The theory of precautionary saving is shown in this paper to be isomorphic to the Arrow-Pratt theory of risk aversion, making possible the application of a large body of knowledge about risk aversion to precautionary saving, and more generally, to the theory of optimal choice under risk In particular, a measure of the strength of precautionary saving motive analogous to the Arrow-Pratt measure of risk aversion is used to establish a number of new propositions about precautionary saving, and to give a new interpretation of the Oreze-Modigliani substitution effect

Precautionary Saving in the Small and in the Large

https://dspace.mit.edu/bitstream/handle/1721.1/99175/Hanlon_A%20review%20of%20tax%20research.pdf;sequence=1

A review of tax research

lapse using the highly unequal geographic distribution of wealth losses across the United States. We estimate a large elasticity of consumption with respect to housing net worth of 0.6 to 0.8, which soundly rejects the hypothesis of full consumption risk-sharing. The average marginal propensity to consume (MPC) out of housing wealth is 5–7 cents with substantial heterogeneity across ZIP codes. ZIP codes with poorer and more levered households have a significantly higher MPC out of housing wealth. In line with the MPC result, ZIP codes experiencing larger wealth losses, particularly those with poorer and more levered households, experience a larger reduction in credit limits, refinancing likelihood, and credit scores. Our findings highlight the role of debt and the geographic distribution of wealth shocks in explaining the large and unequal decline in consumption from 2006 to 2009. JEL Codes: E21, E32, E44, E60.

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Household Balance Sheets, Consumption, and the Economic Slump*

THE POPULAR PRESS is full of speculation that the United States, as well as other countries, is in a "housing bubble" that is about to burst. Barrons, Money magazine, and The Economist have all run recent feature stories about the irrational run-up in home prices and the potential for a crash. The Economist has published a series of articles with titles like "Castles in Hot Air," "House of Cards," "Bubble Trouble," and "Betting the House." These accounts have necessarily raised concerns among the general public. But how do we know if the housing market is in a bubble? The term "bubble" is widely used but rarely clearly defined. We believe that in its widespread use the term refers to a situation in which excessive public expectations of future price increases cause prices to be temporarily elevated. During a housing price bubble, homebuyers think that a home that they would normally consider too expensive for them is now an acceptable purchase because they will be compensated by significant further price increases. They will not need to save as much as they otherwise might, because they expect the increased value of their home to do the saving for them. First-time homebuyers may also worry during a housing bubble that if they do not buy now, they will not be able to afford a home later. Furthermore, the expectation of large price increases may have a strong impact on demand if people think that home prices are very unlikely to fall, and certainly not likely to fall for long, so that there is little perceived risk associated with an investment in a home. If expectations of rapid and steady future price increases are important motivating factors for buyers, then home prices are inherently unstable. Prices cannot go up rapidly forever, and when people perceive that prices have stopped going up, this support for their acceptance of high home prices could break down. Prices could then fall as a result of diminished demand: the bubble bursts. At least one aspect of a housing bubble--the rapid price increases--has clearly been seen recently. A rapid surge in home prices after 2000, as tabulated, for example, by the Economist Intelligence Service, has been seen in almost all the advanced economies of the world, with the exception of Germany and Japan. In some of these countries, price-to-rental ratios and price-to-average income ratios are at levels not seen since their data begin in 1975. (1) But the mere fact of rapid price increases is not in itself conclusive evidence of a bubble. The basic questions that still must be answered are whether expectations of large future price increases are sustaining the market, whether these expectations are salient enough to generate anxieties among potential homebuyers, and whether there is sufficient confidence in such expectations to motivate action. In addition, changes in fundamentals may explain much of the increase. As we will show, income growth alone explains the pattern of recent home price increases in most states. Falling interest rates clearly explain much of the recent run-up nationally; they can also explain some of the cross-state variation in appreciation because of differences in the elasticities of supply of homes, including land. To shed light on whether the current boom is a bubble and whether it is likely to burst or deflate, we present two pieces of new evidence. First, we analyze U.S. state-level data on home prices and the "fundamentals," including income, over a period of seventy-one quarters from 1985 to 2002. Second, we present the results of a new questionnaire survey conducted in 2003 of people who bought homes in 2002 in four metropolitan areas: Los Angeles, San Francisco, Boston, and Milwaukee. The survey replicates one we did in these same metropolitan areas in 1988, during another purported housing bubble, after which prices did indeed fall sharply in many cities. The results of the new survey thus allow comparison of the present situation with that one. …

/pdf/is-there-a-bubble-in-the-housing-market-3xduoktd1v.pdf

Is There a Bubble in the Housing Market

We conduct a within-county analysis using detailed ZIP code—level data to document new findings regarding the origins of the biggest financial crisis since the Great Depression. The sharp increase in mortgage defaults in 2007 is significantly amplified in subprime ZIP codes, or ZIP codes with a disproportionately large share of subprime borrowers as of 1996. Prior to the default crisis, these subprime ZIP codes experience an unprecedented relative growth in mortgage credit. The expansion in mortgage credit from 2002 to 2005 to subprime ZIP codes occurs despite sharply declining relative (and in some cases absolute) income growth in these neighborhoods. In fact, 2002 to 2005 is the only period in the past eighteen years in which income and mortgage credit growth are negatively correlated. We show that the expansion in mortgage credit to subprime ZIP codes and its dissociation from income growth is closely correlated with the increase in securitization of subprime mortgages.

