Urban Intelligence
NYC publishes millions of 311 complaints every year. But immigrant communities don't call at the same rate. Lower-income neighborhoods under-report. Every tool built on that data inherits the same blind spot. Beaconz corrects for it.
The Problem
NYC and NYS publish 311 dashboards. They show who called. They can't show who didn't.
A peer-reviewed NYU study confirmed systematic under-reporting in immigrant, low-income, and limited-English communities for critical categories like heat and hot water.
Every property decision — buy, hold, renew, develop — relies on signals that structurally exclude the neighborhoods with the most hidden risk.
What We're Building
Composite score blending 311 complaints, HPD violations, and infrastructure signals — weighted, normalized per housing unit, and bias-corrected for under-reporting demographics.
Our bias-correction model reveals neighborhoods where conditions are worse than the data suggests — calibrated against NYU Marron Institute methodology on demographic reporting gaps.
Our signal validation framework tests 7 causal chains across 5 years of data. The goal: prove friction predicts evictions, price shifts, and business closures 1–3 months in advance.
Product Preview
Built On
Origin
We don’t just report what is visible; we reveal what is hidden. Our mission is to dismantle the data bias that defines our cities.
- Sid Virkar, Founder
Former AWS Identity EM
We're validating the signal now. Join the list to get first access.