language:en license:apache-2.0 size_categories:1K<n<10K region:us lamudi facebook ohmyhomes real-estate-property real-estate real-estate-properties AI-generated-real-estate-properties watermarked-dataset watermarked-real-estate-properties real-estate-images
---
license: apache-2.0
language:
- en
tags:
- lamudi
- facebook
- ohmyhomes
- real-estate-property
- real-estate
- real-estate-properties
- AI-generated-real-estate-properties
- watermarked-dataset
- watermarked-real-estate-properties
- real-estate-images
size_categories:
- 1K<n<10K
---
## Real Estate Property Dataset

**Overview**

This dataset is designed to train and evaluate a fraud filter system and image matching system for real estate properties. It comprises a diverse range of real estate images, including authentic images and AI-generated images.

**Dataset Composition**

1. **AI-Generated Images:**
   * **Source:** Perchance AI
   * **Generation Process:**
     - A diverse set of properties was generated using Gemini AI, covering various property types, architectural styles, and features.
     - Gemini AI generated a dataset for each property type by randomly choosing from various sets of architectural styles, property categories (interior/exterior), and feature names (e.g., bedroom, facade):
       - **Property Types (11):** Condominium, House and Lot, Townhouse, Apartment, Bungalow, Villa, Office Space, Industrial Space, Commercial Lot, Land, Farm, Hotel
       - **Architectural Styles (10):** Colonial, Victorian, Tudor, Ranch, Cape Cod, Mediterranean, Modern, Contemporary, Industrial, Rustic
       - **Property Categories (2):** Interior, Exterior
       - **Interior Features (13):** Living Room, Dining Room, Kitchen, Bedrooms, Bathrooms, Hallways, Stairs, Basement, Attic, Closet, Pantry, Laundry Room, Family Room, Office
       - **Exterior Features (14):** Lot, Facade, Roof, Windows, Doors, Porch, Patio, Deck, Balcony, Garage, Driveway, Landscaping, Fence
     - Image descriptions were created based on these properties.
     - Perchance AI was used to generate images based on these descriptions.
   * **Data Format:** CSV file containing:
     - `ID`
     - `Image Description`
     - `Image Title`
     - `Property Type`
     - `Architectural Style`
     - `Property Category`
     - `Feature Name`
   * **Total Images:** 1000 (training) + 1000 (testing)

2. **Authentic Images:**
   * **Source:** Facebook Marketplace
   * **Data Collection:** Scraped using Python, Beautiful Soup, and Selenium.
   * **Data Format:** CSV file containing:
     - `image_paths`
     - `title`
     - `price`
     - `post_url`
     - `location`
   * **Total Properties:** 1000
   * **Total Images:** 8158

3. **Copyrighted Images:**
   * **Source:** Lamudi and Ohmyhome
   * **Data Collection:** Scraped using Python and Beautiful Soup.
   * **Data Types:**
     - **No EXIF Data:**
       - **Source:** Lamudi
       - **Data Format:** CSV file containing:
         - `ID`
         - `Title`
         - `Price`
         - `Address`
         - `Num_of_Bedrooms`
         - `Num_of_Bathrooms`
         - `Floor_Area`
         - `Description`
         - `List_of_Amenities`
         - `Image URLs`
         - `Property URL`
       - **Total Properties:** 1000
       - **Total Images:** 3000
     - **Watermarked Images:**
       - **Source:** Ohmyhome
       - **Data Collection:** Python was used to generate watermarked images by adding watermarks to the original images. Watermarks were added randomly in terms of font style, size, case, and location on a contrasting background. Three types of watermarks were created: logo, transparent logo, and text.
       - **Data Format:** CSV file containing:
         - `Image URL`
         - `Developer`
         - `Logo URL`
       - **Total Images:** 1500 (500 transparent logo, 500 text watermark, 500 logo watermark)


**Ethical Considerations:**

* **Data Privacy:** Sensitive information, such as personal addresses and contact details, was removed from the dataset.
* **Copyright Compliance:** Images were used in accordance with fair use principles and relevant copyright laws.
* **Data Bias:** Efforts were made to ensure a balanced and representative dataset, considering factors like property type, location, and image quality.

This dataset provides a valuable resource for training and evaluating models for real estate image matching and fraud detection. By combining authentic and AI-generated images, the dataset enables the development of robust models that can accurately distinguish between real and fake images and identify potential copyright infringements.