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.
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?
Last week the U.S. Commerce Department reported the advance estimate for annual economic growth for 2020. For the full year, US gross domestic product contracted 3.5 percentage points, the largest annual decline since the 11.6 percentage point contraction in 1946. The similarities pretty much end there. In 1946 the US economy was demobilizing after finishing the fight against fascism. Factories were shuttered that had been producing munitions, fighter planes, tanks and liberty ships, while the G.
Earlier today I tweeted out a chart of house prices using my inari color theme. House price growth lifting off pic.twitter.com/TvNJcOZrTF — 📈 Len Kiefer 📊 (@lenkiefer) January 26, 2021 Below is the R code to generate the plot. # load libraries library(tidyquant) library(tidyverse) # list of FRED Tickers tickers<- c("LXXRSA","SPCS20RSA","LVXRSA","SEXRSA", "SFXRSA","NYXRSA","BOXRSA","SDXRSA","CHXRSA", "DNXRSA","PHXRNSA","DAXRNSA","WDXRSA", "ATXRNSA","MIXRNSA","POXRSA","MNXRSA","DEXRNSA","TPXRSA","CRXRSA","CEXRSA") # list of city names cities <- c("Los Angeles","20-city","Las Vegas","Seattle", "San Francisco","New York","Boston","San Diego","Chicago", "Denver","Phoenix","Dallas","Washington DC", "Atlanta","Miami","Portland","Minneapolis","Detroit","Tampa","Charlotte","Cleveland") df.
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.
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.
I’ve been thinking about distributional forecasts. In particular I’ve been considering Quantile Autoregressions (QAR) as defined in KOENKER AND XIAO 2006. There are some handy lecture notes I’ll borrow from at this link (pdf) in the exercise here. This is all speculative, but I think this might be a useful way to think about the assymetry in likely outcomes given the uncertainty inherent in today’s economic forecasts. Setup Let’s define the QAR(1) model for quantile \(Q(\tau)\),
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.
R statistics dataviz housing mortgage data
rstats data visualizations of housing data
R statistics dataviz remix flexdashboard
R statistics dataviz ggplot2 housing mortgage data
R statistics dataviz housing mortgage data
R statistics rstats mortgage rates dataviz
R statistics dataviz housing mortgage data
R statistics dataviz housing mortgage data
R statistics dataviz housing mortgage data
Charts and graphs exploring house price trends
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).
Charts and graphs exploring house price trends
Charts and graphs exploring house price trends
Charts and graphs exploring house price trends
Charts and graphs exploring house price trends
Charts and graphs exploring house price trends
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 releasedupdated population figures for 2015. These data revealed changes in population across the country. Jed Kolko published a nice summaryof 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: