ames_iowa_housing
This dataset contains information on residential properties sold in Ames, Iowa, USA, from 2006 to 2010, compiled by Dean De Cock. It serves as an educational resource to replace the older Boston Housing dataset. Detailed documentation is available in `./originals/DataDocumentation.txt`; structured feature metadata are manually extracted into `./features.json`. The primary data file is `AmesHousing.csv`, a lightly pre‑processed version of the original data.
Dataset description and usage context
Dataset Card: Ames Iowa – Alternative to the Boston Housing Dataset
Dataset Overview
The dataset provides information on residential properties sold in Ames, Iowa, between 2006 and 2010, supplied by the Ames City Assessor's Office. It mirrors the original data and is intended to simplify usage.
Dataset Details
Description
- Task Categories: Tabular Regression, Tabular Classification
- Language: English
- Dataset Name: Ames Iowa: Alternative to the Boston Housing Dataset
- Size: 1K<n<10K
- License: Unknown
Configuration
-
Configuration Name: default
- Data File: AmesHousing.csv
- Default: Yes
- Separator: Comma
-
Configuration Name: features
- Data File: features.json
Source
-
Original Data:
- Excel (xls): https://jse.amstat.org/v19n3/decock/AmesHousing.xls (mirrored file:
./originals/AmesHousing.xls) - Text (tsv): https://jse.amstat.org/v19n3/decock/AmesHousing.txt (mirrored file:
./originals/AmesHousing.txt)
- Excel (xls): https://jse.amstat.org/v19n3/decock/AmesHousing.xls (mirrored file:
Intended Use
The dataset is meant to serve as a modern replacement for the classic Boston Housing dataset, primarily for teaching purposes.
Creation
Motivation
The original author aimed to assemble a larger, more contemporary dataset: the Boston Housing data dates from the 1970s and contains only 506 observations with 14 variables.
Source Data
The original data were obtained directly from the Ames City Assessor's Office.
Citation
BibTeX:
@article{de2011ames,
title={Ames, Iowa: Alternative to the Boston housing data as an end of semester regression project},
author={De Cock, Dean},
journal={Journal of Statistics Education},
volume={19},
number={3},
year={2011},
publisher={Taylor & Francis}
}
Pair the dataset with AI analysis and content workflows.
Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.