Apartment Rent Dataset

Key Features

• Unique Identifiers & Geographic Details: Every apartment is uniquely identified and tagged with geographic details such as address, city name, state, latitude, and longitude, ensuring precise localization.

Property Specifications

• Apartment Category: The dataset includes various property types, helping to differentiate apartments based on layout and usage, including studios, single-family homes, and more.
• Apartment Description: Detailed text fields like title and body provide insights into the apartment’s features, including mentions of upgrades, view quality, and unique selling points.

Amenities & Features

• Amenities Data: A full breakdown of available amenities, including but not limited to furnished units, in-unit laundry, balconies, air conditioning, etc. This allows you to identify trends in amenities preferences and predict their impact on rental pricing.
• Pet-Friendly Listings: Whether pets are allowed is a critical factor in rental searches, and this dataset includes that information, making it useful for both tenants and landlords to analyze trends based on pet policy.

Pricing & Financial Details

• Price & Fee Structures: The dataset provides a detailed look at the financial aspects of each listing. It includes both the rental price and any associated fees, such as security deposits or parking fees. Pricing is consistent and clearly defined, ensuring a comprehensive financial analysis.
• Currency Information: Every listing includes the currency of the rent price, which is essential for analyzing global rental trends and converting prices into a unified format.

Listing Quality

• Visual Data Availability: Apartments with photos are indicated, giving insight into how images can affect rental decision-making and listing visibility.
• Time of Listing: The dataset tracks the exact time and date when each listing was created, allowing users to analyze seasonal trends and the frequency of listing updates.

Ready for Machine Learning

• Cleaned Data: Critical fields like price and square footage are consistently populated, minimizing the need for extensive data cleaning. This feature makes the dataset particularly useful for developing machine learning models with minimal preprocessing.

Applications

This dataset is perfect for tasks like:

• Clustering: Grouping rental properties based on characteristics like size, amenities, or location.
• Classification: Predicting whether a property is high or low-cost based on its features.
• Regression: Estimating the price of a property given its square footage, location, and amenities.