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).
The original data are 506 observations on 14 variables, 'medv' being the target variable:
per capita crime rate by town
proportion of residential land zoned for lots over 25,000 sq.ft
proportion of non-retail business acres per town
Charles River dummy variable (=
1 if tract bounds river;
nitric oxides concentration (parts per 10 million)
average number of rooms per dwelling
proportion of owner-occupied units built prior to 1940
weighted distances to five Boston employment centres
index of accessibility to radial highways
full-value property-tax rate per USD 10,000
pupil-teacher ratio by town
1000(B - 0.63)^2 where B is the proportion of blacks by town
percentage of lower status of the population
median value of owner-occupied homes in USD 1000's
The original data have been taken from the UCI Repository Of Machine Learning Databases at
See Statlib and references there for details on the corrections. Converted to R format by Friedrich Leisch.
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.]