Index

shiller - Data

Author

François Geerolf

U.S. Real House Prices

1990 - 2013

(ref:house-prices-2000) U.S. Real House Prices (1990-2013). Source: Shiller.

Code 
data_house_prices_qtr %>%
  select(date, real_house_index) %>%
  filter(date >= as.Date("1990-01-01"), 
         date <= as.Date("2013-01-01")) %>%
  na.omit %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = real_house_index)) + 
  ylab("Real House Price Index") +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.3) + 
  scale_y_continuous(breaks = seq(50, 200, 20)) + 
  scale_x_date(breaks = as.Date(paste0(c(1990, 1991, 1998, 2001, 2006, 2007, 2009, 2013), "-01-01")),
               labels = date_format("%y"),
               limits = c(as.Date("1990-01-01"), as.Date("2013-01-01"))) +
  theme_minimal() + xlab("")

(ref:house-prices-2000)

1890 - 2019

(ref:house-prices-long-run) U.S. Real House Prices (1890-2019). Source: Shiller.

Code 
data_house_prices_qtr %>%
  select(date, real_house_index) %>%
  na.omit %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = real_house_index)) + 
  ylab("Real House Price Index") +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.3) + 
  scale_y_continuous(breaks = seq(50, 200, 20)) + 
  scale_x_date(breaks = as.Date(paste0(seq(1890, 2100, 10), "-01-01")),
               labels = date_format("%y"),
               limits = c(as.Date("1890-01-01"), as.Date("2021-08-01"))) +
  theme_minimal() + xlab("")

(ref:house-prices-long-run)

Cyclically-Adjusted PE

1880 - 2020

Code 
data %>%
  select(date, CAPE) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = CAPE)) + 
  ylab("Cyclically Adjusted P/E Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1800, 2025, 20), "-01-01")),
               labels = date_format("%Y")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak >= as.Date("1880-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(5, 90, 5)) +
  theme_minimal() + xlab("") + 
  geom_vline(xintercept = as.Date("1929-10-24"), linetype = "dashed", color = viridis(3)[2])

1995 - ?

Code 
data %>%
  select(date, CAPE) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = CAPE)) + 
  ylab("Cyclically Adjusted P/E Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1800, 2025, 2), "-01-01")),
               labels = date_format("%Y")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak >= as.Date("1995-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(5, 90, 5)) +
  theme_minimal() + xlab("")

1920 - 1942

Code 
data %>%
  select(date, CAPE) %>%
  filter(date >= as.Date("1920-01-01"), 
         date <= as.Date("1942-01-01")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = CAPE)) + 
  ylab("Cyclically Adjusted P/E Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1920, 1942, 2), "-01-01")),
               labels = date_format("%y")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1920-01-01"), Peak < as.Date("1942-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(5, 30, 5)) +
  theme_minimal() + xlab("") +
  geom_vline(xintercept = as.Date("1929-10-24"), linetype = "dashed", color = viridis(3)[2])

CPI

1871-1940

Code 
data %>%
  filter(date <= as.Date("1940-01-01")) %>%
  mutate(CPI = 100*CPI / CPI[date == as.Date("1923-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = CPI)) + 
  ylab("U.S. Price Level (1871-1940)") + theme_minimal() + xlab("") + 
  scale_x_date(breaks = c(nber_recessions$Peak),
               labels = date_format("%y"),
               limits = c(1871, 1940) %>% paste0("-01-01") %>% as.Date) + 
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(30, 150, 10)) +
  geom_vline(xintercept = as.Date("1933-04-20"), linetype = "dashed", color = viridis(3)[2])

1920-1942

(ref:gold-standard-1920-1942) U.S. Prices (1920-1942)

Code 
data %>%
  filter(date >= as.Date("1920-01-01"),
         date <= as.Date("1942-01-01")) %>%
  mutate(CPI = 100*CPI / CPI[date == as.Date("1923-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = CPI)) + 
  ylab("Price Level") + theme_minimal() + xlab("") + 
  scale_x_date(breaks = c(nber_recessions$Peak, nber_recessions$Trough),
               labels = date_format("%y"),
               limits = c(as.Date("1920-01-01"), as.Date("1942-01-01"))) + 
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(50, 400, 5)) +
  geom_vline(xintercept = as.Date("1933-04-20"), linetype = "dashed", color = viridis(3)[2])

(ref:gold-standard-1920-1942)

Earnings Yield

Code 
data %>%
  select(date, CAPE) %>%
  na.omit %>%
  filter(date >= as.Date("1980-01-01")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = 1/CAPE)) + 
  ylab("Cyclically Adjusted Eearnings / Price Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1980, 2100, 5), "-01-01")),
               labels = date_format("%y"),) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1980-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = 0.01*seq(0, 30, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  theme_minimal() + xlab("")

Ex 3: Dividend / Earnings Yield

Code 
data %>%
  mutate(dividend_yield = dividend / s_p_price,
         earnings_yield = earnings / s_p_price) %>%
  select(date, dividend_yield, earnings_yield) %>%
  gather(variable, value, -date) %>%
  na.omit %>%
  filter(date >= as.Date("1980-01-01")) %>%
  mutate(variable_desc = case_when(variable == "dividend_yield" ~ "Dividend Yield (%)",
                                   variable == "earnings_yield" ~ "Earnings Yield (%)")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable_desc)) + 
  ylab("Dividend / Earnings Yield (%)") + xlab("") + theme_minimal() + 
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1980, 2100, 5), "-01-01")),
               labels = date_format("%y"),) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1980-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 30, 1),
                     labels = scales::percent_format(accuracy = 1))

Ex 4: War Bear Market

Code 
data %>%
  select(date, CAPE) %>%
  filter(date >= as.Date("1939-01-01"), date <= as.Date("1955-01-01")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = CAPE)) + 
  ylab("Cyclically Adjusted P/E Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1920, 1960, 2), "-01-01")),
               labels = date_format("%y")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1939-01-01"), Peak < as.Date("1955-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_y_continuous(breaks = seq(5, 30, 1)) +
  theme_minimal() + xlab("")

Ex 5: Post-War ups and downs

Code 
data %>%
  select(date, CAPE) %>%
  filter(date >= as.Date("1945-01-01"), date <= as.Date("2020-01-01")) %>%
  na.omit %>%
  ggplot(.) + geom_line(aes(x = date, y = CAPE)) + 
  ylab("Cyclically Adjusted P/E Ratio") +
  scale_x_date(breaks = as.Date(paste0(seq(1920, 2100, 5), "-01-01")),
               labels = date_format("%y")) +
  scale_y_continuous(breaks = seq(0, 100, 5)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1945-01-01"), Peak < as.Date("2020-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  theme_minimal() + xlab("")