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An exploratory analysis of the Ames Housing dataset can show how recorded property characteristics vary with sale price, but the dataset describes sales in Ames, Iowa, from 2006 through 2010—not today’s housing market. The available documentation establishes the dataset’s scope and structure, but not the charts or computed findings behind a particular deep-dive. So no specific feature ranking, correlation, or model result should be treated as a finding without the underlying analysis.
What does an exploratory data analysis of Ames Housing cover?
Dean De Cock’s 2011 paper describes 2,930 residential property sales in Ames, Iowa, spanning 2006–2010. It reports 80 explanatory variables: 23 nominal, 23 ordinal, 14 discrete, and 20 continuous. The feature set covers sale price and a broad range of property characteristics, including lot and location, building style and quality, construction and remodeling, rooms and baths, basement and garage, outdoor features, and sale timing and conditions. De Cock’s paper provides the original dataset context.
For an EDA, sale price is a natural outcome to examine alongside these features. The goal is to understand distributions, data quality, and descriptive relationships before choosing a model—not to establish that a feature causes a higher or lower sale price.
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Why do field counts differ between versions?
The maintained AmesHousing package documents 82 recorded fields overall, while De Cock’s paper reports 80 explanatory variables. Those counts describe different things: the paper’s explanatory-variable count is not the total number of recorded fields in the package representation. The package provides a raw ames_raw form and a processed make_ames() form, so an analysis should identify which representation it uses before stating a column count or interpreting a result. The AmesHousing package documentation describes these forms and notes six properties with identical geotags.
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Which features can be explored against sale price?
A useful EDA can compare sale price with property and location characteristics, provided each field is interpreted according to its documented meaning. For example, overall quality is ordinal, while a nominal property class is a category; converting either to numeric codes does not automatically make the differences between codes continuous or equally spaced.
- Property and site: lot area, shape, access, slope, zoning, and neighborhood.
- Building attributes: dwelling type, style, overall quality and condition, construction year, and remodeling year.
- Interior and systems: rooms, baths, basement characteristics, heating, and electrical systems.
- Garage and outdoor features: garage attributes, porches, deck, pool, and fence.
- Sale context: sale date, type, and condition.
These are sensible dimensions for investigation, not documented results from a particular analysis. Any chart or summary should identify the data representation, filters, sample size or denominator, units, and whether it is descriptive or inferential. Comparisons should use consistent categories and should check group sizes before drawing conclusions.
How should location and data quality be handled?
Location can be useful for exploring how prices differ across Ames, but a geographic analysis needs to state the coordinate source and account for the package documentation’s note that six properties have identical geotags. Identical coordinates may affect map displays or spatial summaries; they should be investigated rather than silently treated as distinct precise locations.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Missing values and category definitions also matter. Before comparing prices, inspect missingness in the selected fields and preserve documented categorical and ordinal meanings. A chart built after dropping incomplete records should say how many records remain; otherwise, the reader cannot tell whether its pattern reflects the full dataset or a filtered subset.
What can the findings say—and what can’t they say?
Descriptive patterns can indicate which recorded characteristics are associated with sale price in this set of Ames transactions. They do not establish that changing a property characteristic would cause a particular price change. Nor can results from one city and sales from 2006–2010 be generalized automatically to current transactions, other regions, or different market conditions.
The Tidy Modeling with R Ames chapter treats EDA as an important step toward building a reliable model and uses sale price as the outcome. It is a useful learning reference for analysis workflow, not evidence for any unreported chart or result. Read the Tidy Modeling with R chapter on Ames.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a specific “what I learned” claim needs
A claim that one feature has the strongest relationship with price, that a particular neighborhood ranks highest, or that a model achieves a stated performance requires the actual notebook or computed output. Without that artifact, those are open questions rather than established takeaways. A reproducible account should name the dataset form, filters, sample counts, handling of missingness, variable definitions, and the method behind each reported result.
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