Real estate risk scoring is one of the most compelling hackathon projects you can build with location intelligence. Judges love it because it solves a real problem, demonstrates spatial reasoning, and produces visually impressive results. In this guide, we'll walk through building a risk scorer using Camino AI's location intelligence API.

What We're Building

A tool that takes a property address and returns a risk score based on location context: nearby amenities, neighborhood characteristics, environmental factors, and accessibility. Think of it as a "location health check" for any address.

Why This Wins Hackathons

  • Real-world impact: Real estate investors, homebuyers, and insurers all need location risk assessment.
  • Visual demo: Maps, scores, and charts are easy to present in 3 minutes.
  • Technical depth: Combines API calls, spatial reasoning, and data aggregation — shows you can build, not just prototype.

Architecture Overview

The stack is intentionally simple — you're at a hackathon, not building enterprise software:

User enters address
    ↓
Camino AI: Place context + spatial relationships
    ↓
Scoring engine: Weighted risk calculation
    ↓
Dashboard: Map + score breakdown + recommendations

Step 1: Set Up Camino AI

Sign up at app.getcamino.ai and grab your API key. Camino's free tier is generous enough for a hackathon — you won't burn through credits during a 48-hour sprint.

Step 2: Query Location Context

The core of your risk scorer is understanding what's around a property. Use Camino's natural language query to get rich context:

// Query neighborhood amenities
const amenities = await camino.query(
  "grocery stores, parks, and public transit within 1 mile of 123 Main St, Austin TX"
);

// Query safety and environmental factors  
const environment = await camino.placeContext(
  "123 Main St, Austin TX",
  { include: ["safety", "walkability", "flood_risk", "noise"] }
);

// Query spatial relationships
const spatial = await camino.spatialRelationship(
  "123 Main St, Austin TX",
  "nearest hospital, fire station, and school"
);

Step 3: Build the Scoring Engine

Create a weighted scoring model. Here's a simple approach that works well for demos:

function calculateRiskScore(amenities, environment, spatial) {
  const weights = {
    amenityAccess: 0.25,    // Nearby grocery, transit, parks
    safetyScore: 0.25,      // Crime data, lighting, traffic
    environmentalRisk: 0.20, // Flood zones, pollution, noise
    emergencyAccess: 0.15,  // Distance to hospital, fire station
    walkability: 0.15       // Pedestrian infrastructure
  };

  // Score each category 0-100
  const scores = {
    amenityAccess: scoreAmenities(amenities),
    safetyScore: scoreSafety(environment),
    environmentalRisk: scoreEnvironment(environment),
    emergencyAccess: scoreEmergencyAccess(spatial),
    walkability: scoreWalkability(environment)
  };

  // Weighted total
  const total = Object.entries(weights).reduce(
    (sum, [key, weight]) => sum + scores[key] * weight, 0
  );

  return { total: Math.round(total), breakdown: scores };
}

Step 4: Build the Dashboard

For a hackathon demo, keep it visual. A map centered on the property with color-coded overlays for each risk factor, plus a score card breakdown, is all you need. Libraries like Leaflet or Mapbox GL JS work well here.

Key UI elements that impress judges:

  • Overall score badge: Big, color-coded number (green/yellow/red) — immediately communicates the result.
  • Category breakdown: Horizontal bar chart showing each risk factor's contribution.
  • Map overlays: Toggle layers for amenities, emergency services, and environmental factors.
  • Comparison mode: Enter two addresses side-by-side to compare risk profiles.

Step 5: Add the "Wow" Factor

What separates winners from participants is the extra touch. Pick one:

  • Natural language summary: Use an LLM to generate a plain-English risk report: "This property has excellent transit access but is in a moderate flood zone. The nearest hospital is 12 minutes away, which is above average for this area."
  • Investment recommendation: Based on the risk score and comparable property data, suggest whether the location is undervalued or overpriced for its risk profile.
  • Time-of-day analysis: Show how the risk profile changes between daytime and nighttime (noise levels, safety, transit availability).

Demo Script (3 Minutes)

  1. The hook (30s): "Before you buy a house, you check the price. But do you check the location risk? We built a tool that scores any address in seconds."
  2. Live demo (90s): Enter a well-known address, show the score populating in real-time, click through the map overlays, then compare it against a second address.
  3. Technical depth (30s): "We use Camino AI for location intelligence — natural language queries that understand context, not just coordinates. The scoring engine weighs five risk factors using spatial relationship data."
  4. Impact (30s): "Real estate investors spend hours researching locations manually. This does it in seconds. Insurance companies could use this to price policies more accurately."

Ready to Build?

Grab your free Camino AI API key at app.getcamino.ai and start building. Our quickstart guide will have you making location queries in under 5 minutes.

Check out our guide to the best hackathons for location intelligence developers in 2026 to find your next event.