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


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).

table-one

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.

table-two

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.

Photo of Jessica Dill By Jessica Dill, economic policy analyst in the Research Department and

Photo of Carl Hudson Carl Hudson, director of the Center for Real Estate Analytics

June 9, 2016 in Credit conditions | Permalink | Comments ( 1)

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

Chart-one

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.

Chart-two

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.

Takeaways

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.

Chart-three

Chart-four

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.

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

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i Private nonresidential spending is comprised of lodging, office, commercial, health care, educational, religious, amusement and recreation, transportation, communication, power, and manufacturing structures.

May 4, 2016 in Affordable housing goals | Permalink | Comments ( 0)

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.

Photo of Kris GerardiBy Kris Gerardi, financial economist and associate policy adviser at the Federal Reserve Bank of Atlanta.

April 27, 2016 in Affordable housing goals | Permalink | Comments ( 0)

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.

chart-one

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.

References

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.

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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 24, 2015 in Financial crisis , Mortgage crisis | Permalink | Comments ( 0)

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.

chart-1

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

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

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

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

November 3, 2015 in Homeownership , Housing boom , Housing crisis , Housing prices , Rental homes | Permalink | Comments ( 1)

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.

Median-distance-of-purchase-from-cbd-core

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.
Percentage_distance_from_city_core

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.

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

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

September 17, 2015 in Housing crisis | Permalink | Comments ( 1)

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.

Apartment-yield-spreadsApartment-yield-spreads

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).

Us-population-of-key-apartment-renting-cohorts

Apartment-vacancy-growth

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.

Photo of Carl HudsonBy Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed's research department

August 27, 2015 in Housing boom , Housing demand | Permalink | Comments ( 1)

July 01, 2015


Are Millennials Responsible for the Decline in First-Time Home Purchases? Part 2

Recall that, in our last post, we investigated the claim that millennials were to blame for the decline in first-time home purchases. Our data analysis confirmed that home purchases by first-time buyers have indeed plummeted since the crisis. We did not, however, find evidence that millennials were driving this decline. We found that, if anything, first-time homebuyers have become younger since the crisis, not older. By contrast, location appeared to be a much stronger predictor of declines in first-time buying than age.

Notwithstanding, many commentators still believe that millennials are behind sluggish sales. In this post, we take a closer look at the timing of first-time home purchases and the credit trends of first-time homebuyers with an eye towards the changing composition of homebuyers. We use the same credit bureau data set that we used in the previous post (take a look for a description of the data and our definition of first-time homebuyer). Using this data, we dig a bit deeper into two theories that are often cited for why millennial homebuyers are not buying as many homes as in the past. We first analyze whether millennials delayed the purchase of their first home in response to the crisis. Then we investigate what role, if any, credit tightening has played.

In short, we can't confirm any delay in the timing of home purchases. What we do find is that the distribution of first-time home purchases changed after the crisis. First-time home purchases by younger buyers peak earlier and persist at an elevated level over a longer period of time than before. We also find, contrary to the popular theory that credit became too tight for millennials to buy homes, that mortgage credit actually became tighter for older first-time buyers than for younger first-time buyers. Taken together, we think these data observations help to explain why the median age of the first-time buyer shifted downwards (instead of upwards) after the housing downturn.

Timing

To examine how the housing downturn affected the timing of purchases by young first-time homebuyers, we separated this group out by birth year and examined the number of home purchases from 2000 to 2014. We looked at millennial homebuyers born in 1983 and 1985 and compared them to Gen X homebuyers born in 1975 and 1977.

Chart 1 shows the number of first-time home purchases for each year, with each line representing a different birth year. The time series for the older birth years peaks between the ages of 27 and 29 while the time series for the younger birth years peaks between the ages of 24 and 25. For Gen Xers who came of age before the crash, their peak appears to be the culmination of a steep increase in purchases and an almost equally steep decline resulting in a curve that looks roughly like an inverted V. For the millennial birth years, who came of age after the real estate crash, the peak in first-time purchases occurs earlier and the decline of the curve is much more gradual. The change in the distribution of purchases after the crash suggests that the younger first-time home buyers are still purchasing homes at relatively high rates, but purchases are spread out over a wider time period.

