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 Jessica Dill, Kristopher Gerardi, Carl Hudson, and analysts, as well as the Boston Fed's Christopher Foote and Paul Willen.
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June 20, 2011
The housing wealth effect revisited
A lot of economic researchers have struggled to answer what seems like a simple question: how much does consumption rise when housing prices go up? Many economists believe that, by increasing the wealth of homeowners, rising house prices during the housing boom had a substantial positive effect on consumption (see this speech by former Fed Chairman Alan Greenspan). Along the same lines, the dramatic decline in home values during the Great Recession is thought by many to be a drag on consumption today.
Conventional wisdom among market participants, and to some extent academics, is that the marginal propensity to consume (MPC) out of housing wealth is somewhere between 3 and 5 cents per dollar for households in the United States (see this paper by Bostic, Gabriel, and Painter for a nice survey of the literature on housing wealth effects). In other words, on average, every $1 increase in housing wealth results in a 3–5 cent increase in consumption. However, many researchers are highly skeptical of these numbers for a number of reasons (see, for example, this 2009 critique). First, a basic life-cycle model that incorporates Friedman's permanent income hypothesis (PIH) would predict that modest changes in housing wealth (both anticipated and unanticipated) should result in changes in consumption that are smaller than the 3–5 cent estimate.
Second, most research that finds big MPCs out of housing wealth estimate these with aggregate consumption and wealth data (see, for example, this 2005 study by Case, Quigley, and Shiller). Aggregate regressions like these are highly susceptible to an omitted variables critique; it may be that both consumption and housing prices are driven by some other variable not accounted for in the regressions. One possibility is that as expected future income in a particular area goes up, the anticipated increase would cause residents in this area to consume more—because their future incomes are higher—at the same time house prices are rising—because more people would want to live in that particular area and enjoy these higher incomes. Ideally, one would use micro-level consumption and housing data to control for these confounding variables. Unfortunately, high-quality panel data on both housing values and household consumption have been hard to find.
An innovative new paper by Jie Gan of the Hong Kong University of Science and Technology recently published in the Review of Financial Studies may have partly solved these data issues. The paper uses property-transaction and credit-card data from Hong Kong to estimate the causal effect of changes in housing wealth on individual-level consumption behavior. For her mortgage and housing data, Gan uses a data set similar in quality and scope to those now being used in the United States (including some papers that we have written). Specifically, Gan obtained detailed mortgage data and some demographic information from a large Hong Kong bank for the period 1988–2004. She also obtained government data on the universe of all housing and mortgage transactions in Hong Kong from the early 1990s to the mid-2000s, which allowed her to construct district-level house price indices. For her consumption data, Gan used monthly credit card statements from the largest credit card issuers in Hong Kong, available for the early 2000s. While credit card spending is certainly not a perfect measure of consumption, Gan argues that credit
cards account for more than 20 percent of consumer spending in Hong Kong and are used to purchase a diverse, representative set of products.
Merging these three data sets yields a panel of about 12,000 homeowners along with monthly information on credit expenditures and home values during the 2000–2 period. Gan then estimates a regression of quarterly consumption growth, measured with the individual-level credit-card data, on quarterly growth in lagged housing values, measured at the district level, and a set of individual fixed effects that control for time-invariant individual characteristics. The regression also includes a set of interaction variables between quarterly time dummies and occupational categories. These variables control for changes in income growth that may be correlated with both house price movements and consumption growth.
Gan finds that the elasticity of consumption growth to house price growth is about 0.17, which implies that a 10 percent increase in house price growth leads to a 1.7 percent increase in consumption growth. While this is a large effect, it translates into a slightly lower MPC out of housing wealth than the 3–5 cent effect common in previous studies. The reason is that the estimated sensitivity of consumption growth to house price growth that Gan estimates must be multiplied by the consumption-to-housing wealth ratio in Hong Kong in order to construct an estimated MPC out of housing wealth. The consumption-to-housing-wealth ratio is relatively small in Hong Kong (11.5 percent) because housing is extremely expensive. Consequently, the MPC out of housing wealth in Hong Kong is about 2 cents per dollar. While 2 cents seems low, especially given previous empirical evidence, Gan argues that this seemingly low MPC should not be construed as evidence that changes in housing wealth have only a small effect on economic activity. To the contrary, because housing is so expensive in Hong Kong, a small MPC implies substantial impacts of changes in house prices on the real economy.1
Given these large and statistically significant results, an obvious question is whether they truly correspond to causal effects, or whether omitted variables might still be causing problems. The answer is probably mixed. On one hand, Gan's data is a significant improvement over previous studies on multiple dimensions. The micro-level credit-card panel data allow her to use individual-level fixed effects, something not possible when using aggregate consumption data. As Gan points out, a fixed-effects specification controls for unobserved household heterogeneity that could lead to biased estimates. Other studies have used micro-level consumption data from the Panel Study of Income Dynamics (PSID), but that data set only measures expenditures on food and has been found to contain substantial measurement error (see Runkle 1991 for evidence of substantial measurement error in the PSID measure of food consumption). Furthermore, the district-level house price indexes used to calculate estimates of home values are more disaggregated than the state-level and even MSA-level house price indexes that previous studies have used. As a result, Gan's price indexes probably suffer less from measurement error than the price indexes in other work.
But even with these significant improvements in data quality, Gan may not have solved the endogeneity issue. As Gan writes in her paper, "[s]ince housing prices are available only at the aggregate level or at the level of the metropolitan statistical areas (MSAs), the observed consumption sensitivities may be driven by economic- or MSA-wide shocks that simultaneously affect housing prices and consumption." In other words, from an econometric standpoint, a regression of consumption growth at the individual level on house price growth at the district level is only identified from time-series changes in average consumption growth at the district level and house price growth at the district level.2 That means that other, unobserved district-level variables could potentially be driving the correlation between consumption growth and house price growth. As noted above, one potential omitted variable is expectation of higher future income at the district level.
Gan downplays this issue by noting that many Hong Kong residents tend to work and reside in different districts. As a result, district-level shocks are less likely to simultaneously influence both housing prices and consumption. But as an empirical matter, it is not clear from the paper how common it is to work and reside in separate districts. And from a theoretical perspective, it is not clear whether this argument effectively rules out the simultaneity issue. If a significant fraction of individuals who work in one district reside in the same outside district, then a shock to employment in a district could cause prices and consumption to co-move in the outside district. In addition, there are other types of district-level shocks that could create simultaneity regardless of the commuting patterns of its residents. For example, a public works project that improved existing infrastructure or that developed new park space (or created some other desirable public good) in a district would be expected to increase the attractiveness of the district—thus increasing housing demand, which would raise prices and possibly also average district-wide consumption, by changing the income/wealth composition of its population. To completely solve the simultaneity issue, one would need time-series variation in home values at the individual level. Unfortunately, that data is simply not available at this point—in Hong Kong or anywhere else.
Innovative tests of the housing wealth channel
Still, while Gan's data may not completely solve the simultaneity issue, some of her additional empirical tests go a long way toward alleviating these concerns. The first is a check on whether the consumption–housing wealth relationship is stronger for people who own multiple homes. A pure housing wealth effect would predict that the consumption behavior of individuals with higher levels of housing wealth would be more sensitive to changes in wealth. This is exactly what Gan finds in her data.
