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

Graph and download economic data for All-Transactions House Price Index for Massachusetts (MASTHPI) from Q1 1975 to Q1 2025 about MA, appraisers, 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.

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

All-Transactions House Price Index for Massachusetts was 1268.37000 Index 1980 Q1=100 in January of 2025, according to the United States Federal Reserve. Historically, All-Transactions House Price Index for Massachusetts reached a record high of 1268.37000 in January of 2025 and a record low of 66.46000 in April of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for All-Transactions House Price Index for Massachusetts - last updated from the United States Federal Reserve on July of 2025.

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.

Title: Boston Housing Price Prediction Dataset

Description:

This dataset contains information about housing prices in Boston and is often used for regression analysis and predictive modeling. The dataset is based on the classic Boston Housing dataset, which is frequently used as a benchmark in machine learning.

Attributes:

1. CRIM (Per Capita Crime Rate): The per capita crime rate in the neighborhood.
2. ZN (Proportion of Residential Land Zoned for Large Lots): The proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS (Proportion of Non-Retail Business Acres): The proportion of non-retail business acres per town.
4. CHAS (Charles River Dummy Variable): A binary variable indicating whether the Charles River bounds the tract (1 if bounded, 0 otherwise).
5. NOX (Nitric Oxides Concentration): Nitric oxides concentration (parts per 10 million).
6. RM (Average Number of Rooms per Dwelling): The average number of rooms per dwelling.
7. AGE (Proportion of Owner-Occupied Units Built Prior to 1940): The proportion of owner-occupied units built prior to 1940.
8. DIS (Weighted Distances to Employment Centers): Weighted distances to five Boston employment centers.
9. RAD (Index of Accessibility to Radial Highways): An index representing accessibility to radial highways.
10. TAX (Full-Value Property Tax Rate per $10,000): The full-value property tax rate per $10,000.
11. PTRATIO (Pupil-Teacher Ratio): The pupil-teacher ratio by town.
12. B (1000(Bk - 0.63)^2 where Bk is the Proportion of Black Residents): A measure of the proportion of Black residents adjusted for an offset.
13. LSTAT (Percentage of Lower Status of the Population): The percentage of lower-status residents in the population.
14. MEDV (Median Value of Owner-Occupied Homes): The median value of owner-occupied homes in $1000s (Target Variable).

Objective:

Predict the median value of owner-occupied homes (MEDV) based on various features to gain insights into factors influencing housing prices.

Usage:

This dataset is suitable for regression tasks, machine learning practice, and understanding the dynamics of housing markets.

Citation:

The dataset is derived from the UCI Machine Learning Repository and can be cited as follows:

Harrison Jr., D., & Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102.

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 ---

The U.S. housing market continues to evolve, with the median home price forecast to reach ******* U.S. dollars by the second quarter of 2026. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices, but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with more modest price increases of *** percent in 2022 and *** percent in 2023. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in the fourth quarter of 2023, with Rhode Island and Vermont leading the pack at over ** percent appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.

Graph and download economic data for Housing Inventory: Median Listing Price Year-Over-Year in Massachusetts (MEDLISPRIYYMA) from Jul 2017 to Jul 2025 about MA, listing, median, price, and USA.

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

United States House Price Index: FHFA: Massachusetts data was reported at 792.670 Mar1980=100 in Jun 2018. This records an increase from the previous number of 777.560 Mar1980=100 for Mar 2018. United States House Price Index: FHFA: Massachusetts data is updated quarterly, averaging 316.220 Mar1980=100 from Mar 1975 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 792.670 Mar1980=100 in Jun 2018 and a record low of 66.040 Mar1980=100 in Jun 1975. United States House Price Index: FHFA: Massachusetts data remains active status in CEIC and is reported by Federal Housing Finance Agency. The data is categorized under Global Database’s United States – Table US.EB014: House Price Index.

The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting ****** U.S. dollars per square foot in 2024. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

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.

Dataset

This dataset was created by Vishal Kushwaha

Contents

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.

House prices grew year-on-year in most states in the U.S. in the first quarter of 2025. Hawaii was the only exception, with a decline of **** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Rhode Island—the state where homes appreciated the most—the increase was ******percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2025, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2025.

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.

(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

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