Chart-01-first-time-home-purchases-by-birth-year

The distribution of millennial first-time homebuyers has clearly shifted. Not only has the distribution of first-time homebuyers become younger over time (refer to previous post) but first-time home buying among the most recent birth years is peaking at an earlier age. Why might this be? We think a closer look at credit trends can shed some light on this question.

Credit scores

In Chart 2, we examine the number of first-time home purchases and median credit scores of first-time home purchasers by age bracket. The two age brackets are adults under 35 years old and adults between the ages of 35 and 48. By grouping first-time home purchases into age brackets, we are able to examine whether credit is tighter for younger borrowers than for older borrowers using the median Equifax risk score as a proxy for credit tightness.1

Chart-02-credit-scores-of-first-time-home-buyers-by-age-bracket

From 2001 to 2014, median FICO scores increased by 5.0 percent for the younger group and 5.1 percent for the older group. In general, the median credit scores of both groups appear to behave similarly, except during the years when subprime lending prevailed. The median credit scores for both younger and older buyers shifted down between 2003 and 2006, signaling that there were more purchases by higher-risk buyers. With that said, the decline in the median credit score was more pronounced for older buyers (down 4.1 percent, from 689 in 2003 to 661 in 2006) than for younger buyers (down just 1.6 percent, from 693 in 2003 to 682 in 2006). Since the crisis, the gap between the median credit scores of younger and older buyers has closed (in other words, credit has become tighter for older buyers), which may explain why first-time home purchases have fallen faster for older buyers than it has for younger buyers. Indeed, between 2001 and 2014, first-time home purchases fell by 36 percent for younger homebuyers and by 54 percent for older buyers.

The table and Charts 3 and 4, below, delve deeper into the credit trends of younger and older first-time homebuyers, showing home purchases by credit bracket, year, and age. We determined credit brackets by taking the quartiles of every individual with a credit record in the time period. As the charts show, purchases by those with the lowest credit scores, marked in blue, have plummeted steeply. Credit scores in the middle, marked in green and orange, fell sharply, too, but particularly among older buyers. Older first-time homebuyers with moderate credit scores were much less likely to buy homes after than before the crisis, falling by 50 to 60 percent. Purchases by those with stellar credit, marked in purple, were barely affected by the crisis. Perhaps a more interesting observation is that young homebuyers in the highest credit bracket were the one subgroup to increase their purchases during and after the recession. First-time purchases by this group of young buyers actually rose by 25 percent.

First-time-home-purchases-by-age-credit-bracket

Charts-3-and-4

We believe this collection of charts demonstrates in more detail that credit became tighter for older homebuyers during the crisis—and also that an uptick in home purchases by the most creditworthy millennials has buoyed purchases for that age bracket.

The Federal Reserve Bank of New York Consumer Credit Panel/Equifax data is an unusual data set in that it allows us to compare first-time homebuyers without first conditioning by age. By comparing older and younger first-time homebuyers, we have been able to examine the claim that millennial homebuyers are behind the stagnation in home sales. In addition to our earlier findings that first-time buyers have become younger, not older, since the crisis, we find that the distribution of first-time home purchases has changed since the housing downturn. Specifically, first-time purchases by younger buyers tend to peak at an earlier age and persist at an elevated level over a longer period of time. This is in contrast to the trend before the downturn, when first-time purchases by younger buyers peaked at an older age and dropped off precipitously after peaking. Moreover, the data reveal that, while younger and older first-time buyers have similar credit trends when tracked as the median credit score, credit may have loosened more for older first-time buyers than younger first-time homebuyers during the run-up and as a consequence tightened more for older buyers than younger buyers during the recovery, resulting in a lower number of first-time purchases by this older group. Despite the fact that many believe tight mortgage credit, student loan debt burdens, and stagnating wages have made it more difficult for millennials to buy homes, it appears that credit tightness has actually weighed more heavily on older first-time homebuyers.