In a series of other tests, Gan tries to distinguish between the role of credit constraints and a precautionary savings motive in generating the positive housing-wealth effect. The credit constraint story is that increasing housing wealth relaxes borrowing constraints for individuals, which results in higher consumption. This effect is expected to be relevant only for households that are borrowing-constrained. The precautionary savings motive refers to the tendency for risk-averse individuals to accumulate wealth in order to self-insure against negative future income or wealth shocks. If individuals consider housing equity to be a component of precautionary savings, then an increase in housing wealth might increase consumption by reducing other components of precautionary savings. For example, if a household is saving a certain percentage of each paycheck for precautionary motives, then an increase in housing equity might be considered to be a viable substitute, and the household might be expected to decrease the percentage saved of each paycheck, and thus increase consumption.
Gan focuses on households that refinance as a first test in distinguishing between these two effects. She argues that borrowing-constrained households would need to refinance in order to access any equity increases, while precautionary savers would not need to refinance, since they could increase consumption by decreasing other forms of saving. Thus, if the relaxation of borrowing constraints is driving the positive elasticity of consumption growth to changes in housing wealth, then the elasticity estimate should be larger among households that refinance.3 Gan finds evidence of both effects: the estimated elasticity is significantly higher for households that refinance but is still positive and statistically significant for households that do not.
In a second test, she identifies households that are likely borrowing-constrained based on their use of credit card lines and separately estimates regressions for households that are close to their credit limit and those that are far from their limit. If the credit-constraint channel is present, the elasticity should be higher for the households that are close to their limit, while the precautionary savings channel, in contrast, predicts that the elasticity should be higher for less-constrained households and thus households that are far from their credit limit. The results of this exercise provide support for the precautionary savings motive, as less-leveraged households have a stronger consumption elasticity than more-leveraged households.
Gan performs a few more clever tests to try to distinguish between the credit-constraint and precautionary-savings channels. She finds strong evidence in favor of the precautionary savings channel and little evidence of an important role for credit constraints. It appears that in Hong Kong, households use housing wealth as an important component of an overall self-insurance strategy and view increases in housing wealth as a substitute for other types of savings and thus an opportunity to increase consumption. This is a very interesting and important finding on its own, but we also view it as strong evidence that Gan has truly identified a causal relationship between housing prices and consumption. The reason is that if simultaneity bias is truly responsible for the positive estimate of the consumption elasticity, then we wouldn't expect the estimate to be sensitive to different samples. The fact that it is, and in ways that are consistent with theory, suggests that Gan has really identified the impact of housing wealth on consumption behavior.
One final caveat is that Gan focuses solely on homeowners, leaving renters out of her analysis. We would expect renters to be hurt by increases in housing values. Thus, while Gan finds a significant positive effect of housing wealth on consumption for the population of homeowners in Hong Kong, we interpret her results as an upper bound of the effect of housing wealth on aggregate consumption in Hong Kong.
Research economist and assistant policy adviser at the Federal Reserve Bank of Atlanta
1 I believe that what Gan means here, although it's not completely clear, is that since the housing stock in Hong Kong is so highly valued, a 1 percent change in house prices translates into a lot of additional consumption, even with a marginal propensity to consume out of housing wealth of 2 cents.
2 The reason is that any time the variation in a right-hand side variable is at a more aggregated level than the dependent variable, the coefficient estimate associated with that right-hand side variable is identified from the variation in both variables at the more aggregated level.
3 Another potential way that a borrowing-constrained household could access increases in housing equity is through home equity lines of credit (HELOC). HELOCs do not require households to refinance their mortgage. This possibility is not discussed in the paper, perhaps because HELOCs are not quite as popular in Hong Kong as they are in the United States.
September 16, 2010
Answering the bloggers
In this post, we depart from our usual practice of addressing research by other authors and discuss a recent paper of our own: "Reasonable people did disagree: Optimism and pessimism about the U.S. housing market before the crash." We first review the main points of the paper, and then touch on some of the comments and criticisms that have been raised about it in various blogs.
