# Dataviz

## Housing Starts Stall

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.

## JOLTS update

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.

## Facets in space and time

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.

## Core Inflation Viz with Progress Bar

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.

## State employment dataviz

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.

## Charts within charts

Maybe you are of the opinion that charts should have their y axis extend all the way down to 0, even if the data live far away from zero. I’m not sure if that’s always the right thing to do. But if you are strict about this, how can you use the space? One thing I experimented with in my Mortgage rates in the 21st century post was filling the area under a line with progressively fainter area.

## Global house price trends

In this post I want to share updated plots comparing house price trends around the world. Or at least part of the world. Our view will be somewhat limited, based on data, but will at least allow us to see how U.S. house prices compare to a few other countries. The details behind these plots are explained in more detail in this post, but some of the images were lost due to my blog transition.

## House price gif that keeps on giffing

This tweet turned out to be popular: 👀house price trends👀 pic.twitter.com/JXB5P0H84A — Leonard Kiefer (@lenkiefer) August 1, 2018 It’s a remix of a chart we made here, though it uses a different index. In the earlier post, we used the FHFA house price index, but this one used the Case-Shiller Index, which was released today. Let me just post two gifs and then below will be the R code I used to create them.

## Getting animated about new home sales

Indications are that U.S. housing market activity in the middle part of 2018 has moderated. Home sales estimates for both new home sales and existing home sales declined on a seasonally adjusted basis in June relative to May. House price growth has also moderated recently. Some folks have gotten animated about the recent trends. I’m more sanguine about the recent data. Certainly a slowdown in housing market activity would be cause for concern.

## House price gifski

I saw today, via Ropensci a blog post about a new package for making animated gifs with R called gifski now available on CRAN. Let’s adapt the code we shared last week to use the gifski package. See that post for additional details. If we run the R code below we’ll generate this animated plot: This plot shows the evolution of house prices in two states, California (CA) and Texas (TX) versus the United States (USA).

## Connected scatterplot

On Twitter Claus Wilke asks: Dear Lazyweb: Is there an accepted name for a plot showing a two-variable time series as a path in the x-y plane? #dataviz@Elijah_Meeks @albertocairo @lenkiefer @sharoz @dataandme pic.twitter.com/N8Edmf8qii — Claus Wilke (@ClausWilke) July 21, 2018 I call them connected scatterplots, and we’ve made a few here. See for example this post. But we can intensify things and make a plot like this: hey @ClausWilke why stop at a 2-d connected scatterplot* when you could go to 3-d

## Mortgage rates in the 21st century

Let’s compare two charts. “Your chart”, or a chart that might come virtually unedited from spreadsheet software versus the chart your boss told you not to worry about: Your chart is perfectly serviceable and for a quick exploration might be perfectly fine. However, why routinely generate such charts if you have the ability to make something a bit more dynamic? Being able to produce more interesting charts might not be necessary, but it also probably doesn’t hurt.

## Exploring housing data with R and IPUMS USA

In this post I want to share some observations on housing in the United States from 1980 to 2016, share some R code for data wrangling, and tri (no that’s not a typo, just a pun) out a visualization techniques. Let’s get to it. I’ve been carrying a running conversation with folks on Twitter regarding the U.S. housing market and its future. Much of that depends on the evolution of demographic forces.

## Plotting house price and income trends

In this post we will create some plots of house prices and incomes for the United States and individual states. We will also try out the bea.R package to get data from the U.S. Bureau of Economic Analysis. We’ll end up with something like this: Per usual we’ll do it with R and I’ll include code so you can follow along. Data We’re going to use two sources of data.

## Pomological Plots

In the real world, when I give talks and use slides I am typically constrained in my aesthetic. Often I’m speaking at a work-related thing and we have a corporate template and color scheme. They serve us well and I’ve found restraint helps focus on the message. Usually I’m setting out to inform, so direct, repeatable and easy to follow are key. But I also like to explore new ideas and different themes on the side.

## Spotlight on housing affordability

IN MY LINE OF WORK, (finance/economics) you see a lot of dual axis line charts. I am of the opinion that dual y axis charts are sort of evil. But in this post I’m going to make one. It’s for a totally legit reason though. Like in an earlier post we’ll make a graph similar to one I saw on xenographics. Xenographics gives examples of and links to “weird, but (sometimes) useful charts”.

## Jobs Friday May 2018

TODAY WAS JOBS FRIDAY. LET’s create a couple plots to show the trend in employment growth. Each month the U.S. Bureau of Labor Statistics (BLS) releases its employment situation report. Let’s make a couple plots looking at trends in U.S. nonfarm payrolls. Per usual, let’s make a graph with R. Data We can easily get the data via the Saint Louis Federal Reserve’s FRED database. If you followed my post from back in April of last year you know what we can do if we combine FRED with the quantmod package.

## What's up? VSUP, that's what's up.

IN THIS POST WE SHALL EXPLORE VALUE-SUPRESSING UNCERTAINTY PALETTES. One of my favorite new sites is xenographics that gives examples of and links to “weird, but (sometimes) useful charts”. The examples xenographics gives are undoubtedly interesting and might help inspire you if you’re looking for something new. One new (to me) graphic was something called Value-Suppressing Uncertainty Palettes (VSUP). See this research paper (pdf). VSUPs “allocate larger ranges of a visual channel when uncertainty is low, and smaller ranges when uncertainty is high”.

## Expanding Expansions, Contracting Recessions

IN THIS POST I WANT TO SHARE A GRAPH looking at the length of economic expansions and recessions in the United State over time. Earlier today, Andrew Chamberlain (on Twitter), observed that at the end of this month the current economic expansion in the U.S. would be the second longest in history. Let’s explore. In the United States, the National Bureau of Economic Research (NBER) dates expansions and recessions. See for example http://www.

## April 2018 Housing Market Update

LAST WEEK I POSTED A THREAD ON TWITTER COVERING RECENT HOUSING MARKET TRENDS AND THE OUTLOOK FOR MORTGAGE RATES: #Mortgage rates are now at their highest level since January 2014. Will these higher borrowing costs dampen the spring homebuying season? Some thoughts (+ charts)... pic.twitter.com/BegOb7p6iq — Leonard Kiefer (@lenkiefer) April 19, 2018 Let’s unpack that thread and add a few more charts I’ve tweeted out in the past week.

## Rate Cloud

TIME FOR A FUN NEW MORTGAGE RATE CHART. This one: We’ll use R to plot a new visualization of mortgage rates. Let’s make it. Data As we did with our majestic mortgage rate plot post we’ll plot mortgage rates using the Freddie Mac Primary Mortgage Market Survey. We’ll get the mortgage rates data via the Saint Louis Federal Reserve’s FRED database. If you followed my post from back in April of last year you know what we can do if we combine FRED with the quantmod package.

