Commercial Real Estate Data API - CRE Property Intelligence

CRE Data is Fragmented

Commercial real estate data is scattered across listing platforms, broker sites, and property databases. Getting a complete market view requires aggregating data from multiple sources.

Traditional CRE data providers are expensive and often have limited coverage. You need flexible data collection that covers your target markets.

WebScraping.AI Solution

Listing Data: Office, retail, industrial, and multifamily properties
Lease Intelligence: Asking rents, lease terms, and concessions
Market Coverage: Data from any CRE platform or broker site
Structured Output: Clean data ready for analysis and modeling

Commercial Property Data

Comprehensive CRE intelligence

Property Details

Square footage, year built, floors, parking, and amenities.

Lease Rates

Asking rent, price per SF, lease terms, and tenant incentives.

Availability

Vacancy rates, available spaces, and sublease inventory.

Broker Info

Listing brokers, contact info, and brokerage firms.

Code Examples

Extract commercial real estate data

Extract commercial property listing data

curl -G "https://api.webscraping.ai/ai/fields" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://cre-platform.com/property/downtown-office-tower" \
  --data-urlencode "fields[property_name]=Building or property name" \
  --data-urlencode "fields[property_type]=Office, retail, industrial, multifamily" \
  --data-urlencode "fields[address]=Full property address" \
  --data-urlencode "fields[total_sf]=Total square footage" \
  --data-urlencode "fields[available_sf]=Available square footage" \
  --data-urlencode "fields[floors]=Number of floors" \
  --data-urlencode "fields[year_built]=Year constructed" \
  --data-urlencode "fields[asking_rent]=Asking rent per SF per year" \
  --data-urlencode "fields[lease_type]=NNN, Full Service, Modified Gross" \
  --data-urlencode "fields[available_spaces]=List of available units with size and rent" \
  --data-urlencode "fields[amenities]=Building amenities" \
  --data-urlencode "fields[parking]=Parking ratio and type" \
  --data-urlencode "fields[broker_name]=Listing broker name" \
  --data-urlencode "fields[broker_company]=Brokerage firm"
# Response:
# {
#   "property_name": "One Financial Plaza",
#   "property_type": "Office",
#   "address": "100 Main Street, Boston, MA 02110",
#   "total_sf": 450000,
#   "available_sf": 75000,
#   "floors": 32,
#   "year_built": 1985,
#   "asking_rent": "$65.00 PSF/Year",
#   "lease_type": "Full Service",
#   "available_spaces": [
#     {"floor": 15, "sf": 25000, "rent": "$62.00"},
#     {"floor": 22, "sf": 50000, "rent": "$68.00"}
#   ],
#   "amenities": ["Fitness center", "Conference facility", "Retail"],
#   "parking": "2.5/1,000 SF covered",
#   "broker_name": "John Smith",
#   "broker_company": "CBRE"
# }

# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
result = client.fields(
    "https://cre-platform.com/property/downtown-office-tower",
    fields={
        "property_name": "Building or property name",
        "property_type": "Office, retail, industrial, multifamily",
        "address": "Full property address",
        "total_sf": "Total square footage",
        "available_sf": "Available square footage",
        "floors": "Number of floors",
        "year_built": "Year constructed",
        "asking_rent": "Asking rent per SF per year",
        "lease_type": "NNN, Full Service, Modified Gross",
        "available_spaces": "List of available units with size and rent",
        "amenities": "Building amenities",
        "parking": "Parking ratio and type",
        "broker_name": "Listing broker name",
        "broker_company": "Brokerage firm",
    },
)
print(result)
# Response:
# {
#   "property_name": "One Financial Plaza",
#   "property_type": "Office",
#   "address": "100 Main Street, Boston, MA 02110",
#   "total_sf": 450000,
#   "available_sf": 75000,
#   "floors": 32,
#   "year_built": 1985,
#   "asking_rent": "$65.00 PSF/Year",
#   "lease_type": "Full Service",
#   "available_spaces": [
#     {"floor": 15, "sf": 25000, "rent": "$62.00"},
#     {"floor": 22, "sf": 50000, "rent": "$68.00"}
#   ],
#   "amenities": ["Fitness center", "Conference facility", "Retail"],
#   "parking": "2.5/1,000 SF covered",
#   "broker_name": "John Smith",
#   "broker_company": "CBRE"
# }

// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const result = await client.fields({
  url: 'https://cre-platform.com/property/downtown-office-tower',
  fields: {
    property_name: 'Building or property name',
    property_type: 'Office, retail, industrial, multifamily',
    address: 'Full property address',
    total_sf: 'Total square footage',
    available_sf: 'Available square footage',
    floors: 'Number of floors',
    year_built: 'Year constructed',
    asking_rent: 'Asking rent per SF per year',
    lease_type: 'NNN, Full Service, Modified Gross',
    available_spaces: 'List of available units with size and rent',
    amenities: 'Building amenities',
    parking: 'Parking ratio and type',
    broker_name: 'Listing broker name',
    broker_company: 'Brokerage firm',
  },
});
console.log(result);
// Response:
// {
//   "property_name": "One Financial Plaza",
//   "property_type": "Office",
//   "address": "100 Main Street, Boston, MA 02110",
//   "total_sf": 450000,
//   "available_sf": 75000,
//   "floors": 32,
//   "year_built": 1985,
//   "asking_rent": "$65.00 PSF/Year",
//   "lease_type": "Full Service",
//   "available_spaces": [
//     {"floor": 15, "sf": 25000, "rent": "$62.00"},
//     {"floor": 22, "sf": 50000, "rent": "$68.00"}
//   ],
//   "amenities": ["Fitness center", "Conference facility", "Retail"],
//   "parking": "2.5/1,000 SF covered",
//   "broker_name": "John Smith",
//   "broker_company": "CBRE"
// }

<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$result = $client->fields('https://cre-platform.com/property/downtown-office-tower', [
    'property_name'    => 'Building or property name',
    'property_type'    => 'Office, retail, industrial, multifamily',
    'address'          => 'Full property address',
    'total_sf'         => 'Total square footage',
    'available_sf'     => 'Available square footage',
    'floors'           => 'Number of floors',
    'year_built'       => 'Year constructed',
    'asking_rent'      => 'Asking rent per SF per year',
    'lease_type'       => 'NNN, Full Service, Modified Gross',
    'available_spaces' => 'List of available units with size and rent',
    'amenities'        => 'Building amenities',
    'parking'          => 'Parking ratio and type',
    'broker_name'      => 'Listing broker name',
    'broker_company'   => 'Brokerage firm',
]);
print_r($result);
// Response:
// {
//   "property_name": "One Financial Plaza",
//   "property_type": "Office",
//   "address": "100 Main Street, Boston, MA 02110",
//   "total_sf": 450000,
//   "available_sf": 75000,
//   "floors": 32,
//   "year_built": 1985,
//   "asking_rent": "$65.00 PSF/Year",
//   "lease_type": "Full Service",
//   "available_spaces": [
//     {"floor": 15, "sf": 25000, "rent": "$62.00"},
//     {"floor": 22, "sf": 50000, "rent": "$68.00"}
//   ],
//   "amenities": ["Fitness center", "Conference facility", "Retail"],
//   "parking": "2.5/1,000 SF covered",
//   "broker_name": "John Smith",
//   "broker_company": "CBRE"
// }

# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
result = client.fields(
  'https://cre-platform.com/property/downtown-office-tower',
  fields: {
    property_name:     'Building or property name',
    property_type:     'Office, retail, industrial, multifamily',
    address:           'Full property address',
    total_sf:          'Total square footage',
    available_sf:      'Available square footage',
    floors:            'Number of floors',
    year_built:        'Year constructed',
    asking_rent:       'Asking rent per SF per year',
    lease_type:        'NNN, Full Service, Modified Gross',
    available_spaces:  'List of available units with size and rent',
    amenities:         'Building amenities',
    parking:           'Parking ratio and type',
    broker_name:       'Listing broker name',
    broker_company:    'Brokerage firm',
  }
)
puts result.inspect
# Response:
# {
#   "property_name": "One Financial Plaza",
#   "property_type": "Office",
#   "address": "100 Main Street, Boston, MA 02110",
#   "total_sf": 450000,
#   "available_sf": 75000,
#   "floors": 32,
#   "year_built": 1985,
#   "asking_rent": "$65.00 PSF/Year",
#   "lease_type": "Full Service",
#   "available_spaces": [
#     {"floor": 15, "sf": 25000, "rent": "$62.00"},
#     {"floor": 22, "sf": 50000, "rent": "$68.00"}
#   ],
#   "amenities": ["Fitness center", "Conference facility", "Retail"],
#   "parking": "2.5/1,000 SF covered",
#   "broker_name": "John Smith",
#   "broker_company": "CBRE"
# }

// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    result, _ := client.Fields(context.Background(), &webscrapingai.FieldsOptions{
        URL: "https://cre-platform.com/property/downtown-office-tower",
        Fields: map[string]string{
            "property_name": "Building or property name",
            "property_type": "Office, retail, industrial, multifamily",
            "address": "Full property address",
            "total_sf": "Total square footage",
            "available_sf": "Available square footage",
            "floors": "Number of floors",
            "year_built": "Year constructed",
            "asking_rent": "Asking rent per SF per year",
            "lease_type": "NNN, Full Service, Modified Gross",
            "available_spaces": "List of available units with size and rent",
            "amenities": "Building amenities",
            "parking": "Parking ratio and type",
            "broker_name": "Listing broker name",
            "broker_company": "Brokerage firm",
        },
    })
    fmt.Println(result.Result)
}
// Response:
// {
//   "property_name": "One Financial Plaza",
//   "property_type": "Office",
//   "address": "100 Main Street, Boston, MA 02110",
//   "total_sf": 450000,
//   "available_sf": 75000,
//   "floors": 32,
//   "year_built": 1985,
//   "asking_rent": "$65.00 PSF/Year",
//   "lease_type": "Full Service",
//   "available_spaces": [
//     {"floor": 15, "sf": 25000, "rent": "$62.00"},
//     {"floor": 22, "sf": 50000, "rent": "$68.00"}
//   ],
//   "amenities": ["Fitness center", "Conference facility", "Retail"],
//   "parking": "2.5/1,000 SF covered",
//   "broker_name": "John Smith",
//   "broker_company": "CBRE"
// }

// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.FieldsOptions;
import ai.webscraping.result.FieldsResult;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
FieldsResult result = client.fields(FieldsOptions.builder()
    .url("https://cre-platform.com/property/downtown-office-tower")
    .addField("property_name", "Building or property name")
    .addField("property_type", "Office, retail, industrial, multifamily")
    .addField("address", "Full property address")
    .addField("total_sf", "Total square footage")
    .addField("available_sf", "Available square footage")
    .addField("floors", "Number of floors")
    .addField("year_built", "Year constructed")
    .addField("asking_rent", "Asking rent per SF per year")
    .addField("lease_type", "NNN, Full Service, Modified Gross")
    .addField("available_spaces", "List of available units with size and rent")
    .addField("amenities", "Building amenities")
    .addField("parking", "Parking ratio and type")
    .addField("broker_name", "Listing broker name")
    .addField("broker_company", "Brokerage firm")
    .build());
System.out.println(result.getResult());
// Response:
// {
//   "property_name": "One Financial Plaza",
//   "property_type": "Office",
//   "address": "100 Main Street, Boston, MA 02110",
//   "total_sf": 450000,
//   "available_sf": 75000,
//   "floors": 32,
//   "year_built": 1985,
//   "asking_rent": "$65.00 PSF/Year",
//   "lease_type": "Full Service",
//   "available_spaces": [
//     {"floor": 15, "sf": 25000, "rent": "$62.00"},
//     {"floor": 22, "sf": 50000, "rent": "$68.00"}
//   ],
//   "amenities": ["Fitness center", "Conference facility", "Retail"],
//   "parking": "2.5/1,000 SF covered",
//   "broker_name": "John Smith",
//   "broker_company": "CBRE"
// }

// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var result = await client.FieldsAsync(new FieldsRequest {
    Url = "https://cre-platform.com/property/downtown-office-tower",
    Fields = new Dictionary<string, string> {
        ["property_name"] = "Building or property name",
        ["property_type"] = "Office, retail, industrial, multifamily",
        ["address"] = "Full property address",
        ["total_sf"] = "Total square footage",
        ["available_sf"] = "Available square footage",
        ["floors"] = "Number of floors",
        ["year_built"] = "Year constructed",
        ["asking_rent"] = "Asking rent per SF per year",
        ["lease_type"] = "NNN, Full Service, Modified Gross",
        ["available_spaces"] = "List of available units with size and rent",
        ["amenities"] = "Building amenities",
        ["parking"] = "Parking ratio and type",
        ["broker_name"] = "Listing broker name",
        ["broker_company"] = "Brokerage firm",
    },
});
Console.WriteLine(result.Result);
// Response:
// {
//   "property_name": "One Financial Plaza",
//   "property_type": "Office",
//   "address": "100 Main Street, Boston, MA 02110",
//   "total_sf": 450000,
//   "available_sf": 75000,
//   "floors": 32,
//   "year_built": 1985,
//   "asking_rent": "$65.00 PSF/Year",
//   "lease_type": "Full Service",
//   "available_spaces": [
//     {"floor": 15, "sf": 25000, "rent": "$62.00"},
//     {"floor": 22, "sf": 50000, "rent": "$68.00"}
//   ],
//   "amenities": ["Fitness center", "Conference facility", "Retail"],
//   "parking": "2.5/1,000 SF covered",
//   "broker_name": "John Smith",
//   "broker_company": "CBRE"
// }

Ask the AI to evaluate market positioning

curl -G "https://api.webscraping.ai/ai/question" \
  --data-urlencode "api_key=YOUR_API_KEY" \
  --data-urlencode "url=https://cre-platform.com/property/downtown-office-tower" \
  --data-urlencode "question=How does this property compare to the market? Is the asking rent competitive? What are the key selling points?"

