR/1BA at Oak Park Apartments
Predicted: 35 days predicted vacancy (revenue-optimal rent: $1,280)
Top contributing features
Asking rent vs market average
+3.8% above market
30% attribution
Low photo count vs similar listings
6 vs 12 avg
24% attribution
Seasonal demand (March = below peak)
85/100 index
19% attribution
Ground floor unit premium penalty
Floor 1 = longer DOM
15% attribution
Not renovated vs renovated comps
Unrenovated
12% 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 with 20,000 units saves $8-15M annually by pricing units based on predicted vacancy duration. Kumo's leasing graph connects unit attributes, market conditions, and seasonal patterns to find the revenue-maximizing price point for each unit.
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.
