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 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.

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.

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.

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.

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

The median house prices in the most expensive zip codes in New England, United States ranged from *** to *** million U.S. dollars. Boston (zip code 02199) was the most expensive in New England with a median house price of *** million U.S. dollars. Nevertheless, that was more affordable than in the ten zip codes with the highest median house price in the entire United States.

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 in Massachusetts (MEDLISPRIMA) from Jul 2016 to Jun 2025 about MA, listing, median, price, and USA.

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.

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.

The LTR Genie Score of Boston, MA is 45 and STR Genie Score is 65, indicating a moderate score for long-term rental and a high score for short-term rental investment. The low LTR Rentability suggests that long-term rental properties may not be as profitable in this market compared to short-term rentals. The higher STR Genie Score could be attributed to the strong STR Net ROI of 16.75% and high STR Occupancy rate of 73.33%. The 1-Year Price Appreciation Forecast of -0.76% for Boston indicates a slight decline in property values, which may impact long-term rental investments. Boston, MA is a historically significant city with a strong economy driven by industries such as education, healthcare, and technology. The city is known for its high demand for rental properties due to its large student population and young professionals. Real estate investors should consider the market trends and economic factors when evaluating investment opportunities in Boston.Based on the metrics provided, Boston, MA may be more attractive for short-term rental investments due to the higher STR Genie Score and potential for higher returns. However, investors should carefully consider the market conditions and property values before making a decision. It may be beneficial to diversify investment portfolios with a mix of both long-term and short-term rental properties in Boston to maximize returns and mitigate risks.

Dataset

This dataset was created by Arpit Kumar

Contents

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 2022. 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.

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 ---

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.

(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

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Inspiration

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