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Miami Property Value Dataset
Data Science and Analytics
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About
This dataset provides detailed information on 13,932 single-family homes sold in Miami. Its primary purpose is to facilitate the prediction of Miami house prices, offering a valuable resource for predictive analysis in the real estate market. The data captures various aspects influencing property values, making it ideal for developing models to forecast housing costs.
Columns
- PARCELNO: A unique identifier for each property. Note that approximately 1% of entries may appear multiple times.
- SALE_PRC: The sale price of the property, denominated in US Dollars ($).
- LND_SQFOOT: The land area of the property, measured in square feet.
- TOT_LVG_AREA: The total floor area of the living space, measured in square feet.
- SPEC_FEAT_VAL: The monetary value attributed to special features of the property (e.g., swimming pools), in US Dollars ($).
- RAIL_DIST: The distance from the property to the nearest rail line, serving as an indicator of potential noise, measured in feet.
- OCEAN_DIST: The distance from the property to the ocean, measured in feet.
- WATER_DIST: The distance from the property to the nearest body of water, measured in feet.
- CNTR_DIST: The distance from the property to the Miami central business district, measured in feet.
- SUBCNTR_DI: The distance from the property to the nearest subcenter, measured in feet.
- HWY_DIST: The distance from the property to the nearest highway, also indicating potential noise levels, measured in feet.
- age: The age of the structure in years.
- avno60plus: A dummy variable indicating whether airplane noise exceeds an acceptable level (1 for yes, 0 for no).
- structure_quality: A numerical rating representing the quality of the structure.
- month_sold: The month in 2016 when the property was sold (1 = January).
- LATITUDE: The geographical latitude coordinate of the property.
- LONGITUDE: The geographical longitude coordinate of the property.
Distribution
The dataset is provided as a CSV data file, named
miami-housing.csv, with a file size of 1.64 MB. It comprises 13,932 records (rows) and 17 distinct columns. Each column contains valid data for all records, with no missing values. A sample file will be updated separately to the platform.Usage
This dataset is ideally suited for developing and testing predictive models for real estate valuation. It can be used for regression analysis to forecast housing prices based on various property attributes and location factors. Potential applications include:
- Building house price prediction models.
- Analysing the impact of geographical features and amenities on property values.
- Supporting real estate market analysis and investment decisions.
- Educational purposes in data science and econometrics courses.
Coverage
The dataset focuses exclusively on single-family homes located within Miami, Florida, USA. The geographical scope is defined by the latitude and longitude coordinates provided for each property. All sales records pertain to the year 2016, with specific sale months indicated. There are no explicit notes on data availability for certain demographic groups, as the data is focused on properties rather than population segments.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for a wide range of users, including:
- Aspiring data scientists and students looking for a clear, real-world regression problem.
- Real estate analysts and investors seeking to understand market dynamics and predict property values in Miami.
- Researchers interested in urban economics, geographical influences on property, or noise pollution impacts.
- Software developers building applications for real estate valuation or market insights.
Dataset Name Suggestions
- Miami Real Estate Price Predictor
- Miami Homes 2016 Sales Data
- Miami Property Value Dataset
- South Florida Housing Data
- Miami Residential Sales Prices
Attributes
Original Data Source: Miami Property Value Dataset
QUALITY