Real Estate Research

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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.


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.

SE-Home-Sales

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.

Broker-Home-Sales

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.

Photo of Carl Hudson By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed´s research department, and

 

Photo of Jessica DillJessica Dill, senior economic research analyst in the Atlanta Fed's research department

November 18, 2014 in House price indexes, Housing demand, Housing prices | Permalink | Comments (0)

October 15, 2014


Bringing Foreign Investment into Economically Distressed Markets: The EB-5 Immigrant Investor Program (Part I)

The saying goes that all real estate is local. But investment from overseas—real dollars for both residential and commercial projects—is becoming a more common opportunity for developers, especially those working in economically challenged markets. Some euro zone countries—Greece and Spain, for example—offer visas to wealthy Chinese home buyers.

One source for commercial projects that is gaining the attention of community and economic development practitioners is the Immigrant Investor Program, called EB-5, a federal program designed to attract foreign investment to real estate projects in challenged markets. The EB-5 program provides residency status (green cards) to foreign investors in exchange for personal capital investments that create jobs in the United States.

EB-5 has been around since 1990, but growing wealth overseas (largely in China) and the financial crisis of 2008 created more demand for the program. More recently, the program has attracted the attention of a variety of economists, academics, and the media. In July 2014, for example, the unlikely political tripartite of mega-investors Sheldon Adelson, Warren Buffet, and Bill Gates endorsed the program in a New York Times op-ed. A few short months later, a Fortune Magazine piece referred to the EB-5 program as "a magnet for amateurs, pipe dreamers, and charlatans, who see it as an easy way to score funding for ventures that banks would never touch."

In this first of two posts, I present the rough mechanics of EB-5 and some of the trends and challenges facing the program. In the next post, I will address the program's impacts in the southeastern region and further assess its strengths and weaknesses.

The mechanics of EB-5
A recent report from the Initiative for a Competitive Inner City (ICIC) profiled the EB-5 program. The ICIC report has a detailed infographic on the anatomy of an EB-5 investment. According to authors Kim Zeuli and Brian Hull, the major components of the EB-5 program are as follows (we've summarized their information in this flow chart):

Rer-chart

EB-5 investments can be made either directly into projects or through third-party entities called "regional centers." The city of Miami, for example, was recently authorized to act as a regional center. These regional centers can source deals, pool investment capital, and provide a number of other advantages, including the ability to count indirect and induced employment toward the program's job requirement. (Direct EB-5 investments can count only direct job creation.) Note that EB-5 investments can be used anywhere in a project's capital stack. Regional centers usually have the finance expertise to leverage EB-5 for additional equity or debt, or to reduce risk for more traditional finance.

The United States has more than 575 regional centers, up from 13 in 2007. The ICIC, citing data from the U.S. Department of State, reports that from 2003 to 2013, 85 percent of all EB-5 visas were issued to investors from China, South Korea, Taiwan and the United Kingdom; 65 percent of them were issued to Chinese investors.

Immigrant-petitions

Impacts and challenges
Since Congress first authorized the EB-5 program, it has captured about $5 billion in direct investments and created more than 85,000 full-time jobs, according to a recent report by Brookings. But the Brookings report also points out that the scarcity of reliable historical data makes it very difficult to evaluate the entirety of the program's impact. The ICIC report includes a summary and analysis of several hundred EB-5 projects. According to ICIC, the largest challenges facing EB-5 are uncertainty around the immigration approval process and the up-front capital and technical knowledge required to execute a project with EB-5 investment.

The next installment of this series will address the program's impacts in the southeastern region and further assess its strengths and weaknesses.

As part of the Atlanta Fed Community and Economic Development program's efforts to bring attention to economically distressed communities in the Southeast, we will be examining specific tools and policies—like EB-5—and sharing what we learn, sometimes in this blog.

Will Lambe is a senior adviser with the Atlanta Fed's Community and Economic Development program, focusing on community development finance.

October 15, 2014 | Permalink | Comments (0)

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.

Instrumental-variable-map
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.

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 Georgia Institute of Technology

September 18, 2014 in Foreclosure laws, Housing crisis, Housing prices | Permalink | Comments (0)

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.

Southeast-construction-activity

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.)

Builder-contact-poll

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.

Photo of Carl Hudson By Carl Hudson, director for the Center for Real Estate Analytics in the Atlanta Fed´s research department, and


Photo of Jessica DillJessica Dill, senior economic research analyst in the Atlanta Fed's research department

August 29, 2014 in Housing prices | Permalink | Comments (0)

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