# LoopNet + Crexi Scraper · CRE Listings · CoStar Alternative (`kazkn/commercial-real-estate-brokerage-intel`) Actor
LoopNet + Crexi commercial real estate scraper. Aggregates listings, normalizes cap rates (NOI/price + asset-class median fallback), tracks days-on-market, dedups cross-platform, exposes broker contacts. The CoStar alternative for SMB CRE brokers — pay-per-result, $5/1K listings.
- **URL**: https://apify.com/kazkn/commercial-real-estate-brokerage-intel.md
- **Developed by:** [KazKN](https://apify.com/kazkn) (community)
- **Categories:** Lead generation, Real estate, Automation
- **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet
## Pricing
from $5.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## LoopNet + Crexi Scraper · Commercial Real Estate Listings · CoStar Alternative
> 🏢 The autonomous **commercial real estate API** that aggregates LoopNet and Crexi listings, normalizes cap rates, and deduplicates cross-platform. **$5 per 1,000 results** — 1/40th of CoStar's $40k/yr median.
---
### 💰 Why CRE brokers switch to this
CoStar gates the U.S. commercial real estate listings market behind **$14k–$40k/year per seat**. Every existing scraper on Apify Store covers a single source (LoopNet OR Crexi). **None** normalize cap rates. **None** track days-on-market. **None** deduplicate cross-platform.
This actor does. And it runs **fully autonomously, 24/7**.
| 💵 Annual cost | 🏢 Coverage | 📊 Intelligence layer |
|---|---|---|
| **This actor** ~$1,500/yr (typical Pro broker use) | LoopNet + Crexi | ✅ Cap rate, DOM, dedup |
| CoStar Suite entry | $14,000–$20,000/yr | ⚠️ Web UI only, no API |
| CoStar Suite median | $40,000/yr | ⚠️ Multi-seat |
| Reonomy / Compstak | $5,000–$18,000/yr | Off-market focus |
---
### ⚡ Quick output preview
```json
{
"source": "loopnet",
"source_listing_id": "29769721",
"address": {
"street": "2921 E 17th St",
"city": "Austin",
"state": "TX",
"zip": "78702",
"lat": 30.278541,
"lng": -97.709889
},
"asset_class": "office",
"sub_type": "Loft/Creative Space",
"sqft": 7500,
"asking_price_usd": 2500000,
"cap_rate_listed": null,
"cap_rate_normalized": 7.5,
"cap_rate_estimated": true,
"price_per_sqft": 333,
"days_on_market": 32,
"status": "active",
"broker": {
"name": "Isaac Gutierrez",
"company": "ECR",
"phone": null
},
"also_listed_on": [],
"photo_urls": ["https://images1.loopnet.com/i2/.../900x675/image.jpg"],
"description": "Creative office building in the East Austin submarket available for sale."
}
````
***
### 🎯 Quick start
#### 🏙️ Search by city + state (most common)
```json
{
"city": "Austin",
"state": "TX",
"sourcesEnabled": ["loopnet", "crexi"],
"assetClasses": ["office", "retail"],
"priceMin": 500000,
"priceMax": 5000000,
"maxResults": 200
}
```
#### 🔄 Daily monitoring — only NEW listings
```json
{
"city": "Dallas",
"state": "TX",
"monitoringMode": true,
"maxResults": 500
}
```
Run on a daily schedule. The actor maintains an internal snapshot per query. **Only listings not seen in previous runs are emitted.**
#### 📊 Track CLOSED deals
```json
{
"city": "Phoenix",
"state": "AZ",
"transactionTrackingMode": true
}
```
Emits ONLY listings that disappeared since the last run (likely sold/leased) or whose status flipped to `sold` / `under_contract`. Each item carries `transaction_tracking.close_status`, `close_detected_at`, `previous_price`.
