Iowa Housing Data

The original Ames data that is being used for the competition House Prices: Advanced Regression Techniques and predicting sales price is edited and engineered to suit a beginner for applying a model without worrying too much about missing data while focusing on the features.

Contents

The train data has the shape 1460x80 and test data has the shape 1458x79 with feature 'SalePrice' to be predicted for the test set. The train data has different types of features, categorical and numerical.

A detailed info about the data can be obtained from the Data Description file among other data files.

Transformations

a. Handling Missing Values: Some variables such as 'PoolQC', 'MiscFeature', 'Alley' have over 90% missing values. However from the data description, it is implied that the missing value indicates the absence of such features in a particular house. Well, most of the missing data implies the feature does not exist for the particular house on further inspection of the dataset and data description.

Similarly, features which are missing such as 'GarageType', 'GarageYrBuilt', 'BsmtExposure', etc indicated no garage in that house but also corresponding attributes such as 'GarageCars', 'GarageArea','BsmtCond' etc are set to 0.

A house on a street might have similar front lawn area to the houses in the same neighborhood, hence the missing values can be median of the values in a neighborhood.

Missing values in features such as 'SaleType', 'KitchenCond', etc have been imputed with the mode of the feature.

b. Dropping Variables: 'Utilities' attribute should be dropped from the data frame because almost all the houses have all public Utilities (E,G,W,& S) available.

c. Further exploration: The feature 'Electrical' has one missing value. The first intuition would be to drop the row. But on further inspection, the missing value is from a house built in 2006. After the 1970's all the houses have Standard Circuit Breakers & Romex 'SkBrkr' installed. So, the value can be inferred from this observation.

d. Transformation: There were some variables which are really categorical but were represented numerically such as 'MSSubClass', 'OverallCond' and 'YearSold'/'MonthSold' as they are discrete in nature. These have also been transformed to categorical variables.

e. X Normalizing the 'SalePrice' Variable: During EDA it was discovered that the Sale price of homes is right skewed. However on normalizing the skewness decreases and the (linear) models fit better. The feature is left for the user to normalize.

Finally the train and test sets were split and sale price appended to train set.

Acknowledgements

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

Inspiration

The data after the transformation done by me can easily be fitted on to a model after label encoding and normalizing features to reduce skewness. The main variable to be predicted is 'SalePrice' for the TestData csv file.

Dataset Card for 'Ames Iowa: Alternative to the Boston Housing Data Set'

This dataset contains information from the Ames Assessor’s Office about residential properties sold in Ames, IA from 2006 to 2010. This repository is a mirror the original dataset meant to facilitate its consumption. The dataset was originally published by Dean De Cock in Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project, it is meant as a resource for teaching machine… See the full description on the dataset page: https://huggingface.co/datasets/cloderic/ames_iowa_housing.

Dataset Description: Ames Housing Dataset with Engineered Features

This dataset is a cleaned and enhanced version of the popular Ames Housing Dataset, originally compiled by Dean De Cock. It is designed for regression tasks, specifically predicting house sale prices.

Key Transformations and Features:

• Missing Value Handling: Missing values have been addressed through dropping columns with excessive missing data and imputing remaining missing values using appropriate strategies (mode for categorical, median for numerical).
• Categorical Encoding: Categorical features have been converted into numerical formats using a combination of Ordinal Encoding for variables with a natural order and One-Hot Encoding for nominal variables.
• Feature Engineering: Several new features have been created to potentially improve model performance, including:
  • HouseAge: The age of the house calculated from the year it was built and the year it was sold.
  • Log_LotArea: A log transformation of the 'Lot Area' to address skewness.
  • TotalSF: The total square footage of the house, combining basement, first floor, and second floor areas.
• Feature Selection: Highly correlated features have been identified and some have been removed to mitigate multicollinearity.
• Outlier Handling: Outliers in numerical features have been capped using the Interquartile Range (IQR) rule.
• Skewness Handling: Skewed numerical features have been transformed using a log transformation to achieve a more normal distribution.
• Duplicate Removal: Duplicate rows have been identified and removed.

