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 Housing Inventory: Median Days on Market in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDDAYONMAR14460) from Jul 2016 to Sep 2025 about Boston, NH, MA, median, and USA.

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

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

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

We are predicting the House prices for Boston Dataset based on the various features given. The features are numeric and therefore we apply linear regression algorithm to predict a continuous target value. We are using scikit-learn's dataset boston.

There are 506 rows and 13 attributes (features) with a target column (price). Here are the features listed. 1. CRIM per capital 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) 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 centers 9. RAD index of accessibility to radial highways 10.TAX full-value property-tax rate per 10,000 USD 11. PTRATIO pupil-teacher ratio by town 12. Black 1000(Bk — 0.63)² where Bk is the proportion of blacks by town 13. LSTAT % lower status of the population

Target: Price

Explore Boston, MA rental market 2025. The average long-term prices $3,342 and short-term $4,567, with trends shaping housing in a city of 663,972 residents.

View monthly updates and historical trends for Case-Shiller Boston, MA Home Price Index YoY. Source: Standard and Poor's. Track economic data with YCharts…

Dataset

This dataset was created by Umashree31

Released under MIT

Contents

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.

View yearly updates and historical trends for Boston-Cambridge-Newton, MA-NH Housing Affordability Index. Source: National Association of Realtors. Track …

(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?

View quarterly updates and historical trends for Boston, MA (MSAD) House Price All-Transactions Index. Source: Federal Housing Finance Agency. Track econo…

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

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?

Housing Inventory: Median Days on Market Year-Over-Year in Boston-Cambridge-Newton, MA-NH (CBSA) was 15.38% in August 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 October of 2025.

Housing Inventory: Median Days on Market Month-Over-Month in Boston-Cambridge-Newton, MA-NH (CBSA) was 16.88% in August of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Month-Over-Month in Boston-Cambridge-Newton, MA-NH (CBSA) reached a record high of 52.63 in July of 2021 and a record low of -54.63 in February of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Month-Over-Month in Boston-Cambridge-Newton, MA-NH (CBSA) - last updated from the United States Federal Reserve on October of 2025.

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.

Iowa Housing Data

The original Ames data that is being used for the competition House Prices: Advanced Regression Techniques and predicting sales price is edited and engineered to suit a beginner for applying a model without worrying too much about missing data while focusing on the features.

Contents

The train data has the shape 1460x80 and test data has the shape 1458x79 with feature 'SalePrice' to be predicted for the test set. The train data has different types of features, categorical and numerical.

A detailed info about the data can be obtained from the Data Description file among other data files.

Transformations

a. Handling Missing Values: Some variables such as 'PoolQC', 'MiscFeature', 'Alley' have over 90% missing values. However from the data description, it is implied that the missing value indicates the absence of such features in a particular house. Well, most of the missing data implies the feature does not exist for the particular house on further inspection of the dataset and data description.

Similarly, features which are missing such as 'GarageType', 'GarageYrBuilt', 'BsmtExposure', etc indicated no garage in that house but also corresponding attributes such as 'GarageCars', 'GarageArea','BsmtCond' etc are set to 0.

A house on a street might have similar front lawn area to the houses in the same neighborhood, hence the missing values can be median of the values in a neighborhood.

Missing values in features such as 'SaleType', 'KitchenCond', etc have been imputed with the mode of the feature.

b. Dropping Variables: 'Utilities' attribute should be dropped from the data frame because almost all the houses have all public Utilities (E,G,W,& S) available.

c. Further exploration: The feature 'Electrical' has one missing value. The first intuition would be to drop the row. But on further inspection, the missing value is from a house built in 2006. After the 1970's all the houses have Standard Circuit Breakers & Romex 'SkBrkr' installed. So, the value can be inferred from this observation.

d. Transformation: There were some variables which are really categorical but were represented numerically such as 'MSSubClass', 'OverallCond' and 'YearSold'/'MonthSold' as they are discrete in nature. These have also been transformed to categorical variables.

e. X Normalizing the 'SalePrice' Variable: During EDA it was discovered that the Sale price of homes is right skewed. However on normalizing the skewness decreases and the (linear) models fit better. The feature is left for the user to normalize.

Finally the train and test sets were split and sale price appended to train set.

Acknowledgements

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.

Inspiration

The data after the transformation done by me can easily be fitted on to a model after label encoding and normalizing features to reduce skewness. The main variable to be predicted is 'SalePrice' for the TestData csv file.