Real Estate Research provides analysis of topical research and current issues in the fields of housing and real estate economics. Authors for the blog include the Atlanta Fed's Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
November 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
November 18, 2014
Can the Atlanta Fed Construction and Real Estate Survey Predict Home Sales?
The slow recovery in housing remains an item of note in statements from the Financial Open Market Committee. That it's still something of a concern means that many people pay attention to housing-related data releases, several of which are due out this week, because they can shed some light on the direction of housing and the economy. The builder confidence index, released today, got things off to a good start by showing a four-point rise, from 54 to 58 (values greater than 50 mean that more builders view conditions as good rather than as poor). House starts and existing sales are due Wednesday and Thursday, respectively.
At the Atlanta Fed, we conduct a monthly survey of regional builders and real estate brokers to get their perspectives of the market. In August, we began to look at the results a little differently to see if they could tell us anything about subsequent housing-market data releases. In that exercise, we investigated the correlation between the expectations of our homebuilder contacts for construction activity and subsequent housing starts. We found that our builders are on point, more or less, and we reported on that discovery in an August post. We recently repeated the exercise, this time to explore the predictive power of the outlook for home sales of our homebuilders and residential brokers for subsequent new and existing home sales data releases. We report on our findings in this post.
Brokers and builders expect new home sales to rise
The September home sales data showed us that existing single-family home sales increased by 1.9 percent from the year-earlier level and new home sales increased by 22.6 percent. This news is fairly consistent with the reports we received from our real estate business contacts about September sales activity; more brokers and builders noted an increase than noted a decrease in home sales activity from the year-ago level.
But what exactly did our survey respondents tell us about their outlook for home sales? Diving deeper into the data, we find that brokers' and builders' outlooks remain mildly positive and that the two groups have tracked each other fairly closely in recent years. (In the pre-2011 period, brokers and builders diverged more sharply.) Specifically:
- Of builder respondents, 40.0 percent indicated that they expect new home sales to increase over the next three months, 32.0 percent expect activity to decline, and 28.0 percent expect home sales activity to remain about the same. The home sales outlook diffusion index value for builders was 0.08.
- Of broker respondents, 22.5 percent indicated that they expect new home sales to increase over the next three months, 27.5 percent expect activity to decline, and 50.0 percent expect home sales activity to remain about the same. The home sales outlook diffusion index value for brokers was -0.05.
The chart below features two scatter plots of the diffusion index value for the broker and builder home sales outlook on the horizontal axis and the year-over-year change in the three-month moving average of single-family home sales (for Alabama, Florida, Georgia, Louisiana, Mississippi, and Tennessee) on the vertical axis. Given that we are asking contacts to be forward-looking, we lag the contact responses.
Do home sales expectations correlate with subsequent sales data?
Three things stand out on this chart. First, if builders and especially brokers (who tend to be an optimistic lot) predict a decline, the subsequent home sales data release will probably be poor. Only a modest bit of net optimism is of little comfort—some of the worst declines occurred in years with net positive (albeit small net positive) outlooks.
Second, for builders, if the index is greater than 0.3, we find that sales generally grow—except for between August 2012 and April 2013, when sales did not match builders' optimism. When the broker index is above 0.3, sales either grow or decline by a smaller amount than when the index is negative. Like the builders, the broker panel missed the sales declines from August 2012 and April 2013. The brokers also missed the declining real estate market in 2006 to early 2007 (see the green triangles in the chart above)—despite a declining market, the broker index remained lofty until May 2007.
Third, the official statistics on housing sales could go either way when index values are between ‑0.1 and 0.3. This shouldn't come as a complete surprise, particularly because a diffusion index value near zero (regardless of whether that value is positive or negative) indicates that responses from contacts were mixed. And as we can see in the scatter plot above, large declines were much more likely given the time period covered.
A simple regression indicates that the outlook could explain just under 50 percent of the variation in sales measure, which indicates that our poll does a decent job of predicting subsequent sales. Given this finding, what do we now expect home sales to look like? The most recent downward trend in respondents' outlook puts the diffusion index in the center, suggesting that declines in seasonally adjusted sales over the next several months are just as likely as increases in sales.
The poll was conducted October 6–15, 2014. Sixty-five business contacts across the Southeast participated (40 residential brokers and 25 builders). To explore the latest poll results in more detail, please visit our Construction and Real Estate Survey page.
By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed´s research department, and
Jessica Dill, senior economic research analyst in the Atlanta Fed's research department
September 18, 2014
The Economic Effects of Urban Renewal
Editor's note: An earlier version of this post inadvertently included a paragraph from last week's post. The corrected post is below, and we apologize for the oversight.
