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 Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
April 14, 2017
Is the Share of Real Estate Sales to Investors Increasing?
In early February, our monthly Construction and Real Estate Survey came back with a few comments that called attention to increasing investor home buying activity in certain Southeast markets.
Central Alabama: "We are as busy in early February as we usually are in May! I heard today that a busload of investors came to town recently because they'd been told [we have] a great cash flow market. Haven't heard that kind of talk since 2005."
Metro Atlanta: "I checked on the percent of homes we sell to investors. The answer is 19% so far this year. That is the highest level since the recession. Actually, that is a little scary because with rate increases and a fall in investor confidence, this section of the market will go away overnight. Before the last recession when we were in the bubble, investors were making up about 50% of the market in some lower-price neighborhoods. Keep an eye on sales to investors; anything over 10% is a little scary to me."
Given that we did not solicit comments on this specific topic, we wondered what these comments may signal. Was investor activity increasing in isolated markets, or was this increase more widespread? We decided to dig a bit deeper in our March poll, using special questions to gather more information on the trend in investor activity. We also turned to other data sources for additional clues. In short, what we found is that, while there may have been an increase in investment activity in some Southeast markets, investor activity does not appear to have increased in a material way at a national level.
In our March 2017 poll, we asked residential brokers and homebuilders to indicate whether home sales to investors had increased, remained unchanged, or decreased over the course of the previous year. While some respondents did see an increase in investor buying in their markets, the majority of builders indicated no change. Broker responses came in mixed. Interestingly, more brokers than builders indicated that investor activity was down.
To get a more complete picture, we also asked our business contacts to describe the distribution of home buyers in their market in the previous month (that is, the shares of sales to first-time buyers, repeat/move-up buyers, and investor buyers). On average, respondents indicated that 13.9 percent of home sales in February 2017 across the Southeast were to investors. Because we asked this question in the past, we were able to compare the response to previous periods. Interestingly, the investor share has trended downward since we started asking this question in 2012, and registered its lowest reading to date.
Out of curiosity, we wondered how our results compared with those from the National Association of Realtors (NAR), which asks a similar question about the share of sales to investors in its monthly survey, the Realtors® Confidence Index. The main difference between our survey and the NAR's survey is that our respondents are limited to the Atlanta Fed's Sixth District while the NAR's respondents are spread across the nation. After plotting our Southeast results on the same axis as the national NAR time series (see below), we found that both series appeared to trend downward over time.
Observing a similar trend in both series provided some assurance that investor activity has not ramped up to the extent that it had prior to the housing downturn. However, it is difficult to say what influence (if any) the results of the ongoing NAR survey had on our panel's responses. To get a third perspective, we turned to the Campbell/Inside Housing Mortgage Finance HousingPulse Tracking survey, a proprietary national-level monthly survey that ran from July 2009 through November 2016. The Campbell survey asked a similar question. This survey also shows a downward shift in the trend in the investor share of all home sales.
While these three separate surveys point to a broadly similar trend of declining investor share of sales, we felt it was important to consider other measures for tracking investor activity. Another potential proxy measure could be the share of flipped properties to home sales. CoreLogic's Insights Blog recently featured a couple of posts (here and here) that highlighted the current state of flipping. The author of the posts, Bin He, defined a flipped property as a property that was bought and sold within a 12-month period. He found "the ratio of flips to sales stands at 4.9 percent in 2016, which is well below the peak value of 7.5 percent reached in 2005." While a property flipper is just one type of investor, this analysis serves as one more piece of evidence that pushes back against the idea that investor activity has picked up in a material way.
To conclude, certain areas around the Southeast may have seen an increase, but investment activity does not appear to have increased in a material way across the nation. Although the measures we refer to above do not necessarily provide an apples-to-apples comparison, they independently but collectively provide some reassurance that investor activity has not returned to where it was at the height of the bubble (which of course is hard to pinpoint exactly because few of these more robust measures date back that far). The hope, of course, is that one or more of these measures would provide some type of signal if investment activity were heading in that direction again.
By Jessica Dill, economic policy analyst specialist in the Research Department
February 13, 2017
Investigating the Trend in Office Renovations
Have you noticed more talk of office property renovations lately? Over the course of the past year, we've been hearing a lot of talk about office renovations from business contacts engaged with the Atlanta Fed's Regional Economic Information Network, as well as reading about more office renovations in our markets (see here, here, and here for just a few of many examples). This motivated our search for data that could help us understand the trend in office renovation activity, particularly as it relates to new office construction.
To our dismay, there was not much readily available data on office renovations. We turned to Dodge Data & Analytics, formerly known as McGraw Hill Construction, which collects project-level data on construction activity across 382 metropolitan markets in the United States. The data set not only tracks nonresidential construction activity by property type and stage of construction on a monthly basis, but also identifies the type of construction (which includes new construction, addition, alteration, and conversion).