/pdf/the-consequences-of-mortgage-credit-expansion-evidence-from-iavm0vilte.pdf

The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis

We construct measures of the annual cost of single-family housing for 46 metropolitan areas in the United States over the last 25 years and compare them with local rents and incomes as a way of judging the level of housing prices. Conventional metrics like the growth rate of house prices, the price-to-rent ratio, and the price-to-income ratio can be misleading because they fail to account both for the time series pattern of real long-term interest rates and predictable differences in the long-run growth rates of house prices across local markets. These factors are especially important in recent years because house prices are theoretically more sensitive to interest rates when rates are already low, and more sensitive still in those cities where the long-run rate of house price growth is high. During the 1980s, our measures show that houses looked most overvalued in many of the same cities that subsequently experienced the largest house price declines. We find that from the trough of 1995 to 2004, the cost of owning rose somewhat relative to the cost of renting, but not, in most cities, to levels that made houses look overvalued.

/pdf/assessing-high-house-prices-bubbles-fundamentals-and-586rsrgbsl.pdf

Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

How does one tell when rapid growth in house prices is caused by fundamental factors of supply and demand and when it is an unsustainable bubble? In this paper, we explain how to assess the state of house prices—both whether there is a bubble and what underlying factors support housing demand—in a way that is grounded in economic theory. In doing so, we correct four common fallacies about the costliness of the housing market. For a number of reasons, conventional metrics for assessing pricing in the housing market such as price-to-rent ratios or price-to-income ratios generally fail to reflect accurately the state of housing costs. To the eyes of analysts employing such measures, housing markets can appear "exuberant" even when houses are in fact reasonably priced. We construct a measure for evaluating the cost of home owning that is standard for economists—the imputed annual rental cost of owning a home, a variant of the user cost of housing—and apply it to 25 years of history across a wide variety of housing markets. This calculation enables us to estimate the time pattern of housing costs within a market. As of the end of 2004, our analysis reveals little evidence of a housing bubble.

/pdf/assessing-high-house-prices-bubbles-fundamentals-and-n6agczq8tx.pdf

Many people assume that the most significant risk in the housing market is that homeowners are exposed to fluctuations in house values. However, homeownership also provides a hedge against fluctuations in future rent payments. This paper finds that, even though house price risk endogenously increases with rent risk, the latter empirically dominates for most households so housing market risk actually increases homeownership rates and house prices. Further, the net effect of rent risk on the demand for homeownership increases with a household's expected length of stay in its home, as the cumulative rent volatility rises and the discounted house price risk falls. Using CPS data, the difference in the probability of homeownership between households with long and short expected lengths of stay is 2.9 to 5.4 percentage points greater in high rent variance places than low rent variance places. The sensitivity to rent risk is greatest for households that devote a larger share of their budgets to housing, and thus face a bigger gamble. Similarly, the elderly who live in high rent variance places are more likely to own their own homes, and their probability of homeownership falls faster with age (as their horizon shortens). This aversion to rent risk might help explain why older households do not consume much of their housing wealth. Finally, we find that house prices capitalize not only expected future rents, but also the associated rent risk premia. At the MSA level, a one standard deviation increase in rent variance increases the house price-to-rent ratio by 2 to 4 percent.

/pdf/owner-occupied-housing-as-a-hedge-against-rent-risk-whbwn68m55.pdf

Owner-Occupied Housing as a Hedge Against Rent Risk

The conventional wisdom that homeownership is very risky ignores the fact that the alternative, renting, is also risky. Owning a house provides a hedge against fluctuations in housing costs, but in turn introduces asset price risk. In a simple model of tenure choice with endogenous house prices, we show that the net risk of owning declines with a household's expected horizon in its house and with the correlation in housing costs in future locations. Empirically, we find that both house prices, relative to rents, and the probability of homeownership increase with net rent risk.

/pdf/owner-occupied-housing-as-a-hedge-against-rent-risk-252mg8t53j.pdf

We examine two factors that explain air traffic congestion: network benefits due to hubbing and congestion externalities. While both factors impact congestion, we find that the hubbing effect dominates empirically. Hub carriers incur most of the additional travel time from hubbing, primarily because they cluster their flights in short time spans to provide passengers as many potential connections as possible with a minimum of waiting time. Non-hub flights at the same hub airports operate with minimal additional travel time. These results suggest that an optimal congestion tax might have a relatively small impact on flight patterns at hub airports. (JEL L2, L5, L9, D6)

/pdf/network-effects-congestion-externalities-and-air-traffic-1yyjej70vl.pdf

Todd Sinai

Papers

Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions

Owner-Occupied Housing as a Hedge Against Rent Risk

Owner-Occupied Housing as a Hedge Against Rent Risk

Network Effects, Congestion Externalities, and Air Traffic Delays: Or Why Not All Delays Are Evil