Property Valuation
“What is this property worth?”
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A real-world example
What is this property worth?
Traditional AVMs (Automated Valuation Models) rely on comparable sales within a radius, missing the neighborhood graph: how proximity to specific amenities, school districts, transit, and commercial corridors affects value. Median AVM error is 5-8%, meaning a $500K home has a $25K-$40K uncertainty band. For a portfolio lender with $10B in real estate exposure, reducing valuation error by 2% prevents $50M in over-lending losses and missed opportunities annually.
How KumoRFM solves this
Graph-powered intelligence for real estate
Kumo connects properties, transactions, neighborhoods, amenities, and market data into a real estate graph. The GNN learns how value propagates through the neighborhood network: how a new restaurant cluster affects nearby residential values, how school rating changes ripple through associated properties, and how transit access creates non-linear value premiums. PQL predicts current market value per property with built-in confidence intervals.
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
PROPERTIES
| property_id | type | sqft | bedrooms | year_built |
|---|---|---|---|---|
| PROP001 | Single Family | 2,400 | 4 | 2005 |
| PROP002 | Condo | 1,100 | 2 | 2018 |
| PROP003 | Single Family | 3,200 | 5 | 1995 |
TRANSACTIONS
| txn_id | property_id | sale_price | date | days_on_market |
|---|---|---|---|---|
| TXN201 | PROP001 | $485,000 | 2023-06-15 | 22 |
| TXN202 | PROP002 | $320,000 | 2024-01-10 | 45 |
| TXN203 | PROP003 | $620,000 | 2022-09-20 | 18 |
NEIGHBORHOODS
| neighborhood_id | name | median_income | school_rating | crime_index |
|---|---|---|---|---|
| NBH01 | Oak Park | $95,000 | 8.2 | Low |
| NBH02 | Downtown Lofts | $78,000 | 6.5 | Medium |
| NBH03 | Hillcrest | $120,000 | 9.1 | Low |
AMENITIES
| amenity_id | type | name | distance_to_prop001 |
|---|---|---|---|
| AMN01 | School | Oak Park Elementary | 0.4 mi |
| AMN02 | Transit | Metro Station | 0.8 mi |
| AMN03 | Retail | Shopping Center | 0.3 mi |
MARKET_DATA
| neighborhood_id | month | median_price_sqft | inventory_months | yoy_change |
|---|---|---|---|---|
| NBH01 | 2025-02 | $245 | 2.1 | +4.2% |
| NBH02 | 2025-02 | $380 | 3.8 | -1.5% |
| NBH03 | 2025-02 | $285 | 1.5 | +6.8% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(TRANSACTIONS.sale_price, 0, 90, days) FOR EACH PROPERTIES.property_id
Prediction output
Every entity gets a score, updated continuously
| PROPERTY_ID | TYPE | LAST_SALE | ESTIMATED_VALUE | CONFIDENCE |
|---|---|---|---|---|
| PROP001 | Single Family | $485,000 | $528,000 | +/- 2.8% |
| PROP002 | Condo | $320,000 | $308,000 | +/- 4.1% |
| PROP003 | Single Family | $620,000 | $695,000 | +/- 2.2% |
Understand why
Every prediction includes feature attributions — no black boxes
Property PROP003 -- 5BR Single Family in Hillcrest
Predicted: Estimated value: $695,000 (+12.1% since last sale)
Top contributing features
Neighborhood YoY price appreciation
+6.8%
28% attribution
School district rating improvement
9.1 (was 8.7)
24% attribution
Low inventory in neighborhood
1.5 months
20% attribution
Comparable sales in last 90 days
$280/sqft avg
17% attribution
New transit access within 1 mile
Metro opened 2024
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 portfolio lender with $10B in real estate exposure prevents $50M in annual losses by reducing valuation error 2%. Kumo's real estate graph captures amenity impacts, school district effects, and market momentum that radius-based comp models miss.
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.
