Building the forecast layer for residential real estate.
Homecastr helps people understand where home values may be headed, not just what a home is worth today.
Why Homecastr exists
Most home valuation tools give you a single number for what a property is worth today. That number is useful, but it does not help you think about risk, timing, or the range of outcomes that could unfold over the next few years.
Homecastr was built to fill that gap. We produce property-level and neighborhood-level forecasts with calibrated probability bands—downside, base case, and upside scenarios—so homeowners, investors, and professionals can make more informed decisions. We believe that honest uncertainty quantification is more valuable than false precision.
What makes it credible
Probabilistic forecasts
P10/P50/P90 percentile bands express uncertainty explicitly rather than hiding it behind a single estimate.
Property-level coverage
Forecasts at the neighborhood and tract level, not just metro-wide averages. The house next door can have a different outlook.
Backtested reporting
Published accuracy metrics by geography and horizon. We report how well the model has performed, not just what it predicts.
Founder
Daniel Hardesty Lewis
Founder, Homecastr
Background in large-scale scientific computing and geospatial machine learning. Previously at the Texas Advanced Computing Center (TACC) and Columbia University, working on computational modeling and applied research.
Previous affiliations
Research foundation
Homecastr's methodology draws on applied research in large-scale probabilistic modeling, geospatial data science, and computational forecasting. The model architecture emphasizes interpretability and honest uncertainty quantification over black-box point estimates.
Work with us
API & Institutional Access
Access property and neighborhood forecasts through API for portfolio analysis, underwriting, or product integration.
View API docsResearch Collaborations
Interested in collaborating on housing market research, model validation, or applied forecasting projects.
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