## Pipe Tweenr

I LIKE TO MAKE ANIMATIONS WITH R. Sometimes folks ask me how they add to understanding. They don’t always, but often, particularly when you are working with time series, I find they help visualize trends and understand the evolution of variables. I’ve written several posts on animation, see particularly this recent post on making a simple line plot and this post about improving animations with tweenr. Tweenr is a handy package that lets you interpolate data and make smooth animations.

## Forecasting Game

LET’S PICK BACK UP where we left off and think about communicating forecast results. To help guide our thinking, let’s set up a little game. Basic setup Like last time we’re going to focus on a situation where a forecaster observes some information about the world and makes an announcement about a future binary outcome. A decision maker observes the forecaster’s announcement and takes a binary action. Then the outcome is realized and the forecaster receives a payoff.

## Charting Jobs Friday with R

LAST FRIDAY WAS JOBS FRIDAY, the day when the U.S. Bureau of Labor Statistics (BLS) releases its monthly employment situation report. This report is blanketed with media coverage and economist and financial analysts all over the world pay close attention to the report. The employment situation gives a read on trends in the world’s largest economy’s labor market. It also provides a clue about how monetary policy might unfold, affecting bond yields around the world.

## Rock that dadbod plot!

Spring is nearly upon us, or at least we can hope. Let’s examine how housing activity typically rounds into shape as the weather warms up. We’ll make some fun plots with R. Seasonality in housing data Housing market activity in the United States is highly seasonal. Consider this animated plot. This plot shows U.S. new home sales. Often the data are presented seasonally adjusted, but this plot is for non seasonally adjusted data.

## Employment growth and house price trends

LET US TAKE A LOOK AT HOUSE PRICE AND EMPLOYMENT TRENDS. House prices in the Unitest States have been increasing at a rapid pace, about 7 percent on an annual basis. How does that relate to employment growth? And how do those trends vary by geography. Let’s take a look. Per usual, I will post R code and you can follow along. Data Following recent posts (see here and here for example), we will use the Freddie Mac House Price Index an Excel spreadsheet can be downloaded here.

## More house price plots

SO TODAY I SPENT SOME TIME WITH THE KIDDOS and contemplated the Enlightenment, so I didn’t have time to write up some code. But I will post a couple images that I think are interesting. I’ve got two plots for you, both using geofacets. See this post on using the geofacet package in R to make plots like these. The first plot shows U.S. house price trends by metro area from January 2015 to December 2017.

## February 2018 housing market update

EARLIER THIS WEEK I TWEETED out a poll asking whether or not folks wanted to see a thread/tweetstorm with slides from an upcoming presentation on the economy and housing markets that I’m giving. Over 90 percent voted for a thread. So I shared it. In this post let me add a little more commentary on the individual slides. Here’s the thread I ended up posting: Thread (0/5). I'm giving an update on economy, #housing and #mortgage market trends.

## Comparing recent periods of mortgage rate increases

THIS MORNING I SAW AN INTERESTING CHART OVER ON BLOOMBERG. In this post they compared recent 10-year Treasury yield movements with the Taper Tantrum in 2013. The chart you can see here was an area chart with overlapping line plots. I thought it would be a fun exercise to remix a similar chart with R. Eventually it will look like this: Let’s make our remix and try out a few alternative plots.

## Me on a podcast

Hey check it out! Me on a podcast: https://policyviz.com/podcast/episode-111-len-kiefer/. We talk about data visualization and how I use it at work. A bit about using R too. I got the opportunity to talk with Jon Schwabish on the Policyviz podcast. Jon’s PolicyViz blog has a lot of cool data visualization stuff, including chart remakes and thoughtful discussion. There’s a bunch of other neat stuff on the page too. So check out the podcast and take a look around the page, there’s a lot of useful stuff there.

## Simple animated line plot with R

IN THIS POST I WANT TO SHARE R code for a simple animated line plot using ggplot2. In the past I’ve shared similar code, but over time my workflow has evolved. So I thought it would be good to post an updated bit of code. We’ll use economic data from the St. Louis Federal Reserve Economic Database (FRED). See for example this post for more on using FRED with the tidyquant package.

## Plotting U.S. Macroeconomic Trends with FRED and R

LET’S TAKE A LOOK AT RECENT U.S. macroeconomic trends by making a couple plots with R code. Since we’re going to be looking at U.S. macroeconomic data, the data we’ll need is available in the St. Louis Federal Reserve Bank Economic Database FRED. Below I’ll walk you through creating the plots. Where are we in the business cycle After the BLS released their employment situation report (the Jobs report) last week I tweeted out:

## Housing market dataviz: week of November 22, 2017

IT IS THANKSGIVING WEEK HERE IN THE UNITED STATES. I’m getting ready to go out for a nice casual drive down Interstate I-95. Should be fun. After I get back stuffed with turkey and whatnot, we’ll get back to data visualizations and analysis. But let me leave you a couple animated gifs showing recent housing and mortgage market trends. Mortgage rate trends First, let’s just look at a line plot of trends in the 30-year fixed mortgage rate:

## Majestic mortgage rate plot

COME AND MAKE A MAJESTIC MORTGAGE RATE PLOT WITH ME. We’ll use R to plot a few visualizations of mortgage rates. I recently gave a number of talks about the economic outlook and housing. One point I like to make is that mortgage rates are low. I’ve shown this through a variety of visualizations. But one of my favorites looks like this: Let’s make it. Data We’ll plot mortgage rates using the Freddie Mac Primary Mortgage Market Survey.

## Tour of U.S. metro area house price trends

HEY! HERE IS A VIDEO SHOWING HOUSE PRICE TRENDS around the United States. Earlier this year we looked at how to get the data and plot it using R. I made the video using the PowerPoint to .mp4 workflow I outlined here. Below I’ll review how to build this file. Get data We are going to use house price data from the publicly available Freddie Mac House Price Index.

## JOLTS a dataviz trilogy

LET’S TAKE A LOOK AT RECENT LABOR MARKET TRENDS IN THE UNITED STATES. Below we’ll plot labor market trends using the U.S. Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS). Last year we looked at how to get the data and plot it using R. Look to that post for more details, though I’ll include all the code we need below at the bottom. A trilogy of plots First, let’s look at some static plots.

## Combining PowerPoint and R's tweenr for smooth animations

IN THIS POST I WANT SHARE A METHOD FOR MAKING SMOOTH POWERPOINT ANIMATIONS USING R. Also see other posts in this series: Crafting a PowerPoint Presentation with R LINK PURRRTY PowerPoint with R LINK PURRRtier PowerPoint with R LINK Motivation Why would you want to do this? We’ve covered how to make an animated gif with R and that works pretty well. But there are a couple advantages with this approach.

