Housing

U.S. housing supply and demand

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.

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.

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.

U.S. housing starts are still super low

I try not to use too much jargon (jargon monoxide can be deadly) on this blog. But I’ve got a bit of a technical term I’ve been using the describe U.S. residential construction: super-low. To be sure, housing construction has been grinding higher, but it’s been taking a while for activity to get back close to historical averages. Once you account for the larger population, which all else equal needs more housing units, the level of construction is quite low.

Housing in the Golden State

I am headed out west, to California to talk housing at the Western Secondary Market Conference. After my talk they might post my slides online somewhere. If they do I’ll link to them, but for now you can get a preview in this twitter thread. Like many western states, California is facing a imbalance between housing supply and housing demand. Strong economic growth has bolstered demand, but supply has not kept up.

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.

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”.

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. I’ll also share some links to posts where I’ve shared R code on how to make the particular plots we discuss.

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.

State population growth and house prices

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.

Housing construction and employment trends

THE UNITED STATES IS NOT building enough homes to meet demand. Be sure to check out my upcoming presentation at Realtor University to learn more about whether or not this could mean a house price bubble. One reason often cited for low levels of construction is a lack of labor. How do construction trends compare to construction employment? Let’s take a look. Financial blogger Logan Mohtashami (Twitter, blog) tweeted out (he’s a power Twitter user so I’m not sure exactly when or where, so I probably saw it multiple places) an interesting observation on housing construction and employment (see blog post).

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:

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.

Recent trends in U.S. housing markets: 2017Q3 update

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.

Home sales in expansions and recessions

LET’S LOOK AT NEW HOME SALES. Today the U.S. Census Bureau joint with the Department of Housing and Urban Development (HUD) released new home sales estimates through September of 2017. New home sales have been grinding higher along with housing starts, though they dipped last month (maybe). This month’s report was the strongest since 2007, as I tweeted earlier today: New home sales in September highest since 2007. report: https://t.co/IDHMLCkY0D pic.

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.

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.

Mortgage origination trends

IT IS SEPTEMBER AND THAT MEANS it is data release season. One of the most important September data releases for me is the annual HMDA data release. These data provide the closest thing to a publicly-available comprehensive summary of U.S. mortgage market activity that we’ll get (for right now). The recently released data is for 2016 and provides a detailed view of mortgage market activity across the country. Let’s take a look.

New home sales fall (maybe)

NEW HOME SALES FALL according to the latest new residential sales report from the U.S. Census Bureau and Department of Housing and Urban Development (HUD). errr probably. Remember, housing data is uncertain and there’s quite a large margin of error. Per the Census/HUD report sales fell 3.4 percent, but with a confidence interval of plus or minus 13 percent. Here’s a chart with the line showing the estimate and the shaded area the confidence interval around that estimate.

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.

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.

A (Tidyquant)um of solace

LET’S WRANGLE SOME HOUSING DATA. We’ll try something different with how posts are organized. In the past I have generally mixed data wrangling, R code and graphs all in one post. Now I’m going to break it up. Posts like yesterday will just show some data and discuss it. Then, if the data wrangling or code is complicated enough I’ll follow up with another post with details. You’ll be able to find all my posts on data wrangling, under the data wrangling tag and R code under the R tag.

If housing inventory is so tight, why are so many homes vacant?

LET US REVIEW SOME INTERESTING TRENDS IN HOUSING VACANCIES for the United States. Earlier this year we talked about how limited housing supply was helping to drive accelerating house prices across the country. In such an environment you would expect to see housing vacancies decline. Indeed, if you look at the rate of rental or homeowner vacancies you see a substantial reduction. The chart below shows the homeowner and rental vacancy rates reported by the U.

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.

Gather round and spread the word: Wrangling global house price data

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.

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.

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.

Visual meditations on house prices, Part 7: Don't cross the streams!

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.

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.

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.

Brexit, State of the Nation's Housing, and home sales: the week in charts.

IT WAS A BUSY WEEK FOR ECONOMIC AND HOUSING DATA. Existing and new home sales came out, the Joint Center for Housing Studies of Harvard University released their annual State of the Nation’s Housing, and the U.K. voted to leave the European Union (the Brexit). We’ll recap these data and events through charts I’ve created and shared throughout the week. In this post, I’ll share each of the charts with some commentary, and then below, I’ll include the R code I used to generate the charts.

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

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.

Vacant housing: from surplus to shortage

EARLIER THIS WEEK the Census Bureau released the latest Housing Vacancy Survey (HVS) data for the first quarter of 2016. Much attention went to the homeownership rate estimates, which showed a decline in homeownership rates near a 48-year low. The gif below shows the history of the homeownership rate as estimated by the HVS. And here’s a still image of the same data: Vacancy rates In addition to the homeownership rate, the HVS data contained estimates of both the homeowner and rental vacancy rates.

The week that was in charts

THIS WAS A BUSY WEEK for housing data. On Monday, the NAHB released the NAHB/Wells Fargo Housing Market Index (HMI), which tracks home builder sentiment. On Tuesday we got New Residential Construction from Census/HUD which gives us housing starts and permits. On Wednesday we got Existing Home Sales (EHS) from the National Association of Realtors. And on Thursday (along with mortgage rates), we got the FHFA House Price Index.

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.

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.