Name: Housing Market Sample Dataset
Creator: Stratum
License: https://creativecommons.org/publicdomain/zero/1.0/

Fifteen hundred residential listings with prices, size, condition, and dates — a mixed numeric-and-categorical dataset that's ideal for histograms, correlation, and your first regression.

Columns

Column	Type	Description
`ListingID`	ID	Unique listing identifier (e.g. L00001).
`City`	Categorical	City the property is in.
`Neighborhood`	Categorical	Neighborhood within the city.
`PropertyType`	Categorical	Condo, Townhouse, or Detached.
`Condition`	Categorical	Reported condition (Fair → Excellent).
`Bedrooms`	Integer	Number of bedrooms.
`Bathrooms`	Numeric	Bathrooms, in halves (e.g. 2.5).
`SqFt`	Integer	Interior floor area, square feet.
`LotSize_sqft`	Integer	Lot size in square feet (0 for condos).
`Age_years`	Integer	Age of the property in years.
`GarageSpaces`	Integer	Number of garage spaces.
`ListPrice`	Integer	Asking price in dollars.
`PricePerSqFt`	Numeric	List price divided by interior area.
`DaysOnMarket`	Integer	Days the listing has been active.
`ListingDate`	Date	Date listed (YYYY-MM-DD).

Things to try

See the price distribution. A histogram of ListPrice shows a classic right skew — perfect for talking about mean vs median. → Histograms lesson
Correlate the numerics. How tightly do SqFt, Bedrooms, and ListPrice move together? → Correlation lesson · What is r?
Build a regression. Predict ListPrice from size, age, and condition, then read the R². → Regression lesson
Compare groups. Box plots of price by PropertyType reveal how condos, townhouses, and detached homes differ. → Box & violin lesson

How to open it. Download the CSV, then drag it onto Stratum (or use File ▸ Import). Columns are typed automatically — numbers, the date, and the categories all detected — so you're charting within seconds. Get Stratum →

Housing market dataset

At a glance

Columns

Things to try

See also