## Purrrtier PowerPoint with R

WE ARE ON OUR WAY TOWARDS BUILDING a tidy PowerPoint workflow. In this post I want to build on my earlier posts (see here for an introduction and here for a more sophisticated approach) for building a PowerPoint presentation with R and try to make it even purrrtier. I saw that somebody shared my posts on reddit and I thought I would take a look at the comments. Folks on the internet are known for kindness and offering helpful advice right?

## Mortgage rates are low!

MORTGAGE RATES ARE LOW IN THE UNITED STATES. How low? Let’s take a look. We’ll use R to plot a few visualizations of mortgage rates. We’ll also try out some of the nice features in the tibbletime package that help when working with time series data. For more on using tibbletime see this post and this one on making animated plots. Since I was already called out for alleged chartcrimes, I’m going to go ahead and let loose here.

## Time to animate with tibbletime

IN THIS POST I WANT TO SHARE SOME CODE TO CREATE AN ANIMATED CHART of housing starts. Per usual we’ll use R and we’ll also use the tibbletime package. Running the code below will generate: (see this post for more on animations with the R package tweenr) ##################################################################################### ## Load libraries ## ##################################################################################### library(tidyquant) library(tibbletime) library(tweenr) ##################################################################################### ## Get housing starts data ## ##################################################################################### df <- tq_get("HOUST1F",get="economic.data",from="1959-01-01") ##################################################################################### ## function for rolling windows ## ##################################################################################### mys <- function(win=12){ rolling_mean <- rollify(mean, window = win) #function creates rolling average based on win df %>%mutate(dy=rolling_mean(price), w=as.

## Unemployment Flexdashboard

IN THIS POST I WANT TO REVISIT FLEXDASHBOARDS. Back in January we made several Flexdashboards with R to display economic data. See my guide to building a flexdashboard for some examples. In this post, I want to use the tidyquant package to wrap some of the plots we made earlier into a flexdashboard. I’ll have more to say about this in the near future, but I just wanted to make a simple flexdashboard (partially to remind myself how to do it).

## Bivariate tilegridmaps with R

I HAVE BEEN EXPERIMENTING WITH A NEW WAY TO VISUALIZE DATA, a bivariate tilegridmap. When I get around to rolling out my tidyPowerPoint workflow we’re going to want something other than bars and lines to fill it up. A graph like this might be a fun option. We’ll build one, but first, just let me show you one I tweeted earlier today: bivariate #tilegridmap map anyone? pic.twitter.com/y3G5XExzoN — Leonard Kiefer (@lenkiefer) October 11, 2017 In this post, let’s go over how to make this plot with R.

## quick geofacet plots

WHILE I WAS TRYING TO MAKE TIME FOR TIBBLETIME yesterday I got distracted and made this plot: hey! here's a #dataviz: tile plot of U.S. state unemployment pic.twitter.com/vH8fSu63ve — Leonard Kiefer (@lenkiefer) October 10, 2017 In this post, let’s go over how to make this plot with R. And we’re going to make it quick. Setup In order to create a plot like this we’ll need several packages, including the tidyverse, geofacet and the tidyquant package.

## What time is it? Time for tibbletime!

WHAT TIME IS IT? TIME FOR TIBBLETIME! In this post, I’m going to take the tibbletime package out for a spin. Turns out this package is quite useful for things I tend to do. We’ll use the tibbletime package to write some R code to extend our ongoing analysis of trends in the U.S. mortgage market (see here for example). Davis Vaughan (on Twitter) one of the authors of the tibbletime package suggested I take a look:

## Analyzing mortgage data with R

TIME FOR ANOTHER DATA WRANGLING AND VISUALIZATION EXTRAVAGANZA. This time we are going to work hard to turn some big data into little data. That is, we’re going to work hard to aggregate several million loan level records into useful summary graphics to tell us about the U.S. mortgage market in 2016. I’ve been working on a lot of different ways to visualize trends in the mortgage market (see here and here for examples).

## Arizona housing market trends

I AM HEADED OUT TO ARIZONA to talk with mortgage professionals. I wanted to share some charts I’ve put together for the Arizona and Phoenix metro economy. These charts were put together using R and tidyquant as I described here. I am working on applying tidy data principles to constructing presentation slides, something I’m calling “tidy PowerPoint”. Preparing these charts, or ones like them, would fall under that workflow. Hopefully I’ll be able to tell you about them more later.

## ggplot as it was meant to be

I’ve BEEN MESSING AROUND MORE WITH R and OFFICER and having too much fun for a Monday. I’m going to dive into some details later, but I’ll just leave a couple files here. See the attached PowerPoint .pptx file for all the charts. Here’s a gif version I started with: Then after I created the PowerPoint I started messing around with the drawing tools and made increasingly ill-advised edits.

## Charting housing starts with R

IN THIS POST I WANT TO SHARE SOME R CODE to create charts of U.S. housing starts we studied last week. Get data We’ll use tidyquant (see e.g. this post for more) to go get our data from the St. Louis Federal Reserve Economic Database (FRED). We’ll also use cowplot to arrange multiple ggplot2 graphs on one page. Let’s load libraries and grab the data. ##################################################################################### ## Step 0: Load Libraries ## ##################################################################################### library(tidyquant) library(tidyverse) library(cowplot) library(lubridate) library(scales) library(ggridges) # replaces ggjoy ##################################################################################### ## Step 1: Prepare for data ## ##################################################################################### tickers=data.

## Crafting a PowerPoint Presentation with R

LOOK I DON’T HAVE ANYTHING BAD TO SAY about PowerPoint. Others have said it (see for example Tufte and Harvard Business Review). It’s a tool and a fact of life for many of us. I am interested in making better PowerPoints. In this post we’ll use some R tools to generate a PowerPoint deck. OfficeR The package officer allows you to access and manipulate ‘Microsoft Word’ and ‘Microsoft PowerPoint’ documents from R.

## Housing market update September 2017

THIS WEEK WAS BUSY, with a lot of data releases. Earlier this week we talked about housing starts, but there were a bunch of other key releases. Let’s review some of them here. We’ll just do a quick description of the data and then follow up with a static chart and an animated gif. Mortgage rates are still super low U.S. weekly average mortgage rates ticked up this week, but remain down from the start of the year.

## Housing starts grinding higher or grinding to a halt?

ARE HOUSING STARTS GRINDING HIGHER, OR GRINDING TO A HALT? Today the U.S. Census Bureau joint with the U.S. Department of Housing and Urban Development published updated estimates of housing starts through August of 2017. Per the report privately-owned housing starts in August were at a seasonally adjusted annual rate of 1.18 million, down 0.8 percent from July’s revised estimate and up 1.4 percent from a year ago. Neither the month-over-month or year-over-year changes were significant.

## Low-key dataviz

IN THIS POST I WANT TO REWORK SOME VISUALIZATIONS we’ve already made. Inspired by this compendium of clean graphics in R, I want to remake some of visualizations we made earlier this year in a low-key manner. Sometimes the visualizations we make here can be pretty intense. Sometimes they are quite intricate, or use animation, or have a lot going on with them. But sometimes you need a simple, low-key visualization to make your point.

## Forecasting is hard (work)-bonus charts

THIS POST IS A QUICK FOLLOW UP to my previous post on forecasting. I’m not going to add any R code or commentary on graphics, instead I’m just going to leave a few extra graphs related to the prior post. Check out the earlier post for more details on the data and how to build the charts. Static plots Here are some static images: Gifs And here are associated animated gifs:

## Adding an animation progress bar

I LIKE TO MAKE ANIMATED GIFS with my data. But frankly, sometimes they can be confusing. I got a good suggestion via twitter in response to a gif I shared there: This is very cool. Has anyone added a visual “progress bar” to a gganimate loop to show the frame visually? It's hard to grasp time as text. — Jonathan Spring (@JustTheSpring) August 6, 2017 Now it’s very likely that others have tried this, but I haven’t seen it.

## Joyswarm: Joyplot + beeswarm = awesome

I LOOK AT A LOT OF DATA and the thing about data is it’s not always what it seems to be. A lot of data are uncertain, and based on estimates. We’ve talked about this before. See for example, this post on visualizing uncertainty in housing data. In this post I’m going to combine one of my favorite new plot types, the joyplot (see this post) with another of my favorite plot types: the beeswarm plot (see this post for more on beeswarm plots).

## Mortgage Rate Kandinsky

THINGS ARE ABOUT TO GET A BIT MORE ABSTRACT IN THIS SPACE. Today we make some Kandinsky-style images with R. This summer I was fortunate to spend some time at the Pompidou Centre in Paris. The Pompidou Centre houses the largest collection of modern art in Europe. I really enjoyed their collection of abstract and minimalist paintings. Well, turns out we can make our own abstract-style art using a Kandinsky R package from Giora Simchoni.

## Joyful dataviz

I TOOK SOME TIME OFF OVER THE SUMMER, away from data visualizations. It’s good to get away from time to time, but oh boy did I miss out. I wasn’t gone long, but in the short time I was gone people came up with some wonderful things. Let me dive back into it with some joyful dataviz. Joy plots Claus Wilke (Twitter) authored a new R package for creating joy plots ( LINK for ggjoy vignette).

## More on housing affordability

LET US FOLLOW UP ON YESTERDAY’S POST with some more analysis of housing affordability. Per usual, we’ll use R to generate the plots and I’ll share the code below. Measuring affordability First, let’s talk a little bit more about what we are seeing in the plots. What are we measuring? Affordability metrics are often based on market level summary statistics. In our case we are looking at various ways to measure housing costs at a market level.

## Housing supply, population, and house prices: Tableau Dashboard

I MADE A LITTLE TABLEAU VISUALIZATION TO ANLAYZE TRENDS in population, housing supply and house prices. If you like interactive dataviz, then the best thing might be to jump down below and explore. But I’ll frame the viz with a bit of discussion. This post extends the analysis from last time looking at population, housing supply and house prices. Last time we restricted ourselves to national trends, but here we’ll dig into local trends.

## Housing supply, population, and house prices: The macro view

## 'getSymbols' currently uses auto.assign=TRUE by default, but will ## use auto.assign=FALSE in 0.5-0. You will still be able to use ## 'loadSymbols' to automatically load data. getOption("getSymbols.env") ## and getOption("getSymbols.auto.assign") will still be checked for ## alternate defaults. ## ## This message is shown once per session and may be disabled by setting ## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details. I travel around the United States giving talks, usually updates on recent trends in housing and mortgage markets.

## Housing market recap

QUITE A LOT OF HOUSING DATA CAME OUT THIS WEEK. Let’s recap with some graphs. Mortgage rates back below 4 percent The 30-year fixed rate mortgage fell back below 4 percent this week. New home sales New home sales data was released and came in weaker than expected for April 2017. March has been an extremely strong number, so a decline was anticipated, but the drop was bigger than most expected.

## Index starting points and dataviz

SO WE HAVE BEEN PLOTTING A LOT OF INDEX VALUES LATELY. It’s been great. But you have questions. Great questions. I got an interesting response to my house price dot chart over Twitter regarding the house price index we were plotting. User [@chrisschnabel](https://twitter.com/chrisschnabel) wondered how the choice of starting point influenced how the house price dot chart looked. @lenkiefer This is a great viz, but conclusions will be drawn based on the date of the index.

## Facet my geo!

TIME TO TRY OUT ANOTHER HOUSE PRICE VISUALIZATION. In this post we’ll try out a new way to visualize recent house price trends with R. Just this wekeend I saw a new package geofacet for organizing ggplot2 facets along a geographic grid. It allows use to construct a small multiple graph that roughly looks like the United States. (Thanks to [@yoniceedee](https://twitter.com/yoniceedee) for recommending geofacet). Let’s try it out using the same house price data we visualized recently.

## Consumer prices, household debt

LET’S TAKE A LOOK AT RECENT TRENDS IN CONSUMER PRICES AND HOUSEHOLD DEBT. Along the way we’ll refresh some visualizations of consumer prices (see here) and household debt (see here) we made last year, as well as think up some new ones. As usual we’ll use R to generate the plots and I’ll share the code below. But before we get into the details of constructing the charts, let’s just look at two plots to help set the stage.

## House price growth and employment trends

IN THIS POST I WANT TO REVIEW RECENT EMPLOYMENT AND HOUSE PRICE TRENDS at the metropolitan statistical area. No R code here, but you can recreate the graphs we’ll explore today by following the code in this post. This week the U.S. Bureau of Labor Statistics (BLS) released updated metro employment data (LINK) and Freddie Mac released its Freddie Mac House Price Index for over 300 metro areas as well as the 50 states, the District of Columbia and the United States.

## Animate a bivariate choropleth

IN THIS POST I WANT TO EXTEND ON yesterday’s post and build an animated bivariate choropleth. We’ll use the same data as yesterday and create a combined scatterplot with bivariate choropleth map and animate it with R. Let’s get right to it. Load data We’ll follow from yesterday and load our data and do some manipulations. In order for this to work we’ll need data from three sources: House Price Index Data in a .

