# 💾 Dortmund real estate market analysis: obtaining and tidying data

Back in 2013 I spent two amazing months of my life in Dortmund. Taking into account that a number of my friends who moved (are moving) to Germany is increasing, I thought it would be nice to get an insight of the last imperfect market of real estate in Dortmund.

The largest listning of property in Germany is immobilienscout24, which is used as a data source. By utilizing the power of rvest package and SelectorGadget (I totally recommend to check out these easy-readable links), it is possible to scrap the data from immobilienscout24. The following code simply loads packages, defines a function that checks whether or not the lengths of its arguments are the same. We will need this function later on. Then, urls are defined by pasting numbers of pages between the first and second parts of urls. Furthermore, we define the data frame property where the data will be stored.

options(stringsAsFactors = FALSE)
library("rvest")
library("stringr")

rm(list = ls())

equal_lengths <- function(...) {
sizes <- sapply(X = list(...), FUN = length)
return(all(rep(sizes[1], length(sizes)) == sizes))
}

url_part1 <- "https://www.immobilienscout24.de/Suche/S-T/P-"
url_part2 <- "/Wohnung-Miete/Nordrhein-Westfalen/Dortmund?pagerReporting=true"
pages <- 1:45

urls <- paste0(url_part1, pages, url_part2)

property <- data.frame(price = character(),
area = character(),
rooms = character(),


At this step it seems natural to use a for loop, and go over each link. Unfortunately, guys from immobilienscout24 use rate limiting (I am not an expert, but it seems like). Thus, I figured out that we need to download the data in while loop instead, for which the condition to stop would be the length of urls equals to 0. Inside the loop each page is parsed, and we extract the following information about entries: price, area (living space), rooms, and address. If the rate limiting is triggered, then a part of data is lost, and therefore, the lengths of these variables will be different. Conditioning on the length equality, the content of variables is copied to property, and the processed url is removed from urls.

The rvest package was designed to work in conjunction with magrittr package, allowing for usage of the so-called pipe (also worth reading).

while(length(urls) > 0) {

price <- immo %>%
html_nodes(".result-list-entry__primary-criterion:nth-child(1) .font-line-xs") %>%
html_text()

area <- immo %>%
html_nodes(".result-list-entry__primary-criterion:nth-child(2) .font-line-xs") %>%
html_text()

rooms <- immo %>%
html_nodes(".result-list-entry__primary-criterion:nth-child(3) .font-line-xs") %>%
html_text()

#     html_nodes(".margin-bottom-xs") %>%
#     html_text()

html_text()

property <- rbind(property,
urls <- urls[-1]

}
}


Having collected all the required data, we need to tidy it and coerce to appropriate formats. For numeric data things are quite simple. Desired characters, price and area, contain in the end units (e.g. euro currency sign or square meter signs). We get rid of them using built-in function gsub. Further, we need to drop . as thousands separator, and replace , with . for a decimal point (including rooms variable). Finally, we can convert characters to numerics.

property$price <- property$price %>%
gsub(pattern = " \u20AC", replacement = "", fixed = TRUE) %>%
gsub(pattern = ".", replacement = "", fixed = TRUE) %>%
gsub(pattern = ",", replacement = ".", fixed = TRUE) %>%
as.numeric()

property$area <- property$area %>%
gsub(pattern = " m\u00B2", replacement = "", fixed = TRUE) %>%
gsub(pattern = ",", replacement = ".", fixed = TRUE) %>%
as.numeric()

property$rooms <- property$rooms %>%
gsub(pattern = ",", replacement = ".", fixed = TRUE) %>%
as.numeric()


With addresses it’s a little bit trickier. We will focus on the district treating it as categorical variable for further regression models. However, not all entries has the nice and full address. Some of them contain only the district and city name. First off, we extract the district from full address observations, and then extract the district from the rest of entries:

property$part <- str_match(string = property$address,
pattern = "(?<=, )(.+?),")[, 2]
property$part[is.na(property$part)] <-
gsub(pattern = ", Dortmund", "", property$address[is.na(property$part)])


Finally, removing NAs and throwing away duplicated rows yield nice and clean data, on which we will calibrate our models in the further post.

nrow(property)
# > 900
nrow(unique(property))
# > 889
sum(complete.cases(property))
# > 889
property <- property[!duplicated(property) & complete.cases(property), ]
nrow(property)
# > 888


As result of these code snippets we obtained the nice real estate data set of 888 rows including such columns as price, area, rooms, address, and part. The data is free from missed values and duplicated rows. So, Set a course for regression, full speed ahead!

Please, note, that with a bit of pain in neck it’s possible to get more detailed info, like the level of building, balcony, cellar, etc. But for a time being it’s reasonable to operate with available data.

The source file is available at my gist together with data file downloaded on 2017 September 8.

Update: how could I miss that immobilienscout24 has an API? However, it’s not yet clear whether the time spent learning API would take less than writing this code.