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

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

_______________________________________

1 Examining credit scores over time can be misleading. Credit scores measure a person's ranking of creditworthiness at a given time. A credit score does not give an absolute measure of someone's default probability, just where they are relative to others. So, someone with a 700 credit score in one time period may have a different default probability than someone in another time period, though their rank relative to others remains the same. This becomes relevant if the creditworthiness of the American public as a whole shifts dramatically over time

July 1, 2015 in Credit conditions , Financial crisis | Permalink | Comments ( 0)

May 20, 2015


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

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

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

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

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

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

Image

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

First-time-homebuyers-2001-14

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

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

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

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

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

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

_______________________________________

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

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

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

May 20, 2015 in Financial crisis , Homeownership , Housing boom , Housing crisis | Permalink | Comments ( 0)

April 20, 2015


Income Growth, Credit Growth, and Lending Standards: Revisiting the Evidence

Almost a decade has passed since the peak of the housing boom, and a handful of economics papers have emerged as fundamental influences on the way that economists think about the boom—and the ensuing bust. One example is a paper by Atif Mian and Amir Sufi that appeared in the Quarterly Journal of Economics in 2009 (MS2009 hereafter). A key part of this paper is an analysis of income growth and mortgage-credit growth in individual U.S. ZIP codes. The authors find that from 2002 to 2005, ZIP codes with relatively low growth in incomes experienced high growth in mortgage credit; that is, income growth and credit growth were negatively correlated during this period.

Economists often cite this negative correlation as evidence of improper lending practices during the housing boom. The thinking is that prudent lenders would have generated a positive correlation between area-level growth in income and mortgage credit, because borrowers in ZIP codes with high income growth would be in the best position to repay their loans. A negative correlation suggests that lenders instead channeled credit to borrowers who couldn't repay.

Some of the MS2009 results are now being reexamined in a new paper by Manuel Adelino, Antoinette Schoar, and Felipe Severino (A2S hereafter). The A2S paper argues that the statistical evidence in MS2009 is not robust and that using borrower-level data, rather than data aggregated up to the ZIP-code level, is the best way to investigate lending patterns. The A2S paper has already received a lot of attention, which has centered primarily on the quality of the alternative individual-level data that A2S sometimes employ.1 To understand the relevant issues in this debate, it's helpful to go back to MS2009's original statistical work that uses data aggregated to the ZIP-code level to get a sense of what it does and doesn't show.

Chart 1: Measures of the Relationship between within-County Income Growth and Credit Growth for U.S. ZIP Codes

Chart 1 summarizes the central MS2009 result. We generated this chart from information we found in either MS2009 or its supplementary online appendix. The dark blue bars depict the coefficients from separate regressions of ZIP-code level growth in new purchase mortgages on growth in ZIP-code level incomes.2 (These regressions also include county fixed effects, which we discuss further below.) Each regression corresponds to a different sample period. The first regression projects ZIP-level changes in credit between 1991 and 1998 on ZIP-level changes in income between these two years. The second uses growth between 1998 and 2001, and so on.3 During the three earliest periods, ZIP-level income growth enters positively in the regressions, but in 2002–04 and 2004–05, the coefficients become negative. A key claim of MS2009 is that this flip signals an important and unwelcome change in the behavior of lenders. Moreover, the abstract points out that the negative coefficients are anomalous: "2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated."

