My recent economic and housing market talks see for example here have been titled: “Will the U.S. housing market get back on track in 2019?”. My general conclusion has been cautiously optimistic. There is enough strength in the broader economy and enough of a tailwind from demographic forces to push the U.S. housing market to modest growth next year. I still think that’s true, but as I have said in my talks, risks are weighted to the downside.
The year is winding down, and folks are starting to think about next year. With lots of folks reviewing strategic plans and whatnot, there’s increased demand for me to talk about my 2019 economic outlook. Over on LinkedIn I posted a summary of my most recent chartbook: Will the housing market get back on track in 2019?. Do check it out. Slidecraft For these slides I used a mixture of R and Excel.
U.S. housing markets have slowed down in 2018. Housing construction, which is still running well below both historical averages and what the U.S. currently needs to meet rising demand has stalled out this year. The current level of housing construction is close to the level we’ve seen in recession periods. And the historical comparison stretching back decades is comparing a nation with significantly fewer households. Total U.S. households for example, in 1970 were about 1/2 (63 million) of what they were in 2017 (126 million) FRED chart.
Supply and demand, isoquants, indifference, the lists goes on. Economists love curves. One attracting extra attention these days is the Phillips curve. Last week I was in Boston for the annual meeting of the National Association for Business Economics (NABE). The overall conference was quite good, and certainly one of the highlights was a lunchtime speech by Federal Reserve Chairman Jerome Powell. You can find the speech here (pdf).
In this post I want review some trends in U.S. housing supply and demand. Specifically I want to look at county level trends in population, housing supply (the total number of housing units) and house prices. We’ll uncover some interesting trends. Per usual we will make our graphics with R. Preparing the data required several steps that I will outline in a follow up post. For now we’ll just proceed with the data I’ve put together.
It’s been a while since I posted here. I’ve got some longer form things in the works, but let’s ease back into it. Let’s take a look at the latest Job Openings and Labor Turnover Survey (JOLTS) data via the U.S. Bureau of Labor Statistics. This post is an update of this post. Per usual we will make our graphics with R. First, let’s look at aggregate trends.
My studies involve a lot of data organized in space and across time. I look at housing data that usually captures activity around the United States, or sometimes the world, and almost always over time. In my data visualization explorations I like to study different ways to visualize trends across both space and time, often simultaneously. Let’s consider a couple here in this post. Per usual we will make our graphics with R.
I saw this fun bit of R code in a tweet by user aschinchon. df <- data.frame(x=0, y=0) for (i in 2:500) { df[i,1] <- df[i-1,1]+((0.98)^i)*cos(i) df[i,2] <- df[i-1,2]+((0.98)^i)*sin(i) } ggplot2::ggplot(df, aes(x,y)) + geom_polygon()+ theme_void()#rstats pic.twitter.com/cgNjyk405f — Antonio Sánchez Chinchón (@aschinchon) August 16, 2018 Let’s remix it to make a fun animation. We’ll zoom in and out and have the colors shift. Click for R code
About a year ago I shared code for a dataviz with a progress bar. Let’s update that R code using gifski and tweener. The code below will generate this animated gif: Gif code. Click for details. # CPI VIz with progress bar---- # set up your directory mydir <- "PATH_TO_YOUR_DIRECTORY" # libraries ---- library(data.table) library(tidyverse) library(tweenr) library(gifski) library(ggridges) library(extrafont) library(scales) library(cowplot) # make plots ---- # Get data----- # CPILFESL is FRED mnemonic for CPI: All Items Less Food and Energy # https://fred.
Today was JOLTS Tuesday, when the U.S. Bureau of Labor Statistics releases updated data from the Job Openings and Labor Turnover Survey. I was talking about it earlier today, but before we get into that… If you care about dataviz check this out I saw this on Twitter today via Jon Schwabish. Link to a handy dataviz cheatsheet outlining Jon’s core dataviz principles. Prints out nicely on pdf. Back to the JOLTS.