## Bivariate choropleth maps with R

NOTE: After I posted this (like within 5 minutes) I found this post which also constructs bivariate chropleths in R. IN THIS POST I WANT TO REVISIT SOME MAPS I MADE LAST YEAR. At that time, I was using Tableau to create choropleth maps, but in this post I want to reimagine the maps and make them in R. Last year in this post we looked at the relationship between population growth and the growth in housing units from 2010 to 2015.

## What's that on the horizon? An awesome dataviz!

This post is everything you want it’s everything you need it’s every viz inside of you that you wish you could see it’s all the right viz at exactly the right time but it means nothing to you and you don’t know why LET US MAKE SOME HORIZON CHARTS. What is a horizon chart you ask? That’s exactly what I was thinking earlier this weekend. Well, not exactly.

## Treemapify those pies!

TIME FOR ANOTHER DATAVIZ REMIX. Saw on Twitter that [@hrbrmstr](https://twitter.com/hrbrmstr) posted a remix of a Wall Street Journal visualization over at rud.is. The original WSJ article used pies of various size to compare recent store closings. As we usually do in this space, we’ll use R to create our plots. Let’s mix things up and go remix the remix. Pies But first let’s consider the original. I’m not going to copy the original from the WSJ (click the link above to check out the story), but I am going to make my own pie version.

## Housing gets off to a good start

IN 2016 HOUSING IN THE UNITED STATES HAD ITS BEST YEAR IN A DECADE (see my review or my flexdashboard remix) and so far 2017 has gotten off to a good start. Let’s take a look at residential construction, particularly housing starts and see how they stack up to prior years. Per usual we will use R, and the libraries of data.table() and the tidyverse for data management and plotting, and animation and tweenr for animating.

## Let's Pixelate America

LET’S PIXELATE AMERICA. This morning I happened across a fun blog post on how to generate Pixel maps with R via R weekly. The basic code is so easy, all you need is ggplot2 (which I get from the tidyverse). library(tidyverse) ## Warning: package 'tidyverse' was built under R version 3.5.1 ## -- Attaching packages -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 -- ## v ggplot2 3.0.0 v purrr 0.2.5 ## v tibble 1.

## Of kernels and beeswarms: Comparing the distribution of house values to household income

BACK IN JANUARY WE LOOKED AT HOUSING microdata from the American Community Survey Public Microdata that we collected from IPUMS. Let’s pick back up and look at these data some more. Glad you could join us. Be sure to check out my earlier post for more discussion of the underlying data. Here we’ll pick up where we left off and make some more graphs using R. Just a quick reminder (read the earlier post for all the details), we have a dataset that includes household level observations for the 20 largest metro areas in the United States for 2010 and 2015 (latest data available).

## Mortgage rates after dark

TONIGHT WE VISUALIZE MORTGAGE RATES AFTER DARK. Last year I shared 10 amazing ways to visualize mortgage rates (and more ways and even more ways). In this post I have one more DATA VISUALIZATION (dataviz) for you. I was putting together a presentation using remark.js via the xaringan R package (see my discussion of how to do this in this post) and decided to try a dark theme. The code below, modifies our mortgage rate code to make this graph:

## QR code or dataviz?

TODAY I MADE A KIND OF SILLY DATAVIZ, a tile plot of weekly changes in mortgage rates. A colleague happened by my viz terminal, pointed at my monitor and asked “what is that, a QR code?” Nope, it was a tile plot. We’re going to make the plot with R. As before, we’ll use data we used in our mortgage rate post to explore weekly average mortgage rates in the United States based on Freddie Mac’s Primary Mortgage Market Survey.

## Resampling

THIS PAST MONTH HAS BEEN BUSY. People have been traveling, I’ve been traveling, kids have been sick, and we’ve had the March Madness basketball keeping me occupied. Today I wanted to just explore a little analysis I’ve put together on resampling. Because reasons I’ve recently been interested in sample sizes and how quickly certain estimates might converge. There is of course, a vast literature on this topic. But armed with powerful computers maybe we can avoid too much mathy work and try to simulate our way through some problems.

## A grand tour of house price trends

LET US BUILD ON YESTERDAY’S POST (LINK) and construct more VISUALIZATIONS of house prices. In this post, I’ll include some R code so you can play along. We are going to construct our own GRAND TOUR (Wikipedia) except instead of touring European antiquity, we will explore recent trends in house prices around the continental United States. But we will perhaps still pick up some culture, or at least a new ggplot2 theme.

## Recent trends in house prices

IN LATE 2016 HOUSE PRICES recovered back to their pre-recession peak. At least nationally. At least not adjusted for inflation. Let’s talk about it. National trends The chart below shows the Freddie Mac House Price Index (link to source) for the United States from December 2000 to December 2016. Prior to the Great Recession, house prices reached their seasonally-adjusted peak in March of 2007. Prices fell from 2007 (with a brief interruption due to the first time homebuyer tax credit) to December 2011 when they reached their post-recession minimum.

## Experimenting with expanding axes

LET US EXPERIMENT A BIT WITH AXES. In this post I’m going to try out some data visualization ideas expanding on our earlier work with ticks marks (see post ticks out). We’re going to make the following plot and some variations with R. As before, we’ll use data we used in our mortgage rate post to explore weekly average mortgage rates in the United States based on Freddie Mac’s Primary Mortgage Market Survey.

## House prices are highest in coastal metros

TODAY THE NATIONAL ASSOCIATION OF REALTORS (NAR) released (press release) data on metro area median sales prices of existing single-family homes (the U.S. Census and HUD report data on new home sales prices in a joint release). NAR makes the data available (Excel file). Let’s take a look at the data: ## Warning: Removed 1 rows containing missing values (position_stack). ## Warning: Removed 1 rows containing missing values (geom_text). ## Warning in grid.

## Ticks out!

YOU HAVE SPOKEN and we will go with ticks out, at least 54% of the time. In a graph, should axis ticks face in or out? — Leonard Kiefer (@lenkiefer) February 5, 2017 To celebrate, let’s make an animated gif where the axis expands over time. We’ll use data we used in our mortgage rate post. Let’s start by loading the data and plotting a static graph (with ticks out of course).

## Hello Ninja! Crafting a browser-based presentation and how I got (re)started with R

I GIVE A LOT OF TALKS. Some are formal presentations or keynotes to large groups, while many are in small group settings. Sometimes I get impromptu requests so I have to be ready pretty much at all times to give some sort of talk. On this blog I’ve shared many different DATA VISUALIZATIONS which could be part of a talk. Many of the more complex visualizations-probably most of the animated gifs-wouldn’t work great in most presentation settings.

