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 Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q1 2025 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDDAYONMARYY14460) from Jul 2017 to Jun 2025 about Boston, NH, MA, median, and USA.

Dataset

This dataset was created by Masayu Anandita

Released under Data files © Original Authors

Contents

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

The Boston Housing dataset contains information about housing prices in the suburbs of Boston, 1970.

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 ---

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

Graph and download economic data for Housing Inventory: Active Listing Count in Boston-Cambridge-Newton, MA-NH (CBSA) (ACTLISCOU14460) from Jul 2016 to Jun 2025 about Boston, NH, MA, active listing, listing, 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?

This statistic shows the housing markets with the largest year-on-year change in house flips in the United States in 2018. The house flipping rate in Boston, Massachusetts was 33 percent higher in 2018 than in 2017. House flipping is a real estate term which refers to the practice of an investor buying property with the aim of reselling them for a profit. The investor either invests capital into each respective property in the form of renovations or simply resells the properties if home prices are on the rise.

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.

Graph and download economic data for Housing Inventory: Median Days on Market Year-Over-Year in Essex County, MA (MEDDAYONMARYY25009) from Jul 2017 to Jun 2025 about Essex County, MA; Boston; MA; median; and USA.

Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) was 10.64% in May of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) reached a record high of 66.67 in April of 2023 and a record low of -62.00 in May of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) - last updated from the United States Federal Reserve on July of 2025.

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.

RentSmart Boston compiles data from BOS:311 and the City's Inspectional Services Division to give prospective tenants a more complete picture of the homes and apartments they are considering renting, assisting them in understanding any previous issues with the property, including: housing violations, building violations, enforcement violations, housing complaints, sanitation requests, and/or civic maintenance requests.

You can look up individual properties using the RentSmart dashboard here.

The median rent for one- and two-bedroom apartments in Boston, Massachusetts, amounted to about ***** U.S. dollars by the end of 2023. Rents decreased slightly after the beginning of the coronavirus pandemic,this trend reversed in 2021 and as of December 2023, the annual rental growth stood at **** percent. Among the different states in the U.S., Massachusetts ranks as one of the most expensive rental markets.

Property managers are hired to oversee operations for apartment complexes and other rental sites. In recent years, the property management industry has seen an oversupply of high-end apartments, leading to heightened competition among property managers and slower lease-ups. This has resulted in downward pressure on rent growth and flattened or declining rents in certain regions. In the office space sector, elevated interest rates have significantly decreased new office construction. Limited new stock increases the appeal of prime buildings and gives owners a strong negotiating position, leading to rent gains for Class A buildings. Demand for apartments has remained robust, as climbing home prices and elevated mortgage rates have made home ownership unaffordable for many households. Through the end of 2025, industry revenue has climbed at a CAGR of 1.9% to $134.2 billion, including a boost of 1.9% in 2025 alone. The gain of short-term rental platforms like Airbnb and VRBO has revolutionized the rental market, with property management firms adapting their services to accommodate these changes. However, persistent inflation and high interest rates present operational challenges for the industry and may strengthen costs. Property managers adopt various strategies to offset these expenses, such as adjusting rents, optimizing costs, streamlining operations through software and technology and renegotiating contracts for fixed-rate agreements. Through the end of 2030, housing affordability issues and slow construction activity will continue to boost the residential property management sector. E-commerce growth will stimulate demand in retail property management, with property managers needing to offer more flexible lease agreements adapted to omnichannel retail strategies. Technological advancements will be pivotal in the industry: AI, predictive tools and digital lease management platforms can streamline operations, improve efficiency and offer valuable insights through data analysis. While adopting these technologies may involve upfront costs, they will likely lead to long-term savings and positive transformations within the industry. Altogether, revenue will climb at a CAGR of 1.8% to reach $146.9 billion in 2030.

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.

In 2022, San Jose, CA, was the hottest market for millennial homebuyers in the United States. Millennials in San Jose were responsible for nearly ** percent of the house purchase requests. Denver, CO, and Boston, MA, completed the top three with over ** percent of purchase requests. Which are the states with the youngest population in the U.S.? It should come as no surprise that the demographic composition plays a central role in the development of the housing market in different states. In 2020, the median age in the United States was 38.2 years, but some states, such as Alaska, District of Columbia, and Utah had much younger population. In contrast, Maine, Puerto Rico, and Hampshire had the highest median age of population. Millennials’ attitudes towards homeownership While many millennials have given up on homeownership, one in ***** people share that they are in the process of saving for a home purchase. These results suggest that young Americans have not entirely given up on the American dream of owning a home of their own.