On Friday a colleague showed me an interesting chart, a map of maps. I believe the original was made in Tableau, but I decided to spin one up in R. I tweeted out the picture: A map of maps, showing the correlation between state house price growth rates You see pretty strong spatial correlation, with some interesting exceptions. Florida correlated with AZ, NV pic.twitter.com/9hzwZLkb41 — 📈 𝙻𝚎𝚗 𝙺𝚒𝚎𝚏𝚎𝚛 📊 (@lenkiefer) March 6, 2020 In this post I will supply the R code to make one.
Let us take a look at house price trends in the United States and across states and metro areas. Earlier this week I tweeted out a few charts on housing market trends. In most of the middle part of the country over the past 44 years there has been little growth in real (inflation-adjusted) house prices. In coastal states, a very different story. pic.twitter.com/PLbiNftha3 — 📈 𝙻𝚎𝚗 𝙺𝚒𝚎𝚏𝚎𝚛 📊 (@lenkiefer) July 10, 2019 In this post we’ll analyze real house prices since 1975, and per usual use R to wrangle data and make plots.
Updated May 28, 2019 I’m giving a seminar about my new working paper “What Happens in Vegas Doesn’t Always Stay in Vegas”“. The slides for the talk are posted below. I made the slides with R and the xaringan package. You can easily print the html to pdf with Chrome. The pdf version is below and available at this link. Long seminar slides .html or [.pdf](../../../../img/charts_may_22_2019/what happens in vegas preso long.
I’ve got a new working paper with Hua Kiefer (FDIC) and Diana Wei (OCC) that studies the dynamics of house prices and foreclosure rates across space and time. We estimate a model using a panel of state/quarters where nearby states influence one another. Link to paper (pdf): What Happens in Vegas Doesn’t Always Stay in Vegas Note Updated May 17, 2019 I’m giving a talk on this paper at the American Real Estate and Urban Economics National Conference later this month.
Recently the U.S. Census Bureau released updated population estimates through 2018 for the United States, states, counties, and metropolitan statistical areas (MSA). Press release I tweeted out the following chart comparing house prices and state population dynamics. demographics are an important driver of #housing market trends. here's a comparison of growth in state population and nominal house prices since the year 2000 left to right: more people bottom to top: higher home prices pic.
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.
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.
This tweet turned out to be popular: 👀house price trends👀 pic.twitter.com/JXB5P0H84A — 📈 𝙻𝚎𝚗 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.
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).
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.
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.
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.
LAST YEAR WE TOURED recent house price trends Post. Let’s update the data visualizations with data through December 2017. We are going to show house price trends using data from the publicly available Freddie Mac House Price Index. Animation: Here’s an updated animation showing trends in the top 20 metro areas, based on population. Below, I’ll plot stills for each of the 20 metro areas. Later I’ll follow up with some additional visualizations and some commentary.
EARLIER TODAY THE U.S. CENSUS BUREAU released new estimates of population for U.S. states from 2010 through 2017. Let’s see how population trends look compared to recent house price growth. We’ll combine the Census estimates for state population with the Freddie Mac House Price Index. The chart below plots population growth rates by state from July of 2010 to July of 2017 against house price appreciation rates over that same period.
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.
LET US REVIEW HOUSING MARKET TRENDS in the United States through the first three quarters of 2017. Economic background The overall economic environment remains favorable for housing. Interest rates are low, the labor market has been solid and income growth, while modest, has begun to tick up. Low mortgage rates For most of 2017 mortgage rates have declined. Rates entered the year above 4 percent for the 30-year fixed rate mortgage, but after peaking in March, declined through September.
IT IS TIME FOR AN UPDATE ON HOUSE PRICE TRENDS AROUND THE UNITED STATES. I have been experimenting with some new visualizations and updating some old favorites. Let’s collect them here. This post will be an extension of my Visual Meditations on House Prices series from last year. Check out those posts for additional visualizations. Data We’ll use the recently updated Freddie Mac House Price Index (link to source) data and use R to create some plots.
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.
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.
## '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.
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.
IN THIS POST I WANT TO SHARE SOME R data wrangling strategy and use it to prepare an update to some global house price plots I shared last year. In last year’s post I did some data manipulation by hand and mouse in Excel before getting into R. In this post I’m going to use the newly updated readxl library to do the data manipulations entirely in R. If you follow along, then you should be able to use this code to recreate my graphs.
IN THIS POST I AM going to share some useful code to create some custom plots using the St Louis Federal Reserve Economic Database (FRED). While the FRED page has some nice chart customization options, I’m going to import the data into R with the quantmod package and draw the plots. I find myself doing these types of things often enough that I thought you might find these bits o’ code useful.
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.
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.
Introduction ON FRIDAY I MADE SOME ACCIDENTAL data art. I ended up with something pretty much useless, but kind of pretty. I shared it on Twitter: made an unintentional sorta streamgraph #graphfail pic.twitter.com/uW4IOnbrcL — Leonard Kiefer (@lenkiefer) December 1, 2016 We’ll see if we can redeem this graphfail in another Visual Meditation on house prices. Visual Meditations I’ve been collecting various graphical thoughts about house prices in my Visual Meditations series.
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.
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).
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:
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.
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.
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.
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.
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.
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: