mlbench (version 2.1-1)

BostonHousing: Boston Housing Data

Description

Housing data for 506 census tracts of Boston from the 1970 census. The dataframe BostonHousing contains the original data by Harrison and Rubinfeld (1979), the dataframe BostonHousing2 the corrected version with additional spatial information (see references below).

Usage

data(BostonHousing)
data(BostonHousing2)

Format

The original data are 506 observations on 14 variables, medv being the target variable:

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 prior to 1940
dis	weighted distances to five Boston employment centres
rad	index of accessibility to radial highways
tax	full-value property-tax rate per USD 10,000
ptratio	pupil-teacher ratio by town
b	\(1000(B - 0.63)^2\) where \(B\) is the proportion of blacks by town
lstat	percentage of lower status of the population

The corrected data set has the following additional columns:

cmedv	corrected median value of owner-occupied homes in USD 1000's
town	name of town
tract	census tract
lon	longitude of census tract
lat	latitude of census tract

References

Harrison, D. and Rubinfeld, D.L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5, 81--102.

Gilley, O.W., and R. Kelley Pace (1996). On the Harrison and Rubinfeld Data. Journal of Environmental Economics and Management, 31, 403--405. [Provided corrections and examined censoring.]

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Pace, R. Kelley, and O.W. Gilley (1997). Using the Spatial Configuration of the Data to Improve Estimation. Journal of the Real Estate Finance and Economics, 14, 333--340. [Added georeferencing and spatial estimation.]

Examples

# NOT RUN {
data(BostonHousing)
summary(BostonHousing)

data(BostonHousing2)
summary(BostonHousing2)
# }