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Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.

Authors for Policy Hub: Macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.

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February 1, 2021

Ecommerce's Spillover Effects on the Local Retail Real Estate Market

Over the past decade, online shopping has become the primary shopping method for many. Consequently, concerns that ecommerce will replace traditional retail outlets and make physical stores obsolete have increased. However, the expansion of ecommerce drives online retailers to establish large fulfillment centers nationwide. To ensure that customers receive packages within two days, online retailers aggressively accelerated fulfillment center openings. A major online retailer, one that accounts for about 50 percent of total ecommerce sales, has opened more than 150 fulfillment centers, located across 88 U.S. counties as of 2018. Each fulfillment center hires its own workers, and thus a new fulfillment center's local economic impact might be the opposite of the typical negative belief about ecommerce's effects on brick-and-mortar stores. To estimate the overall effect of ecommerce on the local retail market, I use a fulfillment center of this online retailer as a proxy to an ecommerce local presence. Specifically, I focus in this post on retail real estate transaction prices, since store sales heavily influence property values. (Retail property sales price is commonly estimated using the direct capitalization method.)

Among the counties that received a fulfillment center, I select counties with a fulfillment center that opened between 2013 and 2015, giving clear three-year periods before and after the opening. Control counties are the matched counties that are strong potential fits for a fulfillment center establishment yet did not receive one. Fulfillment centers are more likely to be in counties where population density and education level are higher than average but median household income and median age are lower than average. These counties experience slightly faster retail establishment growth, but other demographic or industry factors did not influence whether a firm decided to establish a fulfillment center or not. Using those economic and demographic factors that may affect fulfillment center establishment, I select control counties that have very similar growth rates to the treatment counties with a fulfillment center establishment. Figure 1 indicates the average retail property price changes relative to fulfillment center establishment between treatment and control counties. Both treatment and control counties exhibit similar price movement prior to a fulfillment center opening, thus meeting the parallel trend assumption. However, the average retail property values move differently following the local fulfillment center establishment.

Impact of ecommerce on retail property transaction values
Using a difference-in-difference specification, I test how the expansion of ecommerce affects local retail property values. The model includes property characteristics and market controls. If ecommerce affects brick-and-mortar stores negatively, we should expect to see lower transaction values for local retail properties, but results exhibit contrary behavior. The entry of an ecommerce fulfillment center has a positive effect on local retail store values. Retail properties located in counties where a fulfillment center operates sold at a 5.2 percent premium relative to properties in similar counties without a fulfillment center. The premium persists through the fifth year, with the largest gains seen in year three. The natural environment of commercial real estate transactions explains the delayed effect on the retail property market. The average time on market for a retail property in the full sample is 357 days, which helps explain why retail properties' transaction prices experience a delay.

This positive pricing effect is significant in the retail and multifamily markets, but not in the office and warehousing markets. This state of affairs indicates that the local market's stronger overall economic growth drives results. A concern stems from clustering effects—that is, the pricing effect will move upward if other retailers open their warehouses in the same counties at a similar time. Using the largest big box retailer as a means to isolate this effect, I exclude the counties where this retailer also opened a warehouse within three years of the opening of the proxy online retailer's fulfillment center. The results are robust as they persist even after I account for the clustering effects involving another retailer.

Local labor market structure changes
Establishing fulfillment centers changes the composition of the local labor market of treatment counties as businesses focus on warehousing services and generate greater demand for labor. The share of employment in the warehousing-related industries (NAICS codes 48 and 49), their salaries relative to a county's average, and the number of warehouse establishments relative to total business establishments increase by 0.9 percent, 1.1 percent, and 0.5 percent, respectively, relative to control counties following a local fulfillment center opening. The positive effects may spill over into the local retail labor market as overall labor demand increases. The retail trade sector also exhibits positive spillover effects on establishment and payroll in the third year (see figure 2). However, the increase in retail employment is not statistically significant. If general economic growth of the treatment counties drives positive labor market effects, I might expect to find that the finance and insurance industry (NAICS code 52) also grows following the establishment of a fulfillment center, which is not the case.

Figure 2: Trends in Payroll, Establishment, and Employment

Transportation and Warehousing

Retail Trade

Finance and Insurance
Source: Census County Business Patterns and author's calculations

Which areas are most sensitive to these effects?
Areas where warehousing employees tend to live—that is, areas with higher population densities, lower median incomes, higher renter-occupied rates, lower levels of education, younger population, and lower rates of internet access—tend to pay higher premiums for retail properties and exhibit a stronger, positive effect on local retail property values after the establishment of a fulfillment center.

