House Prices Lift Off

Earlier today I tweeted out a chart of house prices using my inari color theme.

House price growth lifting off pic.twitter.com/TvNJcOZrTF

— 📈 Len Kiefer 📊 (@lenkiefer) January 26, 2021

Below is the R code to generate the plot.

# load libraries
library(tidyquant)
library(tidyverse)

# list of FRED Tickers
tickers<- c("LXXRSA","SPCS20RSA","LVXRSA","SEXRSA",
            "SFXRSA","NYXRSA","BOXRSA","SDXRSA","CHXRSA",
            "DNXRSA","PHXRNSA","DAXRNSA","WDXRSA",
            "ATXRNSA","MIXRNSA","POXRSA","MNXRSA","DEXRNSA","TPXRSA","CRXRSA","CEXRSA")

# list of city names
cities <- c("Los Angeles","20-city","Las Vegas","Seattle",
            "San Francisco","New York","Boston","San Diego","Chicago",
            "Denver","Phoenix","Dallas","Washington DC",
            "Atlanta","Miami","Portland","Minneapolis","Detroit","Tampa","Charlotte","Cleveland")

df.all <-
  data.frame(symbol = tickers,
             city = cities,
             stringsAsFactors = FALSE)

# get data from FRED
hpi.all <- tq_get(tickers,
                  get = "economic.data",
                  from = "2000-01-01")

df.out2 <-
  hpi.all %>%
  left_join(df.all, by = "symbol") %>%
  group_by(symbol, city) %>%
  mutate(price = 100 * price / price[date == "2000-01-01"]) %>%
  ungroup %>%
  select(-symbol) %>%
  group_by(date) %>%
  tidyr::pivot_wider(names_from = city,
                     values_from = price,
                     id_cols = date) %>%
  filter(year(date) > 1989) %>%
  ungroup()

df_plot <-
  df.out2 %>% tidyr::pivot_longer(-date, names_to = "city", values_to = "price")  %>%
  filter(city != "20-city") %>%
  group_by(city) %>% mutate(hpa = price / lag(price, 12) - 1) %>% ungroup() %>%
  filter(year(date) > 2014)

# define colors
inari  <- "#fe5305"
inari1 <- "#e53a00"
inari2 <- "#a90000"

# make plot 1

df_plot %>%
  ggplot(aes(
    x = date,
    y = hpa,
    ymin = 0,
    ymax = hpa
  )) +
  geom_ribbon(alpha = 0.45,
              color = NA,
              fill = "white") +
  geom_line(size = 1.01, color = "black") + facet_wrap( ~ city) +
  scale_y_continuous(
    sec.axis = dup_axis(),
    #labels=scales::percent,
    breaks = c(0, .05, 0.1, 0.15),
    labels = c("0%", "5%", "10%", "15%"),
    limits = c(-0.02, .15)
    
  ) +
  theme_dark(base_size=8) +
  theme(
    plot.caption = element_text(hjust = 0),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic")
  ) +
  labs(
    y = "",
    x = "date (monthly)",
    title = "House price trends by metro area",
    subtitle = "12-month percent change in house price index: Jan 2015 to Nov 2020",
    caption = "@lenkiefer Source: S&P Dow Jones Indices LLC, S&P/Case-Shiller, seasonally adjusted index"
  ) +
  scale_x_date(date_breaks = "1 years", date_labels = "%y") +
  theme(
    plot.background = element_rect(fill = inari),
    text = element_text(color = "white"),
    axis.text = element_text(color = "white"),
    panel.grid.major = element_line(color = "white", size = 0.25),
    plot.title = element_text(face = "bold"),
    panel.grid.minor = element_blank(),
    plot.caption = element_text(hjust = 0),
    panel.background = element_rect(fill = inari1)
  )

# make plot 2
df.out2 %>% pivot_longer(-date, names_to = "city", values_to = "price")  %>%
  filter(city != "20-city") %>%
  group_by(city) %>% mutate(hpa = price / lag(price, 12) - 1) %>% ungroup() %>%
  filter(year(date) > 1989) %>%
  ggplot(aes(
    x = date,
    y = hpa,
    ymin = 0,
    ymax = hpa
  )) +
  geom_ribbon(alpha = 0.95,
              color = NA,
              fill = inari1) +
  geom_line(size = 1.01, color = inari2) + 
  facet_wrap( ~ city) +
  scale_x_date(date_breaks = "2 years", date_labels = "%y") +
  scale_y_continuous(sec.axis = dup_axis(), labels = scales::percent) +
  theme_dark(base_size=8) +
  theme(plot.caption = element_text(hjust = 0),
                         plot.title = element_text(face = "bold"),
                         plot.subtitle = element_text(face = "italic")
                       ) +
                       labs(
                         y = "",
                         x = "date (monthly)",
                         title = "House price trends by metro area",
                         subtitle = "12-month percent change in house price index: Jan 2000 to Nov 2020",
                         caption = "@lenkiefer Source: S&P Dow Jones Indices LLC, S&P/Case-Shiller, seasonally adjusted index"
                       ) +
                       
                       theme(
                         plot.background = element_rect(fill = inari),
                         text = element_text(color = "white"),
                         strip.text = element_text(face = "bold", size = rel(1.2)),
                         axis.text = element_text(color = "white"),
                         panel.grid.major = element_line(color = "white", size = 0.25),
                         plot.title = element_text(face = "bold", size = rel(1.5)),
                         plot.subtitle = element_text(face = "italic", size = rel(1.5)),
                         panel.grid.minor = element_blank(),
                         plot.caption = element_text(hjust = 0, size = rel(1.5)),
                         panel.background = element_rect(fill = "#ff8436")
                       )

## Warning: Removed 12 row(s) containing missing values (geom_path).

 Share!

• Tweet