Using the construction type variable, we selected all office projects that were marked as additions, alterations, or conversions and created a time series that provides a proxy measure for office renovation starts across the nation. Our time series runs from January 2003 to September 2016. Chart 1 shows the composition of office starts (that is, the share of new office construction starts versus office renovation starts) in any given month to provide insight into the mix of projects over time.
From 2003 through 2008, new office-construction projects comprised a larger share of office-related starts. But since 2008, the majority of office starts have been office renovations. We drilled down a bit further to understand the composition of the office renovations activity and found that alterations accounted for the majority of all renovations captured in this database over the course of the time series.
In addition to examining the makeup of the office-related construction activity, we also sought to understand the level of office construction activity. Ideally, we would have liked to examine the trend in renovations activity in terms of square feet under way because it would have given insight into the volume of activity (that is, size of projects) while controlling for changes in things like cost of land, materials, and labor (which have the potential to distort the project value), thus providing the best apples-to-apples comparison of activity over time. Unfortunately, this was not possible due to data limitations. Specifically, square footage under way is captured only for projects that add to the existing stock of office space (that is, where the construction type is listed as new construction or additions). Since additions make up such a small share of overall renovations activity, square footage data are missing for the majority of the observations.
As a result of this data limitation, we examined the trend in the number of office projects started over time. In chart 2, the blue line represents the number of new office construction starts, and the orange line represents the number of office renovation starts.
New office construction starts increased sharply from 2003 until 2005, when the number of projects leveled off. Office renovation starts increased during the same period, though at a much more modest pace, then also leveled off in 2005. New office construction starts began to drop in the first few months of the recession and fell rather sharply until the middle of 2010, while office renovation starts appeared to continue at a steady pace until halfway through the recession before softening a bit. Both new office construction and office renovation starts flattened out and continued at an unchanged pace throughout the early years of the recovery. New office construction starts experienced very little in the way of an uptick until 2016. Office renovation starts, on the other hand, began to rise at a fairly quick pace starting in 2015.
By creating a proxy measure for office renovations, we were able to take anecdotes from business contacts to the data and confirm that office renovation projects have, indeed, been on the rise over the past year. Stay tuned for our follow-up report, when we will drill down and explore renovations activity across the Southeast in more detail.
November 23, 2016
Commercial Construction Update: Third-Quarter 2016
The Atlanta Fed's Center for Real Estate Analytics conducts a quarterly commercial construction poll to keep a finger on the pulse of the industry as it relates to the performance of the economy. In this post, we will discuss a few of the more interesting results from our third-quarter poll. To view all of the results, please visit the Construction and Real Estate Survey web page.
Pace of multifamily construction appears to be slowing
After several years with most incoming reports indicating that the pace of multifamily construction activity had increased from the year-earlier level, it seemed noteworthy that indications from contacts were much more mixed in the third-quarter report. Half of respondents noted that activity had increased from the year-ago level, but the rest indicated that activity was flat to down.
These reports seem to align with the incoming Census Bureau data on multifamily starts through November 17, which, when aggregated to a quarterly frequency, reveal a slight decline (-6.2 percent) from the year-earlier level.
Available finance perceived to be sufficient to meet demand
Since about the second quarter of 2013, the majority of our commercial construction contacts have indicated that the amount of available commercial construction finance has been sufficient to meet demand. Interestingly, the share reporting that credit was insufficient to meet demand spiked in the first quarter of 2016 and remained high into the second quarter. The reports from our commercial construction contacts seemed to align closely with the results of the April and July Federal Reserve Board's Senior Loan Officer Opinion Survey (SLOOS) on the lending environment in the first and second quarters. The survey suggested that banks had tightened their standards for commercial real estate loans.
Interestingly, the most recent survey results deviated from the SLOOS. The share of contacts in our commercial construction poll that indicated credit was insufficient to meet demand continued to drop in the third quarter despite the fact that results from the October 2016 SLOOS indicated that banks continued to tighten their standards for commercial real estate loans. Granted, our commercial construction poll and the SLOOS pose slightly different questions to different types of respondents, but the divergence in results that have typically trended in a similar fashion seems notable nonetheless.
More hiring on the horizon?
Each quarter, we poll our contacts about their hiring plans. The majority (74 percent) in the third quarter indicated that their fourth-quarter hiring plans entail increasing head count by a modest to significant amount. This increase is more or less consistent with the entire history of responses; most respondents have always indicated their hiring plans were flat to up.
