Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q4 2025 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.

The S&P Case Shiller Boston Home Price Index has been on an upward trend in the past years. The index measures changes in the prices of existing single-family homes. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given month, for example, it means that the house prices have increased by 30 percent since 2000. The value of the S&P Case Shiller Boston Home Price Index amounted to nearly ****** in November 2025. That was above the national average.

The Boston Housing Dataset originally contains data on housing values in various neighbourhoods of Boston, USA, collected in 1978. It originally contains 506 instances and 14 columns, representing various factors that influence housing prices in different neighbourhoods in Boston. It was commonly used for regression analysis and to understand how different socio-economic and environmental factors impact property values.

Due to ethical concerns, the B column (proportion of African American residents) has been removed. The remaining columns predict house prices with MEDV as the target.

**Original Columns: ** 1. CRIM: Crime rate (per capita) by town. 2. ZN: Proportion of residential land zoned for large lots. 3. INDUS: Proportion of non-retail business acres per town. 4. CHAS: Charles River dummy variable (1 if adjacent to the river, 0 otherwise). 5. NOX: Nitrogen oxide concentration (air pollution level). 6. RM: Average number of rooms per dwelling. 7. AGE: Proportion of homes built before 1940. 8. DIS: Weighted distance to employment centers. 9. RAD: Accessibility to radial highways. 10. TAX: Property tax rate by town. 11. PTRATIO: Pupil-teacher ratio by town. 12. B: Proportion of African American residents (removed due to ethical concerns). 13. LSTAT: Percentage of lower-status population. 14. MEDV: Median value of owner-occupied homes (target variable).

**Important Note: ** This dataset has been modified from the original Boston Housing Dataset. I am not the original source.

Graph and download economic data for S&P Cotality Case-Shiller MA-Boston Home Price Index (BOXRSA) from Jan 1987 to Dec 2025 about Boston, NH, MA, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Housing Inventory: Median Listing Price in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDLISPRI14460) from Jul 2016 to Feb 2026 about Boston, NH, MA, listing, median, price, and USA.

About Dataset

The Boston Housing dataset contains information on housing values in suburbs of Boston. Each record represents a suburb or town and includes 14 attributes such as crime rate, proportion of residential land, average number of rooms, air pollution, property taxes, and pupil-teacher ratio. The target variable is the median value of owner-occupied homes (MEDV) in $1000s.

Boston Housing Dataset – Column Descriptions

• CRIM: Per capita crime rate by town.
• ZN: Proportion of residential land zoned for lots over 25,000 sq.ft.
• INDUS: Proportion of non-retail business acres per town.
• CHAS: Charles River dummy variable (1 if tract bounds river, 0 otherwise).
• NOX: Nitric oxides concentration (parts per 10 million).
• RM: Average number of rooms per dwelling.
• AGE: Proportion of owner-occupied units built prior to 1940.
• DIS: Weighted distances to five Boston employment centers.
• RAD: Index of accessibility to radial highways.
• TAX: Full-value property-tax rate per $10,000.
• PTRATIO: Pupil-teacher ratio by town.
• B: 1000 × (Bk − 0.63)², where Bk is the proportion of blacks by town.
• LSTAT: Percentage of lower status of the population.
• MEDV: Median value of owner-occupied homes in $1000s (target variable).

Source: MANIMALA - Boston House Prices

This dataset is based on the classic Boston Housing dataset, widely used for regression and machine learning tasks. It contains features related to housing in Boston suburbs, such as crime rate, average number of rooms per dwelling, and accessibility to highways, along with the target variable — median value of owner-occupied homes (in $1000s).

The dataset is ideal for:

:- Predicting house prices using regression models

:- Practicing feature engineering and data preprocessing

:- Applying machine learning algorithms (Linear Regression, Random Forest, XGBoost, Neural Networks, etc.)

:- Educational and research purposes in real estate price prediction

This dataset consolidates Rent prices in different towns and cities in Massachusetts, house prices and other relevant information. The main objective of the app is to prioritize towns in MA where house price and rent relationship is the most favorable for a potential investor.

