Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q1 2025 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.

The S&P Case Shiller Boston Home Price Index has risen steadily since February 2020. 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 335.36 in August 2024. That was above the national average.

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

All-Transactions House Price Index for Boston, MA (MSAD) was 479.76000 Index 1995 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Boston, MA (MSAD) reached a record high of 479.76000 in January of 2025 and a record low of 24.75000 in October of 1977. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Boston, MA (MSAD) - last updated from the United States Federal Reserve on July of 2025.

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

Graph and download economic data for Home Price Index (High Tier) for Boston, Massachusetts (BOXRHTSA) from Jan 1987 to Apr 2025 about high tier, Boston, HPI, housing, price index, indexes, price, and USA.

Dataset

This dataset was created by Nilay Chauhan

Contents

Estimated using sales prices and appraisal data.

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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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

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Boston housing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/altavish/boston-housing-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Domain: Real Estate

Difficulty: Easy to Medium

Challenges: 1. Missing value treatment
2. Outlier treatment 3. Understanding which variables drive the price of homes in Boston

Summary: The Boston housing dataset contains 506 observations and 14 variables. The dataset contains missing values.

--- Original source retains full ownership of the source dataset ---

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

Dataset

This dataset was created by Arpit Kumar

Contents

Dataset

This dataset was created by Domenico Morabito

Contents

The median listing price in a given market during the specified month.

With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDLISPRIPERSQUFEEYY14460) from Jul 2017 to Jun 2025 about Boston, NH, MA, square feet, listing, median, price, and USA.

(https://www.kaggle.com/c/house-prices-advanced-regression-techniques) About this Dataset Start here if... You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.

Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting Acknowledgments 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.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset

This dataset was created by Polu Sreekanth Reddy.

Contents

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

1) Data Introduction • The Real Estate DataSet consists of 506 examples, including home prices in the Boston suburbs and various residential and environmental characteristics.

2) Data Utilization (1) Real Estate DataSet has characteristics that: • The dataset provides 13 continuous variables and one binary variable, including crime rate, house size, environmental pollution, accessibility, tax rate, and population characteristics. (2) Real Estate DataSet can be used to: • House Price Forecast: It can be used to develop a regression model that predicts the median price (MEDV) of a house based on various residential and environmental factors. • Analysis of Urban Planning and Policy: It can be used for urban development and policy making by analyzing the impact of residential environmental factors such as crime rates, environmental pollution, and educational environment on housing values.

Graph and download economic data for All-Transactions House Price Index for Middlesex County, MA (ATNHPIUS25017A) from 1975 to 2024 about Middlesex County, MA; Boston; MA; HPI; housing; price index; indexes; price; and USA.