The last time such a large fraction of respondents indicated they had plans to increase their head count was more than two years ago, back in the second quarter of 2014. Since a large share of respondents answered the same way, can this be taken as a signal that hiring will indeed increase in the coming quarter? To investigate, we charted quarterly figures for construction new hires using the Bureau of Labor Statistics' Job Openings and Labor Turnover Survey to get a sense for what happened the last time contacts overwhelmingly indicated they had plans to increase hiring and used markers to call attention to second- and third-quarter figures of 2014.
It appears the number of construction hires did in fact increase between the second and third quarters that year, so perhaps this most recent result will serve as a leading indicator. We will keep an eye on this series to see if there is an increase in the number of construction hires in the fourth quarter of 2016.
June 09, 2016
Construction Lending Update: Have the Banks Finally Opened the Spigots?
When we last blogged about at bank call report data, in June 2014, we found that "aggregate lending remained well below its 2008 peak," but "more than half of banks with a construction lending business line were expanding" their lending. Fast forwarding two years, where does construction lending stand now? We pulled bank call report data through the first quarter of 2016 and found that construction lending has continued to grow, albeit at a measured pace (see table 1).
Of the insured banks with a construction lending business line, 62.2 percent have stepped up their lending relative to the year-earlier level. Not only are there more banks actively lending, but half of these banks increased their lending by at least 11.9 percent.
Despite this seemingly good news, it appears that most banks remain selective about the loans they make, and a few large banks are largely responsible for the increase in aggregate lending. In the first quarter of 2016, the top 20 construction lenders accounted for more than one-third of all construction lending (that is, 0.4 percent of active construction lenders are responsible for 37 percent of all construction lending). To provide some perspective, the top 20 banks accounted for 32 percent of all construction lending in 2005 and 42 percent in 2010. Slicing the data this way suggests that it is not particularly unusual for the top 20 to play such a large role in construction lending and that smaller lenders have made some progress toward recouping the market share of the top 20, though they aren't as active as they were in 2005.
Shifting attention now to the second and third set of columns in table 1, we'd like to point out that call report data in 2010 started breaking down total construction lending data into "Residential 1–4 family construction loans" and "Other loans, all land development and other land" categories. Note that this "Other" category includes construction loans for nonresidential and multifamily properties. While lending in both categories has increased over the past two years, growth has been much stronger for "Residential 1–4 family construction" relative to "Other construction, all land development and other land." Our interpretation of this divergence remains quite similar to our assessment two years earlier: the slower growth in "Other" is likely the outcome of fairly strong growth in multifamily construction lending weighed down by banks' continued reluctance to lend on land and lot development.
While the data seem to indicate that the construction lending spigots have opened up a little over the past two years, it is less clear who is able to access this credit. Bank call report data is aggregated in a way that prevents us from knowing anything about the borrowers. Anecdotally, using our monthly poll of Southeast homebuilders, we have not picked up much in the way of improved access to construction credit (see table 2). The majority of builders in our monthly poll continued to report that the amount of available credit for construction and development falls short of demand.
About a year ago, we asked our builder respondents to self-identify as small, medium, or large. By tagging respondents with a size, we've been able to break out the results to see how small-builder responses compared to all responses. Not surprisingly, small builders find credit to be less available than the group as a whole. Moreover, there has only been a slight change in the responses over the past year (three out of four small builders still find credit to be insufficient compared to four out of five one year ago). While a few smaller builders may have had better luck in securing construction and development lending over the past year, we haven't been able to detect much in the way of broad improvement in access to credit for construction and development.
We also looked to the April 2016 Senior Loan Officer Opinion Survey (SLOOS), published by the Federal Reserve Board, for insights into construction lending. The results seem to paint a construction lending picture that is similar to but not completely aligned with the one we outlined above. In short, the SLOOS reports that a "significant net fraction of banks reported tightening standards for construction and land development loans" while a "moderate net fraction of banks reported stronger demand for construction and land development loans." It is not clear that the call report data and the SLOOS are telling the same story on construction lending behavior, but perhaps this difference is simply an early signal of what we can expect from the second quarter call report.
By Jessica Dill, economic policy analyst in the Research Department and
Carl Hudson, director of the Center for Real Estate Analytics
May 04, 2016
Construction Spending Update
Looking at the latest construction spending report can be an informative exercise, despite the fact that the data lag other releases, because it bundles together various measures of construction activity for one comprehensive look. The latest report, released on May 2, revealed continued growth in construction spending. Private construction spending increased 8.5 percent on a year-over-year basis. The breakdown of growth by segment shown in chart 1 reveals that private residential (the sum of new single-family, multifamily, and residential improvements) and private nonresidential spending contributed almost equally to this increase (4.0 and 4.5 percent respectively).i
Growth in private residential and nonresidential spending from the year-earlier level has persisted since July 2011, but how does the level of spending compare to the previous cycle? The seasonally adjusted annual rate of private nonresidential spending has rebounded to a level just 1.8 percent below its previous peak. Private residential construction spending, on the other hand, remains 35.8 percent below its previous peak. With that said, after zooming out to look at spending over the entire horizon of the series and adjusting for inflation (see chart 2), it doesn't seem particularly wise to judge the health of construction spending relative to the past peak. In hindsight, the last peak was clearly an aberration, especially for residential spending.