Average Fair Market Rent Prices information was scraped from https://www.rentdata.org/states/massachusetts/ from 2006 to 2022. Massachusetts has the 3rd highest rent in the country out of 56 states and territories.

Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2019. U.S. Census Bureau Population Division. May 21, 2020. The data is available for 351 towns in MA.

Home prices in MA were scraped from Boston Magazine web portal: https://www.bostonmagazine.com/property/single-family-home-price-chart-2021/. SOURCES: Boston neighborhood and town median home prices, sales volumes, and days on market provided by the Massachusetts Association of Realtors (marealtor.com) and MLS Property Information Network (mlspin.com).

Massachusetts is the second wealthiest state in the United States of America, with a median household income of $77,378 (as of 2019). The income per household per town was retrieved from https://en.wikipedia.org/wiki/List_of_Massachusetts_locations_by_per_capita_income.

Median Home Sale Price: All Residential: Boston, MA data was reported at 555.000 USD th in Jul 2020. This records an increase from the previous number of 540.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Boston, MA data is updated monthly, averaging 419.500 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 555.000 USD th in Jul 2020 and a record low of 290.000 USD th in Feb 2012. Median Home Sale Price: All Residential: Boston, MA data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

Explore Boston, MA rental market 2026. The average long-term prices $3,224 and short-term $4,567, with trends shaping housing in a city of 663,972 residents.

Graph and download economic data for All-Transactions House Price Index for Massachusetts (MASTHPI) from Q1 1975 to Q4 2025 about MA, appraisers, HPI, housing, price index, indexes, price, and USA.

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Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town 2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft. 3) INDUS: proportion of non-retail business acres per town 4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M] 6) RM: average number of rooms per dwelling 7) AGE: proportion of owner-occupied units built prior to 1940 8) DIS: weighted distances to five Boston employment centres 9) RAD: index of accessibility to radial highways 10) TAX: full-value property-tax rate per $10,000 [$/10k] 11) PTRATIO: pupil-teacher ratio by town 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

Output variable: 1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

Overview

This dataset is a cleaned and updated version of the classic Boston Housing Dataset, originally made available by the U.S. Census and later popularized in machine learning communities. It contains detailed information about housing prices in Boston suburbs, along with environmental, structural, and socio-economic indicators for each neighborhood.

The dataset is widely used as a benchmark for regression tasks and offers an excellent opportunity to explore linear modeling, feature engineering, multicollinearity analysis, bias mitigation, and more. 📚 Context

Originally published by Harrison and Rubinfeld in 1978, this dataset has been widely adopted in the machine learning and statistics communities. It contains 506 observations, each representing a town or neighborhood in the Boston metropolitan area.

However, some features in the dataset—particularly the B column which encodes race-based information—have become the subject of ethical scrutiny in recent years. Therefore, this version may have undergone data cleaning, feature selection, or modification to ensure it is more appropriate for modern and ethical ML applications. 📊 Features Feature Description CRIM Per capita crime rate by town ZN Proportion of residential land zoned for lots over 25,000 sq. ft. INDUS Proportion of non-retail business acres per town CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) NOX Nitric oxides concentration (parts per 10 million) RM Average number of rooms per dwelling AGE Proportion of owner-occupied units built before 1940 DIS Weighted distance to five Boston employment centers RAD Index of accessibility to radial highways TAX Property tax rate per $10,000 PTRATIO Pupil-teacher ratio by town B 1000(Bk - 0.63)^2 where Bk is the proportion of Black residents LSTAT Percentage of lower-status population MEDV Median value of owner-occupied homes in $1000s (Target Variable)

🟡 Note: Some features (e.g., CHAS, B, or RAD) may have been removed or modified in this version depending on your ethical preprocessing or cleaning steps.

🎯 Target Variable

MEDV: Median value of owner-occupied homes (in $1000s). This is the value we aim to predict in regression tasks.