Optimists, pessimists, and agnostics: Revisiting the three housing-boom camps
The paper basically asks a simple question: What did professional economists think about the housing boom while it was in full swing? Did most of them warn that the boom was really a bubble, sustained only by its own momentum and destined to crash someday? Or did most professional economists think that the run-up in housing values was entirely justified by fundamental factors, like low interest rates or higher rents? Our paper mainly discusses, rather than evaluates, the housing literature at this time. As a result, the paper is really more of a literature review rather than formal economic research. But we think that this type of review is helpful if people think that a disruptive gyration in asset prices could happen again.
By looking at the research papers and opinion pieces of professional economists, we concluded that these economists could be separated into three camps. All three camps agreed that house prices had risen dramatically—that was obvious from the data—but they differed on why. The "optimist" camp argued that there were good reasons why house prices had gone up, while the "pessimists" argued that there were no good reasons. A third group, whom we label "agnostics," refused to take a position one way or the other. Remarkably, most economists fell into this third camp. We argue that one reason for the popularity of the agnostic position is that economists generally believe that asset prices reflect fundamental factors unless there is a lot of strong evidence to the contrary. After all, asset prices are set in free markets, by people using their own money. Who are economists to say that the prices that people have agreed upon are wrong?
The strongest forms of this view comprise the various flavors of efficient-market theory, which argues that asset prices are always correct in some sense. While most economists would not go that far, they would still demand a lot of evidence before they would label some asset-price increase a bubble. This tendency to view asset prices as reflecting all available information makes economists generally reluctant to predict where asset prices are headed, or to say that some asset price is unsustainable. As we write in the paper:
In a sense, this reluctance to commit should not surprise anyone familiar with modern asset-pricing theory. The "Fundamental Theorem of Asset Pricing" implies that the evolution of asset prices is, to a first approximation, unpredictable. If housing was so obviously overvalued, as the pessimists suggested, then investors stood to make huge profits by betting against housing. By doing so, investors would have ensured that house prices would have fallen immediately. Regardless of whether the theory of the unpredictability of asset prices is correct, the theory is part of the basic training of almost every economist. Consequently, any economist who suggests to his or her peers that an asset is over- or undervalued faces a heavy burden of proof.
The optimists were reasonable people
Among bloggers, the paper has generated a mixed reaction. Naturally, we think the people that like the paper are absolutely correct in their assessments. But more seriously, we also think that our critics fail to appreciate the major point we were trying to make. In fact, some of their arguments actually provide further support for our position.
First off, many critics have said that housing-market optimists were right-wing ideologues who were deluded by efficient-market theory into thinking that asset bubbles were impossible. This story simply doesn't square with the facts. Two of the most prominent optimists were Chris Mayer and Todd Sinai. Both are professors at top business schools (Columbia and Wharton respectively), and both serve as co-heads of the Real Estate program at the National Bureau of Economic Research, a research body that includes economists of all ideological stripes. Even more importantly, one of Chris Mayer's seminal real-estate papers discusses "loss aversion" among potential house sellers. Loss aversion is a behavioral theory that blends psychology and economics, so it is not exactly high on the right-wing ideological research agenda, which is firmly rooted in the free market, rational decision-making tradition. By virtually any definition, Chris Mayer and Todd Sinai are reasonable people. Even so, based on their joint research, Mayer identified people who were predicting a collapse in house prices at the end of 2005 as "Chicken Littles."
The second issue has to do with economics as a science. Paul Krugman argues that "the evidence just screamed bubble." He points to the following picture, which shows real housing prices from the late 1970s through 2005.
This picture might convince the lay person that housing prices were headed for a fall. But the point of our paper is that convincing economists of this fact would have required much more work. Despite what some influential commentators might have us believe, large and persistent increases in real house prices have occurred in the U.S. without subsequent crashes. For example, Robert Shiller has found that U.S. housing prices increased by 60 percent between 1942 and 1947, adjusted for inflation. (This increase basically reversed a long-lasting period of low housing prices that occurred during the Depression.) After 1947, house prices were basically flat until the close of the 20th century. Nobody refers to the 1940s period as a house price bubble, because housing prices remained high for a very long time.
