Seasonally adjusted greetings to you and yours. For you I have an animated chart, a variation on our rate cloud with a wintry theme. R code below. We’ll grab mortgage rate data, make a few new variables and then plot the chart using ggridges::geom_density_ridges2. Using the raincloud option for the poisition argument in ggridges::geom_density_ridges2 places the individual data points below the density plots. Using various shades of white we can turn the rain cloud into a snow cloud.
Today I got to talk #dataviz and shared a bunch of my charts, which always sort of feels like sharing my vacation photos. But it was fun to talk about why I made some of these charts, what they mean, and how different data visualization techniques can bring out new insights in familiar data. The pdf version is below or here. The pptx version has animations, get it here.
VISUAL MEDITATIONS are the analysis of repeated graphs of the same data with variations on a graphical theme. When altering the mapping of data to aesthetics sometimes interesting patterns emerge. I find it a useful practice. I made a series of these a few years ago with different charts. The chart images have been lost to past blog migrations, but the code should still work. In this post, I want to consider several alternative ways to visualize house prices.
For several months now, I’ve been working on a new research paper with Sumit Agarwal, Souphala Chomsisengphet, Hua Kiefer, and Paolina Medina studying refinance activity this year. When we started the project back in the spring we were not sure that the pandemic would allow households to refinance at rates similar to prior periods. It seemed possible that the pandemic would disrupt the mortgage market and make it difficult for households to take advantage of historically low rates.
Recently I’ve been putting my y-axis labels on the right for some time series. I think this idea has been rattling around in my head since it was suggested on Twitter by Maarten Lambrechts: A y-axis on the left is almost always the default, while the most recent and usually most relevant data are on the right. Then shouldn't a y-axis on the right be the default (when there are no data labels), to improve legibility?
Yesterday I shared with you observations on the economy, which form the core of many of my recent economic outlook talks. In that article I used some charts with alternative formatting. No not spooky, but a blue theme kind of like those alternative road uniforms some sportsball teams wear. Here, I will share with you the R code for these delicious plots. Setup First we’ll need to set up our chart theme, tweak some ggplot2 defaults and load some libraries.
We are entering the homestretch for 2020, with just about two months left. It has been an intense year, with many twists and turns. Given all the uncertainty many people have been asking me to share my perspective on the outlook for US housing and mortgage markets. I’ve given many talks, in this article I’m laying out the basic view that forms the core of my recent presentations. The COVID-19 pandemic and the associated recession are of course the dominant drivers of the outlook.
Today the FHFA released their house price index for August 2020. Per the report house prices in August 2020 increased 1.5 percentage points over the prior month (19.6% at an annualized rate). Over the last 12-months US house prices have increased 8%, and over the last 3-months they have risen over 15% on an annualized basis. That is an acceleration of over 7.5 percentage points, the largest turnaround in house price growth since the inflection point in 2009.
Earlier today I tweeted out an update of our skyline mortgage rate chart. Gray Kimbrough (follow him on Twitter to get all your Millennial Myths busted) pointed out that my chart style was close to famous art style of Joy Division’s 1979 album Unknown Pleasures. That was exactly right, because I arrived at the skyline mortgage rate chart by tweaking my original application of ‘ggridges::geom_density_ridges’ (formerly known as Joyplot) code to arrive there.
Let’s update our earlier analysis to examine the Federal Reserve’s Beige Book. Following my earlier post, we can construct a sentiment measure for each report. It turns out that after turning sharply negative in spring, the October 2020 report returned to positive territory. The sentiment index looks at all words and after adjusting for economics terms (like gross) we score them for sentiment. We could just count up the number of times we see words like “stong” vs words like “weak”.