Housing Values in Suburbs of Boston The medv variable is the target variable.

Data description The Boston data frame has 506 rows and 14 columns.

This data frame contains the following columns:

crim per capita crime rate by town.

zn proportion of residential land zoned for lots over 25,000 sq.ft.

indus proportion of non-retail business acres per town.

chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

nox nitrogen oxides concentration (parts per 10 million).

rm average number of rooms per dwelling.

age proportion of owner-occupied units built prior to 1940.

dis weighted mean of distances to five Boston employment centres.

rad index of accessibility to radial highways.

tax full-value property-tax rate per $10,000.

ptratio pupil-teacher ratio by town.

black 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.

lstat lower status of the population (percent).

medv median value of owner-occupied homes in $1000s.

Source Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.

Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Boston Housing Data

Sources

(a) Origin: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. (b) Creator: Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. (c) Date: July 7, 1993

Past Usage

• Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley,
  1. N.B. Various transformations are used in the table on pages 244-261.
  2. Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

Relevant Information

Concerns housing values in suburbs of Boston.

Number of Instances

Number of Instances: 506

Number of Attributes

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

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

Missing Attribute Values

Missing Attribute Values: None.

The Boston Housing Dataset originally contains data on housing values in various neighbourhoods of Boston, USA, collected in 1978. It originally contains 506 instances and 14 columns, representing various factors that influence housing prices in different neighbourhoods in Boston. It was commonly used for regression analysis and to understand how different socio-economic and environmental factors impact property values.

Due to ethical concerns, the B column (proportion of African American residents) has been removed. The remaining columns predict house prices with MEDV as the target.

**Original Columns: ** 1. CRIM: Crime rate (per capita) by town. 2. ZN: Proportion of residential land zoned for large lots. 3. INDUS: Proportion of non-retail business acres per town. 4. CHAS: Charles River dummy variable (1 if adjacent to the river, 0 otherwise). 5. NOX: Nitrogen oxide concentration (air pollution level). 6. RM: Average number of rooms per dwelling. 7. AGE: Proportion of homes built before 1940. 8. DIS: Weighted distance to employment centers. 9. RAD: Accessibility to radial highways. 10. TAX: Property tax rate by town. 11. PTRATIO: Pupil-teacher ratio by town. 12. B: Proportion of African American residents (removed due to ethical concerns). 13. LSTAT: Percentage of lower-status population. 14. MEDV: Median value of owner-occupied homes (target variable).

**Important Note: ** This dataset has been modified from the original Boston Housing Dataset. I am not the original source.

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Overview

This dataset is a cleaned and updated version of the classic Boston Housing Dataset, originally made available by the U.S. Census and later popularized in machine learning communities. It contains detailed information about housing prices in Boston suburbs, along with environmental, structural, and socio-economic indicators for each neighborhood.

The dataset is widely used as a benchmark for regression tasks and offers an excellent opportunity to explore linear modeling, feature engineering, multicollinearity analysis, bias mitigation, and more. 📚 Context

Originally published by Harrison and Rubinfeld in 1978, this dataset has been widely adopted in the machine learning and statistics communities. It contains 506 observations, each representing a town or neighborhood in the Boston metropolitan area.

However, some features in the dataset—particularly the B column which encodes race-based information—have become the subject of ethical scrutiny in recent years. Therefore, this version may have undergone data cleaning, feature selection, or modification to ensure it is more appropriate for modern and ethical ML applications. 📊 Features Feature Description CRIM Per capita crime rate by town ZN Proportion of residential land zoned for lots over 25,000 sq. ft. INDUS Proportion of non-retail business acres per town CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) NOX Nitric oxides concentration (parts per 10 million) RM Average number of rooms per dwelling AGE Proportion of owner-occupied units built before 1940 DIS Weighted distance to five Boston employment centers RAD Index of accessibility to radial highways TAX Property tax rate per $10,000 PTRATIO Pupil-teacher ratio by town B 1000(Bk - 0.63)^2 where Bk is the proportion of Black residents LSTAT Percentage of lower-status population MEDV Median value of owner-occupied homes in $1000s (Target Variable)

🟡 Note: Some features (e.g., CHAS, B, or RAD) may have been removed or modified in this version depending on your ethical preprocessing or cleaning steps.