This year, the 50th anniversary of the "War on Poverty," has seen an effort in the news media and among policy commentators to review the success and failure of past efforts to address poverty (see, for example, this, this, this, and this). Some of these efforts have included place-based policies such as the Model Cities program, which attempted to improve housing stock and reduce urban blight at the neighborhood level. In part, this renewed interest is policy-relevant: many cities are struggling with blight in the wake of the foreclosure crisis, and place-based policy has returned to popularity. For these reasons and more, I was quite interested to read a recent article in the American Economic Journal: Applied Economics. "Slum Clearance and Urban Renewal in the United States" by William J. Collins and Katherine L. Shester revisits the topic of urban renewal programs in the latter part of the last century.
The set of policies loosely referred to as "urban renewal" has been controversial since implementation. In fact, the programs changed a lot from 1950 to 1974, largely in reaction to the outraged response and perceived failures of early efforts. Title I of the 1949 Housing Act, which focused on "slum clearance," was a precursor to the 1954 Housing Act, which shifted the emphasis away from demolition and towards rehabilitation and preservation. Later legislation added programs to smooth the relocation process for those who were displaced by Title I programs and to direct resources towards the elderly poor. Throughout the 1960s, policy shifted away from changing the quality of housing stock towards creating a suite of policies focused on healthy communities. In 1965, as a result of a major reorganization, the Housing and Home Finance Agency, which had administered Title I, became the Department of Housing and Urban Development, commonly known as HUD. Finally, in 1968, the Fair Housing Act passed, further affecting the dispersal of funds.
In the early sixties, Jane Jacobs was one of the more famous critics of the destruction of historic neighborhoods and reconstruction along rationalist, modernist lines. In her 1961 classic, The Death and Life of Great American Cities, she argued that cities embodied organized complexity and that so-called "disorderly" slums were better than the rationally planned spaces that displaced them, both economically and socially. Other research on urban renewal has focused on political, social, and legal implications. This line of inquiry has focused on the impact of eminent domain on property rights, aesthetic concerns about how to incorporate historic preservation into revitalization, and concerns of justice and equity, primarily the issue that urban renewal placed the burden of displacement and disruption onto poor and minority residents without due consultation or compensation (see Gans 1962, Gotham 2001, Jacobs 1961).
The 2013 Collins and Shester paper cites this literature, but is distinct from it in its quantitative, nationwide study of economic impacts. It evaluates the effect of a series of programs over a 30-year period across 458 cities, and calculates that effect on broad economic outcomes. The authors measure urban renewal by combining the dollars allocated under the various programs implemented between 1950 and 1974. They evaluate the combined effect of these programs using a regression model. This model estimates the impact of federal dollars spent on the change in economic health of each city between 1950 and 1980. Using census-region fixed effects, the authors evaluate the impact of expenditures on median income, median property values, the employment rate, and the percentage of people living in poverty.
The authors' first-stage findings show that federal dollars spent on urban renewal projects between 1950 and 1974 had a negative effect on various economic outcomes. However, Collins and Shester suspect there is endogeneity in the relationship they are trying to uncover. That is, they say we cannot be sure what causes what: did urban renewal cause economic growth or decline, or did blighted cities pursue more urban renewal? In the latter case, even if the program improved the economy, these cities might still be doing more poorly than cities that had no blight to begin with.
The authors deal with endogeneity using an instrumental variable approach. That is, they seek to use exogenous variation in the allocation of federal funds. The variable they use is the year in which a state passed enabling legislation that made these sorts of projects legal. At first glance, this isn't a great instrument. Instrumental variables have to meet what's called the "exclusion restriction" to be credible. That restriction is untestable; you have to evaluate this claim on its merits. So, for us to believe this instrument delivers credible result, we have to be convinced that a state's decision to pass enabling legislation affects economic outcomes only by the way it influences urban renewal expenditures. There can't be any other chain of effects of related issues that connect those two events—the instrument and the outcome.
Collins and Shester perform several tests to justify their instrument. First, they look just at the effect of the instrument in places where court cases affected the timing of the laws passing. Then they perform a test of known effects to see whether their model predicts the economic growth in rural areas where urban renewal was not pursued. Finally, they use an alternate specification of the instrument. The instrument holds up under these examinations.
The authors then use their metric to predict the urban renewal funds distributed, and then use that predicted value in the original model. In this specification, urban renewal dollars have a strong positive effect on income and property values. These findings are consistent across several specifications and robustness checks. Furthermore, they find no effect on employment or poverty rates, leading them to posit that the positive effects they observe were not generated by displacement of poorer residents from inner cities. As a whole, these results suggest that overall, urban renewal programs created positive growth in average wages and property values.
A concern is that these conclusions rest on the credibility of the instrumental variable, and I'm not sure that the instrumental variable meets the exclusion restriction. I also wonder whether the average effects might reflect underlying variation in the effect of individual programs in urban renewal as well as different contexts where the program was applied. A map of the instrument (below) shows a strong spatial component to the instrument. Of the 458 cities that the authors measured in 1950–80, 68 percent of the cities, or 311, were in states that passed enabling legislation immediately. Regions in the Northeast, Midwest, and West pursued urban renewal programs immediately. These states were the most industrialized parts of the country; they experienced sectoral change and decline of their manufacturing center. The more agricultural, conservative areas of the country pursued funds relatively later, and received funds under later programs.
Source: Collins and Shester 2014, author's calculations
This makes me wonder if there isn't sufficient variation in the manufacturing states, and that the instrumental variable instead down weights these cases, providing in essence a regional estimate. Looking at the first stage results within each census region, we find that the results vary by region. For heavily industrial regions—the Mid-Atlantic, East North Central, and East South Central—urban renewal funding had a negative on growth. The other regions show a positive relationship between urban renewal and growth and economic growth.
There is also inconsistency in the second-stage, or instrumented, results within each region. The two regions in the Midwest, stretching from Wisconsin to New York, drop out as there is no variation. The regions on the eastern half of the nation show a positive effect, while those in the West show a negative effect.
Collins and Shester want to evaluate the treatment effect of urban renewal dollars by creating as-if-random variation in the administration of urban renewal funds. But if we aren't convinced that the instrument meets the exclusion restriction, or that the policy is having a constant effect, then what can we make of the results generated by this instrumental variable? We might surmise that the instrument is telling us something about the impact of the program in the subset of cities where the instrumental variable generates variation. If we believe that the study design can actually capture the effects of urban renewal, we might think of these new estimates as telling us the average effect of later urban renewal projects in 158 cities in the South and rural West, and not so much the effect of the program in the 311 cities where urban renewal was most intensively pursued.
By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department, and doctoral student, School of City and Regional Planning at Georgia Institute of Technology
August 29, 2014
Real Estate Business Contacts on Target
After several months of year-over-year declines in housing starts and increasing concern that momentum in the housing market was slowing, we received some positive news this past week. The U.S. Census Bureau and Department of Housing and Urban Development, or HUD, jointly released the July 2014 construction statistics: total housing starts were up 15.7 percent from the upwardly revised June estimate and were 10.7 percent higher than the year-earlier level.
This news is fairly consistent with the reports we received from our real estate business contacts. Each month since 2006, the Atlanta Fed has conducted a poll of brokers and builders from six states in the Southeast in an effort to gather anecdotal intelligence. (These states are Alabama, Florida, Georgia, Louisiana, Mississippi, and Tennessee.) This intelligence not only helps form the basis for the Atlanta Fed’s construction and real estate submission to the Beige Book, but it is also considered a helpful tool for collecting insight on current market conditions before the release of the official housing statistics.
Our August poll results came in last week. Altogether, 60 business contacts participated—44 of them were residential brokers and 16, builders. The results reflect activity in July 2014. What did we find? As it relates to housing starts:
- Sixty-three percent of respondents indicated that current construction activity was ahead of the year-earlier level, 19 percent said construction activity was down, and the rest indicated that activity was about the same. In August 2014, the current construction activity diffusion index value was 0.44.
- Fifty-seven percent of respondents indicated that they expect construction activity to increase going forward (specifically, over the next three months), 13 percent said they expect activity to decline, and 31 percent said they expect the level of construction activity to remain about the same. The construction activity outlook diffusion index value also happened to be 0.44.
We compute a diffusion index of responses to help us more easily track the trend over time. Basically, we subtract the share of respondents reporting decreasing activity from the share of respondents reporting increasing activity to arrive at a diffusion index value. (We do not factor those reporting no change in activity into the diffusion index equation.)
We tend to think of any diffusion index value higher than zero as an indication that construction activity is increasing, so we’d like to think that our signal from real estate business contacts was somewhat accurate. Using this same rule of thumb, though, it is hard not to notice that our construction activity diffusion indexes have been tracking above zero since 2011, and this does not necessarily jibe with recent data releases.
So how useful is the Atlanta Fed’s Southeast housing market poll? Well, we’ve spent some time evaluating survey data against the actual outcomes to better understand what we can reasonably take away from the results. We ask contacts what their expectation is for construction activity next three months versus the year-ago period. The chart below features a scatter plot of the diffusion index on the horizontal axis and the year-over-year change in the three-month moving average of single-family housing starts on the vertical axis. (Given that we are asking them to be forward-looking, we lag the contact responses.)