Using this longer-running and inflation-adjusted time series to help put current spending in context, it's hard not to notice that the level of private nonresidential spending has surpassed the level seen in earlier peaks (the most recent peak excluded) while private residential spending now looks to be about on par with levels seen in earlier peaks. This surface-level comparison is a bit short-sighted, as this is not a mean-reverting time series. An upward trend in aggregate real construction spending seems perfectly reasonable as the population and economy grow over time.
Shifting focus to the dashed trend lines in chart 2, we see that spending on residential construction has yet to catch up with trend but is much closer than when compared with the previous peak, while spending on nonresidential construction is at a level that exceeds its trend.
Two high-level questions emerge after reviewing the latest construction spending data. First, does construction spending really provide a comprehensive look at construction? The construction spending data could confound the underlying trend because it reflects activity, costs, and timing of payment (for some categories). Data on activity (that is, square feet and units under construction) for all subcategories are not available, but charts 3 and 4 (below) provide some indication for the trend in residential and some categories of nonresidential construction activity.
The construction of single-family and multifamily units as well as the square footage under way for warehouse and office properties have all resumed upward trajectories. Because these measures of construction activity tell a consistent story with the spending data, they provide some reassurance that the costs aren't the primary driver of the growth in construction spending.
Second, does the recovery in real estate still have legs? This one is hard to say for certain but, taking the construction spending and construction activity data together, it seems fairly likely that there is still room for growth.
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
i Private nonresidential spending is comprised of lodging, office, commercial, health care, educational, religious, amusement and recreation, transportation, communication, power, and manufacturing structures.
April 27, 2016
Teachers Teaching Teachers: The Role of Networks in Financial Decisions
Nearly every homeowner goes through the process of refinancing a mortgage at least once, and usually several times. The process itself can be rather daunting, especially for someone experiencing it for the first time. Determining the optimal time to refinance, the best lender to refinance with, and the best mortgage product to refinance into are all fairly complicated decisions, even for a research economist like me who studies housing and mortgage markets for a living.
Fortunately, in my case, I was able to draw on the experiences of an older relative who had refinanced numerous times and was willing to provide advice and, more importantly, a referral to a fantastic mortgage broker. The importance of social networks and peer effects in the refinancing decision is something that many housing economists have long believed in, largely based on anecdotal evidence. Now, a new study has come out that confirms this belief using a unique data set of school teachers and a novel empirical design that cleanly identifies the influence of peer effects on refinancing decisions. The paper, titled "Teachers Teaching Teachers: The Role of Networks on Financial Decisions," is written by Gonzalo Maturana (Emory) and Jordan Nickerson (Boston College). It was presented at a housing finance conference that our very own Center for Real Estate Analytics held in New Orleans back in December (a copy of the agenda and links to the presentations are available here). In addition, I recently discussed the paper at the Midwest Finance Association meetings held in Buckhead last month (a copy of my discussion slides can be found here).
One of the main innovations in the paper is the data set that the authors compile. They start with administrative data on public school teachers in Texas. These data contain detailed demographic information, employment information (the school district and school where each teacher works and the exact employment dates), and, most importantly, information on each teacher's daily class schedule.
For example, the authors know the exact time of the classes that each teacher is scheduled to teach as well as the exact timing of all teachers' break periods. The teachers' data are then matched to a public voting records database in order to obtain the exact street addresses of the teachers' places of residence. Finally, armed with the street addresses, the authors are able to merge the data with public property records. The property records come from county deed registries in Texas and contain detailed information on property transactions (addresses, names of the buyers and sellers, and property characteristics obtained by tax assessors) as well as information on every mortgage that is originated in the state (the type of mortgage—purchase or refinance, the loan amount, the interest rate type—fixed or adjustable, and the identity of the lender). Thus, the authors are left with a data set that contains detailed information on the refinancing decisions of Texas public school teachers (the timing of the refinances, characteristics of the loans, and the identities of the lenders), and detailed information on the employment history and status of the teachers including the exact campus where each teacher works, and the exact daily schedule that each teacher follows.