Chart 2: Serious Delinquency Rates by Loan Vintage

There are, however, at least three reasons to doubt that the MS2009 coefficients tell us anything about lending standards. First of all, the coefficients for the 2005–06 and 2006–07 regressions are positive—for the latter period, strongly so. By MS2009's logic, these positive coefficients indicate that lending standards improved after 2005, but in fact loans made in 2006 and 2007 were among the worst-performing loans in modern U.S. history. Chart 2 depicts the share of active loans that are 90-plus days delinquent or in foreclosure as a share of currently active loans, using data from Black Knight Financial Services. To be sure, loans made in 2005 did not perform well during the housing crisis, but the performance of loans made in 2006 and 2007 was even worse.4 This poor performance is not consistent with the improvement in lending standards implied by MS2009's methodology.

A second reason that sign changes among the MS2009 coefficients may not be informative is that these coefficients are not really comparable. The 1991–98 regression is based on growth in income and credit across seven years, while later regressions are based on growth over shorter intervals. This difference in time horizon matters, because area-level income and credit no doubt fluctuate from year to year while they also trend over longer periods. A "high-frequency" correlation calculated from year-to-year growth rates may therefore turn out to be very different from a "low-frequency" correlation calculated by comparing growth rates across more-distant years. One thing we can't do is think of a low-frequency correlation as an "average" of high-frequency correlations. Note that MS2009 also run a regression with growth rates calculated over the entire 2002–05 period, obtaining a coefficient of -0.662. This estimate, not pictured in our graph, is much larger in absolute value than either of the coefficients generated in the subperiods 2002–04 and 2004–05, which are pictured.

A third and perhaps more fundamental problem with the MS2009 exercise is that the authors do not report correlations between income growth and credit growth but rather regression coefficients.5 And while a correlation coefficient of 0.5 indicates that income growth and credit growth move closely together, a regression coefficient of the same magnitude could be generated with much less comovement. MS2009 supply the data needed to convert their regression coefficients into correlation coefficients, and we depict those correlations as green bars in chart 1.6 Most of the correlations are near 0.1 in absolute value or smaller. To calculate how much comovement these correlations imply, recall that the R-squared of a regression of one variable on another is equal to the square of their correlation coefficient. A correlation coefficient of 0.1 therefore indicates that a regression of credit growth deviated from county-level means on similarly transformed income growth would have an R-squared in the neighborhood of 1 percent. The reported R-squareds from the MS2009 regressions are much larger, but that is because the authors ran their regressions without demeaning the data first, letting the county fixed effects do the demeaning automatically. While this is standard practice, this specification forces the reported R-squared to encompass the explanatory power of the fixed effects. The correlation coefficients that we have calculated indicate that the explanatory power of within-county income growth for within-county credit growth is extremely low.7 Consequently, changes in the sign of this correlation are not very informative.

How do these arguments relate to A2S's paper? Part of that paper provides further evidence that the negative coefficients in the MS2009 regressions do not tell us much about lending standards. For example, A2S extend a point acknowledged in MS2009: expanding the sample of ZIP codes used for the regressions weakens the evidence of a negative correlation. The baseline income-credit regressions in MS2009 use less than 10 percent of the ZIP codes in the United States (approximately 3,000 out of more than 40,000 total U.S. ZIP codes). Omitted from the main sample are ZIP codes that do not have price-index data or that lack credit-bureau data.8 MS2009 acknowledge that if one relaxes the restriction related to house-price data, the negative correlations weaken. Our chart 1 conveys this information with the correlation coefficients depicted in red, which are even closer to zero. A2S go farther to show that if the data set also includes ZIP codes that lack credit-bureau data, the negative correlation and regression coefficients become positive.

