How accurate are LandPlanner's construction cost predictions?

Our gradient-boosted ensemble model is trained on thousands of actual construction projects and calibrated with conformal prediction to produce 95% confidence intervals. Every estimate shows P10 (optimistic), P50 (median), and P90 (conservative) bounds.

What geographic areas does LandPlanner cover?

Full parcel analysis including zoning, assessor data, and jurisdiction intelligence is live across 15 US states — including Utah, Idaho, California, Texas, Florida, Arizona, Colorado, Georgia, Oregon, Washington, Pennsylvania, Tennessee, North Carolina, Nevada, and Ohio — with more added on a rolling basis. Environmental and federal data (FEMA, USGS, EPA, NOAA, Census, NREL) is available nationwide for any US coordinate.

What data sources does LandPlanner use?

We aggregate data from 15+ federal, state, and local agencies including FEMA, USGS, Census Bureau, USDA SSURGO, NREL, USFWS, EPA, NOAA, USFS, BLS, Walk Score, and county assessors.

Can I analyze multiple parcels at once?

Yes. Pro plans and above include batch screening — upload a CSV of coordinates and our engine scores each parcel for feasibility, construction cost, and environmental risk. Enterprise plans also get batch API access with webhooks.

How is LandPlanner different from other site analysis tools?

Most tools show raw data and leave interpretation to you. LandPlanner combines authoritative data from 15+ sources with machine learning to produce actionable outputs: calibrated cost predictions with confidence intervals, automated pro formas, entitlement risk scoring, permit outcome predictions, foundation analysis, stormwater BMP design, buildable area calculations, and fire access compliance checks.

Construction Cost Prediction: How ML Models Beat Rules of Thumb

Why $/sqft averages fail and how ML models produce better predictions.

The Problem with $/sqft Averages

Regional averages miss site conditions, seismic categories, labor markets, lot geometry, and regulatory burden.

How ML-Based Cost Prediction Works

Gradient-boosted ensembles learn non-linear relationships from thousands of actual construction projects.

Why Confidence Intervals Matter

P10/P50/P90 bounds quantify cost uncertainty for better feasibility decisions.

Conformal Prediction

Distribution-free method with 92-96% empirical coverage on holdout validation.