#### 🔗 Paste specific listing URLs
```json
{
"startUrls": [
{"url": "https://www.loopnet.com/Listing/12345678/example-property/"},
{"url": "https://www.crexi.com/properties/87654321/example-listing"}
]
}
```
***
### 🏢 Who uses this
| Persona | Use case |
|---|---|
| 💼 **CRE investment broker** | Daily morning brief: new sub-$5M listings in target market |
| 🏛️ **REIT acquisition team** | Track multifamily + retail across 5 metros, normalize cap rates |
| 🔍 **ETA / search fund** | Identify owner-operated deals, dedupe LoopNet+Crexi to skip duplicate underwriting |
| 📈 **CRE consulting firm** | Build normalized comp datasets for client reports |
| 🏰 **Family office** | Filter for opportunity-zone office, weekly digest |
| 🏦 **Lender / appraiser** | Days-on-market by asset class for valuation models |
***
### 📊 The intelligence layer (the actual moat)
#### Cap-rate normalization
Listing data is **inconsistent across platforms**. Some declare cap rate, others NOI only, auctions and unpriced listings have neither. The actor handles all 4 cases:
| Source data | Action | Output `cap_rate_estimated` |
|---|---|---|
| NOI + price both declared | **Recompute** cap = NOI/price × 100 | `false` ✅ |
| Only cap\_rate declared | Pass-through, derive implied NOI | `false` ✅ |
| Neither declared, price known | **Estimate** with asset-class median (US Q1 2024-25) | `true` ⚠️ |
| No price + no cap\_rate | — | `null` |
**Asset-class medians used:**
| Class | Median | Class | Median |
|---|---|---|---|
| 🏠 Multifamily | 5.4% | 🛍️ Retail | 6.8% |
| 🏭 Industrial | 5.9% | 🏢 Office | 7.5% |
| 🏗️ Mixed-use | 6.5% | 🎯 Specialty | 7.0% |
| 🏨 Hotel | 8.5% | | |
#### 🔄 Cross-platform deduplication
Property listed on both LoopNet AND Crexi appears in BOTH datasets. The actor:
1. Computes a **stable `dedup_key`** = hash(normalized\_street + city + state + sqft\_bucket + asset\_class)
2. Groups listings by key
3. Picks the most complete record as primary (highest non-null field count)
4. Marks `also_listed_on: ["crexi"]` on the LoopNet primary (or vice versa)
5. Outputs **one** deduplicated record per property
📌 No other Apify actor does this. It's the difference between raw scrape and intelligence.
***
### 💰 Pricing — fully transparent
Pay-per-event on Apify Store. **New Apify users get $5 platform credit on signup** (~1,000 listings test on this actor included).
| Event | Price | When |
|---|---|---|
| 🚀 **Actor Start** | $0.05 / run | Once per scrape job |
| 📍 **Result** ⭐ | $0.005 / listing | Each unique listing returned (primary cost) |
| 📋 **Listing detail enrichment** | $0.003 / listing | Only when `includeListingDetails: true` (NOI, photos, year built) |
#### 💵 Realistic cost estimates
| Volume | Without details | With details |
|---|---|---|
| 100 listings (test) | **$0.55** | $0.85 |
| 1,000 listings (city scan) | **$5.05** | $8.05 |
| 10,000 listings (multi-city) | **$50.05** | $80.05 |
| 25,000 listings/mo (active broker) | **~$125/mo** | ~$200/mo |
📈 At 25K listings/mo the actor costs **~$1,500/yr** vs CoStar's **$40,000/yr median**. ROI = **27×**.
