mortgage

Yesterday I gave a virtual talk at JSM 2021 on Visualizing Economic and Financial Market Trends in Volatile Times. Here is a summary of my remarks. The COVID-19 pandemic and associated economic slowdown has led to unprecedented volatility in many economic and financial time series. With swings well outside of historical ranges, many forecasting models break down. Data visualization techniques are a powerful tool to help the analyst understand evolving economic and financial market trends.

Seasonally adjusted greetings to you and yours. For you I have an animated chart, a variation on our rate cloud with a wintry theme. R code below. We’ll grab mortgage rate data, make a few new variables and then plot the chart using ggridges::geom_density_ridges2. Using the raincloud option for the poisition argument in ggridges::geom_density_ridges2 places the individual data points below the density plots. Using various shades of white we can turn the rain cloud into a snow cloud.

For several months now, I’ve been working on a new research paper with Sumit Agarwal, Souphala Chomsisengphet, Hua Kiefer, and Paolina Medina studying refinance activity this year. When we started the project back in the spring we were not sure that the pandemic would allow households to refinance at rates similar to prior periods. It seemed possible that the pandemic would disrupt the mortgage market and make it difficult for households to take advantage of historically low rates.

We are entering the homestretch for 2020, with just about two months left. It has been an intense year, with many twists and turns. Given all the uncertainty many people have been asking me to share my perspective on the outlook for US housing and mortgage markets. I’ve given many talks, in this article I’m laying out the basic view that forms the core of my recent presentations. The COVID-19 pandemic and the associated recession are of course the dominant drivers of the outlook.

Earlier today I tweeted out an update of our skyline mortgage rate chart. Gray Kimbrough (follow him on Twitter to get all your Millennial Myths busted) pointed out that my chart style was close to famous art style of Joy Division’s 1979 album Unknown Pleasures. That was exactly right, because I arrived at the skyline mortgage rate chart by tweaking my original application of ‘ggridges::geom_density_ridges’ (formerly known as Joyplot) code to arrive there.

Yesterday I gave a virtual talk at Gonzaga University hosted by Ryan Herzog. Below are the slides I used, here is a link (pdf).

This week the Freddie Mac Primary Mortgage Market Survey reported a record low for the U.S. weekly average 30-year mortgage rate. First some charts, then below I post R code. R code Load libraries library(fredr) library(tidyverse) library(patchwork) library(cowplot) library(gganimate) library(lubridate) # updated You’ll need a custom color scale (see below for code). R code to wrangle data data preparation code source(paste0(mydir,"len_color_scales.R")) #custom color scale code copied below fredr_set_key("YOURKEY") df <- fredr(series_id = "MORTGAGE30US", observation_start = as.

Mortgage interest rates have moved about a percentage point lower from where they were a year ago. The housing market seems to have responded favorably. On my way into D.C. the other day to do some business, I joined a Twitter exchange originally between [at]Graykimbrough and Adam Ozimek, [at]ModeledBehavior about the effects of Federal Reserve interest policy on the housing market. Seems unlikely housing market was slowed by trade war.

Today I tweeted something that turned out to be pretty popular: US consumer prices pic.twitter.com/LxIxvEnGFe — 📈 Len Kiefer 📊 (@lenkiefer) October 10, 2019 This was an update to a visualization we have talked about here. In this post I want to provide some updated R code to make this visualization taking advantage of the new gganimate api. I’ll also share some code for mortgage rate plots. The code for the inflation plot is pretty simple.

The U.S. housing market stalled out a bit in 2018 and we aren’t building enough homes to match demand. See my recent speech for details on what’s going on. Abbreviated version: in 2018 mortgage interest rates slowed housing activity, but demographic forces support housing demand and should provide a lift in years to come. Together with a recent moderation in mortgage rates there’s reason to be optimistic about housing market activity in 2019.

I went up to New York and spoke with Barry Ritholtz on his Masters in Business podcast. Some links: The podcast A transcript Bloomberg View: Every Graph Tells a Story I am really glad I got the chance to chat with Barry and share some of my story. Have a listen if you want to learn more about my work and background, the mortgage finance industry, and how I use data visualization.

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.

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.

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.

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.

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:

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).

I AM WORKING ON ADDING some more analysis around mortgage origination trends (see here for a high level summary). It’s on the way, but let me just leave a few graphs for you. These are updated versions of the same ones we made last year. These infographics show the distribution of mortgage loan amounts by state/county and metro area. For the beeswarm plots (see for example, Flowing Data) plots I have randomly sampled 2,000 loans from each state/metro area.

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.

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.

rstats data visualizations of housing data

R statistics dataviz housing mortgage data

R statistics rstats mortgage rates dataviz

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:

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

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

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. Rates in the week of May 12, 2016 were the lowest in three years.

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) reportedon existing home sales (EHS) on Monday. Existing home sales for February surprised most by dropping 7.

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 reportedon 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. One state I’ve been focused on is Texas due to its reliance on oil and gas drilling.

{% 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.

{% 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 Constructionjoint 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.