Armed with this unique data set, the authors implement a strategy to test whether one teacher's decision to refinance influences other teachers' refinancing decisions who are part of that teacher's same "peer group." The term "peer group" typically refers to the group of people that an individual interacts with on a frequent basis and thus, whose economic or financial decisions are most likely to influence those of the individual. There are two major challenges that this study along with every other empirical study on social interactions and peer effects must confront with respect to peer groups. The first challenge is determining exactly what constitutes a given individual's "peer group" in a particular context, and then identifying those groups in the data. The second challenge is finding peer groups that an individual is randomly assigned to rather than groups that an individual explicitly chooses to join. This latter challenge is especially crucial, but very difficult to overcome in a non-experimental setting, as individuals typically choose which groups to associate with and the factors that determine those choices are often unobservable to the researcher and hence, can lead to severe omitted variable bias that conflates inference.
In Texas, teachers apply for jobs in a specific school district, but then are more-or-less randomly assigned to specific schools within the district. Therefore, one teacher peer group that the authors consider in the paper is the set of teachers who work in the same school. This peer group is rather large, however, so it is unclear how much interaction actually occurs between teachers in the same school. To address this issue, the authors use their detailed information on teacher schedules and identify groups of teachers in the same school that have significant overlap in their respective break schedules (at least 40 minutes of overlap in off-periods each day). The idea is that if two teachers are on break together fairly often, then it is more likely that they will directly interact with each other and discuss aspects of their lives including their financial decision making. This is a particularly compelling strategy because teachers often spend their break periods in the faculty lounge, near other teachers on break, which maximizes the potential of significant social interaction.
Using this detailed information on teacher schedules and the data on mortgage refinancing from the property records, the authors define their main variable of interest to be the number of teachers with significant overlap in break periods (at least 40 minutes per day) who have refinanced their mortgage debt within the previous three-month period. They then estimate a regression to determine whether an individual teacher's choice to refinance is influenced by the number of teachers in her peer group who had previously refinanced their mortgages. The results show that this indeed the case. Specifically, a one standard deviation increase in the percentage of a teacher's peer group who refinanced their loans with the previous three months is found to increase the likelihood that an individual teacher in the peer group refinances his or her loan by around 6.5 basis points. While 6.5 basis points does not sound like a large amount, it corresponds to almost 10 percent of the unconditional monthly hazard of refinancing in the data (which is approximately 56 basis points), so the effect is nontrivial.
In addition to testing whether increased refinancing by a teacher's peers influences that teacher's own decision to refinance, the study looks at whether there is a tendency for teachers within the same peer group to use the same lender. This is a natural extension since it would seem likely that during the course of discussing their refinancing experience with each other, teachers would share the identity of and their personal experience with the lender. We also know anecdotally that referrals are a large source of business for mortgage brokers. Sure enough, the authors find that teachers within the same peer group use the same lender to refinance at a significantly higher rate than would be the case if simple random chance were driving lender decisions. On average, teachers within the same peer group use the same lender approximately 8.2 percent of the time. Assuming a world in which there were no peer effects in refinancing behavior, and lenders were chosen randomly, teachers within the same peer group would be expected to use the same lender roughly 3 percent of the time. This difference is highly statistically significant, suggesting that teachers within the same peer group share their lender experiences and refer those lenders with whom they have had good encounters.
Broadly speaking, the results in the paper appear to confirm our belief that people tend to seek and receive advice on major financial decisions from individuals within their social network. In particular, determining the optimal time to refinance a mortgage and the best lender to perform the refinance with are complicated decisions with potentially large consequences, as mortgage debt accounts for the majority of total outstanding debt for many U.S. households.
While I find the results of the paper fairly convincing and believe the authors have implemented a very careful analysis, there is an important and open question of external validity. That is, should we generalize these results to other types of individuals besides just public schoolteachers in Texas, who, it turns out, are predominantly female and highly educated (approximately three-fourths of the sample has at least a bachelor's degree)? This is always an issue with studies that do not use a representative sample of the population, but in this case, there are huge advantages that the data set provides in facilitating the analysis of peer effects on refinancing behavior, which I think dominate the drawbacks of not having a representative sample.
By Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta.
November 24, 2015
The Pass-Through of Monetary Policy
In the wake of the Great Recession, the Federal Reserve instituted three rounds of large-scale asset purchases (LSAPs) in 2008, 2010 and 2012, more commonly known as "quantitative easing 1" (QE1), "QE2" and "QE3." The objective of these interventions was to keep interest rates low in an attempt to stimulate household consumption and business investment.1
In the United States, housing is the single largest asset on the household balance sheet, accounting for 73 percent of nonfinancial assets for the average U.S. household and an even higher share for homeowners.2 Mortgage payments represent the largest class of household debt obligation.
