Housing values in suburbs of Boston

Housing Values in Suburbs of Boston

Boston

Format

A data frame with 506 rows and 14 variables:

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: 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.

Details

The Boston data frame was obtained from Venables and Ripley's MASS package. The data utilize census tracts in the Boston Standard Metropolitatn Statistical Area in 1970. Two changes have been made from this original dataset. The dollar values for tax and medv have been converted to 2020 US dollars (assuming a 299.4% cumulative inflation rate). Additionally, the black variable has been transformed from its original metric (1000*(proportion of blacks by town - 0.63)^2) to a simple the proportion of blacks by town.

Examples

qacr::contents(Boston)
#> 
#> The data frame Boston has 506 observations and 14 variables.
#> 
#> Overall
#>  pos varname type    n_unique n_miss pct_miss
#>   1  crim    numeric 504      0      0%      
#>   2  zn      numeric  26      0      0%      
#>   3  indus   numeric  76      0      0%      
#>   4  chas    integer   2      0      0%      
#>   5  nox     numeric  81      0      0%      
#>   6  rm      numeric 446      0      0%      
#>   7  age     numeric 356      0      0%      
#>   8  dis     numeric 412      0      0%      
#>   9  rad     integer   9      0      0%      
#>  10  tax     numeric  66      0      0%      
#>  11  ptratio numeric  46      0      0%      
#>  12  black   numeric 357      0      0%      
#>  13  lstat   numeric 455      0      0%      
#>  14  medv    numeric 229      0      0%      
#> 
#> Numeric Variables
#>           n    mean     sd  skew    min     p25  median     p75     max
#> crim    506    3.61   8.60  5.19   0.01    0.08    0.26    3.68   88.98
#> zn      506   11.36  23.32  2.21   0.00    0.00    0.00   12.50  100.00
#> indus   506   11.14   6.86  0.29   0.46    5.19    9.69   18.10   27.74
#> chas    506    0.07   0.25  3.39   0.00    0.00    0.00    0.00    1.00
#> nox     506    0.55   0.12  0.72   0.38    0.45    0.54    0.62    0.87
#> rm      506    6.28   0.70  0.40   3.56    5.89    6.21    6.62    8.78
#> age     506   68.57  28.15 -0.60   2.90   45.02   77.50   94.07  100.00
#> dis     506    3.80   2.11  1.01   1.13    2.10    3.21    5.19   12.13
#> rad     506    9.55   8.71  1.00   1.00    4.00    5.00   24.00   24.00
#> tax     506 1628.87 672.46  0.67 746.13 1113.21 1316.70 2657.34 2836.89
#> ptratio 506   18.46   2.16 -0.80  12.60   17.40   19.05   20.20   22.00
#> black   506    1.22   0.11 -3.34   0.65    1.24    1.26    1.26    1.26
#> lstat   506   12.65   7.14  0.90   1.73    6.95   11.36   16.96   37.97
#> medv    506   89.91  36.70  1.10  19.95   67.93   84.59   99.75  199.50