Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Valuation errors destroy deal economics and lead conversion models waste millions on buyers who never close. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict property values, lead conversion, and market trends. KumoRFM learns directly from the relationships in your data and is pre-trained on tens of thousands of datasets, delivering higher accuracy than any internally-built model, in hours, not months.
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Why real estate companies choose Kumo
Here's how Kumo transforms real estate operations with relational AI.
34%
More accurate property valuations
KumoRFM learns from the full property graph: listings, transactions, neighborhoods, amenities, school zones, and market comparables. It captures spatial and temporal valuation signals that regression models miss.
3x
Better lead conversion scoring
Pre-trained on thousands of relational schemas, KumoRFM understands buyer behavior patterns, property preferences, and engagement signals across your CRM, portal, and marketing data.
Days
From idea to production
Property valuation, lead scoring, market trend prediction, tenant churn, investment scoring, and price optimization. One platform replaces the custom model you'd build for each use case.
Loved by data scientists, ML engineers & CXOs at
Use cases
From property valuation to tenant churn, Kumo learns from every relationship in your data to deliver predictions that traditional ML misses.
Predict accurate property valuations by learning from comparable sales, neighborhood trends, property attributes, and market conditions — all connected in a single relational graph.
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Score buyer and seller leads based on engagement patterns, property interactions, agent touchpoints, and historical conversion signals across your CRM data.
Forecast hyperlocal market trends by learning from transaction histories, inventory levels, pricing patterns, and demographic shifts across interconnected markets.
Identify tenants at risk of non-renewal by analyzing lease history, maintenance requests, payment patterns, and comparable rental market conditions.
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Score investment opportunities by learning from property performance histories, market trajectories, tenant quality signals, and portfolio-level risk patterns.
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Predict lease renewal likelihood by connecting tenant behavior, market comparables, property condition, and historical renewal patterns across your portfolio.
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Forecast maintenance costs by learning from property age, equipment histories, vendor performance, weather patterns, and similar property benchmarks.
Predict neighborhood-level demand shifts by analyzing migration patterns, employment data, amenity development, and buyer search behavior across regions.
Match buyers to properties by learning from preference histories, viewing patterns, offer behaviors, and the relational signals between buyer profiles and property attributes.
The real estate data advantage
Feature engineering destroys signal. Even with a great data science team, flattening relational tables into feature vectors discards the nuanced relationships that actually predict outcomes. Your real estate data connects properties → listings → agents → buyers → transactions → neighborhoods → market comps. When you flatten that into a single row, you lose the structural signals that separate accurate valuations from expensive guesses. This is a structural limitation of traditional ML, not a team quality problem.
You only have your data. KumoRFM is pre-trained on thousands of relational schemas across industries. It already knows what predictive patterns look like across hundreds of database structures — the same advantage GPT has over a custom NLP model. Your team, no matter how talented, cannot replicate this breadth of relational knowledge from a single company's data.
Your existing team will love it. KumoRFM 10x's your data science team. Feature engineering disappears entirely. Teams go from 3–5 models per year to 50+ per quarter. The interesting work — defining business problems, interpreting results, driving action — remains. The drudgery vanishes.
One platform powers property valuation, lead scoring, tenant churn, investment analysis, and every other real estate prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
10–30%
Valuation accuracy improvement
Over traditional appraisal models
20x
Faster to production
From months to hours
$2.4M
Average annual savings
Per enterprise deployment
KumoRFM isn't limited to your data alone. Pre-trained on billions of relational patterns across diverse datasets and fine-tuned to your schema, it sees what no in-house model can. As per the SAP SALT benchmark.
LLM
GPT4 + AutoML
PhD Data Scientist
Feature eng. + XGBoost
KumoRFM
Relational Foundation Model
40%
lift in prediction accuracy
“Beating internal XGBoost model on key metrics with far less data/features — on Kumo pre-trained. We replaced six months of pipeline work with a single afternoon.”
Matt Loskamp
GTM Data Science Leader, Enterprise Financial Customer
From startups to enterprises, leading organizations rely on Kumo to deliver predictive insights at scale.
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD. The methodology is public and reproducible.
K. Huang, M. Fey, J. Leskovec et al.
A foundation model for relational data - pre-trained across schemas, it delivers accurate predictions out of the box and improves with fine-tuning on your specific data.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Introduces learning predictive models directly on relational databases, eliminating the feature engineering pipeline that has historically bottlenecked enterprise ML.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks. Kumo consistently outperforms traditional ML baselines.
Read paper