Evidence of the effectiveness of the asset purchase programs on real economic activity has until recently been limited due to the lack of data and a credible identification strategy (by which we mean a way to separate the causal impact of the LSAPs on the economy from other government programs and market factors that were occurring at the same time). When we chart a timeline of the three LSAPs against the primary mortgage rate, we can see that the primary mortgage market rate effectively dropped below 6 percent when the Fed began buying $600 billion in mortgage-backed securities during QE1. Indeed, the rate dropped following each of the subsequent LSAPs.
Since 2009, a number of papers have been published that evaluate the effectiveness of the policy interventions through different transmission channels. One such paper (Keys, Piskorski, Seru, and Yao, 2014) reports on borrowers with adjustable-rate mortgages (ARMs) who automatically receive the benefits of lower interest rates with no frictions or transaction costs, unlike borrowers with fixed-rate mortgages (FRMs) who must refinance in order to take advantage of lower interest rates. The paper provides new evidence on the effectiveness of the LSAPs.
Our strategy is to compare the change in the household balance sheets of 7/1 ARM borrowers to those of 5/1 ARM borrowers, using credit bureau data linked to mortgages. These two ARMs are the most popular ARM products among prime borrowers with very similar credit quality and risk preferences, yet they differ only in years 6 and 7, when the 5/1 ARM is eligible for a rate reset and the 7/1 is still locked (that is, the rate is still fixed). This creates a natural experiment that allows us to isolate other factors that might affect the mortgage rate.
By controlling for borrower characteristics and economic environments, we estimate that mortgage rates in the treatment group (5/1 ARMs) dropped in the first year by 1.14 percentage points, from 5.1 percent, and that payments dropped by $150 per month, or about a 20 percent reduction on average. The average borrower had a cumulative two-year savings of $3,456.3 We also subsequently found that borrowers spent 18 percent of the money saved on paying off credit card balances and that there was an 11 percent increase in new car purchases for the group. As a result, the leverage of U.S. households' dropped considerably from its peak during the financial crisis.4
We also find significant heterogeneity for these effects across different populations. Less creditworthy and more liquidity-constrained borrowers appear to have benefited the most from LSAPs as they experienced significantly greater reductions in mortgage rates and payments and larger improvements in mortgage and credit card performance. In terms of how they spent the extra liquidity received, highly leveraged borrowers (high credit utilization) spent 40 to 50 percent of the extra liquidity received during the first year, or $814 out of $1,740, to repay their revolving debts, then spent 20 percent of the extra liquidity received during the second year. Borrowers in the top quartile of credit utilization rates allocated about 70 percent of the extra liquidity toward repaying their credit card debt. We found similar effects among borrowers in the bottom quartile of credit scores. (The low-wealth borrowers with low credit utilization experienced a much larger increase in auto debt or new car purchases.) In other words, the LSAP programs effectively stimulated household investment and consumption.
We also find, as a result of the estimated effects at the micro level, a significant impact on local (nontradable) employment growth, consumer spending, and house price recovery in regions that were more exposed to ARMs. For example, a 10 percentage point increase in the ARM share, which is associated with about a 20-basis-point average reduction in ZIP code mortgage rates, is associated with about a 0.25 percentage point increase in quarterly home price growth, or about 1 percent annual appreciation, a very meaningful increase.
By Vincent Yao, visiting scholar at the Federal Reserve Bank of Atlanta and associate professor in the Real Estate Department in the J. Mack Robinson College of Business at Georgia State University.
Di Maggio, Marco; Amir Kermani; and Rodney Ramcharan. 2014. "Monetary Pass-Through: Household Consumption and Voluntary Deleveraging," Working Paper.
Hancock, Diana and Wayne Passmore. 2014. "How the Federal Reserve's Large-Scale Asset Purchases (LSAPs) Influence Mortgage-Backed Securities (MBS) Yields and U.S. Mortgage Rates," Finance and Economics Discussion Series 2014–12. Board of Governors of the Federal Reserve System.
Keys, Benjamin J.; Tomasz Piskorski; Amit Seru; and Vincent Yao. 2014. "Mortgage Rates, Household Balance Sheets, and the Real Economy," NBER Working Paper No. 20561.
1 The LSAPs involved purchases of long-term securities issued by the U.S. Treasury, agency debts, and agency mortgage-backed securities (MBS). They ultimately affected the yields of the MBS as well as the mortgage rates offered to borrowers in the primary mortgage market through several potential transmission channels: (1) the signaling of the Fed's commitment to keeping rates low, (2) a portfolio rebalance between assets and deposits and among different durations, and (3) increasing the liquidity value of MBS (Hancock and Passmore, 2014).