We found that when the diffusion index value is greater than 0.3, starts subsequently grew. When the diffusion index values falls below -0.3, starts tended to fall. But we found that between -0.3 and 0.3, the official statistics on housing starts could go either way. This shouldn’t come as a complete surprise, particularly because a diffusion index value near zero (regardless of whether it is positive or negative) indicates that responses from contacts were mixed. Real estate markets are in fact local, so it doesn’t mean that our contacts were wrong. It simply means that their responses are less likely to match the larger picture when aggregated. And as we can see in the scatter plot above, large declines were much more likely given the time period covered.
Using a simple regression model, we would have expected June 2014 starts in the six states we cover to be around 114,000 rather than the roughly 103,000 that were reported. So while the poll results may not serve as a perfect early warning system, they do correlate with subsequent starts in the states we cover. To explore the poll results in more detail, please visit the Atlanta Fed's Construction and Real Estate Survey page.
By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed´s research department, and
Jessica Dill, senior economic research analyst in the Atlanta Fed's research department
March 27, 2014
Limiting Property Tax Assessments to Slow Gentrification
A recent New York Times article on gentrification discussed a number of cities—including Boston, Philadelphia, and Washington, D.C.—that are planning to freeze property tax assessments for long-time homeowners in gentrifying neighborhoods. The concern is that rising house prices will also raise property assessments, forcing low-income residents to move to escape the greater tax burden and thereby accelerating the pace of gentrification. Although the desire to protect existing residents from gentrification appears to be new, laws capping assessment growth for all property or all primary homes ("homesteads") have been around since Californians passed Proposition 13 in 1978. After California, a number of additional states passed laws limiting how quickly an individual property's assessed value could increase. The bulk of these laws passed in the early eighties to the mid-nineties, and advocates for the law were concerned, at least in part, with limiting the size of local government. If this tax backlash of the previous decades is uncorrelated with more recent gentrification pressures, this may be a good test of statewide assessments caps.
Using a data set of low-income central-city neighborhoods that Dan Hartley of the Cleveland Fed assembled from the 2000 census and the 2007 American Community Survey, we can look at the share of neighborhoods that gentrified in capped and uncapped states. Hartley shows that a central city moving from below-median-MSA house price to above-median house price is a good indicator of gentrification. Relying on the table of statewide assessment caps that Haveman and Sexton compiled, we identify 10 states and the District of Columbia (plus the city of New York) with the strictest limits. In these states, assessed value can increase only at the rate of inflation or by a fixed percentage ranging from 2 percent (California) to 10 percent (Texas). Table 1 presents the share of neighborhoods that gentrified in capped and uncapped states.
Note that neighborhoods protected by assessment caps actually gentrified faster than those in states without them.
However, we might worry that the decision to impose statewide assessment caps was not random. In the case of Prop 13, rising home prices was certainly a factor in rising property taxes. It is possible that some underlying factor may drive statewide price up but also cause poor inner-city neighborhoods to appreciate faster than other homes in the metro area. One candidate is restrictive zoning laws that limit densification of already desirable neighborhoods. Such laws could both drive up aggregate house prices and push homebuyers into more marginal neighborhoods, causing them to appreciate relatively faster. However, assessment caps are only one possible response to rising property taxes. If voters wish to limit the growth in property taxes, they don't need capped assessments—they can restrict the growth in property tax revenues directly. At the same time, assessment caps that don't also cap the property tax rate don't actually constrain property taxes, but instead shift the tax burden from longtime owners to new buyers. In Table 2, we limit the sample to states that have a binding revenue growth cap or that jointly cap assessments and municipal tax rates. In this case, we assume that, conditional on imposing a tax expenditure limit, the decision to cap assessments rather than property tax revenue is random. We rely on the work by Hoyt, Coomes, and Biehl (2011) to identify various statewide tax expenditure limits.
Limiting the sample to states that have chosen to constrain the property tax in some way, we still observe assessment caps seeming to accelerate gentrification rather than slow it. How can that be? One possibility is that because these are state-wide limits, the caps have reduced the turnover in more desirable neighborhoods, driving new homebuyers to marginal central-city neighborhoods. In that case, targeted assessment caps that apply only to currently low-priced neighborhoods could still be efficacious. On the other hand, the existence of an assessment cap may increase the long-run return from "pioneering" in a low-priced neighborhood.
So far, we have been using change in relative house prices as our definition of gentrification. However, advocates for assessment caps are plainly concerned about the ability of homeowners to stay in their home in the face of rising home values. While the in-migration of higher-income residents and house prices are highly correlated, we do not observe the duration of time that existing residents remain in their home. Unfortunately, there are few individual-level data sets with sufficiently granular geography to allow such an analysis. As an alternative, we can look at the change in median income of residents. This value is available at the census-tract level in the 2000 census and the 2007 American Community Survey. Table 3 presents change in median income for all census tracts and for gentrifying tracts with and without assessment caps. While median incomes rose in gentrifying neighborhoods (even as they declined nationally), they rose faster in tracts subject to an assessment cap. However, this difference is not statistically different from zero (p value 0.303).