***
### ✅ Key features
- 🔄 **Cross-platform aggregation** — LoopNet + Crexi
- 📊 **Cap-rate normalization** with NOI fallback + asset-class medians
- 🔁 **Cross-platform deduplication** with stable `dedup_key`
- ⏱️ **Days-on-market index** auto-computed
- 🆕 **Monitoring mode** — only new listings since last run
- 🤝 **Transaction tracking** — only closed deals (sold / leased / under contract)
- 📦 **Portfolio expansion** — multi-property listings split into individual items
- 🎛️ **20+ filters** — price, cap rate, sqft, asset class, sub-type, state, city
- 📁 **Apify dataset views** — Overview / Financial / Broker contacts pre-configured
- 🌍 **Multi-locale parsing** — handles English + French, ranges (1.5K-13K SF), K/M multipliers
- 🤖 **Fully autonomous** — runs on Apify residential proxy, no human ritual
***
### ❓ FAQ
**Q: How is this different from existing LoopNet / Crexi scrapers on Apify?**
A: Other scrapers cover a single source and return raw data. This covers BOTH, normalizes cap rates, deduplicates cross-platform, computes days-on-market. The intelligence layer is the value.
**Q: How fresh is the data?**
A: As fresh as LoopNet/Crexi public listings — typically updated every 24–48h on the source side. With monitoring mode, you're notified within 1 day of a new listing.
**Q: Can I get NOI and cap rate that aren't publicly listed?**
A: When the source platform exposes them, yes. When not (~40% of listings), the actor estimates with asset-class medians, clearly flagged with `cap_rate_estimated: true`. Medians refreshed quarterly.
**Q: Are broker contact emails / phones included?**
A: Phone and email when available on source (Crexi exposes more contact info than LoopNet). Use ethically — direct outreach is governed by your local CRE broker license rules.
**Q: Can I use this for off-market data?**
A: No, on-market only. For off-market, look at Reonomy or CompStak (different price tier).
**Q: What about Ten-X auctions?**
A: Out of scope — Ten-X auction listings are gated (bidder registration required). The actor flags listings transitioning to auction status via `status: "under_contract"` when source exposes it.
**Q: Can I run this on a schedule?**
A: Yes — Apify scheduler. Recommended: daily 6am ET in `monitoringMode: true` for new-listing alerts. Combine with Apify webhooks → Slack / Zapier / your CRM.
**Q: How does dedup handle two listings with same address but different asset class?**
A: Dedup key includes `asset_class`, so a "Mixed Use" record and a "Multifamily" record at the same address remain separate items.
**Q: What about international listings?**
A: USA + Canada (LoopNet covers both). International CRE markets not currently supported.
***
### 📋 Output schema
Each dataset row follows this schema:
```typescript
{
source: "loopnet" | "crexi",
source_listing_id: string,
source_url: string,
scraped_at: string, // ISO8601
address: {
raw: string,
street: string | null,
city: string | null,
state: string | null, // 2-letter US/CA code
zip: string | null,
country: "US" | "CA",
lat: number | null,
lng: number | null
},
asset_class:
| "office" | "retail" | "industrial" | "multifamily"
| "land" | "hotel" | "mixed-use" | "specialty" | "unknown",
sub_type: string | null,
sqft: number | null,
units: number | null,
year_built: number | null,
lot_size_sqft: number | null,
asking_price_usd: number | null,
noi_usd: number | null,
cap_rate_listed: number | null,
cap_rate_normalized: number | null,
cap_rate_estimated: boolean,
price_per_sqft: number | null,
listed_at: string | null,
days_on_market: number | null,
status:
| "active" | "under_contract" | "sold" | "leased"
| "removed" | "off_market" | "unknown",
broker: {
name: string | null,
company: string | null,
phone: string | null,
email: string | null,
profile_url: string | null
},
dedup_key: string, // stable hash for dedup
also_listed_on: ("loopnet" | "crexi")[],
photo_urls: string[],
description: string | null,
// Only when transactionTrackingMode=true
transaction_tracking?: {
close_status: "sold" | "leased" | "under_contract" | "removed" | "pending",
close_detected_at: string,
first_seen_at: string,
last_seen_at: string,
previous_price: number | null
}
}
```
***
### 🛠️ Technical notes
- **Stack**: Apify SDK v3 + Crawlee v3.16, TypeScript ESM, Node 22
- **Source extraction**: `pds.loopnet.com` mobile API + `api.crexi.com` JSON
- **Anti-blocking**: Apify residential proxies, auto-rotated
- **Performance**: HTTP-only for normal usage, sub-10s runs for 50 listings
- **Cap-rate medians**: refreshed quarterly from Marcus & Millichap + CBRE Cap Rate Survey
- **Address normalization**: USPS-style street parsing + sqft buckets for dedup
***
### 📞 Contact
Issues, feature requests, custom feeds → open an issue on Apify Store or contact KazKN directly.