## Wrangling employment data, plotting trends

We will get back to house prices soon. IN THIS POST I WANT TO EXPLORE EMPLOYMENT TRENDS at the state and metro area. Today the U.S. Bureau of Labor Statistics (BLS) released data on state and metro area employment trends. Last month we looked at unemployment trends. Today we’ll look at trends in nonfarm employment. Wrangling the data We will be importing, preparing, and plotting our data with R. We can get the data pretty easily using the files BLS prepares, though we have to do a little bit of work to organize the data.

## Populous metros are heavy!

I WANT TO SHARE WITH YOU a little bit of code to make this whimsical data visualization: Make a simple map First we can construct a map of the lower 48 U.S. states and add a marker for each city. These data are available in the us.cities data that come with the maps package. library(tidyverse) library(maps) data(us.cities) # get us city data from the package maps # drop AK and HI to get the lower 48 states: us.

## Simple tweenr animations with ggplot2

Animations with tweenr IN THIS POST WE ARE GOING TO CREATE TWO SIMPLE animated data visualizations using R ggplot2, animation, and tweenr packages. See this post about tweenr for an introduction to tweenr, and more examples here and here. Unlike those examples we are going to create visualizations using the data that ship with the tidyverse packages (mainly ggplot2). This will allow for easier replication. A simple stripped down example.

## Visual meditations on house prices, Part 6: state recovery

Introduction HOUSE PRICES HAVE NOW RECOVERED BACK TO THEIR PRE-RECESSION PEAK, at least according to some indices. The Freddie Mac House Price Index, for example, surpassed its pre-2008 peak in the latest release for data through September 2016. In this post I’ll be exploring trends in house prices and exploring different ways of showing how far house prices have come, and in some cases, how far they still have to go.

## Some animated gifs: Week of Oct 14, 2016

A couple of views of mortgage rates: Mortgage Rates in 2016 This chart shows the weekly average for the 30-year fixed rate morrtgage. Comparing mortgage rates by week This viz compares weekly mortgage rates (30-year fixed rate mortgage) by year. Each line represents a different year. The x-axis display the week of the year (from 1 to 52). Homeownership gif A homeownership rate viz. This shows the U.S. homeownership rate and breaks it out by the age of householder:

## Distribution of mortgage loan amounts in 2015

IN THIS POST I WANT TO CREATE some data visualizations with R using the recently released Home Mortgage Disclosure Act (HMDA) data. For this post I’m going to return to the 2015 HMDA that you can get from the Consumer Financial Protection Bureau (CFPB) webpage and I discussed earlier. Check out my prior post for more discussion of how we build these data visualizations. R code for graphs posted below

## Data visualizations for the week of September 22, 2016

IT WAS A BUSY WEEK FOR ECONOMIC AND HOUSING DATA this week. Below are some data visaulizations I made tracking key trends in economic and housing market data. Homeowner equity increases to $12.7 Trillion in the second quarter of 2016 With house prices rising by nearly 6 percent on a year-over-year basis, homeowners are building back equity. According to the Federal Reserve’s Flow of Funds, owners’ equity in real estate was$12.

## Industry-specific Beveridge Curves

IN MY PREVIOUS POST we looked at the Job Openings and Labor Turnover Survey (JOLTS) data and plotted a Beveridge Curve. In this post I want to add some more code that allows us to plot Beveridge Curves by industry. For more on the analysis of industry-specific Beveridge Curves, see this paper published in the June 2012 Monthly Labor Review that decomposes shifts in the Beveridge Curve and looks at it by industry.

## JOLTS! Job openings and labor turnover trends

IN THIS POST WE’LL LOOK AT recent job openings and hires data from the Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS). R code for selected graphs posted below Job openings and labor turnover Total nonfarm trends Let’s start by looking at aggregate national trends for total nonfarm sector. The plot below compares hires, job openings and separations (the sum of quits, layoffs and discharges, and other separations) over time.

## What we spend: Consumer Expenditures in 2015

.showopt { background-color: #004c93; color: #FFFFFF; width: 100px; height: 20px; text-align: center; vertical-align: middle !important; float: right; font-family: sans-serif; border-radius: 8px; } .showopt:hover { background-color: #dfe4f2; color: #004c93; } pre.plot { background-color: white !important; } EARLIER THIS WEEK THE U.S. BUREAU OF LABOR STATISTICS released data on consumer expenditures in 2015. In this post I want to examine these data and make a few visualizations. R code for graphs posted below

## Recent economic and housing market trends: August 2016

.col2 { columns: 2 200px; /* number of columns and width in pixels*/ -webkit-columns: 2 200px; /* chrome, safari */ -moz-columns: 2 200px; /* firefox */ } .col3 { columns: 3 100px; -webkit-columns: 3 100px; -moz-columns: 3 100px; } IN THIS POST WE’LL REVIEW some recent economic and housing market trends. R code for graphs posted below Low mortgage rates Mortgage rates remain low, with the 30-year fixed mortgage averaging 3.

## Density squared

WE ARE GOING TO EXAMINE THE DISTRIBUTION OF US POPULATION and make an animated gif combining a map and a kernel density estimate of the distribution of county population densities. Density of densities, or density squared. We are going to use the same US County Population Estimates 1790-2010 we used in my previous post. We’ll end up with this: How do we do it? Code First, we’ll load the data and do some manipulations.

## More map visualizations

IN THIS POST I’M JUST GOING TO share a few data visualizations I’ve been working on today. For most, no code, but these build off my previous posts here and here so you can check there for hints and I’ll post some more examples with code later. Population maps This one shows the evolution of population by county without the distribution plots I included last time. We discussed these data in our last post.

## U.S. county population: 1790-2010

SOMETIMES YOU ACTUALLY LEARN SOMETHING from social media. Today on Twitter I happened across this Tweet via @kyle_e_walker: Anybody want population estimates for every US county & metro back to 1790? Well, here are mine. Have fun!https://t.co/QqdA6226kN — Jonathan Schroeder (@j_p_schroeder) August 15, 2016 Seems somebody posted estimates of the U.S. population by county (defined by 2010 county definitions) going back to 1790. This is a perfect dataset to practice my mapping with R.

## Maps, mortgages and me

IN THIS POST I WANT TO DOCUMENT some R code I’ve recently been working on combining maps and distribution plots. As I discussed earlier lots of interesting data will be released in the fall and I want to be ready for it. Some of these snippets can be recycled when the new data is available. Maps One area of data visualization with R I haven’t explored much is mapping. Part of this reason is because I’ve had other tools to use, but usually it’s because I’m in a hurry.

