Dark Mode
Tunisian Real Estate Price Predictor
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This collection of real estate data focuses on predicting property prices within Tunisia. The underlying content was acquired by scraping listings from tayara.tn, a major Tunisian real estate website. The dataset is suitable for training models to understand and forecast property valuations, incorporating variables such as location, size, and property type. It is expected to be updated annually.
Columns
The data includes nine distinct fields detailing property specifications:
- category: Defines the general type of property, such as Appartements (37%) or Terrains et Fermes (27%).
- room_count: The total number of rooms in the property. Values range from -1 (likely indicating missing or unsuitable data) up to 20, with a mean of 1.76.
- bathroom_count: The number of bathrooms available. The mean count is 0.76, with a maximum observed value of 10.
- size: Represents the physical size of the property. The mean size is 131, ranging up to 2,000.
- type: Indicates whether the property is À Vendre (For Sale, 61%) or À Louer (For Rent, 39%).
- price: The original listed property price. The mean price is approximately 16 million.
- city: The city where the property is situated. Key cities include Tunis and Ariana, each accounting for 19% of the listings. There are 24 unique cities recorded.
- region: Provides a more granular geographical location within Tunisia, containing 257 unique regional entries.
- log_price: A log transformation of the property price, often used to normalise distributions for machine learning tasks.
Distribution
The dataset, titled "Property Prices in Tunisia.csv," contains 12.7 thousand valid records. The file size is 1.1 MB. All records are 100% valid with no missing or mismatched values across the entries. Data is typically shared in CSV format.
Usage
This resource is ideal for developing machine learning models aimed at predicting property market values. Specific uses include:
- Building regression models for property price forecasting.
- Analysing regional price disparities within the Tunisian housing market.
- Conducting exploratory data analysis on real estate trends, differentiating between properties listed for sale versus rent.
- Feature engineering practice for handling mixed data types and preparing price variables (e.g., using the log transformation).
Coverage
The data covers real estate listings geographically located across Tunisia. Listings span 24 unique cities, with high concentrations in major hubs such as Tunis and Ariana. It provides fine-grained regional detail across 257 regions. The time range for data collection is not specified in the current description. Listings primarily cover Appartements, Terrains et Fermes, and other property types, segmented by whether they are available for sale or rent.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
Intended users include data scientists looking to apply machine learning to economic prediction, real estate market analysts seeking pricing insights, and students developing portfolio projects focused on regression techniques. They can utilise this dataset to build highly accurate valuation models for North African properties.
Dataset Name Suggestions
- Tunisian Real Estate Price Predictor
- North African Housing Market Listings
- Tayara.tn Property Price Data
- Tunisian Property Valuation Data
Attributes
Original Data Source: Tunisian Real Estate Price Predictor
QUALITYLoading...