***
*🏢 Built to help SMB CRE brokers stop overpaying for data they couldn't differentiate themselves with anyway.*
# Actor input Schema
## `city` (type: `string`):
Target city. Combined with State below, used to build a search bounding box. Leave empty if you provide URLs in the field below.
## `state` (type: `string`):
US state code (CA, NY, TX, FL, NV, …). Required when City is set.
## `assetClasses` (type: `array`):
Restrict to specific commercial property types. Empty = all types.
## `sourcesEnabled` (type: `array`):
Which platforms to query. Both enabled by default (cross-platform dedup needs both).
## `transactionTypes` (type: `array`):
Whether to fetch for-sale properties (purchase prices, cap rates), for-lease spaces (rent quotes, suite breakdown), or both. When both are enabled, an asset listed for both sale AND lease emits 2 separate dataset rows.
⚠️ v0.2 scope: Lease scraping is supported on Crexi. LoopNet for-lease is queued for v0.3 (pending iPhone API reverse-engineering). For lease-only runs prefer sourcesEnabled: \["crexi"].
## `maxResultsPerSource` (type: `integer`):
Hard cap PER SOURCE per run. With both sources enabled and value=10, you get up to 10 LoopNet + 10 Crexi = 20 total.
## `startUrls` (type: `array`):
Optional. Paste search URLs from LoopNet or Crexi. URL filters are auto-extracted when possible:
✅ Crexi URL params auto-extracted: types\[] (asset class), priceMin / priceMax, capRateMin / capRateMax, squareFootageMin / squareFootageMax.
⚠️ Crexi NOT auto-extracted: placeIds (location — set City + State manually), subtypes, tenancy, occupancy, yearBuilt, lotSize, keywords, /lease/ paths.
✅ LoopNet URL parts auto-extracted: spaceType (asset class) + city + state + for-sale/lease detection.
⚠️ LoopNet NOT auto-extracted: the encoded "sk=" filter blob (cap rate, tenancy, year built, etc.) — set those in the form below.
Form fields ALWAYS take priority over URL filters when both are set.
## `priceMin` (type: `integer`):
Lower bound on asking price.
## `priceMax` (type: `integer`):
Upper bound on asking price.
## `capRateMin` (type: `integer`):
Lower bound on the normalized cap rate (e.g. 5 = 5%).
## `capRateMax` (type: `integer`):
Upper bound on the normalized cap rate.
## `buildingSizeMin` (type: `integer`):
Lower bound on building square footage.
## `buildingSizeMax` (type: `integer`):
Upper bound on building square footage.
## `deduplicate` (type: `boolean`):
Merge listings appearing on both LoopNet and Crexi into a single record (with also\_listed\_on field).
## `normalizeCapRate` (type: `boolean`):
Recalculate cap\_rate\_normalized from NOI / asking\_price. Falls back to asset-class median when NOI absent.
## `monitoringMode` (type: `boolean`):
Emit only listings not seen in previous runs. Uses an internal snapshot keyed by your search criteria. Best run on a daily schedule.
## `transactionTrackingMode` (type: `boolean`):
Emit only listings that closed since the last run (sold / leased / under\_contract / removed). Useful for comp data and market velocity tracking.
## `includeListingDetails` (type: `boolean`):
Fetch each listing's detail page for richer data (NOI, year\_built, building class, full description, photos). Slower and adds $0.003 per listing.
## `includePortfolioProperties` (type: `boolean`):
When a listing groups multiple properties under one URL, emit each as a separate dataset item.
