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Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.

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April 17, 2020

Southeast Housing Market and COVID-19

Considering the devastating effects the COVID-19 pandemic is having on the economic landscape, we can think of no better time to revive our Real Estate Research blog. Atlanta Fed staff, often in partnership with researchers at other Reserve Banks, are continually working to track and assess COVID-19-related developments as they unfold in the housing and mortgage markets.

With the relaunch of this blog, we intend to regularly publish posts, like this one, with our commentary, observations on recent data releases and survey results, analysis of recently published academic research, findings from working papers we find relevant and interesting, and takeaways from conferences and guest speakers when those events become possible again. We aim to foster dialogue on topical research and current issues in the field with each post we publish and look forward to your active participation and feedback.

Taking the pulse of the housing market
To gauge the impact COVID-19 is having on housing in the Sixth District, we redeployed our Southeast Housing Market Poll from March 26 to April 2 to existing homebuilder and residential sales agent contacts. To get a reference point for comparison, the poll included a combination of routine questions we've posed in the past. It also included special questions to gather more detailed insights on the unfolding situation. This post highlights the results of the poll.

Two-thirds of Southeast builder respondents indicated that home sales came in below their plan for March 2020 and were down slightly from the year-ago level. The majority of broker respondents, on the other hand, said home sales were in line with their plan for the period and were, on balance, flat from the year-earlier level.

Real Estate Research blog - Chart 1: March 2020 Southeast Home Sales vs. Year Ago

Builders and brokers both reported declining buyer traffic relative to one year earlier. As the results of the special questions will emphasize (see below), buyer traffic appears to be the dimension of business that COVID-19 has most adversely affected through March.

Real Estate Research blog - Chart 2: March 2020 Southeast Buyer Traffic vs. Year Ago

The majority of builders reported that inventory levels remained flat from the year-ago level, while brokers said inventory levels were down. Most brokers and builders reported that home prices either held steady or were up slightly in March. Builders and brokers also indicated that the amount of available mortgage credit was sufficient to meet demand.

Just over half of southeastern builders indicated that construction activity during March 2020 fell short of their plan for the period, with the majority reporting that construction activity was flat to down slightly compared to the year-ago level.

Real Estate Research blog - Chart 3: March 2020 Southeast Construction Activity vs. Year Ago

Most builders said material prices had increased from one year earlier, but were flat relative to the month-earlier level. More than four-fifths reported some degree of upward pressure on labor costs, and half of pointed to modest increases in labor costs of just 1–3 percent from the year-ago level. The majority of builders indicated that the amount of construction and development finance available was sufficient to meet demand.

With regard to the outlook, all Southeast broker respondents said they expect home sales activity to decline over the next three months. Most builders expect construction activity over the next three months to slow considerably as well.

Real Estate Research blog - Chart 4: March 2020 Southeast Outlook vs. a Year Earlier

Results from special questions on COVID-19
Consistent with results from surveys fielded by the National Association of Realtors (like this one) and the National Association of Home Builders (like this one), many Sixth District brokers and builders reported that they had already felt the effects of COVID-19 on their business at the end of March 2020 (see the chart). Buyers' ability to secure financing was the aspect least affected across the Sixth District, followed by the amount buyers were willing to pay for the home. Buyer traffic appeared to be the most significant adverse impact thus far.

Real Estate Research blog - Chart 5: To date, has COVID-19 had a noticeable, adverse impact on the following aspects of your business?

We posed the same set of special questions to brokers and builders, but this time asked them to look three months ahead and anticipate the impact of COVID-19 along the same dimensions. Perhaps not surprisingly, the majority of brokers and builders expect the adverse effects of COVID-19 to worsen over the next three months across all dimensions of their business, but especially as it relates to buyer traffic and the number of offers that come in.

Real Estate Research blog - Chart 6: In the next 3 months, do you expect for COVID-19 to have a noticeable, adverse impact on the following aspects of your business?

We'll continue to keep an eye on the housing situation as it unfolds and report back periodically with updates. In the meantime, feel free to share observations from your local market or resources you are using to the track the situation in the comments section below.

This poll was conducted March 26 to April 2, 2020, and reflects activity in March 2020. Thirty-seven business contacts across the Sixth District participated in the poll: 19 homebuilders and 18 residential brokers.

November 3, 2015

Keeping an Eye on the Housing Market

In a recent speech, Federal Reserve Bank of San Francisco President John Williams suggested that signs of imbalances were starting to emerge in the form of high asset prices, particularly in real estate. He pointed out that the house price-to-rent ratio had returned to its 2003 level and that, while it may not be at a tipping point yet, it would be important to keep an eye on the situation and act before the imbalance grows too large. President Williams is not the only one monitoring this situation. Many across the industry are keeping a watchful eye on the rapid price appreciation (see here, here, and here), including my colleagues and me at the Atlanta Fed.

While it is too soon to definitively know if a bubble is forming, the house price-to-rent ratio seems like a relevant measure to track. Why? Basically, because households have the option to rent or own their home, equilibrium in the housing market is characterized by a strong link between prices and rents. When prices deviate substantially from rents (or vice versa), the cost-benefit calculus in the rent-versus-own equation changes, inducing some households to make a transition. In effect, these transitions stabilize the ratio.

In an effort to better understand house price trends, we chart the house price-to-rent ratio at an annual frequency on top of a stacked bar chart depicting year-over-year house price growth (see chart below). Each stacked bar reflects the share of ZIP codes in each range of house price change. Shades of green indicate house price appreciation from the year-earlier level, and shades of red indicate house price decline. The benefit of considering house price trends through the lens of this stacked bar chart is, of course, that it provides a better sense for the distribution of house price change that is often masked by the headline statistic.

Looking at these two measures in concert paints an interesting picture, one that doesn't appear to be a repeat of the early 2000s. While the house price-to-rent ratio indicates that house prices on a national basis have been increasing relative to rents, the distribution of house price change looks a bit different. In 2003, roughly 20 percent of ZIP codes across the nation were experiencing house price appreciation of 15 percent or more on a year-over-year basis. In 2014 and 2015, less than 5 percent of ZIP codes experienced this degree of appreciation.

chart-1

To better understand the regional variation, we repeated this exercise at a metro level using the Case-Shiller 20 MSAs (see charts below). (House price-to-rent ratios for Las Vegas and Charlotte were not calculated because the Bureau of Labor Statistics does not provide an owners' equivalent rent for primary residence series for these markets.) This more detailed approach reveals that elevated price-rent ratio readings were only present in a few, perhaps supply-constrained, metropolitan areas (see top right corner of each chart for the Saiz supply elasticity measure). Moreover, current home price appreciation across ZIP codes does not have the breadth that was present during the early 2000s.

Notes: (1) All price-to-rent ratios are indexed to 1998, except Dallas and Phoenix, which are indexed to 2002. (2) SE = Saiz's Supply Elasticities. Pertains to city boundaries, not metropolitan areas. For more information, see Albert Saiz, "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics (August 2010) 125

As John Krainer, an economist at the San Francisco Fed, pointed out in a 2004 Economic Letter, "it is tempting to identify a bubble as a long-lasting deviation in the price-rent ratio from its average value. But knowing how large and long-lasting a deviation must be to resemble a bubble is not obvious." We will continue digging and report back when we think we know something more.

Photo of Jessica DillJessica Dill, economic policy analysis specialist in the Atlanta Fed's research department

May 20, 2015

Are Millennials Responsible for the Decline in First-Time Home Purchases?

First-time homebuyers play a critical role in the housing market because they allow existing homebuyers to sell their homes and trade up, triggering a cascade of home sales. While their share of all purchases has remained fairly flat over time (see our previous post on this topic), the number of first-time homebuyers has declined precipitously since the real estate crash. Many think of first-time homebuyers as younger households, and believe millennials are largely behind the decline in first-time homebuying. There are a variety of theories about why millennials have been slow to enter homeownership. One theory says that millennials would rather rent in dense urban areas where land is scarce than buy homes in the suburbs. Another theory blames steep increases in student debt for crowding out mortgage debt and reducing the homeownership opportunities of younger generations. Yet another theory argues that because the recession lowered incomes, younger people can't afford to buy. Finally, underwriting standards tightened after the recession, causing mortgage lenders to require larger down payments and higher credit scores in order to buy a home. Some worry that this more stringent lending environment has raised the bar too high for millennial homebuyers in particular.

We can't examine all these theories in a blog post, but we can examine the validity of the assumption that millennials are driving the decline in first-time homebuyers. We approached this from two angles. We first looked at whether the age distribution of first-time homebuyers has changed, and then we tried to discern patterns in first-time home buying across states. In general, we find that the age distribution of first-time homebuyers has become younger, not older, since the crisis. We also found that the dramatic fall in purchases varies much more strongly across states than by age. The preliminary figures suggest that housing market and local economic conditions may explain as much or more of the decline in first-time homebuyers than a generational divide.