Overall, the findings in this post seem to provide another silver lining to the consolidation among retailers and the increased focus on digital rather than physical footprints. Contrary to the common belief that online retailers destroy local communities, the data appear to indicate that ecommerce could positively contribute to local economies and the value of retail property. In a county with a fulfillment center, retail properties transact 5.2 percent higher compared to properties in counties that appeared to be a strong fit for a fulfillment center but did not receive one. Growth in labor demand for the warehousing sector and a potential positive spillover effect to the retail sector also support these findings. The expansion of ecommerce anchored by the establishment of a fulfillment center might help local economies and could cause local labor markets to specialize in warehousing. Finally, a heterogeneity test suggests that the positive pricing effect on local retail values is stronger in areas where fulfillment center employees are more likely to live.

While these results perhaps cast online retailers in a positive light, the persuasiveness of this post's findings are limited. Higher property values could be the result of local government subsidies to online retailers, and I don't assess the long-term impact on warehousing and retail labor markets. As this analysis concerned only the county level, its ability to capture the overall effects of ecommerce on the servicing areas is limited. To promote a mutually beneficial relationship between ecommerce firms and brick-and-mortar retailers, I suggest that local governments and policymakers need to be aware of the spillover effects resulting from fulfillment centers and implement proactive, clearly beneficial policies for both the warehousing and retail industries.

May 27, 2020

COVID-19 Mortgage Relief—The Role of Income Support

The COVID-19 pandemic has led to a large number of furloughs, layoffs, reductions in hours worked, and wage cuts. Anticipating that many homeowners would consequently have problems paying their monthly mortgage bill, the U.S. Department of Housing and Urban Development ordered all mortgage servicers of federally backed debt to provide forbearance to any homeowners affected by the crisis. In addition, bank regulators encouraged lenders to forbear and restructure mortgages for borrowers affected by the shutdown, actions that staved off an immediate wave of foreclosures. At the end of the forbearance window, borrowers will likely be offered a series of repayment schemes: starting with a period of catch-up payments, then moving to extended terms on their mortgage or possibly even rate reductions. However, if the borrower has not returned to work, paying for what is effectively a new mortgage obviously poses a challenge. Options such as creating a modified repayment plan, lowering the mortgage interest rate, or extending the term of the loan might not be enough for a borrower who has experienced a substantial income loss.

In 2009, researchers at the Boston Fed proposed an alternative policy of supplemental mortgage payment assistance targeted to underwater borrowers experiencing a significant reduction in disposable income due to factors such as employment loss or medical costs associated with illness. That 2009 research built on earlier Boston Fed research demonstrating that—during a previous housing market downturn—most underwater households continued to pay their mortgages unless they were hit with a further reduction in earnings or increase in expenses. The idea that mortgage default is caused by both a negative house price shock and a negative income/employment shock is known as the "double trigger" theory of default. However, the empirical evidence on the double trigger theory was limited. Underwater homeowners in areas with increased unemployment appeared to default more, but this was mostly an interesting correlation, not necessarily a causal relationship.

Since the Great Recession, considerable research (here, for example) has tried to identify the central role income shocks play in default. The econometric challenge is that shocks to income from changes in employment or wages tend to be capitalized into house prices. So a community experiencing the second trigger from widespread job loss, say, will likely also experience a drop in house prices, making it difficult to isolate the real cause of default. In a forthcoming paper we consider the unique sources of changes in employment and income arising from the hydraulic fracking boom in Pennsylvania in the late 2000s to isolate the second trigger from the first.

Fracking involves injecting large amounts of water, sand, and potentially toxic chemicals underground at great pressure to break shale formations and release the trapped natural gas. The fracking process also involves piercing aquifers, storing and treating large quantities of contaminated water, and employing heavy equipment. Some evidence shows that these real or perceived negative features lower the value of homes near fracking wells. At the same time, the shale boom increased demand for middle- and low-skilled workers and generated significant royalty payments to many property owners.

Observing the performance of mortgages that originated before fracking began allows us to treat the resulting shale boom as an experiment where household incomes were sustained (or increased) even as housing prices were flat or declining. Using geological information to predict the location of fracking activity, we find that fracking wells significantly raised total household income, from both wages and royalties, and the wells appear to have increased employment in fracking-related industries. At the same time, fracking does not appear to have raised house prices or made it less likely that a household has negative equity. However, fracking does significantly reduce the probability that a mortgage becomes seriously delinquent (that is, when a borrower misses more than a few payments).

In addition, when we use only geology to predict the location of fracking wells, we get a much larger decline in mortgage delinquency, suggesting that more vulnerable communities were quicker to embrace fracking. Finally, the ameliorative effects of fracking were concentrated among borrowers who are likely to be underwater on their mortgages (the first trigger), consistent with the double trigger hypothesis, since the theory predicts that borrowers with positive equity are unlikely to default in the first place.

Our results suggest that an effective strategy for preventing a foreclosure crisis in the current situation is direct support of household income. Indeed, the Coronavirus Aid, Relief, and Economic Security Act (commonly known as the CARES Act) contains several income transfers to help sustain household budgets, including expanded unemployment insurance, direct cash payments to most households, and loans to small firms that are forgivable on the condition that they sustain employment through the shutdown. It is our view that these programs are not simply helping to sustain families during the crisis, but they're also limiting disruption to the housing market. Depending on how the crisis evolves in the coming months, further income support for affected households may forestall the need for less efficacious interventions to aid distressed borrowers.

June 16, 2016

Experts Debate Policy Options for China's Transition

After nearly three decades of rapid economic growth, China today faces the challenge of economic rebalancing against the backdrop of slow and uncertain global growth. Although investment and exports have been a motor for growth, China is increasingly experiencing structural issues: widening inequality, overcapacity as a consequence of policy distortions, unsustainable environmental costs, volatile financial markets, and rising systemic risk.

On April 28–29, I attended the First Research Workshop on China's Economy, organized jointly by the International Monetary Fund (IMF) and the Atlanta Fed. The workshop, held at the IMF's headquarters in Washington DC, explored a series of questions that have emerged as China shifts toward a new growth model. Is this the end of the growth miracle? Will the Chinese renminbi one day be as important as the U.S. dollar? Should the rapidly increasing shadow banking activity in China be a source of concern? How worrisome is the rapid rise in China's housing prices?

Panelists shared their views on these and other issues facing the world's second-largest economy (or largest, if measured on a purchasing-power-parity basis). Plans are under way for a second workshop to be held in 2017.

The following is a nice summary of the research discussed at the workshop. It was originally published in the IMF Survey Magazine, and was written by Hui He, IMF Institute for Capacity Development, and Nan Li, IMF Research Department. Thanks to the IMF for allowing me to repost it here.

Is China's economic growth sustainable?
Understanding the source of China's tremendous growth was a recurring theme at the workshop. "China's economy combines enormous dynamism with huge distortions," observed Loren Brandt (University of Toronto). Brandt described his research based on China's firm-level data and emphasized that firm dynamics (entry and exit), especially firm entry, have been the main source of the productivity growth in the manufacturing sector.

Echoing Brandt's message, Kjetil Storesletten (University of Oslo) discussed regional growth disparities and showed that barriers preventing firms from entering an industry account for most of the disparities. Such barriers are more severe for privately owned firms in regions in which state-owned enterprises (SOE) dominate, he said.

In his keynote speech, Nicholas Lardy (Peterson Institute for International Economics) offered an upbeat view on China's transition to a new growth model, one in which the service sector plays a larger role than manufacturing. The bright side of the service sector, he noted, is its continued strong productivity growth. The development of financial deepening and the stronger social safety net are contributing to increased consumption, which helps to rebalance the economy.

However, he emphasized, SOE reforms remain critical as the service sector cannot provide a silver bullet for a successful transition.

Central bank's policy decisions
Several participants tried to discern how the People's Bank of China (PBC) conducts monetary policy. Tao Zha (of the Atlanta Fed's Center for Quantitative Economic Research and Emory University) found that the PBC reacts sharply when the gross domestic product's growth rate falls below its target, increasing the money supply by 11.5 percentage points for every 1 percentage point shortfall.

Mark Spiegel (Center for Pacific Basin Studies) discussed the trade-offs involved in Chinese monetary policy—for example, controlling the exchange rate versus maintaining inflation stability. He also argued that the heavy use of reserve requirements on banks as a monetary policy tool might have an unintentional consequence to reallocate capital from SOEs to more efficient privately owned firms and could therefore offset the resource misallocation caused by the easy credit to SOEs that banks granted in the high growth years.

Renminbi versus the dollar
Eswar Prasad (Cornell University and Brookings Institution) argued that China's capital account will become more open and the renminbi will be used more widely to denominate and settle cross-border transactions. But he also noted that legal and institutional constraints in China were likely to prevent the renminbi from serving as a safe-haven currency as the U.S. dollar does today.

Moreover, he said, the current sequencing of liberalization initiatives—that is, removal of capital account restrictions before appropriate financial market supervision and regulation and exchange rate reform—poses financial stability risks.

Shadow banking and the housing market
Recently, volatile Chinese financial markets and continued housing price appreciation have raised serious financial stability concerns.