# pip install webscraping_ai
# https://pypi.org/project/webscraping-ai/
from webscraping_ai import Client

client = Client(api_key="YOUR_API_KEY")
answer = client.question(
    "https://cre-platform.com/property/downtown-office-tower",
    question="How does this property compare to the market? Is the asking rent competitive? What are the key selling points?",
)
print(answer)

// npm install webscraping-ai
// https://www.npmjs.com/package/webscraping-ai
import { WebScrapingAI } from 'webscraping-ai';

const client = new WebScrapingAI({ apiKey: 'YOUR_API_KEY' });
const answer = await client.question({
  url: 'https://cre-platform.com/property/downtown-office-tower',
  question: 'How does this property compare to the market? Is the asking rent competitive? What are the key selling points?',
});
console.log(answer);

<?php
// composer require webscraping-ai/webscraping-ai-php
// https://packagist.org/packages/webscraping-ai/webscraping-ai-php
require 'vendor/autoload.php';

use WebScrapingAI\Client;

$client = new Client('YOUR_API_KEY');
$answer = $client->question(
    'https://cre-platform.com/property/downtown-office-tower',
    'How does this property compare to the market? Is the asking rent competitive? What are the key selling points?',
);
echo $answer;

# gem install webscraping_ai
# https://rubygems.org/gems/webscraping_ai
require 'webscraping_ai'

client = WebScrapingAI::Client.new(api_key: 'YOUR_API_KEY')
answer = client.question(
  'https://cre-platform.com/property/downtown-office-tower',
  question: 'How does this property compare to the market? Is the asking rent competitive? What are the key selling points?'
)
puts answer

// go get github.com/webscraping-ai/webscraping-ai-go/v4
// https://pkg.go.dev/github.com/webscraping-ai/webscraping-ai-go/v4
package main

import (
    "context"
    "fmt"

    webscrapingai "github.com/webscraping-ai/webscraping-ai-go/v4"
)

func main() {
    client, _ := webscrapingai.NewClient(&webscrapingai.Config{APIKey: "YOUR_API_KEY"})
    answer, _ := client.Question(context.Background(), &webscrapingai.QuestionOptions{
        URL:      "https://cre-platform.com/property/downtown-office-tower",
        Question: "How does this property compare to the market? Is the asking rent competitive? What are the key selling points?",
    })
    fmt.Println(answer)
}

// Maven: ai.webscraping:webscraping-ai:4.0.0
// https://central.sonatype.com/artifact/ai.webscraping/webscraping-ai
import ai.webscraping.Client;
import ai.webscraping.Config;
import ai.webscraping.option.QuestionOptions;

Client client = new Client(Config.builder().apiKey("YOUR_API_KEY").build());
String answer = client.question(QuestionOptions.builder()
    .url("https://cre-platform.com/property/downtown-office-tower")
    .question("How does this property compare to the market? Is the asking rent competitive? What are the key selling points?")
    .build());
System.out.println(answer);

// dotnet add package WebScrapingAI
// https://www.nuget.org/packages/WebScrapingAI
using WebScrapingAI;

var client = new WebScrapingAIClient(new WebScrapingAIClientOptions { ApiKey = "YOUR_API_KEY" });
var answer = await client.QuestionAsync(new QuestionRequest {
    Url = "https://cre-platform.com/property/downtown-office-tower",
    Question = "How does this property compare to the market? Is the asking rent competitive? What are the key selling points?",
});
Console.WriteLine(answer);

Why Use WebScraping.AI

Any Platform: Extract from any CRE listing site or broker platform.

All Property Types: Office, retail, industrial, multifamily, land.

Market Coverage: Build databases for any geographic market.

AI Analysis: Get insights beyond raw data points.

Fresh Data: Current listings and market rates.

CRE Use Cases

Investment Analysis

Evaluate acquisition opportunities

Market Research

Track rents, vacancy, and market trends

Tenant Representation

Find available spaces for tenants

Competitive Intelligence

Monitor competing properties

Related Use Cases

Start Collecting CRE Data

Get started with 1,000 free API credits. No credit card required.

Start Free Trial View API Documentation

Commercial Real Estate Data