## `downloadImages` (type: `boolean`):
Save listing photos to the Apify Key-Value Store (collection: photos). Off by default to save bandwidth.
## `maxImages` (type: `integer`):
Cap on photos saved per listing (only used when downloadImages = true). NOT a result-count limit — for that, use "Maximum results PER SOURCE" above.
## `maxConcurrency` (type: `integer`):
Maximum parallel page fetches.
## `minConcurrency` (type: `integer`):
Minimum parallel page fetches.
## `maxRequestRetries` (type: `integer`):
How many times to retry a failed request before giving up.
## `addresses` (type: `array`):
Format: "123 Main St, City, State". Each address queried on every enabled source. Use City + State above instead for new searches.
## `crexiCookies` (type: `array`):
Optional Crexi session cookies (export with EditThisCookie Chrome extension after login). Not required for the default scrape — only useful if you hit Crexi rate limits.
## `proxyConfiguration` (type: `object`):
Apify residential proxies recommended (default).
## `maxResults` (type: `integer`):
Backward-compat alias. Use "Maximum results PER SOURCE" above instead. Leave empty — only kept for older saved tasks.
## Actor input object example
```json
{
"city": "Austin",
"state": "TX",
"assetClasses": [],
"sourcesEnabled": [
"loopnet",
"crexi"
],
"transactionTypes": [
"sale"
],
"maxResultsPerSource": 100,
"startUrls": [],
"deduplicate": true,
"normalizeCapRate": true,
"monitoringMode": false,
"transactionTrackingMode": false,
"includeListingDetails": false,
"includePortfolioProperties": false,
"downloadImages": false,
"maxImages": 10,
"maxConcurrency": 20,
"minConcurrency": 1,
"maxRequestRetries": 5,
"addresses": [],
"crexiCookies": [],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# Actor output Schema
## `results` (type: `string`):
Aggregated, normalized commercial real estate listings. Each row is one unique property after cross-platform dedup.
## `photos` (type: `string`):
Listing photos downloaded to the key-value store, only when downloadImages: true. One image per key, prefixed with photo-.
## `snapshots` (type: `string`):
Per-query snapshot files used by monitoringMode (detect new listings) and transactionTrackingMode (detect closed deals) across runs. JSON, prefixed with cre-snapshot-.
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"city": "Austin",
"state": "TX",
"assetClasses": [],
"startUrls": [],
"addresses": [],
"crexiCookies": []
};
// Run the Actor and wait for it to finish
const run = await client.actor("kazkn/commercial-real-estate-brokerage-intel").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"city": "Austin",
"state": "TX",
"assetClasses": [],
"startUrls": [],
"addresses": [],
"crexiCookies": [],
}
# Run the Actor and wait for it to finish
run = client.actor("kazkn/commercial-real-estate-brokerage-intel").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"city": "Austin",
"state": "TX",
"assetClasses": [],
"startUrls": [],
"addresses": [],
"crexiCookies": []
}' |
apify call kazkn/commercial-real-estate-brokerage-intel --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=kazkn/commercial-real-estate-brokerage-intel",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LoopNet + Crexi Scraper · CRE Listings · CoStar Alternative",
"description": "LoopNet + Crexi commercial real estate scraper. Aggregates listings, normalizes cap rates (NOI/price + asset-class median fallback), tracks days-on-market, dedups cross-platform, exposes broker contacts. The CoStar alternative for SMB CRE brokers — pay-per-result, $5/1K listings.",
"version": "0.2",
"x-build-id": "57FTxENeEUlaOgKCW"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/kazkn~commercial-real-estate-brokerage-intel/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-kazkn-commercial-real-estate-brokerage-intel",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/kazkn~commercial-real-estate-brokerage-intel/runs": {
"post": {
"operationId": "runs-sync-kazkn-commercial-real-estate-brokerage-intel",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/kazkn~commercial-real-estate-brokerage-intel/run-sync": {
"post": {
"operationId": "run-sync-kazkn-commercial-real-estate-brokerage-intel",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"city": {
"title": "🏙️ City",
"type": "string",
"description": "Target city. Combined with State below, used to build a search bounding box. Leave empty if you provide URLs in the field below."
