In this episode of the REconomy Podcast™ from First American, Chief Economist Mark Fleming and Deputy Chief Economist Odeta Kushi discuss how housing affordability has shifted since the early spring and what that means for potential first-time home buyers.
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“Since April, mortgage rates have trended even higher, further reducing house-buying power and dampening demand. And, as the housing market cools, house price growth is likely to moderate as potential buyers pull back from the market, allowing supply and demand to rebalance. But, in the interim, there are more affordable cities, such as Buffalo, New York; Pittsburgh; Oklahoma City; and Detroit that do provide renters the greatest opportunity to purchase a home.” – Odeta Kushi, deputy chief economist at First American
Odeta Kushi - Hello and welcome to episode 45 of the REconomy podcast, where we discuss economic issues that impact real estate, housing and affordability. I am Odeta Kushi, deputy chief economist at First American and here with me is Mark Fleming, chief economist at First American. Hey Mark. In today's episode, we're once again letting the genie out of the bottle.
Mark Fleming - Hi Odeta. Oh, oh, do we get three REconomy wishes? Oh wait, this isn't about Aladdin is it?
Odeta Kushi - Sadly, no REconomy wishes. In today's episode, we're going to update our listeners on a measure that we've discussed before on this podcast -- refer back to episode nine and episode 30 for more -- and that would be our First-Time Home Buyer Outlook report. And, if you haven't heard our earlier podcasts on this, we will provide a little refresher on what genie has to do with it.
Mark Fleming - That's going to make a lot more sense. The Gini coefficient is, I think, what you're talking about. Our First-Time Home Buyer Outlook report was initially inspired by something called a Gini coefficient, not Aladdin. This Gini coefficient is a measure of the distribution of income across a population and was developed by the Italian statistician Corrado Gini in 1912. Yes, a little history here, of course. It's typically represented graphically with a thing called a Lorenz curve. The Lorenz curve was developed by Max O. Lorenz, in 1905, for representing the inequality of the wealth distribution. But the thing is, we can use the Lorenz curve and these Gini coefficient concepts to demonstrate unequal distributions in any kind of system. And, of course, we're interested in the housing market.
Odeta Kushi - That's right, and I appreciate your attempt at an Italian accent. But the reason we're talking about this analysis, again, is because it's a great way to measure affordability, not just for the median buyer, but for any buyer in the income distribution. And, as we'll talk about in a bit, that matters, especially now that affordability has declined. But first, how are we measuring affordability in this analysis?
Mark Fleming - Well, I remember when we were conceptualizing this. Listeners, that's the official economics term for doodling on a whiteboard. And I believe we started talking about how conventional measures of affordability that compare house prices to income didn't differentiate between the renters income and a homeowners income.
Odeta Kushi - And we know from the data that there's a pretty big gap between the two. According to 2021, ASEC data, which is actually reflecting 2020 household incomes, the median renter had a household income of just over $45,000, whereas the median homeowner had a household income of just over $82,000. And, why would we use the median homeowner’s household income anyway? By definition, the homeowner is already able to afford a home.
Mark Fleming - Right, because they're already homeowners. The key to understanding housing affordability is determining whether a renter can afford to purchase a home, or more precisely how many homes they can afford, if any at all.
Odeta Kushi - That second part that you mentioned is the other big piece of this analysis. It's not just how much the renter can afford to buy, because the renter may have a house-buying power of let's just say $200,000. But, if there are no homes priced at $200,000 or below for that renter to buy, then what's the point?
Mark Fleming - Exactly, and we know that it's those lower price starter homes that are the toughest to find. So, rather than measure affordability based on overall income house-buying power compared to the median priced home, we measure the share of homes for sale that that a median renter's house-buying power can afford.
Odeta Kushi - Exactly, so let's dive right into the latest results. By the way, we published a blog post on August 10 that walks through these results that can be found on our Economic Center at firstam.com/economics. If you want to dive deeper into the data, take a look at our Data Center and make sure to click on the First-Time Home Buyer Outlook report. And, just to make the methodology completely clear, we can determine the first-time home buyers house-buying power using the median renter's income, the prevailing 30-year, fixed mortgage rate and the assumption that 1/3 of the first-time home buyer's pre-tax income is used for a mortgage with a 5% down payment. The share of homes for sale in any given market that are affordable for the median renter is estimated by comparing data on home sale transactions from First American Data & Analytics to the median renter's house-buying power. All right. Well, without further ado, Mark, what did the most recent report tell us about the state of affordability?
Mark Fleming - Well, I think, and not surprisingly, it wasn't great news from April of last year in 2021 to April of this year, double-digit house price appreciation chipped away at affordability as the pandemic-driven supply and demand imbalance fueled competition for a limited inventory of homes for sale. We've talked a lot about that on this podcast. Meanwhile, the average 30-year, fixed mortgage rate increased from 3% in April of last year to 5% in April of this year, and as a result, the median U.S. renter, who can also be considered the median potential, first-time home buyer could afford 35% of homes for sale nationally in April of 2022, which is down from 54% a year earlier.
