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.

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 Aug 2025 about Boston, NH, MA, median, and USA.

Gives property, or parcel, ownership together with value information, which ensures fair assessment of Boston taxable and non-taxable property of all types and classifications. To preserve their integrity, the identifiers PID, CM_ID, GIS_ID, ZIPCODE, and MAIL_ZIPCODE all are marked with an underscore ("_") as the last character.

Year-specific documentation for the FY2008 through FY2013 files is not currently available, but the format of those files is equivalent to that described in the FY2014 documentation.

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.

Dataset

This dataset was created by Zohair ahmed

Released under CC0: Public Domain

Contents

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

Dataset

This dataset was created by Arpit Kumar

Contents

It contains the following files:

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 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.

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

Characteristics:

• Number of Instances: 506

• Number of Attributes: 13 numeric/categorical predictive. The Median Value (attribute 14) is the target.

• Attribute Information (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) 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 11. PTRATIO pupil-teacher ratio by town 12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13. LSTAT % lower status of the population 14. PRICE Median value of owner-occupied homes in $1000's

• Missing Attribute Values: None

• Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. You can find the original research paper here.

Financial overview and grant giving statistics of Greater Boston Real Estate Board Foundation

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

Analysis of ‘Real Estate DataSet’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arslanali4343/real-estate-dataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Concerns housing values in suburbs of Boston.

1. Number of Instances: 506

2. Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute.

3. Attribute Information:

   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)
   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
   11. PTRATIO pupil-teacher ratio by town
   12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
   13. LSTAT % lower status of the population
   14. MEDV Median value of owner-occupied homes in $1000's

4. Missing Attribute Values: None.

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Boston-Cambridge-Newton, MA-NH (CBSA) (MEDLISPRIPERSQUFEE14460) from Jul 2016 to Aug 2025 about Boston, NH, MA, square feet, listing, median, price, and USA.

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.

(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

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

In the third quarter of 2024, the districts of Downtown and Black Bay had the highest amount of available office and laboratory space in Boston. Downtown had ************* square feet of directly available space and *** million square feet of space for sublease. This brought its total available space to ** million square feet.

Real Estate Sales Category Archives — Massachusetts Real Estate Lawyer Blog Published by Massachusetts Real Estate Attorneys — Pulgini & Norton, LLP Attorneys at Law | Published by Massachusetts Real Estate Attorneys — Pulgini & Norton, LLP Attorneys at Law