},
"state": {
"title": "🇺🇸 State (US 2-letter)",
"type": "string",
"description": "US state code (CA, NY, TX, FL, NV, …). Required when City is set."
},
"assetClasses": {
"title": "🎯 Asset class",
"uniqueItems": true,
"type": "array",
"description": "Restrict to specific commercial property types. Empty = all types.",
"items": {
"type": "string",
"enum": [
"office",
"retail",
"industrial",
"multifamily",
"land",
"hotel",
"mixed-use",
"specialty"
],
"enumTitles": [
"🏢 Office",
"🛍️ Retail",
"🏭 Industrial",
"🏠 Multifamily",
"🌾 Land",
"🏨 Hotel",
"🏗️ Mixed-use",
"🎯 Specialty"
]
}
},
"sourcesEnabled": {
"title": "📡 Sources",
"uniqueItems": true,
"type": "array",
"description": "Which platforms to query. Both enabled by default (cross-platform dedup needs both).",
"items": {
"type": "string",
"enum": [
"loopnet",
"crexi"
],
"enumTitles": [
"LoopNet",
"Crexi"
]
},
"default": [
"loopnet",
"crexi"
]
},
"transactionTypes": {
"title": "💼 Transaction type",
"uniqueItems": true,
"type": "array",
"description": "Whether to fetch for-sale properties (purchase prices, cap rates), for-lease spaces (rent quotes, suite breakdown), or both. When both are enabled, an asset listed for both sale AND lease emits 2 separate dataset rows.
⚠️ v0.2 scope: Lease scraping is supported on Crexi. LoopNet for-lease is queued for v0.3 (pending iPhone API reverse-engineering). For lease-only runs prefer sourcesEnabled: [\"crexi\"].",
"items": {
"type": "string",
"enum": [
"sale",
"lease"
],
"enumTitles": [
"🛒 For sale (purchase)",
"🏷️ For lease (rent)"
]
},
"default": [
"sale"
]
},
"maxResultsPerSource": {
"title": "🔢 Maximum results PER SOURCE",
"minimum": 1,
"maximum": 10000,
"type": "integer",
"description": "Hard cap PER SOURCE per run. With both sources enabled and value=10, you get up to 10 LoopNet + 10 Crexi = 20 total.",
"default": 100
},
"startUrls": {
"title": "🔗 Search URLs (optional)",
"type": "array",
"description": "Optional. Paste search URLs from LoopNet or Crexi. URL filters are auto-extracted when possible:
✅ Crexi URL params auto-extracted: types[] (asset class), priceMin / priceMax, capRateMin / capRateMax, squareFootageMin / squareFootageMax.
⚠️ Crexi NOT auto-extracted: placeIds (location — set City + State manually), subtypes, tenancy, occupancy, yearBuilt, lotSize, keywords, /lease/ paths.
✅ LoopNet URL parts auto-extracted: spaceType (asset class) + city + state + for-sale/lease detection.
⚠️ LoopNet NOT auto-extracted: the encoded \"sk=\" filter blob (cap rate, tenancy, year built, etc.) — set those in the form below.
Form fields ALWAYS take priority over URL filters when both are set.",
"items": {
"type": "object",
"required": [
"url"
],
"properties": {
"url": {
"type": "string",
"title": "URL of a web page",
"format": "uri"
}
}
}
},
"priceMin": {
"title": "Min asking price (USD)",
"minimum": 0,
"type": "integer",
"description": "Lower bound on asking price."
},
"priceMax": {
"title": "Max asking price (USD)",
"minimum": 0,
"type": "integer",
"description": "Upper bound on asking price."
