Creating and Mutating Dataset

library(dplyr)
library(ggplot2)
library(plotly)
library(readxl)

konutdf <- read_xlsx("EVDS_istanbul_property_data.xlsx",
                    skip = 1,
                    n_max=131,
                    col_names=c('Month','TotalSales','EncumberedSales','NewHouseSales',
                                'UsedSales','SalestoForeigns','NewHousePriceIndex',
                                'HousePriceIndex','HouseUnitPriceTRY'))

usdf <- read_excel("USDbyMonth.xlsx")

konutdf <- konutdf %>% left_join(usdf, by="Month") %>% mutate("HouseUnitPriceUSD" = HouseUnitPriceTRY/MeanUSDTRY)

konutdf$Month <- konutdf$Month %>% readr::parse_date(format = "%Y-%m") %>% lubridate::as_date(.)

Information About Dataset

glimpse(konutdf)
## Rows: 129
## Columns: 11
## $ Month              <date> 2010-01-01, 2010-02-01, 2010-03-01, 2010-04-01, 2…
## $ TotalSales         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ EncumberedSales    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NewHouseSales      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ UsedSales          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ SalestoForeigns    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NewHousePriceIndex <dbl> 35.9, 36.6, 37.4, 38.0, 38.0, 37.6, 37.3, 38.1, 38…
## $ HousePriceIndex    <dbl> 36.0, 36.2, 36.5, 36.9, 37.1, 37.0, 37.2, 37.3, 37…
## $ HouseUnitPriceTRY  <dbl> 1414.9, 1420.1, 1427.9, 1442.7, 1449.0, 1445.4, 14…
## $ MeanUSDTRY         <dbl> 1.4690, 1.5095, 1.5280, 1.4865, 1.5353, 1.5699, 1.…
## $ HouseUnitPriceUSD  <dbl> 963.1722, 940.7751, 934.4895, 970.5348, 943.7895, …

Unit Price per m2 TRY-USD Comparison

a <- ggplot(data = konutdf, aes(x = Month)) +
  geom_line(aes(y = HouseUnitPriceTRY), color = "darkred", group=1) + 
  geom_line(aes(y = HouseUnitPriceUSD*4), color="steelblue", group=1) +
  scale_y_continuous(
    # Features of the first axis
    name = "Mean House Price per m2 TRY",
    # Add a second axis and specify its features
    sec.axis = sec_axis(~./4,name="Mean House Price per m2 USD")
  ) +
  theme(panel.grid.major = element_blank(),
       panel.grid.minor = element_blank(),
       axis.text.x = element_text(angle=90, size=8)) + 
  scale_x_date(date_breaks = "6 month",date_labels = "%b %Y")

a

Sales to Foreigners and Unit Price per m2 USD

after2013 <- konutdf %>% tail(93)
ggplot(data = after2013, aes(x = Month)) +
  geom_bar(aes(y = SalestoForeigns), fill = "darkred", stat = "identity") + 
  geom_line(aes(y = HouseUnitPriceUSD*2), color="steelblue", group=1) +
  scale_y_continuous(
    # Features of the first axis
    name = "Sales to Foreigns (Count) ",
    # Add a second axis and specify its features
    sec.axis = sec_axis(~./2,name="Mean House Price per m2 (USD)")
  ) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), 
        axis.text.x = element_text(angle=90, size=8)) + 
  scale_x_date(date_breaks = "6 month",date_labels = "%b %Y")

Conclusion

Mean price per m2 in TRY usually increases but mean price per m2 in USD draws a wavy graphic.

House sales to foreigners increased with the decrease in the dollar exchange rate and mean house price per m2 in USD. Especially after price per m2 in USD falling below $ 1000, there was a big increase in sales to foreigners because of exchange rate.