✅ Use Cases

This dataset is ideal for:

Predictive modeling using linear regression or advanced ML techniques

Feature engineering and feature selection

Studying the effects of urban and environmental variables on real estate prices

Analyzing multicollinearity and variable importance

Exploring ethical considerations in machine learning

⚖️ Ethical Considerations

The original dataset includes the feature B, which encodes racial information. While historically included for statistical analysis, modern ML best practices recommend caution when using such data to avoid unintended bias or discrimination.

In this version, you may choose to remove or retain the column depending on the intended use and audience.

Always consider the fairness, accountability, and transparency of your ML models.

📁 File Information

Filename: boston_housing_cleaned.csv

Records: 506 rows (observations)

Columns: 13 features + 1 target variable (depending on cleaning)

Missing Values: None (in original); NA if introduced during preprocessing

Source: Based on U.S. Census data (original), sourced from Kaggle and cleaned

📌 Tags

housing-prices · regression · real-estate · data-cleaning · ethical-ml · boston · exploratory-data-analysis · feature-engineering

Graph and download economic data for Housing Inventory: Median Days on Market in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDDAYONMAR14460) from Jul 2016 to Feb 2026 about Boston, NH, MA, median, and USA.

Graph and download economic data for Condo Price Index for Boston, Massachusetts (BOXRCSA) from Jan 1995 to Jan 2026 about condos, Boston, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Housing Inventory: Average Listing Price Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) (AVELISPRIYY14460) from Jul 2017 to Jan 2026 about Boston, NH, MA, average, listing, price, and USA.

Boston Area - Median Home Listing Price - Historical chart and current data through 2026.

The Boston Housing Dataset

The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns: 1. CRIM- per capita crime rate by town 2. ZN- proportion of residential land zoned for lots over 25,000 sq.ft. 3. INDUS- proportion of non-retail business acres per town. 4. CHAS- Charles River dummy variable (1 if tract bounds river; 0 otherwise) 5. NOX- nitric oxides concentration (parts per 10 million) 6. RM- average number of rooms per dwelling 7. AGE- proportion of owner-occupied units built prior to 1940 8. DIS- weighted distances to five Boston employment centres 9. RAD- index of accessibility to radial highways 10. TAX- full-value property-tax rate per $10,000 11. PTRATIO- pupil-teacher ratio by town 12. B- 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13. LSTAT- % lower status of the population 14. MED- Median value of owner-occupied homes in **1000's

Boston Housing Dataset

The Boston Housing dataset is a well-known dataset in the field of predictive modeling and statistics. It contains information collected by the U.S. Census Service concerning housing in the area of Boston Mass.

Dataset Overview

• Number of Instances: 506
• Number of Attributes: 14 (including the target variable)

Attributes

The dataset includes the following features:

1. CRIM - Per capita crime rate by town.
2. ZN - Proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS - Proportion of non-retail business acres per town.
4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise).
5. NOX - Nitric oxides concentration (parts per 10 million).
6. RM - Average number of rooms per dwelling.
7. AGE - Proportion of owner-occupied units built prior to 1940.
8. DIS - Weighted distances to five Boston employment centers.
9. RAD - Index of accessibility to radial highways.
10. TAX - Full-value property tax rate per $10,000.
11. PTRATIO - Pupil-teacher ratio by town.
12. B - ( B ) stands for ( 1000(Bk - 0.63)^2 ) where ( Bk ) is the proportion of Black residents by town.
13. LSTAT - Percentage of lower status of the population.
14. MEDV - Median value of owner-occupied homes in $1000s (target variable).

Use Cases

This dataset can be used for:

• Regression Analysis: To predict the value of homes based on the features provided.
• Exploratory Data Analysis: To analyze the relationships between different variables.
• Machine Learning: As a benchmark dataset for testing regression models.

Citation

Details about the dataset and its original source can be found in the following reference:

• Harrison, D. and Rubinfeld, D. L. (1978). "Hedonic housing prices and the demand for clean air." J. Environ. Economics and Management, 5, 81-102.