Wooldridge Source: Collected from the real estate pages of the Boston Globe during 1990. These are homes that sold in the Boston, MA area. Data loads lazily.
data('hprice1')
A data.frame with 88 observations on 10 variables:
price: house price, $1000s
assess: assessed value, $1000s
bdrms: number of bdrms
lotsize: size of lot in square feet
sqrft: size of house in square feet
colonial: =1 if home is colonial style
lprice: log(price)
lassess: log(assess
llotsize: log(lotsize)
lsqrft: log(sqrft)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Typically, it is very easy to obtain data on selling prices and characteristics of homes, using publicly available data bases. It is interesting to match the information on houses with other information – such as local crime rates, quality of the local schools, pollution levels, and so on – and estimate the effects of such variables on housing prices.
Used in Text: pages 110, 153-154, 160-161, 165, 211-212, 221, 222, 234, 278, 280, 299, 307
#> 'data.frame': 88 obs. of 10 variables: #> $ price : num 300 370 191 195 373 ... #> $ assess : num 349 352 218 232 319 ... #> $ bdrms : int 4 3 3 3 4 5 3 3 3 3 ... #> $ lotsize : num 6126 9903 5200 4600 6095 ... #> $ sqrft : int 2438 2076 1374 1448 2514 2754 2067 1731 1767 1890 ... #> $ colonial: int 1 1 0 1 1 1 1 1 0 0 ... #> $ lprice : num 5.7 5.91 5.25 5.27 5.92 ... #> $ lassess : num 5.86 5.86 5.38 5.45 5.77 ... #> $ llotsize: num 8.72 9.2 8.56 8.43 8.72 ... #> $ lsqrft : num 7.8 7.64 7.23 7.28 7.83 ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"