Another example of a long-lasting swing in housing prices is the boom–bust cycle along the U.S. coasts during the late 1980s and early 1990s. Krugman's post states that the late-'80s price rise in southern California depicted in this figure was a bubble, but that claim is debatable. Even after their recent crash, California housing prices are approximately 20 percent higher than they were during their late-1980s peak.
The responsibility of policymakers to create a robust financial system
What is most puzzling to us is the statement by some bloggers that we are essentially trying to exonerate the economics profession for failing to miss the housing bubble. Exactly the opposite is true. What we try to argue in the paper is that the economics profession simply doesn't have the tools to achieve real-time consensus on whether a bubble exists in this or that asset market. One response to this problem is for economists to relax their views on the correctness of asset prices. Undoubtedly, the recent housing cycle will nudge a lot of economists away from efficient-markets theory and the fundamental unpredictability of asset prices. For policymakers, we would argue the main lesson of our literature review is that they should not depend on economists to achieve consensus on whether some asset price reflects a potentially disruptive bubble. Rather, policymakers should construct a financial system that is robust enough to withstand steep drops in asset prices. If our review of the housing literature is any indication, there will be little warning of the next asset-market crash in the peer-reviewed economics literature.
June 18, 2010
Explaining local supply elasticities: Quantifying the importance of space limitations in housing prices
It's an old joke among real estate professionals: the price of a house depends on three factors—location, location, and location. A half-million dollars will buy a sprawling estate in Wichita but only a modest apartment in New York. Economists have long suspected that geographic space limitations have a lot to do with this discrepancy. The logic goes that houses are cheap in Wichita because there is plenty of surrounding space on which to build new homes, but Manhattan Island isn't getting any larger. Unfortunately, it has been difficult to precisely quantify the importance of space limitations in housing prices, due to data limitations as well as a large number of potentially confounding factors that also matter for housing markets.
An exciting new paper by Albert Saiz of the University of Pennsylvania's Wharton School makes a significant advance in this area by using detailed geographic data to show how both space limitations and local development policies affect housing prices. This paper will be a big help to those who study the geographic pattern of urban development in the United States. It will also be widely cited in future studies of local development policy. But, as we argue below, one must be careful when using the Saiz results to infer anything about the rise-and-fall in housing prices during the recent housing bubble.
The main empirical contribution of the Saiz paper is to calculate, for each major city, the amount of land that cannot be used to build houses because of geographical constraints. Lying next to a major body of water such as an ocean or one of the Great Lakes clearly limits a city's ability to build, and figuring out which cities are affected is trivial. In fact, a "coastal dummy variable" has long been used in models of housing prices. But new construction can also be limited by inland waterways, such as wetlands or lakes. It is also tough to build on steeply sloped terrain, as in the foothills of mountains. To measure the importance of these latter two factors on a city-by-city basis, Saiz uses Geographic Information System (GIS) techniques and highly detailed topographical data from the United States Geological Survey. Specifically, for all the land within 50 kilometers of each large city's center, Saiz measures the geographic characteristics of finely disaggregated parcels (for example, 30-meter-by-30-meter squares). He then adds up the prevalence of water or steep slopes across individual parcels to get overall, city-specific measures of geographically determined space constraints. According to this method, the most constrained city in the country is Ventura (CA), where almost 80 percent of the land within 50 kilometers of the city center is undevelopable. Miami, Fort Lauderdale, and New Orleans are close behind, with about 75 percent of their land essentially off limits to residential construction. At the other end of the spectrum lie cities like Wichita (KS), Indianapolis (IN), Dayton-Springfield (OH), and McAllen-Edinburg-Mission (TX), where less than 2 percent of the land is undevelopable.