},
"capRateMin": {
"title": "Min cap rate (%)",
"minimum": 0,
"maximum": 50,
"type": "integer",
"description": "Lower bound on the normalized cap rate (e.g. 5 = 5%)."
},
"capRateMax": {
"title": "Max cap rate (%)",
"minimum": 0,
"maximum": 50,
"type": "integer",
"description": "Upper bound on the normalized cap rate."
},
"buildingSizeMin": {
"title": "Min building size (sqft)",
"minimum": 0,
"type": "integer",
"description": "Lower bound on building square footage."
},
"buildingSizeMax": {
"title": "Max building size (sqft)",
"minimum": 0,
"type": "integer",
"description": "Upper bound on building square footage."
},
"deduplicate": {
"title": "Cross-platform deduplication",
"type": "boolean",
"description": "Merge listings appearing on both LoopNet and Crexi into a single record (with also_listed_on field).",
"default": true
},
"normalizeCapRate": {
"title": "Normalize cap rates",
"type": "boolean",
"description": "Recalculate cap_rate_normalized from NOI / asking_price. Falls back to asset-class median when NOI absent.",
"default": true
},
"monitoringMode": {
"title": "Monitoring mode (only NEW listings)",
"type": "boolean",
"description": "Emit only listings not seen in previous runs. Uses an internal snapshot keyed by your search criteria. Best run on a daily schedule.",
"default": false
},
"transactionTrackingMode": {
"title": "Transaction tracking (CLOSED deals only)",
"type": "boolean",
"description": "Emit only listings that closed since the last run (sold / leased / under_contract / removed). Useful for comp data and market velocity tracking.",
"default": false
},
"includeListingDetails": {
"title": "Include full listing details",
"type": "boolean",
"description": "Fetch each listing's detail page for richer data (NOI, year_built, building class, full description, photos). Slower and adds $0.003 per listing.",
"default": false
},
"includePortfolioProperties": {
"title": "Expand portfolio listings",
"type": "boolean",
"description": "When a listing groups multiple properties under one URL, emit each as a separate dataset item.",
"default": false
},
"downloadImages": {
"title": "Download images to key-value store",
"type": "boolean",
"description": "Save listing photos to the Apify Key-Value Store (collection: photos). Off by default to save bandwidth.",
"default": false
},
"maxImages": {
"title": "Max images per listing",
"minimum": 0,
"maximum": 50,
"type": "integer",
"description": "Cap on photos saved per listing (only used when downloadImages = true). NOT a result-count limit — for that, use \"Maximum results PER SOURCE\" above.",
"default": 10
},
"maxConcurrency": {
"title": "Max concurrency",
"minimum": 1,
"maximum": 50,
"type": "integer",
"description": "Maximum parallel page fetches.",
"default": 20
},
"minConcurrency": {
"title": "Min concurrency",
"minimum": 1,
"maximum": 50,
"type": "integer",
"description": "Minimum parallel page fetches.",
"default": 1
},
"maxRequestRetries": {
"title": "Max request retries",
"minimum": 0,
"maximum": 50,
"type": "integer",
"description": "How many times to retry a failed request before giving up.",
"default": 5
},
"addresses": {
"title": "Addresses (alternative to URLs)",
"type": "array",
"description": "Format: \"123 Main St, City, State\". Each address queried on every enabled source. Use City + State above instead for new searches.",
"items": {
"type": "string"
}
},
"crexiCookies": {
"title": "Crexi session cookies (advanced)",
"type": "array",
"description": "Optional Crexi session cookies (export with EditThisCookie Chrome extension after login). Not required for the default scrape — only useful if you hit Crexi rate limits."
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Apify residential proxies recommended (default).",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
},
"maxResults": {
"title": "Legacy: max results (alias for maxResultsPerSource)",
"minimum": 1,
"maximum": 10000,
"type": "integer",
"description": "Backward-compat alias. Use \"Maximum results PER SOURCE\" above instead. Leave empty — only kept for older saved tasks."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```