## Mortgage rate gifs: Week of Aug 19, 2016

No code, just some gifs: Comparing mortgage rates by week (2014, 2015,2016) Annotated history of mortgage rates

## Data swarms: Your firearms are useless against them!

AUGUST IS ALMOST OVER, and it’s nearly back to school season. And that means one thing. No, not that we’re about to get a chance to watch the #1 NCAA football program of all time dominate the gridiron (though that’s awesome too). No, it’s data release season! A data swarm is on its way. From American Community Survey to the American Housing Survey to the annual Home Mortgage Disclosure Act Data many statistical data releases come out in September and October.

## Consumer Credit Trends Part 2: Data doesn't drive, it's lucky to be in the car

.col2 { columns: 2 200px; /* number of columns and width in pixels*/ -webkit-columns: 2 200px; /* chrome, safari */ -moz-columns: 2 200px; /* firefox */ } .col3 { columns: 3 100px; -webkit-columns: 3 100px; -moz-columns: 3 100px; } A FEW DAYS AGO I POSTED on trends in household debt using data from the the New York Federal Reserve Bank’s Consumer Credit Panel. The post got many responses, some observing that while student debt has grown a lot the absolute level of it is small relative to mortgage debt.

## Visual meditations on house prices, Part 5: distributions

OVER THE PAST THREE MONTHS I HAVE MADE several new house price visualizations. In these meditations I’ll consider some recent graphs and provide R code for them. For reference, prior meditations are available at: Part 1: data wrangling Part 2: sparklines and dots (animated) Part 3: bubbles and bounce Part 4: graph gallery Meditation 1: Median sales price trends Earlier this week, the National Association of Realtors (NAR) released their quarterly update on metro area median house prices (data here).

## Consumer Credit Trends

TODAY the NEW YORK FEDERAL RESERVE BANK released its Quarterly Report on Household Debt and Credit. These data come from the Center for Microeconomic Data based on credit records from Equifax. R code for the graphs are posted at bottom of page Trends in household debt balances One of the key statistics tracked in the report (full data can be found here) is household debt balances. They break debt balances out by loan type:

## Let's make a dot plot

IN THIS POST WE’RE GOING to make an animated version of the famous Federal Reserve dot plot comparing the dots in March 2016 to June 2016. As far as celebrity charts go, the dot plot has to be up there. Check out how much “dot plot” has grown in search popularity since the Fed introduced their dot plot in 2012: Ultimately, it will look something like this: The dot plot is a special chart that shows the distribution of expectations of the Federal Open Market Committee (FOMC) for the federal funds rate.

## Dataviz Remix: Housing Trilemma

A dataviz remix I came across some interesting analysis from Josh Lehner at the Oregon Office of Economic Analysis about the “Housing Trilemma”. The Housing Trilemma, captured in the chart below is based on the following claim: Every city wants to have a strong local economy, high quality of life and housing affordability for its residents. Unfortunately these three dimensions represent the Housing Trilemma. A city can achieve success on two but not all three at the same time.

## Visualizing the U.S. housing stock

IN THIS POST I wanted to share a few data visualizations I made using the American Housing Survey (AHS). For this exercise I used the metro summary tables which you can download from American Fact Finder. Distribution by year unit built Distribution by units in stucture Distribution by bedrooms in unit Distribution by square footage of units {% include JB/setup

## More tweenr animation examples

IN THIS POST I WANT TO PROVIDE some additional examples of using tweenr and gganimate to create nice smooth transitions in an animated GIF. In this post we’ll look at an animated waterfall chart. For this data I’m going to use the National Income and Products Accounts from the U.S. Bureau of Economic Analysis (BEA). Specifically we’ll be looking at the contributions to growth in Real Gross Domstic Product, which you can find here.

## Improving R animated GIFs with tweenr

.col2 { columns: 2 200px; /* number of columns and width in pixels*/ -webkit-columns: 2 200px; /* chrome, safari */ -moz-columns: 2 200px; /* firefox */ } .col3 { columns: 3 100px; -webkit-columns: 3 100px; -moz-columns: 3 100px; } RECENTLY I HAVE MADE several animated GIFs, primarily using the animation package. These gifs usually work pretty well, coming out something like this (code here). Unfortunately, these gifs tend to come out rather choppy.

## Population growth, housing supply, and house prices

EARLIER THIS WEEK THE U.S. CENSUS BUREAU released data on population and housing units for counties across the U.S. in 2015. These data reveal important trends in population growth, and help shed light on recent house price trends. Housing unit growth One key factor driving housing market dynamics is the expansion of housing supply (or lack thereof). The updated estimates from Census allow us to see which areas have added the most housing units and how that relates to population and house price trends.

## Mortgage rates and the Fed Funds rate

YESTERDAY THE FEDERAL RESERVE RELEASED minutes from their last Federal Open Market Committee (FOMC) meeting in April. The big news was that the FOMC still considered a June rate hike a possibility. How much does Fed policy impact mortgage rates? I put together a few gifs comparing the history of the 30-year mortgage rate, the Fed Funds rate and the 10-year constant maturity Treasury. {% include JB/setup

## Mortgage rates, some perspective

Another mortgage rates animated gif IN THE PAST I’ve told you how I made my mortgage rates gif. In this post I’m make an extension that uses stop motion techniques to reverse course. We’ll end up with this: For reference, here’s the standard gif I share each Thursday after mortgage rates come out: Stop motion animation While thinking about the week-to-week movements in rates it’s easy to lose longer-term perspective.

## Visual meditations on house prices, Part 4: graph gallery

Introduction IN THIS POST we’ll collect several visualizations of house prices I’ve shared on Twitter the past few days and have a little discussion. In prior posts I’ve also included code for some of these graphs, and the others are mostly straightforward extensions of the earlier examples. This is Part 4 of my series of posts on visualizing house prices. Below are the earlier posts that have data and R code for generating plots:

## Visual meditations on house prices, Part 3: bubbles and bounce

IN THIS POST I’m going to describe how to make some additional data visualizations of house prices and discuss a bit about what they mean. For reference, the prior posts are available at: Part 1: data wrangling Part 2: sparklines and dots (animated) Meditation 1: Bubbles Let’s start by recreating this visualization: This visualization plots each state (plus the District of Columbia) as a blue dot in a scatterplot.

## Visual meditations on house prices

Introduction This is a multi-part blog series on visual meditations on house prices. You can find the links at: Part 1: data wrangling Part 2: sparklines and dots (animated) Part 3: bubbles and bounce Part 4: graph gallery Part 5: distributions Part 6: state recovery Part 7: don’t cross the streams and maybe more as I think of them. I’ll add them all here.

## Visual meditations on house prices, Part 1: data wrangling

Introduction THIS POST CONSIDERS recent trends in house prices. Thinking a great deal about house prices, I like to look at many different DATA VISUALIZATIONS of house prices, at the national, state, and metro levels of aggregation. Also, in this post, I will include descriptions of how I built my visualizations, including code. These meditations as I call them, required some data wrangling and some thinking as to how to construct the charts.

