Real estate authenticity

A ML model that provides a reliable means of verifying the authenticity of property images for real estate websites with user-generated content.

Overview

When it comes to Digital Asset Management (DAM) services, the veracity and trustworthiness of digital content are paramount, especially in the real estate sector. The Real estate authenticity verification model is an innovative machine learning solution designed to classify real estate images into two distinct categories:

  • Real images - Authentic images captured via phones and cameras that provide genuine representations of real estate properties.

  • Artificial images - Images that are synthetically crafted using specialized software and provide visually appealing but artificial and unnatural portrayals of properties.

This model can play a pivotal role in authenticating the legitimacy of property images, making it an ideal tool for enhancing real estate websites that utilize user-generated content. Authenticity verification can not only improve user trust but also streamline the management of real estate digital assets.

Typical use cases

Real estate authenticity verification can be employed in a range of scenarios:

  • Real estate listings - Online marketplaces for buying and selling properties can use the model to verify the authenticity of user-submitted property images, enhancing the credibility of their listings and reducing the likelihood of fraudulent ones.

  • Vacation rental - Platforms for vacation rentals can ensure that images accurately represent the properties they advertise, providing peace of mind to travelers.

  • Property management - Property management companies can authenticate images provided by tenants or property owners, aiding in the transparent documentation of property conditions.

  • Property valuation - Real estate valuation services can use the model to confirm the authenticity of images when assessing property values.

API endpoints

Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:

There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.

Example API responses

Input imageAPI response
{
  "status": "success",
  "version": "3.0.2",
  "predictions": [
    {
      "artificial": 1,
      "real": 0
    }
  ],
  "request_uuid": "0b436203-0b20-44d7-ab16-9327c686fff0",
  "sha1": "f41ce5c70aa1d46fd4e2152b00d65b8d241a4067"
}
{
  "status": "success",
  "version": "3.0.2",
  "predictions": [
    {
      "artificial": 0.06,
      "real": 0.94
    }
  ],
  "request_uuid": "a136a26c-0e08-4ca6-844b-4d954b53f09a",
  "sha1": "8cd713ba596b6e57f46b54efedc725a4782649f1"
}

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