Searching the data for first mortgages
Our analysis is based on the Federal Reserve Bank of New York Consumer Credit Panel/Equifax data. This data set provides longitudinal, individual data, using a 5 percent sample of all persons with a credit record and social security number in the United States.i We examined the age, location, and credit scores of people who bought homes for the first time and looked at how these characteristics changed after the crisis.ii

To identify first-time homebuyers, we flagged the first year of the oldest mortgage for each individual in the credit panel. This reveals the first instance of someone obtaining a mortgage, even if they subsequently buy another home or even transition back to renting. The trade-off is we were able to observe only those who use debt finance, and thereby excluded all cash purchases. While many homeowners do own their homes outright, we expect most first-time buyers and certainly most young buyers to have a mortgage.iii

Having isolated first-time homebuyers in this data set, we looked at their purchasing trends and demographic attributes from 2000 to 2014. In this data set, we found that roughly 1 percent to 2 percent of the population purchased a mortgage-financed home for the first time in a given year. Forty-nine percent to 53 percent had no mortgage (this category combines renters and those who own their homes outright), and 45 percent to 50 percent were paying down an existing mortgage.

Image

Buyers aren't getting older
We found that the number of first-time home buyers fell precipitously after the crash, from 3.3 million a year to around 1.5 million to 1.8 million. However, the age distribution of these first-time homebuyers does not change dramatically, though the median age of actually went down slightly since the peak. If we were to believe that the decline in first-time buyers was driven primarily by younger workers requiring more time to amass a down payment or pay off student debts, then we would expect to see first-time buyers getting older.

First-time-homebuyers-2001-14

We did not see a strong explanation for dramatic declines in first-time homebuyers when we compared younger and older adults. It doesn't appear that millennials are driving the decline. By comparison, when we reviewed the number of first-time home purchases by state, we found very dramatic differences that population alone cannot explain. Unsurprisingly, first-time homebuying fell further in places where the housing crisis hit the hardest.

The chart shows the number and percent change in first-time homebuyers from 2001 to 2011 by state. There is a wide variety in the percent change in first-time homebuyers, with declines as strong as 65 percent in some states and as low as 10 percent in others. North Dakota was the only state to have increases in first-time homebuyers, likely due to the oil industry growth there.

Changes-in-first-time-home-buyers-by-state
(enlarge)

This analysis does have some weaknesses. For one, as we mentioned, it omits cash buyers, who are an increasingly important segment of the housing market, especially in hard-hit states like Georgia and Florida. Also, other research has shown that the transition from renter to owner and back can happen many times in a person's lifetime, and this data set does not control for homeownership "spells" older than one year (see Boehn and Schlottman 2008). Notwithstanding, this analysis suggests that the decline in first-time homebuying is driven not by swiftly changing preferences nor the economic constraints of the younger generation but by regional and local economic conditions. Stay tuned for more, as we plan to look further into how the real estate crisis altered the home purchase decisions of young first-time homebuyers relative to older generations.

Photo of Carl Hudson By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department, and doctoral student, School of City and Regional Planning at the Georgia Institute of Technology, and

Photo of Jessica DillJessica Dill, economic policy analysis specialist in the Atlanta Fed's research department

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i The data is a 2.5 percent sample of all individuals with a credit history in the United States. So, for example, this sample resulted in 636,638 records in 2014, which would correspond to an estimated 254,655,200 individuals with credit records and social security numbers in 2014.

ii We excluded anyone who had an older mortgage in a prior year. Doing so resulted in only a very small percentage of records being excluded.

iii Our approach and results are similar to those cited in Agarwal, Hu, and Huang (2014), who find that the homeownership rate between 1999 and 2012 varies between 44 percent and 47 percent for individuals aged 25—60 using a different time frame and age distribution of the same data set. Because our definition—and that of Agarwal, Hu, and Huang—is unique, it differs from the widely cited homeownership rate published by the U.S. Census Bureau. The rate published by the Census Bureau ranges between 65 and 68 percent for individuals over 25 years old and is calculated by dividing the number of owner-occupied households by the total number of occupied households. Homeownership rates have also been derived using other data. Gicheva and Thompson (2014) derive a homeownership rate using the Survey of Consumer Finance and find the mean homeownership rate to be 61 percent between 1995 and 2010. Gerardi, Rosen, and Willen (2007) used the Panel Survey of Income Dynamics (PSID), which tracks households over time and captures changes in tenure status, to identify home purchasers. They reported a range of 5.6 percent (in 1983) to 9.6 percent (in 1978) of households buying homes in the 1969—99 timeframe.

