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Housing Price Analysis Data
Finance & Banking Analytics
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Free
About
This dataset offers a detailed collection of property listings, providing a rich context for understanding housing market dynamics and pricing. It is invaluable for a wide range of data analysis and machine learning applications, including predictive modelling and trend identification.
Columns
- id: A unique identifier for each property listing.
- date: The specific date when the property was listed.
- price: The monetary value of the property in currency.
- bedrooms: The total number of bedrooms in the property.
- bathrooms: The total number of bathrooms in the property.
- sqft_living: The size of the living area in square feet.
- sqft_lot: The total size of the property's lot in square feet.
- floors: The number of floors within the property.
- waterfront: An indicator (0 for no, 1 for yes) if the property has a waterfront view.
- view: A rating (0 to 4) reflecting the quality level of the property's view.
- condition: An overall rating (1 to 5) describing the property's condition.
- grade: An overall grade rating (1 to 13) for the property.
- sqft_above: The living area size located above ground level in square feet.
- sqft_basement: The size of the basement area in square feet.
- yr_built: The year in which the property was originally constructed.
- yr_renovated: The year the property was last renovated (0 if no renovations have occurred).
- zipcode: The postal code corresponding to the property's location.
- lat: The latitude coordinate of the property's location.
- long: The longitude coordinate of the property's location.
- sqft_living15: The average living area size of the 15 nearest properties, in square feet.
- sqft_lot15: The average lot size of the 15 nearest properties, in square feet.
Distribution
The dataset is typically provided as a CSV file and is approximately 2.52 MB in size. It comprises 21 columns and contains over 21,600 individual property records.
Usage
This dataset is well-suited for several applications:
- Predictive Modelling: Forecasting property prices based on features like location, amenities, and condition.
- Market Analysis: Identifying trends and patterns within the real estate market.
- Recommendation Systems: Developing systems to guide homebuyers towards properties that match their preferences.
Coverage
The dataset includes geographic coordinates (latitude, longitude, and zip codes) indicating its spatial scope. The time range for property construction spans from 1900 to 2015, with renovation dates also noted up to 2015. Property listing dates are recorded, with the most common date being 23rd June 2014. Demographic details are not explicitly provided within the dataset.
License
CC0: Public Domain
Who Can Use It
The dataset is beneficial for a variety of professionals and individuals:
- Investors: To make informed decisions by analysing real estate market trends.
- Real Estate Agents: To better understand market dynamics and assist clients.
- Policymakers: To gain insights into housing market behaviours for urban planning and regulation.
- Homebuyers: Indirectly, through recommendation systems built upon this data, to find properties aligning with their needs.
Dataset Name Suggestions
- Housing Price Analysis Data
- Residential Property Values
- Real Estate Market Listings
- Home Pricing Data
- Property Valuation Records
Attributes
Original Data Source: Housing Price Analysis Data
QUALITY