Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q2 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 S&P CoreLogic Case-Shiller MA-Boston Home Price Index from Jan 1987 to May 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.

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

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.

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.

Dataset

This dataset was created by Arpit Kumar

Contents

It contains the following files:

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 Massachusetts (MASTHPI) from Q1 1975 to Q2 2025 about MA, appraisers, HPI, housing, price index, indexes, price, and USA.

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.

Graph and download economic data for Housing Inventory: Median Days on Market Month-Over-Month in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDDAYONMARMM14460) from Jul 2017 to Jul 2025 about Boston, NH, MA, median, and USA.

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.

(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?

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.

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 ---

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.

United States Median Home Sale Price: Single Family: Boston, MA data was reported at 580.000 USD th in Jul 2020. This records an increase from the previous number of 556.000 USD th for Jun 2020. United States Median Home Sale Price: Single Family: Boston, MA data is updated monthly, averaging 430.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 580.000 USD th in Jul 2020 and a record low of 301.000 USD th in Feb 2012. United States Median Home Sale Price: Single Family: 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.

Greater Boston had the largest amount of real estate under construction among the top U.S. research and development and life science real estate markets. In the first half of 2023, approximately **** million square feet of R&D life science real estate was under construction. San Francisco Bay Area followed shortly, with *** million square feet under construction during the same period.

United States Median Home Sale Price: sa: Condo/Co-op: Boston, MA data was reported at 482.000 USD th in Jul 2020. This records an increase from the previous number of 479.000 USD th for Jun 2020. United States Median Home Sale Price: sa: Condo/Co-op: Boston, MA data is updated monthly, averaging 397.000 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 520.000 USD th in Mar 2020 and a record low of 290.000 USD th in Mar 2012. United States Median Home Sale Price: sa: Condo/Co-op: 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.EB057: Median Home Sale Price: by Metropolitan Areas: Seasonally Adjusted.