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.
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.
- Commercial Construction Update: Third-Quarter 2016
- Construction Lending Update: Have the Banks Finally Opened the Spigots?
- Construction Spending Update
- Teachers Teaching Teachers: The Role of Networks in Financial Decisions
- The Pass-Through of Monetary Policy
- Keeping an Eye on the Housing Market
- Do Millennials Prefer to Live Closer to the City Center?
- The Multifamily Market: Is a Hot Market Overheating?
- Are Millennials Responsible for the Decline in First-Time Home Purchases? Part 2
- Are Millennials Responsible for the Decline in First-Time Home Purchases?
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