Finally, assessment caps do nothing for renters, who may be impacted much more immediately by rising neighborhood quality than homeowners. It is possible that assessment caps could still allow a small share of long-time owners to stay, and the observed effects are just dominated by the movement of renters. If we had access to administrative data with finer geographic identifiers, we could look at whether neighborhoods that gentrified with assessment caps now exhibit more income or racial heterogeneity than neighborhoods without. However, looking only at aggregate data, property taxes do not appear to be a primary driver of neighborhood change, and concerns about gentrification do not appear to warrant interfering with the assessment process.
Chris Cunningham, research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
December 23, 2013
Exploring Immigrant Contributions to Housing Demand
Since we relaunched the Real Estate Research blog earlier this year, I've contributed two posts exploring various aspects of housing demand. The first post considered what type of housing demand (renter-occupied versus owner-occupied) we should be expecting. The second post examined the long- and short-run trends in household formations to inform how much housing demand we could expect. A key factor in determining how both aspects play out is the trend in immigration. The inflow of the foreign-born population serves as one of just two channels that drive population growth and in turn household growth. (The other channel is the birth rate of the native-born population.)
Several studies since the housing downturn have explored the trend in immigrant inflows and projected household demand for the coming decade. According to a September 2010 working paper written by George Masnick, Daniel McCue, and Eric Belsky and released by the Joint Center for Housing Studies at Harvard, an increase of between 11.8 million and 13.8 million households, depending on whether you use low- or high-immigration assumptions, is projected between 2010 and 2020. Likewise, Dowell Myers and John Pitkin prepared a report earlier this year for the Research Institute for Housing America projecting an increase of 12.8 million households between 2010 and 2020, of which immigrants account for 4.12 million, or 32.2 percent. Looking back to previous decades, Myers and Pitkin note that the 4.03 million immigrant households that arrived between 2000 and 2010 accounted for 35.9 percent of the growth in households, while the 4.36 million immigrant households that arrived between 1990 and 2000 accounted for 31.8 percent. While it is nice to have a range in mind for how many immigrant households we can expect, it is only meaningful if it is accurate.
So how accurate are these projections? It depends on immigration policy discussions and the pending legislation on immigration reform. As a reminder, the White House released a blueprint in May 2011 in an effort to advance immigration reform. After some chatter in 2011 and 2012, a bipartisan bill (S.744: Border Security, Economic Opportunity, and Immigration Modernization Act) was introduced in April 2013 and then passed by the Senate in June 2013. While not much progress has been made on this particular bill since June, several groups have come to the table with reports on the various impacts of immigration in an effort to better inform the public and policymakers. In this vein, it seems worthwhile to consider some of the literature from academics and trade organizations investigating the impact of immigration on local housing markets.
Most recently, in October 2013, the Bipartisan Policy Center released a report with analysis that "demonstrates that...fixing our broken immigration system will benefit our economy." The sensitivity analysis measured the impact of reform to immigration policy on the housing market and found that "demand for housing units increases as new immigrants enter the economy and form households, accelerating the current housing recovery and fueling growth in this sector of the economy."
Jacob Vigdor, professor of public policy and economics at Duke University, has also looked into the impact of immigration. In a report released by the Partnership for a New American Economy and the Americas Society/Council of the Americas in September 2013, Vigdor estimates that each immigrant adds 11.6 cents to the price of the average home. Vigdor's analysis also revealed four additional findings about the impact of immigrants on the vitality of American communities. First, he found that the effects of immigration on house prices are strongest in the Sun Belt cities. (See this map with complete data for each of the counties studied.) Second, he concluded that immigrants tend to avoid places with the worst housing affordability problems. Third, Vigdor found that immigrants often revitalize less desirable neighborhoods in costly metropolitan areas. And lastly, he pointed out that immigration has stabilized declining rural areas and stanched the decline of Rust Belt cities. For more detail on these and other findings, check out a video clip of Vigdor's presentation of findings to an assembly of Atlanta Fed real estate business contacts.
It is worth noting that Vigdor was not the first to find that an inflow of immigrants causes house prices to rise. In a 2006 Journal of Urban Economics article, Albert Saiz demonstrated that an inflow of immigrants increases demand for housing, thereby causing rents and house prices to rise. Specifically, Saiz found that "an immigration inflow that amounts to 1 percent of the initial metropolitan area population is associated with increases in housing values and rents of about 1 percent." This increase in house price provides some benefit to existing homeowners, but the corresponding increase in rents has negative consequences for renter households, causing some to move away from the area.
