Consider recent house price trends.
After adjusting for inflation, U.S. house prices have increased 76% since January 2000. Just over the past two years, since March of 2020, real house prices have increased over 18%. So even with consumer price inflation that is the highest in about 40 years, house price growth has been even stronger.
National trends understate how strong house prices growth has been in certain states.
Mortgage rates have been increasing. If you measure rates with the last observed value in the month, then March 2022 had the largest 12-month increase of the U.S. average 30-year fixed mortgage rate in the 21st century of 1.5 percentage points. If you go back to the prior century there were five periods where rates increased by at least 1.5 percentage points on a 12-month basis.
Of those periods, two were during a recession, while for the other three, the economy avoided a recession for at least the next 12 months.
Let’s start off the 2022 blog year with some housing starts charts. Well first, we’ll begin with completions, but then end with starts.
Today the U.S. Census Bureau released estimates for residential construction through December 2021. Using those data I made a chart:
This chart shows total housing completions by calendar year in the United States from 1968-2021. US housing completions in 2021 were the highest since 2007, but only higher than 9 of the 40 years from 1968-2007.
On Twitter Ali Wolf asks a question:
I know there's a stat out there that tracks what % of mortgaged homeowners have a rate below 4%. Does anyone have it or know where to find it?
— Ali Wolf (@AliWolfEcon) November 9, 2021 Mortgage rates, that’s something I know a little about.
Fortunately there is some publicly available data that can answer this question. The National Mortgage Database publishes some aggregate data on outstanding mortgages.
As a mental exercise, each week I make a new chart using same data, mortgage rates.
Sometimes I try to make something awesome, other times to stretch the imagination I try to make the most awful chart possible. You can learn a lot from that, but I’ll never tell you which is which!
Making an awful chart is an art unto itself. Sure you could fire up your favorite software and just take the defaults, odds are they would be reasonably awful though defaults are generally less awful these days.
Over on Twitter Bill McBride points out a note on house prices from Goldman
“The supply-demand picture that has been the basis for our call for a multi-year boom in home prices remains intact. … Our model now projects that home prices will grow a further 16% by the end of 2022.”
Goldman: "The supply-demand picture that has been the basis for our call for a multi-year boom in home prices remains intact.
I recently gave a talk on housing markets, beginning with some observations and asking a few pressing questions.
U.S. house prices are increasing at about a 20 percentage point annual rate in recent months, the highest rate of growth ever recorded.
The level of real (inflation-adjusted) house prices is the highest in 131 years of house price data stretching back to 1890.
What does this mean for the housing market?
I continue to experiment with ggfx. This week I made some charts showing U.S. housing starts.
This chart emphasizes the fact that while housing starts have been increasing over the past decade, the absolute level of starts is only moderate when compared to pre-Great Recession (2008-2009) data.
I like to use the ggfx::with_outer_glow functions to create a hazy glow around the density plot.
Here is the R code for that chart.
Yesterday I gave a virtual talk at JSM 2021 on Visualizing Economic and Financial Market Trends in Volatile Times.
Here is a summary of my remarks.
The COVID-19 pandemic and associated economic slowdown has led to unprecedented volatility in many economic and financial time series. With swings well outside of historical ranges, many forecasting models break down. Data visualization techniques are a powerful tool to help the analyst understand evolving economic and financial market trends.
I want to share with you a fun rose chart. No not the Nightingale chart (see Jeff Shaffer). Rather we’re talking about a cool generative chart, that fit in a tweet from @aschinchon [https://twitter.com/aschinchon]
expand.grid(x=., y=.) %>%
ggplot(aes(x=(1-x-sin(y^2)), y=(1+y-cos(x^2)))) +
geom_point(alpha=.05, shape=20, size=0)+
coord_polar()#rtistry #rstats #Maths #generativeart pic.twitter.com/vAVeQU0K2o
— Antonio Sánchez Chinchón (@aschinchon) June 16, 2021 I was inspired to use ggfx to add some features.