Property price heatmaps that update with your data

Why a heatmap (and not just markers)?

Pin clusters get unreadable above a few hundred properties. A weighted heatmap surfaces the patterns — price corridors, undervalued pockets, demand gradients — without forcing you to filter every time. Markers tell you where things are; a price-weighted heatmap tells you where value concentrates. For real estate investors hunting for arbitrage between adjacent zip codes, that difference compounds quickly across a day of underwriting.

Pick the weighting column that matters

List price, sold price, $/sqft, days on market, even custom scores. The kernel updates live as you tweak radius and intensity. For flip analysis, weight by ARV minus rehab estimate. For rental underwriting, weight by gross rent multiplier. For wholesale lists, weight by spread between Zillow Zestimate and asking. Whatever column drives your decision, it can drive the heatmap.

Combine with clustering

Zoom in past a threshold and clusters take over so you can drill into specific properties without losing the macro view. At metro scale you see the heatmap; at neighborhood scale you see clusters with counts; at street scale you see individual markers with full popups. The transitions are smooth and configurable.

Use cases real estate investors actually run

A wholesaler pastes 2,000 driving-for-dollars leads and instantly sees which corridors deserve a second pass. A flipper overlays a year of sold comps weighted by $/sqft against active listings weighted by asking price — the gap between the two heatmaps is the spread to chase. A property manager weights by occupancy rate to spot underperforming buildings before the quarterly review. None of these required custom code.

Recording and exporting heatmap animations

On paid plans, the heatmap recorder captures up to 12 frames at 200ms intervals and exports them as an animated GIF — perfect for showing market evolution over six months in a single slide. Static exports as PNG are unlimited. Both formats preserve the legend, weighting column, and timestamp so they're self-explanatory in a deck.

Performance with large datasets

The heatmap renderer runs on the GPU via Google Maps visualization or Mapbox GL JS. Up to ~50,000 points stay smooth on modern laptops. Beyond that, our web-worker pipeline pre-aggregates points into tiles before they hit the renderer so you can visualize a state-wide MLS export without freezing the browser.

Frequently asked questions