2 Survey for Consumer Finance, Federal Reserve Board of Governors, 2013.
3 Di Maggio, Kermani. and Ramcharan (2014) found much bigger savings for subprime and Alt-A borrowers based on a similar approach.
4 It is notable that in the United States the majority of prime borrowers take out fixed rate mortgages while most subprime borrowers take out adjustable rate mortgages.
November 03, 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.
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.
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
September 17, 2015
Do Millennials Prefer to Live Closer to the City Center?
In past posts (Part 1 and Part 2), we examined whether millennials were driving the decline in first-time homebuyers. We concluded that, if anything, first-time homebuyers were becoming younger over time and location and economic conditions appeared to be a much stronger predictor of declines than a generational divide. In this post, we look into whether millennials prefer to live close to the city core or in the suburbs. Where millennials settle could determine whether our cities continue to grow, what our transportation infrastructure expenditures should be, and whether homebuilders should focus their efforts on multifamily housing in urban locations or traditional single-family homes in the suburbs.
This question has received a fair amount of attention—see here, here, here, and here. A number of observers have speculated whether the recent surge in millennials living in cities represents a change in preferences or whether it's simply an artifact of financial constraints—tighter underwriting standards, weak income growth, or larger student debt. Nielsen's survey of young adults finds that millennials prefer the lifestyle afforded by dense urban environments, but the National Association of Homebuilder's survey of young homebuyers finds that just 10 percent would prefer to live in the city while a whopping 66 percent want to live in the suburbs. Setting preferences aside, others debate whether millennials really are moving to the city. While recent data confirm that young people are moving to the cities at much higher rates than in the 1990s, it's also true that the raw majority of young people choose the suburbs over the city.
This research on young adults tends to combine renters and homeowners in one category. Renters tend to experience credit and financial barriers to location, and are limited in their location choice by the distribution of rental housing stock. That can make it difficult to distinguish whether young people who move to the city do so because they prefer urban life or because there is more rental housing stock in the city than in the suburbs. To shed light on the question of where millennials prefer to live, we segment out a group of young adults who experience relatively fewer restrictions on where to live: first-time homebuyers. Our data set allows us to identify first-time millennial homebuyers and the census tracts where they bought their first homes (a previous post describes the data).
Using this data, we ask if first-time millennial homebuyers are more likely to live near the city center than either existing homeowners or older first-time homebuyers. Finally, we look at how other factors like creditworthiness and student debt levels appear to influence this decision.
Below, we chart the median distance from the central business district (CBD) of first-time and existing homeowners by age bracket from the years 2001 to 2014. We find that existing homeowners tend to live, on average, 6.3 to 6.5 miles from the city center. First-time homebuyers tend to live closer in regardless of age, on average 5.8 to 5.9 miles away from the city center. Beginning in 2003, younger first-time homebuyers trended towards more central locations. During the 2007–09 recession, the spread between older and younger first-time homebuyers collapsed. After the recession, the spread widened again. It's difficult to say whether the shift in purchase patterns is the result of financial constraints or changing preferences, but the tendency appears to be for newer and younger homeowners to purchase homes closer to the city center.
What this chart cannot tell us is whether the trend that has younger people living closer to the city center reflects uniform preferences or whether this is an artifact of stronger economic growth in denser cities. In other words, is this trend the result of strong home buying in compact cities and weak sales in sprawling metropolises (that is, between cities), or is it the result of all buyers nationwide choosing to move closer to the city center (that is, within cities)?
To further investigate whether millennials prefer to live close to the city center, we perform several regressions to see how age relates to first-time homebuyer location decisions before and after the crisis. We control for a few key variables—namely, credit score, mortgage size, and student debt levels. The sample includes first-time homebuyers aged 18–60 who chose to purchase homes in the 50 largest metropolitan statistical areas (MSA) in the United States. We calculate distance by matching the census tract variable in the Federal Reserve Bank of New York Consumer Credit Panel with census tract data on distance from city center provided by CityObservatory.
Because some cities are more compact than others, we add MSA-level fixed effects. To control for the influence of nationwide effects such as the introduction of quantitative easing and the first-time homebuyer tax credit, we control for year-fixed effects as well in each regression. These controls should adjust for all region and time invariant factors that might affect both the age and location choice of home purchases.
Since creditworthiness typically increases with age and households with higher credit scores tend to be less constrained in their location choice, we also add a risk score variable to see whether age is simply a proxy for the ability to borrow. Similarly, we include mortgage balances. Finally, we add student debt balances to see whether the higher student debt burdens of young people can explain the discrepancy between the location choices of older and younger buyers.
The results are featured in the table below. On the right side of the table—from 2001–05 (that is, before the housing market crisis)—age appeared to have had a small impact on location and was not significant. Other factors such as size of mortgage and amount of student debt seemed to be larger determinants of location. Homebuyers with larger mortgages and with more student debt were more likely to live farther from the city center.
On the left side of the table—from 2006 to 2014 (that is, during and after the housing crisis)—age appeared to have had a small but significant relationship with location. Buyers who were one standard deviation younger located 0.03 standard deviations closer to the city center. With more controls included in the regression, this relationship declined to 0.02 standard deviations closer to the city center.
During and after the crisis, risk score became a stronger determinant of location as well. As risk scores increased by one standard deviation, buyers moved closer to the city center by 0.04 to 0.03 standard deviations, depending on the specification. This suggests that credit-constrained homebuyers are more likely to live father away from the city center and that, all else equal, younger homebuyers prefer to live closer to the city center.
While it appears that, on average, younger homebuyers prefer to live closer to the city center, can we say this reflects a preference for urban life? The average distance from city center—five-and-a-half miles—could very well describe areas with moderate density and single-family housing stock in moderate-sized cities. To focus on whether younger homebuyers are interested in living in the central city, we repeat these results using a logistic regression predicting the likelihood a first-time homebuyer will purchase within one mile of the city center. Controlling for all available factors, we find that younger buyers are significantly more likely to live in the heart of downtown. For each additional year, the odds that a buyer will decide to live within one mile of the city center drop by 6 percent.
By using this unique data set, we hope that we have shed some additional light on the age and location decisions of first-time homebuyers. Our interpretation of the data suggests that first-time homebuyers became more likely to buy closer to the city center during and after the housing market crisis and that young homeowners (first-time and existing) are more likely to live closer to the city center than older homeowners. Moreover, creditworthiness, total mortgage balance, and student debt loads appear to matter when the time comes to decide where to buy. In short, although age may not affect whether someone buys a house, our analysis suggests it may influence where they buy.
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
Jessica Dill, economic policy analysis specialist in the Atlanta Fed's research department
August 27, 2015
The Multifamily Market: Is a Hot Market Overheating?
Moody's/RCA National commercial property price index, which is based on repeat-sales transactions, has risen 36 percent over the past two years. Such increases in commercial real estate (CRE) prices have raised concerns that the market is overheating (see here). Multifamily is one CRE property type that for a couple of years has been attracting a great deal of lender interest and thus growing concern regarding potential overheating (see here).
Looking around Midtown Atlanta, it is easy to wonder if multifamily housing construction is getting ahead of itself. According to the Midtown Alliance, within just a 0.5-square-mile portion of Midtown Atlanta, 981 units have been recently delivered, 3,392 units are under construction and 4,732 are in various stages of planning. Dodge Pipeline reports that the entire Midtown/Five Points submarket has 4,865 units under way. For reference, peak activity in the Midtown/Five Points area from 2003 to 2007 was 4,636 units under construction with a total of 10,831 units completed. The question arises as to what extent are happenings in Midtown indicative of the broader market trend.
Yield spreads—the capitalization rate on recent apartment transactions (current rental income divided by sales price) minus the yield on Treasury bonds—serve as one indicator of optimism in a market. A narrow spread is consistent with reduced pricing for risk, which is associated with “frothiness.” According to Real Capital Analytics, apartment yield spreads in the second quarter of 2015 stood at 366 basis points (bps), which is around 250 bps higher than prerecession lows and in line with 2003–04 levels (see chart 1). So by this measure, apartment activity does not appear too frothy on a nationwide market basis.
Of course, yield spreads vary significantly by market area and by property type. Breaking the U.S. market into six major markets (Boston; New York; Washington, D.C.; Chicago; San Francisco; and Los Angeles) and all others reveals that the major markets have seen yield spreads fall relative to all other markets. (The major markets account for 36 percent of transaction dollars with New York and San Francisco alone accounting for 20 percent of the U.S. total.) Though shrinking during the last several quarters, the current 150 bps gap between the major and non-major markets is wider than at any time since 2002. One possible explanation is that the anticipated rent growth of the projects sold in the major markets is higher than in nonmajor markets.
So what to make of this? While multifamily markets have been active during the postrecession period, this activity is not necessarily unjustified. Given that the population of 20- to-34-year-olds will continue to grow, demographics point to greater demand for rental property (see chart 2). Supply has not yet shown signs of deteriorating fundamentals since vacancy rates have remained low as new product has been delivered, and rent growth has held steady (see chart 3).
How long will preferences for renting persist? How long can real rents continue to grow? How is this new activity being financed? If new projects are penciled out using unrealistic rent growth assumptions and demand falls, rent growth expectations won't be met and the projects may look overdone in retrospect. Regardless of whether current activity indicates overheating, it seems important to keep a close eye on demand.
By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed's research department
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