Odeta Kushi - That's a pretty steep decline. And it was coming from an increase in prices and a reduction in house-buying power double whammy. But, as we know, real estate is all about location, location, location, and there are pockets of the country where affordability is higher. In our analysis for a housing market to be considered affordable, the median renter should be able to afford 50% or more of the homes for sale. That sounds fair, right? So I'm gonna go ahead and start because the city where the median renter could find the largest supply of affordable homes was Buffalo, New York. Go Bills. I grew up in Western New York, so I was happy to see Buffalo top that list again. In Buffalo, the median renter with a household income of $52,000 could afford 78% of homes for sale. Alright, what's next on the list?
Mark Fleming - I just gotta say Buffalo, New York - eight, almost eight out of every 10. That's pretty impressive. That's a lot of choice, which is good. Next up Pittsburgh. In Pittsburgh, 70% of homes were affordable to the median renter, followed by Oklahoma City at 62%, Detroit at 61%, and Cincinnati at 60%. Do you see a theme for where these markets tend to be?
Odeta Kushi - Yeah, a little bit. And you'll see another theme when I talk about the most expensive markets. The most expensive market on our list was LA, where the median renter had a median house-buying power of about $330,000. But the median sale price in LA was $925,000. So, the median potential homebuyer could only afford about 1% of homes for sale.
Mark Fleming - That's rough. And I would never have guessed LA. I'm shocked about that.
Odeta Kushi - Oh, shocking.
Mark Fleming - So shocking. But the theme again - right behind LA is San Diego, where the median renter can only afford 3% of homes for sale in April of 2022. 1%, 3%, that's basically the same - hardly anything.
Odeta Kushi - Yeah, these coastal markets a lot more expensive. But let's go back to the beginning. What does any of this has to do with the Gini coefficient and Lorenz curves?
Mark Fleming - Ah, yes, that's right, Ginis and Lorenz's. The way we depict rental affordability using a Lorenz curve and - bear with me listeners, we're going to try and explain it verbally here - the x-axis in this chart is the house-buying power percentile. If you're the Bill Gates of renters, you're on the far right of the axis as the 100th percentile renter. If, on the other hand, you're on the opposite side, you're on the lower end of the house-buying power distribution because your income is lower. The y-axis is the share of homes that are affordable at each point for that income distribution. If your on or above the 45-degree line - what we call the line of equality - then your market is affordable, but below that and your unaffordable. In other words, you can afford to buy less than the percentile of your position on the x-axis of income distribution. We have to say here, since a picture's worth 1,000 words, and these happen to be spoken podcast words. We encourage our listeners to go to our First American Economics Data Center to interact with this data and see these curves for themselves here. You can look at the full distribution for each of the top 50 markets in the United States.
Odeta Kushi - So, if you picture an S shape around that 45-degree line, you will have a market where housing is less affordable for lower income renters, but more affordable for high income renters. The median renter in this scenario can afford half of all homes for sale. So let's look at a couple examples in our least affordable market LA, even a very wealthy, wealthy renter in the 80th percentile with a household income of about $119,000 can only afford 20% of homes for sale. Moving down that curve we find that the 30th percentile renter in LA making about $35,000 cannot afford any homes for sale, you have to get to the 45th percentile renter before being able to afford any homes for sale in LA. Now, juxtapose that to Oklahoma City, where the 80th percentile renter can afford 87% of homes for sale when they're making about $82,000 per year. The 30th percentile person can afford 26% of homes for sale. I agree I was just looking at my home market, Washington D.C. Interestingly, in this market, it isn't until the 74th percentile renter that housing becomes affordable. So, that 74th percentile renter has a household income of about $144,000, which translates to a house-buying power of $768,000 in April. That 74th percentile renter can afford the 74th percentile home that costs about $763,000. However, everyone below that 74th percentile could afford less than their share, and everyone above could afford more. But why does any of this matter anyway?
Mark Fleming - It's because it's really important to highlight that home prices alone do not make or break affordability. Instead, affordability is much better defined as the share of homes for sale that are within someone's house-buying power.
Odeta Kushi - That's a great point. And, we all know that since April, mortgage rates have trended even higher, further reducing house-buying power and dampening demand. And, as the housing market cools, house price growth is likely to moderate as potential buyers pull back from the market, allowing supply and demand to rebalance. But, in the interim, there are more affordable cities, such as Buffalo, New York; Pittsburgh; Oklahoma City; and Detroit that do provide renters the greatest opportunity to purchase a home. And that is it for today. Thank you for joining us on this episode of the REconomy podcast. If you have an economics-related question you'd like us to feature on a future episode, you can email us at economics@firstam.com. We love to hear from our listeners. And, as economists, you know, we love our metrics and data. So, if you enjoy listening to the podcast, please make sure to give us a rating on your favorite platform. And, as always, if you can't wait for the next episode, you can follow us on Twitter. It's @OdetaKushi for me and @MFlemingEcon for Mark. Until next time.
This transcript has been edited for clarity.