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 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
- 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?
- Income Growth, Credit Growth, and Lending Standards: Revisiting the Evidence
- Where Is the Credit Availability Pendulum Now? (Part 2 of 2)
- Has the Pendulum Swung Back to Neutral? Looking at Credit Availability
- April 2016
- November 2015
- September 2015
- August 2015
- July 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- Affordable housing goals
- Credit conditions
- Expansion of mortgage credit
- Federal Housing Authority
- Financial crisis
- Foreclosure contagion
- Foreclosure laws
- Government-sponsored enterprises
- Homebuyer tax credit
- House price indexes
- Household formations
- Housing boom
- Housing crisis
- Housing demand
- Housing prices
- Income segregation
- Individual Development Account
- Loan modifications
- Monetary policy
- Mortgage crisis
- Mortgage default
- Mortgage interest tax deduction
- Mortgage supply
- Negative equity
- Positive demand shock
- Positive externalities
- Rental homes
- Subprime MBS
- Subprime mortgages
- Supply elasticity
- Upward mobility
- Urban growth