But perhaps a deeper contribution of A2S is to remind the researchers that outstanding questions about the housing boom should be attacked with individual-level data. No one doubts that credit expanded during the boom, especially to subprime borrowers. But how much of the aggregate increase in credit went to subprime borrowers, and how did factors like income, credit scores, and expected house-price appreciation affect both borrowing and lending decisions? Even under the best of circumstances, it is hard to study these questions with aggregate data, as MS2009 did. People who take out new-purchase mortgages typically move across ZIP-code boundaries. Their incomes and credit scores may be different than those of the people who lived in their new neighborhoods one, two, or seven years before. A2S therefore argue for the use of HMDA individual-level income data so that credit allocation can be studied at the individual level. This use has been criticized by Mian and Sufi, who believe that fraud undermines the quality of the individual-level income data that appear in HMDA records. We should take these criticisms seriously. But the debate over whether lending standards are best studied with aggregate or individual-level data should take place with the understanding that aggregate data on incomes and credit may not be as informative as previously believed.

1 Mian and Sufi's contribution to the data-quality debate can be found here.

2 Data on new-purchase mortgage originations come from records generated by the Home Mortgage Disclosure Act (HMDA). Average income at the ZIP-code level is tabulated in the selected years by the Internal Revenue Service.

3 Growth rates used in the regressions are annualized. The uneven lengths of the sample periods are necessitated by the sporadic availability of the IRS income data, especially early on. The 1991 data are no longer available because IRS officials have concerns about their quality.

4 Chart 2 includes data for both prime and subprime loans. The representativeness of the Black Knight/LPS data improves markedly in 2005, so LPS loans originated before that year may not be representative of the universe of mortgages made at the same time. For other evidence specific to the performance of subprime loans made in 2006 and 2007, see Figure 2 of Christopher Mayer, Karen Pence, and Shane M. Sherlund, "The Rise in Mortgage Defaults," Journal of Economic Perspectives (2009), and Figure 1 of Yuliya Demyanyk and Otto Van Hemert, "Understanding the Subprime Mortgage Crisis," Review of Financial Studies (2009). For data on the performance of GSE loans made in 2006 and 2007, see Figure 8 of W. Scott Frame, Kristopher Gerardi, and Paul S. Willen, "The Failure of Supervisory Stress Testing: Fannie Mae, Freddie Mac, and OFHEO," Atlanta Fed Working Paper (2015).

5 MS2009 often refer to their regression coefficients as "correlations" in the text as well as in the relevant tables and figures, but these statistics are indeed regression coefficients. Note that in the fourth table of the supplemental online appendix, one of the "correlations" exceeds 1, which is impossible for an actual correlation coefficient.

6 Because a regression coefficient from a univariate regression is Cov(X,Y)/Var(X), multiplying this coefficient times StdDev(X)/StdDev(Y) gives Cov(X,Y)/StdDev(X)*StdDev(Y), which is the correlation coefficient. Here, the Y variable is ZIP-code–level credit growth, demeaned from county-level averages, while X is similarly demeaned income growth. As measures of the standard deviations, we use the within-county standard deviations displayed in Table I of MS2009. Specifically, we use the within-county standard deviation of "mortgage origination for home purchase annual growth" calculated over the 1996–02 and 2002–05 periods (0.067 and 0.15, respectively) and the within-county standard deviation of "income annualized growth" over the 1991–98, 1998–2002, 2002–05, and 2005–06 periods (0.022, 0.017, 0.031, and 0.04, respectively). Unfortunately, the time periods over which the standard deviations were calculated do not line up exactly with the time periods over which the regression coefficients were calculated, so our conversion to correlation coefficients is an approximation.

7 It is true that the regression coefficients in the MS2009 coefficients often have large t-statistics, so one may argue that ZIP-level income growth has sometimes been a statistically significant determinant of ZIP-level credit growth. But the low correlation coefficients indicate that income growth has never been economically significant determinant of credit allocation within counties. It is therefore hard to know what is driving the income-credit correlation featured in MS2009, or what may be causing its sign to fluctuate.

8 Though house prices and credit bureau data are not required to calculate a correlation between income growth and mortgage-credit growth, the authors use house prices and credit bureau data in other parts of their paper.


April 20, 2015 in Credit conditions , Housing boom , Housing crisis , Mortgage crisis , Subprime mortgages | Permalink | Comments ( 1)

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