🎯 Target Variable

MEDV: Median value of owner-occupied homes (in $1000s). This is the value we aim to predict in regression tasks.

✅ Use Cases

This dataset is ideal for:

Predictive modeling using linear regression or advanced ML techniques

Feature engineering and feature selection

Studying the effects of urban and environmental variables on real estate prices

Analyzing multicollinearity and variable importance

Exploring ethical considerations in machine learning

⚖️ Ethical Considerations

The original dataset includes the feature B, which encodes racial information. While historically included for statistical analysis, modern ML best practices recommend caution when using such data to avoid unintended bias or discrimination.

In this version, you may choose to remove or retain the column depending on the intended use and audience.

Always consider the fairness, accountability, and transparency of your ML models.

📁 File Information

Filename: boston_housing_cleaned.csv

Records: 506 rows (observations)

Columns: 13 features + 1 target variable (depending on cleaning)

Missing Values: None (in original); NA if introduced during preprocessing

Source: Based on U.S. Census data (original), sourced from Kaggle and cleaned

📌 Tags

housing-prices · regression · real-estate · data-cleaning · ethical-ml · boston · exploratory-data-analysis · feature-engineering

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Dataset

This dataset was created by Balesh S

Released under CC0: Public Domain

Contents

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Dataset

This dataset was created by Hemant Choudhary

Released under MIT

Contents

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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.

This dataset contains information collected by the US Census Service concerning housing in the area of Boston Massachusetts. It was obtained from the StatLib archive and the dataset has 506 cases.

About Dataset

This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.

• The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Dataset Naming

• The name for this dataset is simply boston. It has two prototasks: nox, in which the nitrous oxide level is to be predicted; and price, in which the median value of a home is to be predicted

Miscellaneous Details

• Origin - The origin of the boston housing data is Natural.
• Usage - This dataset may be used for Assessment.
• Number of Cases- The dataset contains a total of 506 cases.
• Order - The order of the cases is mysterious.

Variables

There are 14 attributes in each case of the dataset. They are:

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

-Note Variable #14 seems to be censored at 50.00 (corresponding to a median price of $50,000); Censoring is suggested by the fact that the highest median price of exactly $50,000 is reported in 16 cases, while 15 cases have prices between $40,000 and $50,000, with prices rounded to the nearest hundred. Harrison and Rubinfeld do not mention any censoring.

1. crim: Per capita crime rate by town.
2. zn: Proportion of large residential lots (over 25,000 sq. ft.).
3. indus: Proportion of non-retail business acres per town.
4. Chas: Binary variable indicating if the property is near Charles River (1 for yes, 0 for no).
5. nox: Concentration of nitrogen oxides in the air.
6. rm: Average number of rooms per dwelling.
7. age: Proportion of old owner-occupied units built before 1940.
8. dis: Weighted distances to Boston employment centers.
9. rad: Index of accessibility to radial highways.
10. tax: Property tax rate per $10,000.

These features provide valuable information about the characteristics of neighborhoods that can influence housing prices.

Property managers are hired to oversee operations for apartment complexes and other rental sites. In recent years, the property management industry has experienced an oversupply of high-end apartments, resulting in increased competition and slower lease-up rates. 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, giving owners a strong negotiating position and 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 2.3% to $136.9 billion, including a 0.1% gain in 2025 alone. The gain of short-term rental platforms like Airbnb and VRBO has revolutionized the rental market, with property managers adapting their services to accommodate these changes. However, persistent inflation and elevated 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 bolster 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, as 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 lead to long-term savings and positive transformations within the industry. Altogether, revenue will climb at a CAGR of 2.0% to reach $150.9 billion in 2030.

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