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Home Prices and Structural Attributes Collection
Comodities & Real Estate
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Free
About
Analysing the American residential housing market through scraped web listings provides a detailed look at property values across various locations. By examining the relationship between physical attributes like square footage and room counts against market pricing, this collection facilitates deep dives into real estate trends and predictive valuation modelling. It serves as a practical foundation for understanding the economic drivers within the domestic property sector.
Columns
- address: The specific geographic location or street identifier for the residential property.
- price: The listed market value of the home, typically recorded in USD.
- beds: The total number of bedrooms available in the dwelling.
- bath: The total number of bathrooms provided within the property.
- size: The total living area of the house, measured in square feet.
- place: The broader municipality or city location, such as Newburgh, NY or Nolensville, TN.
Distribution
The information is delivered in a single CSV file titled
Homes.csv, which is approximately 136.17 kB in size. The collection contains 1,700 records structured across 6 distinct columns. The data exhibits high integrity for the address, price, and place fields, though some missing values exist within the bathroom and size attributes. It holds a usability score of 10.00 and is maintained as a static archive with no future updates scheduled.Usage
This resource is ideal for performing exploratory data analysis to identify regional price variations. It is well-suited for building regression models that predict housing costs based on physical dimensions and room counts. Additionally, it can be used for practising data cleaning techniques on real-world scraped records, particularly those containing missing or varied text formats.
Coverage
The geographic scope focuses on specific regions within the United States, including locations such as Newburgh, New York, and Nolensville, Tennessee. Temporally, the records capture a snapshot of the market at the time of the web scraping. The data covers 1,700 individual properties ranging from standard dwellings to various residential estates.
License
CC0: Public Domain
Who Can Use It
Real estate analysts can leverage these records to benchmark property prices in the covered regions. Data science students may utilise the numeric and categorical fields to practise predictive modelling and explore concepts in machine learning ethics. Furthermore, investors can use the size and bedroom metrics to assess market availability for specific property types.
Dataset Name Suggestions
- US Residential Real Estate: Prices and Property Metrics
- Scraped Housing Market Data: Newburgh and Nolensville Listings
- Residential Property Valuation and Feature Archive
- Home Prices and Structural Attributes Collection
- Predictive Real Estate Metrics: Beds, Baths, and Size Registry
Attributes
Original Data Source:Home Prices and Structural Attributes Collection
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Download Dataset in CSV Format
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