House sales prices, Baltimore, MD 1978

House sales price and characteristics for a spatial hedonic regression, Baltimore, MD 1978. X,Y on Maryland grid, projection type unknown.

Usage

baltimore

Format

A data frame with 211 observations on the following 17 variables.

• STATION: a numeric vector

• PRICE: a numeric vector

• NROOM: a numeric vector

• DWELL: a numeric vector

• NBATH: a numeric vector

• PATIO: a numeric vector

• FIREPL: a numeric vector

• AC: a numeric vector

• BMENT: a numeric vector

• NSTOR: a numeric vector

• GAR: a numeric vector

• AGE: a numeric vector

• CITCOU: a numeric vector

• LOTSZ: a numeric vector

• SQFT: a numeric vector

• X: a numeric vector

• Y: a numeric vector

Source

Prepared by Luc Anselin. Original data made available by Robin Dubin, Weatherhead School of Management, Case Western Research University, Cleveland, OH. http://sal.agecon.uiuc.edu/datasets/baltimore.zip

References

Dubin, Robin A. (1992). Spatial autocorrelation and neighborhood quality. Regional Science and Urban Economics 22(3), 433-452.

Examples

data(baltimore)
str(baltimore)
#> 'data.frame':	211 obs. of  17 variables:
#>  $ STATION: int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ PRICE  : num  47 113 165 104.3 62.5 ...
#>  $ NROOM  : num  4 7 7 7 7 6 6 8 6 7 ...
#>  $ DWELL  : num  0 1 1 1 1 1 1 1 1 1 ...
#>  $ NBATH  : num  1 2.5 2.5 2.5 1.5 2.5 2.5 1.5 1 2.5 ...
#>  $ PATIO  : num  0 1 1 1 1 1 1 1 1 1 ...
#>  $ FIREPL : num  0 1 1 1 1 1 1 0 1 1 ...
#>  $ AC     : num  0 1 0 1 0 0 1 0 1 1 ...
#>  $ BMENT  : num  2 2 3 2 2 3 3 0 3 3 ...
#>  $ NSTOR  : num  3 2 2 2 2 3 1 3 2 2 ...
#>  $ GAR    : num  0 2 2 2 0 1 2 0 0 2 ...
#>  $ AGE    : num  148 9 23 5 19 20 20 22 22 4 ...
#>  $ CITCOU : num  0 1 1 1 1 1 1 1 1 1 ...
#>  $ LOTSZ  : num  5.7 279.5 70.6 174.6 107.8 ...
#>  $ SQFT   : num  11.2 28.9 30.6 26.1 22 ...
#>  $ X      : num  907 922 920 923 918 900 918 907 918 897 ...
#>  $ Y      : num  534 574 581 578 574 577 576 576 562 576 ...

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  baltimore_sf <- baltimore %>% st_as_sf(., coords = c("X","Y"))
  plot(baltimore_sf["PRICE"])
}