Potential Use Cases:

This dataset is suitable for various regression modeling tasks, including:

• Building predictive models for house prices.
• Exploring the impact of different features on sale price.
• Practicing data preprocessing and feature engineering techniques.

This cleaned and engineered dataset provides a solid foundation for developing accurate and robust house price prediction models.

Dataset

This dataset was created by Hamza Jabbar Khan

Contents

Graph and download economic data for All-Transactions House Price Index for Ames, IA (MSA) (ATNHPIUS11180Q) from Q4 1986 to Q3 2025 about Ames, IA, appraisers, HPI, housing, price index, indexes, price, and USA.

This dataset is an engineered version of the original Ames Housing dataset from the "House Prices: Advanced Regression Techniques" Kaggle competition. The goal of this engineering was to clean the data, handle missing values, encode categorical features, scale numeric features, manage outliers, reduce skewness, select useful features, and create new features to improve model performance for house price prediction.

The original dataset contains information on 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, with the target variable being SalePrice. This engineered version has undergone several preprocessing steps to make it ready for machine learning models.

Preprocessing Steps Applied

1. Missing Value Handling: Missing values in categorical columns with meaningful absence (e.g., no pool for PoolQC) were filled with "None". Numeric columns were filled with median, and other categorical columns with mode.
2. Correlation-based Feature Selection: Numeric features with absolute correlation < 0.1 with SalePrice were removed.
3. Encoding Categorical Variables: Ordinal features (e.g., quality ratings) were encoded using OrdinalEncoder, and nominal features (e.g., neighborhoods) using OneHotEncoder.
4. Outlier Handling: Outliers in numeric features were detected using IQR and capped (Winsorized) to IQR bounds to preserve data while reducing extreme values.
5. Skewness Handling: Highly skewed numeric features (|skew| > 1) were transformed using Yeo-Johnson to make distributions more normal-like.
6. Additional Feature Selection: Low-variance one-hot features (variance < 0.01) and highly collinear features (|corr| > 0.8) were removed.
7. Feature Scaling: Numeric features were scaled using RobustScaler to handle outliers.
8. Duplicate Removal: Duplicate rows were checked and removed if found (none in this dataset).

The final dataset has fewer columns than the original (reduced from 81 to approximately 250 after one-hot encoding, then further reduced by feature selection), with improved quality for modeling.

New Features Created

To add more predictive power, the following new features were created based on domain knowledge: 1. HouseAge: Age of the house at the time of sale. Calculated as YrSold - YearBuilt. This captures how old the house is, which can negatively affect price due to depreciation. - Example: A house built in 2000 and sold in 2008 has HouseAge = 8. 2. Quality_x_Size: Interaction term between overall quality and living area. Calculated as OverallQual * GrLivArea. This combines quality and size to capture the value of high-quality large homes. - Example: A house with OverallQual = 7 and GrLivArea = 1500 has Quality_x_Size = 10500. 3. TotalSF: Total square footage of the house. Calculated as GrLivArea + TotalBsmtSF + 1stFlrSF + 2ndFlrSF (if available). This aggregates area features into a single metric for better price prediction. - Example: If GrLivArea = 1500 and TotalBsmtSF = 1000, TotalSF = 2500. 4. Log_LotArea: Log-transformed lot area to reduce skewness. Calculated as np.log1p(LotArea). This makes the distribution of lot sizes more normal, helping models handle extreme values. - Example: A lot area of 10000 becomes Log_LotArea ≈ 9.21.

These new features were created using the original (unscaled) values to maintain interpretability, then scaled with RobustScaler to match the rest of the dataset.