Saiz then confronts his city-specific measure of space constraints with the data. He finds that the fraction of undevelopable land is correlated with house price levels, house price growth from 1970 to 2000, average income levels, the extent of tourism, and a measure of creativity (measured by the number patents awarded to residents of the city). The index is not correlated with the size of cities, or with the share of city residents who have a bachelor's degree or work in manufacturing. Many of these correlations are consistent with the detailed theoretical model that Saiz builds to explain how space constraints should matter for a number of city-specific variables. 1
A question that Saiz explores in depth is how space constraints matter for the way a city adjusts to a positive demand shock, which causes more people to want to live in that city. Cities that are space-constrained have a tough time accommodating a positive shock with a burst of new construction. In formal terms, the supply curve for new homes in space-constrained cities will be inelastic, or close to vertical. As a result, a positive shift in housing demand will result mostly in higher housing prices, not more construction.
These predictions are borne out by the data. Using regression models, Saiz finds that local supply elasticities are determined both by his space-constraint measure and by an index of local building regulations, which was also developed at Wharton.2 Interestingly, the local-regulation index is itself strongly correlated with Saiz's space-constraint index, as space-constrained cities tend to have stricter regulatory limits on new construction. This correlation provides compelling evidence for something that many housing economists have long suspected—local voters seek to protect the values of expensive homes by preventing new homes from being built.3 This finding may be puzzling to some, as it may be hard to imagine why land-constrained cities would need to implement further restrictions on new construction. However, some new development, perhaps via dense apartment buildings, is usually possible. Note that unlike a lot of correlations in economics, we can be reasonably sure that the direction of causality runs from space constraints to local regulations, not the other way around. After all, it is hard to create a new mountain, lake, or ocean through the political process.
The Saiz paper is forthcoming in a top economics journal, and its results are already being used by housing economists. We draw from it ourselves in a paper that investigates why so many economists missed the housing bubble.4 But we caution that one should not push the Saiz results too far. The Saiz paper concerns the slopes of housing supply curves in different cities. As a result, it says nothing about shifts in housing demand that might have occurred during the housing boom. For example, the Saiz results would predict that, during the housing boom, prices in high-supply-elasticity cities like Wichita would rise less than prices in low-elasticity cities like Boston. Sure enough, this is what we find in the data. However, this finding does not prove that the boom was caused by some uniform, nationwide increase in housing demand (arising, for example, from easier subprime lending, or from lower interest rates). It is true we would expect a uniform demand increase to have a small effect on Wichita's prices and a big effect on Boston's prices. But because Wichita has a flat supply curve, its house prices will be stable no matter what happens to demand there. To determine whether Wichita and Boston saw similar increases in demand, one would have to look not only at prices but also at quantities (that is, new construction). Researchers should therefore be careful when using the Saiz results to study the housing boom—a point we hope to revisit in future posts.
By Chris Foote, senior economist and policy adviser at the Boston Fed (with Atlanta Fed economist Kris Gerardi and Boston Fed economist Paul Willen)__________________________________________________________________________________________________________________________
1 We will refer interested readers to the paper for the model's details, but it assumes that people can move freely between cities, so that the utility of people is equalized across cities. The model also assumes all the jobs in a city are located in a central business district, to which each city resident must commute.
2 Specifically, to measure the extent of land-use restrictions in a metropolitan area, Saiz uses the Wharton Residential Urban Land Regulation Index (WRI) that was created by Gyourko, Saiz, and Summers (2007).
3 Because the land-regulation index is endogenous, Saiz also provides some instrumental variables (IV) regressions that depend on city-level variation in general political attitudes toward regulation to identify the specific effect of housing regulations on housing-supply elasticities. The IV results are also consistent with a role for both space constraints and regulations in determining housing-supply elasticities.
4 Specifically, we show that housing prices rose in places like Phoenix and Las Vegas, which, according to the Saiz results, should have had very elastic housing prices (flat supply curves). Thus, we argue that some other factor besides building constraints must be invoked to explain the rapid run-up in prices for cities like these.
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