There are certainly pros and cons on each side of the policy discussion around immigration reform. It seems rather important, though, to not lose sight of the mostly positive impact that immigrants have on local housing markets.
By Jessica Dill, senior economic research analyst in the Atlanta Fed's research department
December 04, 2013
Part 2: What Caused Atlanta's House Prices to Drop Again in 2011?
Note: This post is a follow-up to the November 8 post, "What Caused Atlanta's House Prices to Drop Again in 2011?"
Case-Shiller recently released its September data. Once again, the data show that Atlanta's year-over-year performance outpaced Case-Shiller's 20 metro area index—18.7 percent versus 13.3 percent, and Atlanta's low tier experienced its fifth consecutive month of year-over-year returns in excess of 50 percent (53.2 percent).
In the November 8 post, which explored the Case-Shiller house price tiers, we noted that from July 2011 to March 2012, both Atlanta thresholds—the low–middle and the middle-high—had noticeable declines, which corresponded to the time when the low tier's year-over-year performance began to recover. Given the methodology, we said that either all prices declined or a greater proportion of transactions came from lower-valued houses. Data on mortgage closings provide evidence consistent with the idea that investor activity in Atlanta's low tier influenced the market.
We can distinguish investor activity from "typical" house purchases by looking at the form of financing. Most owner-occupiers use mortgage financing whereas investors usually purchase with cash. Thus, if there is substantial investor activity in one of the three tiers, then we would expect that tier to be underrepresented in the number of mortgage closings.
To dig further into the issue of the 2011 house price drop, we looked at the distribution of mortgage closings by tier in the Lender Processing Services (LPS) Applied Analytics database. Residential mortgage servicing data from that database contain records from the servicing portfolios of the largest residential mortgage servicers in the United States. It covers about two-thirds of installment-type loans in the residential mortgage servicing market.
The chart displays the fraction of mortgage closings by Case-Shiller by tier from third-quarter 2008 to third-quarter 2013. We created the data for the chart by using each mortgage's sales price and assigning it to a tier as defined by the Case-Shiller thresholds for the month the mortgage closed (we excluded refinances). Once we'd bucketed the data this way, we calculated each bucket's percentage of the month's closing. If cash purchases were evenly distributed and the set of servicers in the LPS database is representative of Atlanta's overall market, we would expect each bucket to be one-third of the total.
From September 2008 to October 2011, the closings appear to be evenly distributed among the three buckets, with the shares varying between 25 and 43 percent. The average share for the low, middle, and upper tiers were 36 percent, 30 percent, and 34 percent, respectively. After November 2011, the low tier's share fell to an average of 18 percent, with a low share of 12.8 percent in May 2012.
Although the chart does not conclusively prove that investors entered the market en masse to purchase houses in Atlanta's low tier, the timing of the noticeable decline in the low tier's share of mortgage closings does coincide with the fall in Atlanta's low–middle and middle–high thresholds and the bottoming of the low tier's year-over-year price declines. More recently, the low tier's share of mortgage closings has been at its highest since November 2011—perhaps a sign that investor interest has cooled and we are now looking at a more normal market.
But with year-over-year prices in the low tier rising rapidly, let's hope buyers aren't expecting 50 percent year-over-year gains to be normal.
Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
November 08, 2013
What Caused Atlanta's House Prices to Drop Again in 2011?
What happened in Atlanta real estate the second half of 2011 and the first half of 2012? I asked myself this question after looking at the recent release of the Case-Shiller Home Price Indices for August. Atlanta home prices have recently been increasing at a faster rate than the composite index. Last month, the United States saw a 12.8 percent year-over-year increase in house prices while the Atlanta index rose 18.4 percent.
It wasn't long ago that things were much worse in Atlanta. From the pre-bust peak, the city experienced a 37 percent decrease in its Case-Shiller index versus a 33.8 percent decrease for the Case-Shiller 20-metro composite. From July 2011 to March 2012, Atlanta home prices took a second nosedive, almost as large as the initial bust in 2009 (see Chart 1). So what happened?
The Case-Shiller index is a repeated sales index, which means it uses the price change between two arms-length sales of the same single-family home. One way to gauge the amount of activity in a market is to look at the number of "sales pairs" in a period. To get a sense for whether Atlanta is experiencing particularly high volumes, we can look at Atlanta's sales volume relative to the nation's. In the third quarter of 2011, Atlanta's sales began to grow substantially, and Atlanta's share of composite sales pairs peaked in March 2012 at 9.7 percent, which is a much greater percentage than the 5 percent to 6 percent range from 2000 to 2005.