## Visual meditations on house prices, Part 2: sparklines and dots (animated)

THIS IS PART TWO of my series of meditations on house prices. In our earlier post we covered the data collection, wrangling, and some useful transformations. Recap For convenience, the data files you’ll need to replicate these results are right here: state and national called fmhpi.txt metro called fmhpi2.txt And the data preparation code is here in the previous post Now we’ll need to load and prepare some data.

## House price data viz

TO HELP UNDERSTAND TRENDS in house prices, I have a couple of data visualizations for the Freddie Mac house price index. Viz 1: House Price Dynamics The first compares the quarterly and annual appreciation for house prices across the 50 states plus the District of Columbia. In this visualization, each dot represent a state (or D.C.). The horizontal X axis measures the quarter-over-quarter annualized percentage change in the Freddie Mac house price index.

## Annotated chartbook, Tufte style

I finally got around to putting together an annotated chartbook, Tufte style, using the Tufte package for Rmarkdown. Check it out Some screenshots: Screenshot 1 Screenshot 2 Screenshot 3 Screenshot 4 {% include JB/setup

## Data Viz: Occupation Wages and Regional Cost of Living

A new data viz TODAY THE BLS released data on metro level wages, employment concentration and regional costs of living. I put together a quick plot and sent out the following tweet: Today @BLS_gov released data combining price-adjusted wages and employment concentration. I looked at economists… pic.twitter.com/NkL3q9r5ID — Leonard Kiefer (@lenkiefer) April 14, 2016 This post provides a link to interactive versions of the same plot and a discussion.

## dot plots and distributions

We’re going to make this chart (and talk about it) Wait, what is this? Let’s pause the animation and look at the last frame: This plot shows the distribution of metro area unemployment. These data are available here. Each dot represents a metro area with its unemployment rate depicted on the x axis. The data are bucketed into 0.25 percentage point buckets and stacked when more than one metro falls within that range.

## Real house prices and population growth

EARLIER THIS WEEK the U.S. Census Bureau released updated population figures for 2015. These data revealed changes in population across the country. Jed Kolko published a nice summary of these data, and it got me thinking about the relationship of population growth rates and house prices. In this post I want to consider a few key things I found by exploring these data. First, let’s have a look at the history of real house prices, relative to the year 2000 for 30 large metro areas:

## Tight Inventory: Data Viz Remixed

Tight Inventory EARLIER THIS WEEK, Trulia published a post by Chief Economist Ralph McLaughlin called “House Arrest: How Low Inventory Is Slowing Home Buying”. The article analyzed trends in housing inventory. Trulia broke housing inventory into “starter homes”, “move-up homes”, and “premium homes”. They found that the inventory of available homes for-sale has shifted towards premium homes and away from starter homes that first time homebuyers would typically be buying.

## The week (so far) in charts

Mid-week chart update With existing home sales, house prices, and new home sales being released, this is one of the busiest weeks of the month for housing data. We’ll catch new home sales tomorrow morning, but let’s catch our breath and recap what we’ve learned so far this week. Existing home sales disappoint The National Association of Realtors (NAR) reported on existing home sales (EHS) on Monday. Existing home sales for February surprised most by dropping 7.

## The week that was in charts

This past week I tracked several data releases that give us an idea of the health and vibrancy of the economy and housing markets. State employment trends positive Consistent with the national employment numbers state employment trends are positive. The BLS reported on employment trends by state for January 2016 this week. Eleven states plus DC had a statistically significant month-to-month increases in employment and five states experienced month-to-month declines.

## What the February jobs numbers mean for housing

Resilient job growth SPRING IS ALMOST HERE, and housing market activity will start to accelerate as we enter the peak homebuying season in the spring and summer months. The latest jobs report shows the U.S. labor market continues to pick up steam, adding 242,000 jobs month-over-month and beating expectations. Job growth has been resilient since the end of the Great Recession in June 2009, with monthly job growth averaging over 200,000 since 2011.

## Recent House Price Trends

{% include JB/setup National house prices rise 6.2% Freddie Mac released its full year 2015 house price index and an interactive data visualization. The seasonally-adjusted national index increased 6.2 percent year-over-year and is now 29.6 percent above the post-recession low, and just 4.1 percent below the (nominal) pre-recession peak (see graph below). While national house price growth has been strong, there is considerable variation across the country. Some states and metro areas are already well above their pre-recession (nominal) peak, while other still have lots of ground to make up.

## Annotated Data Viz 2

BELOW IS A VISUALIZATION of household size and composition and homeownership from the Census1 going back to 1980. With this visualization, we can see how household size and tenure choice (own vs rent) has varied by age over time. 1Data from Ipums: IPUMS-USA, University of Minnesota, www.ipums.org. See my data visualizations on Tableau Public

## Annotated Data Viz 1

Homeownership and the Jobs Outlook BELOW IS A VISUALIZATION of job growth and homeownership by occcupation. This viz details expected job growth by occupation compared to homeownership rate by occupation. This viz was originally published on Freddiemac.com. This viz shows compares variation in homeownership rates by occupation with expected job growth for those occupations. The size of the bubbles correspond to the expected number of job openings for that occupation.

## How I make my mortgage rates gif

{% include JB/setup Making a data viz SOMETIMES ANIMATION CAN BE USEFUL, though it is often misused. I’ve been tracking the week-to-week changes in mortgage rates, and animating with a GIF. Example animated gif with mortgage rates from 1/1/2013 to 3/10/2016 I build my gif using the R statistical package. Perhaps I’ll explain more of the details later, but the R code below uses the ggplot2, ggthemes and animation packages to create the plots, style them, and save the animation.

## The week (so far) in charts

Mid-week chart update THERE HAVE NOT been a lot of data releases this week, but that’s no excuse not to get busy charting. I tweeted out several charts so far this week. Here’s a recap of my favorites for this half-week. Are wages increasing…or is it merely a trick of the light? We got this week’s charting started with some data from last week. The jobs report came out last Friday (see our discussion from last week) .

## The week ahead: housing starts and housing market index

{% include JB/setup The week ahead Next week there are several data releases but the two that I’m paying especially close attention to are the NAHB/Wells Fargo Housing Market Index (HMI) and Housing Starts, part of the New Residential Construction joint release by Census and HUD. Will builders maintain their sunny outlook? The HMI is a diffusion index based on survey questions about homebuilder’s attitudes. Values of the index above 50 indicate that on balance, more respondents feel positive than negative about the current conditions in and direction of the single-family housing market.