# Chicago office towers over 500,000 sq ft: energy data

**Use case:** 

Chicago office buildings over 500,000 sq ft with ENERGY STAR score, site EUI, emissions and address from the city benchmarking data.

## Input

```json
{
  "latestYearOnly": true,
  "propertyTypes": [
    "Office"
  ],
  "minFloorAreaSqft": 500000,
  "maxBuildings": 25
}
```

## Output

```json
{
  "propertyName": {
    "label": "Property or building name",
    "format": "string"
  },
  "addressStreet": {
    "label": "Street address",
    "format": "string"
  },
  "city": {
    "label": "City",
    "format": "string"
  },
  "propertyType": {
    "label": "Property type normalized to ENERGY STAR ",
    "format": "string"
  },
  "grossFloorAreaSqft": {
    "label": "Gross floor area",
    "format": "number"
  },
  "energyStarScore": {
    "label": "ENERGY STAR score",
    "format": "number"
  },
  "siteEuiKbtuSqft": {
    "label": "Site energy use intensity",
    "format": "number"
  },
  "ghgEmissionsMtco2e": {
    "label": "Total greenhouse gas emissions",
    "format": "number"
  },
  "complianceStatus": {
    "label": "Normalized status: compliant, not_submit",
    "format": "string"
  },
  "reportingYear": {
    "label": "Data year of the report",
    "format": "number"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Chicago Energy Benchmarking Data: Building Energy Ratings Actor is ready to run with the inputs already set for "Chicago office towers over 500,000 sq ft: energy data". [Sign up or log in](https://console.apify.com/create-task-from-example/h0nkIUQtTp6JtLrvy) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Chicago office towers over 500,000 sq ft: energy data" task

The Chicago Energy Benchmarking Data: Building Energy Ratings Actor will start running based on the input automatically. You can also change all the input fields, like latest year only, property types, and more, to match your specific use case.

### 3. Receive the "Chicago office towers over 500,000 sq ft: energy data" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as property or building name, street address, city, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Chicago office towers over 500,000 sq ft: energy data" task via API](https://apify.com/buildingsignal/chicago-energy-benchmarking.md).

## About this Actor

This example demonstrates how to use [Chicago Energy Benchmarking Data: Building Energy Ratings](https://apify.com/buildingsignal/chicago-energy-benchmarking.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/buildingsignal/chicago-energy-benchmarking.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/buildingsignal/chicago-energy-benchmarking.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).