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Boston Housing Data

Source: R/data.R
boston.Rd

Data on median housing values from 506 census tracts in the suburbs of Boston from the 1970 census. This data frame is a corrected version of the original data by Harrison and Rubinfeld (1978) with additional spatial information. The data were taken directly from mlbench::BostonHousing2() and unneeded columns (i.e., name of town, census tract, and the uncorrected median home value) were removed.

Usage

data(boston)

Format

A data frame with 506 rows and 16 variables.

  • lon Longitude of census tract.

  • lat Latitude of census tract.

  • cmedv Corrected median value of owner-occupied homes in USD 1000's

  • 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 centers.

  • 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.

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.

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.

Friedrich Leisch & Evgenia Dimitriadou (2010). mlbench: Machine Learning Benchmark Problems. R package version 2.1-1.

Examples

head(boston)
#>        lon     lat cmedv    crim zn indus chas   nox    rm  age    dis rad tax
#> 1 -70.9550 42.2550  24.0 0.00632 18  2.31    0 0.538 6.575 65.2 4.0900   1 296
#> 2 -70.9500 42.2875  21.6 0.02731  0  7.07    0 0.469 6.421 78.9 4.9671   2 242
#> 3 -70.9360 42.2830  34.7 0.02729  0  7.07    0 0.469 7.185 61.1 4.9671   2 242
#> 4 -70.9280 42.2930  33.4 0.03237  0  2.18    0 0.458 6.998 45.8 6.0622   3 222
#> 5 -70.9220 42.2980  36.2 0.06905  0  2.18    0 0.458 7.147 54.2 6.0622   3 222
#> 6 -70.9165 42.3040  28.7 0.02985  0  2.18    0 0.458 6.430 58.7 6.0622   3 222
#>   ptratio      b lstat
#> 1    15.3 396.90  4.98
#> 2    17.8 396.90  9.14
#> 3    17.8 392.83  4.03
#> 4    18.7 394.63  2.94
#> 5    18.7 396.90  5.33
#> 6    18.7 394.12  5.21