September 20, 2013

Does the Mortgage Interest Tax Deduction Really Support Upward Mobility?

The mortgage interest tax deduction (MITD), a portion of the tax code that permits homeowners to deduct mortgage interest from their taxes, has widespread popular support. It is the second most common tax deduction, and the largest federal tax expenditure. It is particularly popular among middle class homeowners and the politicians they vote for. But it is often criticized by academics and policy wonks for benefiting well-to-do families who need it least, for encouraging overconsumption of housing, and for subsidizing sprawl. The map below, from a Pew Institute study released last week, shows the spatial distribution of the tax deduction. MITD claimants are concentrated in middle-class suburbs around (but not in) urban areas, where the houses are big and the homeownership rate is high.

Rer-01


A recent white paper released by Chetty, Hendren, Kline, and Saez (hereafter CHKS) from the Harvard/Berkeley Equality of Opportunity Project comes down in support of the MITD. The authors theorize that "these deductions may impact economic opportunity by providing opportunities for credit-constrained middle and low income families to become homeowners." The study finds that the MITD, along with many other tax expenditures, is correlated with higher income mobility. That is, in places where the MITD is larger, children's success in life is not bounded by their parent's wealth or poverty. It seems they are free to succeed or fail on their own merits.

In this blog post, I examine the way the MITD was measured in the CHKS study and propose an opportunity to improve this measurement. Then I re-estimate their results using an alternative metric. Using the CHKS study data set and our alternate measurement, it appears that the regressive nature of MITD expenditures corresponds with reduced mobility overall and has a large, negative association with the mobility of low-income Americans.

CHKS demonstrated that average MITD expenditures have a positive association with intergenerational mobility, but they were unable to find an effect when evaluating the progressivity of the MITD. This is probably because the metric was noisy. The authors calculated the progressivity of the MITD by subtracting MITD over adjusted gross income (AGI; MITD/AGI) for the top and bottom income cohorts. By this measure, the MITD looks progressive: as a percentage of AGI, the wealthy take a deduction that is, on average, 8.6 percent lower than the deduction taken by the poor.

Rer-02

But is this what the data are really telling us? Below is a chart showing the sum of all mortgage interest tax deductions in blue and the tax deduction as a percentage of adjusted gross income in green. We can see that the bulk of the deduction goes to people making between $100,000 and $200,000 a year.

Rer-03


Looking at the tax deduction as a percentage of income (in green), we can see that by just comparing the tails, the deduction looks progressive. Those with an adjusted gross income of less than $10,000 a year deduct a much higher percentage (15 percent) of their AGI than those who make $200,000 or more a year (just 3 percent). After the tax expenditure, the income distribution should be flatter than before. However, if you look at the middle of the distribution, the opposite story is true. The tax deduction is regressive—income brackets with higher incomes claim higher deductions as a percent of income. After the policy, the income distribution is more unequal than before.

Why do the tails tell a different story than the middle of the distribution? And why do people in the lowest income category—who typically don't own homes and can't qualify for a mortgage—have the highest percentage of deductions? Well, as it turns out, according to statisticians at the Internal Revenue Service (IRS), the bottom bracket is inflated with a number of wealthy folk who declare high losses to reduce their adjusted gross income. This explains why, for example, in well-to-do communities like Coral Gables, Florida, or Nantucket, Massachusetts, the average person with an AGI of less than $10,000 a year deducts close to 100 percent of that in mortgage tax interest.

The top bracket, by contrast, seems understated. Per filer, this group claims a much larger deduction than any other group. But because this bucket includes the Bloombergs and Buffetts of the world, this high MITD registers as just a small percentage of even higher AGI.

Because the data in the bottom income bracket is noisy, and the top bracket is skewed by those with extremely high income, it makes more sense to calculate the progressivity of the MITD by comparing the second-highest and second-lowest income cohorts. Below is a map of the results.


What happens if we plug this new metric into the CHKS study data? The initial results show that where the MITD is more regressive, parents' income is a better predictor of children's income, and mobility is lower. The results also show that a more regressive MITD corresponds with steep declines in mobility for low-income Americans. This finding makes sense, given that the benefit bypasses low-income homes, either because they are not homeowners or because they do not make enough money to itemize deductions.

Rer-05

It's important to note that the results are correlational, not causal. But if they have any interpretation at all, they suggest that the overall regressive structure of the MITD may be reducing equality of opportunity and making it harder for low-income families who do not own homes to achieve the American Dream.

By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department, and doctoral student, School of City and Regional Planning at Georgia Institute of Technology