Michael Song (Chinese University of Hong Kong) argued that rapidly rising shadow banking activity is an unintended consequence of financial regulation. Restrictions on deposit rates and loan-to-deposit ratios have led to the issuance by banks of "wealth management products" to attract savers with higher returns. Because these restrictions had a greater impact on small banks, the big state banks had more room to undercut the smaller banks by offering wealth management products with higher returns and then restricting liquidity to them in interbank markets, ultimately making the banking system more prone to liquidity distress and runs.

Hanming Fang (University of Pennsylvania) found that, except in big cities such as Beijing and Shanghai, housing prices in China's urban areas between 2003 and 2013 more or less tracked rising household incomes. In his view, the Chinese housing boom is thus unlikely to trigger an imminent financial crisis. He warned, however, that housing prices may fall rapidly if economic growth slows dramatically, and that such a development could, in turn, amplify the economic downturn.

Rising wage inequality
China's rapid growth over the past two decades has been accompanied by rising wage inequality, an issue highlighted by two conference participants. Dennis Yang (University of Virginia) explored the distributional effects of trade openness in China and found a significant impact on wage inequality of China's accession to the World Trade Organization in 2001.

Chong-En Bai (Tsinghua University) argued that the decline after 2008 of the skill premium—that is, the ratio of the skilled labor wage to the unskilled labor wage—can be explained by the Chinese government's targeted credit extension to unskilled labor-intensive infrastructure sector (as part of the fiscal stimulus following the global financial crisis). Such distortionary policies might have short-run growth benefits but could lead to long-run welfare losses, he said, especially when rural-to-urban migration has run its course.

June 9, 2016

It’s Not Just Millennials Who Aren't Buying Homes

In recent years, much attention has been focused on the growing tendency of millennials to rent. Theories for the decrease in homeownership among young adults abound. They include rising student debt levels that crowd out additional borrowing, a tendency to live in more urban areas where the cost to buy is relatively high, a generally tougher credit environment, and even shifts in the perception of homeownership in the wake of the housing bust. The ideas have been widely debated, and yet no single factor seems to neatly explain the declining share of the millennial population opting to buy a house. (See this webcast by the Atlanta Fed's Center for Real Estate Analytics for a discussion of these issues.)

To the extent that these factors are true, they may be affecting the decisions of other generations as well. Chart 1 below shows the overall average homeownership rate and homeownership rates by age group from 1982 to 2015. It's clear that homeownership rates have declined for everyone during the past 10 years, not just for millennials.

In fact, homeownership among young Generation Xers has fallen by a bit more than the millennial generation since the housing peak—declining 11 percentage points since 2005 compared with a decline of 9 percentage points for those under 35 years old.

Another interesting point of comparison is the mid-1980s to mid-1990s, a period in which the United States had a relatively stable share of owner-occupied housing of around 64.0 percent. During the subsequent housing boom, the homeownership rate climbed to a peak of 69 percent in 2004, only to fall back down to 63.7 percent in 2015, a level similar to that prevailing before 1995. However, each age group under age 65 has a somewhat lower homeownership rate than their same-aged peers had during the 1986–94 period.

Chart 1: Homeownership Rates by Age

The fact that the average U.S. homeownership rate is close to rates seen in the mid-1980s and mid-1990s while homeownership rates within age groups (under 65) are currently lower than their respective averages in the mid-1980s to mid-1990s suggests that factors other than age may be affecting the average person's decision to buy or rent.

To investigate what else may be going on, charts 2 and 3 show homeownership rates by family type and race. Between 2005 and 2015, the trend mirrors what's happening by age group. The tendency to own a home has been falling for all family types and races over the past decade. In general, economic incentives (or cultural attitudes) appear to have shifted the population toward renting and away from buying.

However, the picture is quite different when you compare homeownership rates by family type and race to the pre-1995 period. While homeownership rates within age groups are generally lower today, married couples, one-person households, and nonmarried, multiperson households were all more likely to own their home in 2015. Homeownership rates across race (except for blacks) were also higher in 2015 than in 1994.

Chart 2: Homeownership Rates by Type of Family

Chart 3: Homeownership Rates by Race or Ethnicity

So how do we interpret the fact that the overall homeownership rate is close to its average in the 1986 to 1994 period? Are millennials to blame? Yes. But so is everyone else under the age of 65. The data suggest that whatever is affecting millennials' homeownership decisions is applicable to older individuals as well. Further, it seems there are other, possibly larger, factors affecting homeownership, such as the changing face of America. Although homeownership rates by family types and racial groups are a bit above the level seen in 1994, the average person in 2015 was about as likely to live in a home that is owned or being bought. Thus, the shift in the distribution of the population toward racial groups and family types (and likely other factors) that tend to have lower homeownership rates is likely exerting an important influence on the overall homeownership rate.