Apartment transactions in Korea via API provided by the Ministry of Land, Infrastructure, and Transport

Data preparation

library(XML)
library(RCurl)
library(dplyr)

service_key <- readRDS("apartment_key_datagokr.rds")
datlist <- vector("list", 12)

# combine the data in 2022
for (m in 1:12){
  if (m < 10) {
    dt <- paste0("20220", m)
  } else {
    dt <- paste0("2022", m)
  } 
  uri <- paste0("http://openapi.molit.go.kr/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTradeDev?LAWD_CD=11110&DEAL_YMD=", dt, "&serviceKey=", service_key)

  xml_doc <- xmlTreeParse(uri, useInternalNodes = TRUE, encoding = "UTF-8")
  root_node <- xmlRoot(xml_doc)
  xml_data <- xmlToDataFrame(nodes = getNodeSet(root_node, '//item'))

  datlist[[m]] <- xml_data
}

d <- do.call('rbind', datlist)

I will plot the price

# million won
d$price <- as.numeric(gsub('\\,', "", d$거래금액)) / 100
d$area_sq_meter <- as.numeric(d$전용면적) # q
d$area_category <- NA

for(i in 1:nrow(d)) {
  ar <- d$area_sq_meter[i]
  if(ar < 50){
    d$area_category[i] <- "<50"
  }
  else if(ar >= 50 & ar < 80) {
    d$area_category[i] <- "50-80"
  }
  else if(ar >= 80 & ar < 100) {
    d$area_category[i] <- "80-100"
  }
  else if(ar >= 100) {
    d$area_category[i] <- ">100"
  }
}

d$area_category <- factor(d$area_category, levels=c("<50", "50-80", "80-100",">100"))
                             
d$levels <- as.numeric(d$층)

library(ggplot2)

d |> as.data.frame() |> 
  ggplot()+
  geom_point(aes(area_category, price, color=levels)) +
  labs(x=parse(text=paste0("Area~(m^2)")), y="Price (million won)", color=parse(text=paste0("Levels")))+
  theme_bw()+
  ggtitle("Apartment price in Jongno-gu, Seoul, 2022") 

# ggsave("apt_price.png", width=3.4*1.5, height=2.7*1.5, units="in")