Around this time, many of the Atlanta Fed's local contacts reported that some investors were buying up distressed home to convert into rental property. Case-Shiller breaks its index into three price tiers—low, middle, and high. Looking at the tiers in Atlanta for the most recent data, the high end was up 12.9 percent year over year; the middle tier, 27.7 percent; and the low tier was up 52.5 percent. Looking back, we see that the growth rate in Atlanta's low-tier index started to recover in July 2011 (see Chart 2). It was not until March 2012 when the year-over-year changes in the middle and high tiers started their recent upward trends.
The price thresholds for the three tiers are computed using all sales for each period and are set so that each tier has the same number of sales. From July 2011 to March 2012, both thresholds (low–middle and middle–high) had noticeable declines (see Chart 3). Given the methodology, either all prices declined or a greater proportion of transactions came from lower-valued houses. Note that after March 2012, the breakpoints started to increase, which was the same time as when the year-over-year growth in the middle and high tiers started to improve.
Further work is needed in order to determine whether there really was a ramp-up in activity in the low end of the market. If such activity did occur, it raises the question as to what was driving the activity—could it have been investors? If not, how was this activity financed? Was this a case of inventory being absorbed, prices adjusting, and momentum moving from investors to "normal" buyers?
The low tier warrants attention given the fact that it may have driven Atlanta's recent house price performance. Understanding the July 2011 to March 2012 period may shed light on the factors that could influence the market going forward.
Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
August 09, 2013
Recent Trends in Bank Construction Lending and Sentiment
One of the big questions for the economic recovery is the extent to which the improvement in the housing sector is sustainable. The statements from the Federal Open Market Committee (FOMC) over the past three years reveal an interesting evolution in the way the Committee views housing activity. Consider the subtle changes:
|-||06/2009: Household spending has shown further signs of stabilizing but remains constrained by ongoing job losses, lower housing wealth, and tight credit.|
|-||09/2009: [A]ctivity in the housing sector has increased.|
|-||11/2009: Activity in the housing sector has increased over recent months.|
|-||12/2009: The housing sector has shown some signs of improvement over recent months.|
|-||03/2010: [I]nvestment in nonresidential structures is declining, housing starts have been flat at a depressed level.|
|-||04/2010: Housing starts have edged up but remain at a depressed level.|
|-||06/2010: Housing starts remain at a depressed level.|
|-||09/2010: Housing starts are at a depressed level.|
|-||11/2010: Housing starts continue to be depressed.|
|-||12/2010: The housing sector continues to be depressed.|
|-||08/2011: [T]he housing sector remains depressed.|
|-||04/2012: Despite some signs of improvement, the housing sector remains depressed.|
|-||08/2012: Despite some further signs of improvement, the housing sector remains depressed.|
|-||09/2012: The housing sector has shown some further signs of improvement, albeit from a depressed level.|
|-||12/2012: [A]nd the housing sector has shown further signs of improvement....|
|-||01/2013: [T]he housing sector has shown further improvement.|
|-||03/2013: [T]he housing sector has strengthened further....|
|-||07/2013: [T]he housing sector has been strengthening....|
I'll leave the exact parsing of FOMC statements to private experts. What I want to address is the way banks are reacting, or perhaps contributing or being less of a barrier, to the strengthening housing sector.
In my July 10 posting, I discussed the correlation between bank construction lending and residential construction activity—larger changes in construction lending are associated with a higher level of construction put in place. Last week the Census Bureau reported that total construction spending fell. The good news is that residential construction, on a seasonally adjusted annual rate, was essentially flat from May to June and was up significantly compared to June 2012. So is bank behavior consistent with improving residential construction spending? Two sources help to shed light on what banks are thinking and doing: the Senior Loan Officer Opinion Survey (SLOOS) and bank call reports as of June 30, 2013.
The SLOOS asks how the respondent banks' credit standards for approving applications for commercial real estate loans (CRE) loans have changed over the past three months. Since 2011Q2, the net percentage of domestic banks tightening standards for CRE loans has been negative, which indicates loosening (see the chart). CRE, however, includes not only loans for construction and land development (C&D), but also loans secured by nonfarm, nonresidential properties and multifamily residential properties. The latter two loan types finance existing structures rather than construction activity, thus it is impossible to determine whether the loosening since 2011 applies to construction lending.
Fortunately, the most recent SLOOS had special questions that asked for the changes in standards and demand over the past 12 months for the three different types of CRE loans: C&D loans, loans secured by nonfarm nonresidential properties, and loans secured by multifamily residential properties.
Though on net the standards for all CRE loans type appear to be loosening, multifamily and nonresidential loans are likely to have been the drivers of the easing of overall CRE standards since 2011Q2. Unfortunately, because these were special questions, there is no time series to aid with putting the responses in context.
Do actions speak louder than words?