Data Dictionary

• Original Numeric Features: Kept features with |corr| > 0.1 with SalePrice, such as:
  • OverallQual: Material and finish quality (scaled, 1-10).
  • GrLivArea: Above grade (ground) living area square feet (scaled).
  • GarageCars: Size of garage in car capacity (scaled).
  • TotalBsmtSF: Total square feet of basement area (scaled).
  • And others like FullBath, YearBuilt, etc. (see the code for the full list).
• Ordinal Encoded Features: Quality and condition ratings, e.g.:
  • ExterQual: Exterior material quality (encoded as 0=Po to 4=Ex).
  • BsmtQual: Basement quality (encoded as 0=None to 5=Ex).
• One-Hot Encoded Features: Nominal categorical features, e.g.:
  • MSZoning_RL: 1 if residential low density, 0 otherwise.
  • Neighborhood_NAmes: 1 if in NAmes neighborhood, 0 otherwise.
• New Engineered Features (as described above):
  • HouseAge: Age of the house (scaled).
  • Quality_x_Size: Overall quality times living area (scaled).
  • TotalSF: Total square footage (scaled).
  • Log_LotArea: Log-transformed lot area (scaled).
• Target: SalePrice - The property's sale price in dollars (not scaled, as it's the target).

Total columns: Approximately 200-250 (after one-hot encoding and feature selection).

License

This dataset is derived from the Ames Housing...

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Ames, IA (CBSA) (MEDLISPRIPERSQUFEE11180) from Jul 2016 to Oct 2025 about Ames, IA, square feet, listing, median, price, and USA.

Dataset

This dataset was created by Ninad Rajesh Gawali

Contents

Test split of the Ames Housing Data Set (~10%). The dataset is free to use for educational purposes and was converted to csv. Original publication https://jse.amstat.org/v19n3/decock.pdf and dataset in txt format: https://jse.amstat.org/v19n3/decock/AmesHousing.txt

Dataset

This dataset was created by Zahra Amini

Contents

Context

The Ames Housing dataset is a great alternative to the popular but older Boston Housing dataset.

Content

The Ames Housing dataset contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa with the goal of predicting the selling price.

Acknowledgements

The Ames Housing dataset was compiled by Dean De Cock in 2011, for use in data science education.

Inspiration

The Default task for this dataset is Regression.

Dataset

This dataset was created by Rehan Mohammed

Contents

Housing Inventory: Price Increased Count in Ames, IA (CBSA) was 0.00000 Level in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Increased Count in Ames, IA (CBSA) reached a record high of 52.00000 in July of 2021 and a record low of 0.00000 in December of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Increased Count in Ames, IA (CBSA) - last updated from the United States Federal Reserve on October of 2025.

Comprehensive demographic dataset for Ames, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Ames, IA (CBSA) was -4.07% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Ames, IA (CBSA) reached a record high of 23.70 in February of 2021 and a record low of -5.02 in February of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Ames, IA (CBSA) - last updated from the United States Federal Reserve on November of 2025.

Housing Inventory: Median Home Size in Square Feet Year-Over-Year in Ames, IA (CBSA) was -1.88% in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Home Size in Square Feet Year-Over-Year in Ames, IA (CBSA) reached a record high of 7.85 in March of 2024 and a record low of -7.47 in October of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Home Size in Square Feet Year-Over-Year in Ames, IA (CBSA) - last updated from the United States Federal Reserve on November of 2025.

Dataset

This dataset was created by Ayse Nur Dalfidan

Contents

Graph and download economic data for Housing Inventory: Average Listing Price Year-Over-Year in Ames, IA (CBSA) (AVELISPRIYY11180) from Jul 2017 to Oct 2025 about Ames, IA, average, listing, price, and USA.

Financial overview and grant giving statistics of Ames Ecumenical Housing Inc.

Housing Inventory: Median Home Size in Square Feet Month-Over-Month in Ames, IA (CBSA) was 0.08% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Home Size in Square Feet Month-Over-Month in Ames, IA (CBSA) reached a record high of 3.49 in April of 2018 and a record low of -4.58 in October of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Home Size in Square Feet Month-Over-Month in Ames, IA (CBSA) - last updated from the United States Federal Reserve on December of 2025.