Ideally, to gauge bank lending activity, we would look at the volume of new construction loans—new loans enable new activity. What we observe, however, is total C&D loans outstanding—which net out loan payoffs, write-downs, and reclassifications—continue to be decreasing in aggregate. In contrast to the overall industry where the top 100 banks account for 80 percent of total bank assets, the top 100 C&D lenders, which are not necessarily the top 100 banks, account for only 60 percent of bank C&D lending. Given the available data and the fact that smaller banks are relatively more important in the C&D space, the percentage of banks that are increasing their construction lending may be a better indicator of changing sentiment (see table below).
With 48.1 percent of banks reporting year-over-year growth in C&D loans, activity cannot be classified as robust, but neither is it bouncing along the bottom. Not that we should aspire to the frothy levels of C&D lending that prevailed during the housing bubble, but compared to 2010, it is clear that more banks are reentering the C&D market, which bodes well for housing starts and construction spending going forward.
By Carl Hudson, director of the Center for Real Estate Analytics in the Atlanta Fed's research department
July 24, 2013
The Shape of the Housing Recovery in Atlanta
As with politics, all real estate is local, and it seems that it is always someone else's neighborhood that's doing well. By all indications, housing is recovering in 2013. Nationally, starts and prices show promising growth. Even in Atlanta, Georgia—a hotbed of subprime lending and speculative construction before the crash—home prices have made a strong rebound, showing a 21 percent year-over-year increase in April 2013 (see the chart). In this post, we address the questions: What shape is this recovery taking? Will this rebound look a lot like the early 2000s? Or can we expect permanent changes to the urban landscape post-crisis?
Using zip code-level data from CoreLogic to take a closer look at home price recovery in Atlanta, we see that this rebound is not evenly distributed. Instead, variation in growth rates is much higher than it used to be. In general, Atlanta home prices have been very depressed, but at the same time, contacts have remarked that in hot, single-family markets, prices are up, multiple offers are made as soon as a property is listed, and there is a general shortage of homes "where people want to move." The data confirms and adds to this anecdotal evidence: Atlanta's strong price growth is concentrated in select intown markets, as well as in many of the areas hardest hit.
Prior to the crisis, home prices appreciated at about the same rate throughout the Atlanta metropolitan region. Yes, some areas were expensive and others affordable, but prices grew everywhere at roughly the same pace. But when the real estate market crashed in 2007, Atlanta home price rates of change began to diverge. In the past, zip codes with the highest growth rates grew 20 percent faster than zip codes with the lowest rates. Now, that ratio has risen to 300 percent.
To illustrate, the standard deviation in home price growth increased sharply during the crisis and continued to widen during the last year of recovery (see table 1). This pattern also describes the nationwide trend, although the increases in variation in Atlanta are more dramatic.
The obvious explanation for this is that during the crash, although prices fell everywhere, areas with concentrations of distressed properties fell more steeply, generating this variation. Now that the recovery is under way, though, will areas with a high density of distressed properties rebound? Or are these areas reset permanently at a lower level?
The evidence from Atlanta suggests that we will see a bit of both. Fast growth is concentrated in some of the areas that were hardest hit, as well as in some of the choicest neighborhoods in town. This extremely fast growth is paired with slow growth in many markets that never saw steep declines, generating a higher standard deviation in home prices.
The first map below shows year-over-year home price change during the recovery. The second map depicts peak and trough change, showing the depth of the decline. We see that two recovery stories emerge. First, north of I-20, areas that were quite resilient during the crisis and did not see strong declines are experiencing strong growth. Second, in the areas southeast of the interstate 285 perimeter, we see exurbs that were devastated by devaluation experiencing a strong rebound, with growth rates over 16 percent.
How can we best understand this pattern of recovery? We reviewed a few likely correlates with home price increase: household income, household size, and density of high-risk lending and speculative construction during the bubble. None of these factors was significantly correlated with the 2013 rebound. The peak-to-trough change is significantly correlated with the rate of recovery, suggesting that much of this recovery is a price correction. Longer commute times are also correlated with recovery, revealing that demand is increasing in places that are not close to job centers, though it's possible that commute times are simply a proxy for severity of the crash as these areas also experienced the strongest declines.
Contacts tell us that neighborhoods with better school districts are performing well and recent investor interest may also be playing a role. What is certain is that Atlanta's strong overall house price growth is driven by increases in areas hardest hit by the housing downturn and by a few centrally located markets, and that underneath the citywide average there is a lot more variation than we have experienced in the past.
We'd love to hear your thoughts about these trends!
By Elora Raymond, graduate research assistant, Center for Real Estate Analytics in the Atlanta Fed's research department/PhD student, School of City and Regional Planning, Georgia Institute of Technology, and
Carl Hudson, Director, Center for Real Estate Analytics in the Atlanta Fed's research department
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