«reNtal HousiNg Policy iN tHe uNited states Volume 13, Number 2 • 2011 U.S. Department of Housing and Urban Development | Office of Policy ...»
Owning a house also can provide a hedge for the risk households face about housing costs in a future location. If a renter unexpectedly moves to a new city, housing costs may be more or less expensive than she anticipated. An owner faces a double whammy: Not only does she not know what the price of a house in her new city will be, she does not know at the time of purchase how much she will be able to sell her prior house for when it comes time to move. That sell/buy transaction— selling the current house and purchasing a new one—creates risk if the sale price and purchase price are not equal. If so, the sale/purchase pair will either require an infusion of capital (if the new house is more expensive than the old one) or will yield a cash windfall (if the new house is less expensive). Because, according to Sinai and Souleles (2009), 45 percent of families move in a 5-year period, and 10 percent of families move out of their MSA, the potential risk to either a renter or owner from a move to different housing market is quite high.
In this scenario, the renter faces less risk than the owner if housing costs in the origin and destination cities do not move together much. The renter is exposed just to the risk of the total housing costs in the destination city. In addition, the renter, who invested her equity in nonhousing assets, has a more diversified portfolio overall than the owner and faces less volatility in her wealth at the time of the move. But if housing costs in the origin and destination cities covary positively, owning a house in the origin city hedges housing cost risks in the destination city, a benefit the renter does not enjoy. In essence, when house prices in two cities covary positively, a homeowner is wealthier— she can sell her existing house for more—when housing becomes more expensive in the destination city. Likewise, she is poorer when housing is less expensive. In those cases, the volatility of her wealth net of expected housing costs is reduced by owning the house. By contrast, a renter, who is unable to avoid having low covariance between her assets and future housing costs, is more likely to experience high portfolio returns when housing becomes less expensive in the destination city and low returns when housing becomes more costly. In first case, the renter can afford more housing than before and in the second case, the renter can’t afford as much. That volatility in consumption subsequent to a move is what homeowners moving between two markets with high house price covariance can avoid.
It turns out that for most Americans, the covariance in housing costs between their current housing market and the cities they are likely to move to is remarkably high (Sinai and Souleles, 2009). The median expected correlation in real house price growth is 0.6, and the 75th percentile expected correlation is about 0.9. A similar, but less interpretable, pattern is evident in expected covariances across MSAs in house price growth. A high covariance between wealth and housing costs can be obtained best by investing in a house because the average house price correlation with stock or bond prices is much lower (for example, see Gyourko and Keim, 1992). In the context of the previous example, many homeowners have home sales prices that tend to be high at the same time that the purchase price of their next houses tend to be high. That is, the increase in house value in the high-volatility market to $168,000 is not such a windfall if the house in the next market also appreciated to about $160,000. And a decline in value to $88,000, absent leverage, is not so painful if the price of the new house also fell to $90,000 or so.
The risk of forgoing the investment position embedded in homeownership by renting instead varies critically by geography and household type. In general, houses in markets with little volatility cannot be used as a hedge against volatile house prices elsewhere, whereas houses in cities with
more volatility have the potential to be better hedges. However, the expected covariance—and thus the potential hedge—can vary widely across households within a given city because households are likely to move to different places. Some households in some cities have a zero or negative expected covariance between their house price growth and house price growth in the cities they expect to move to, while households in other cities or other industries may tend to move to more correlated housing markets. In addition, owning a house might provide a good hedge for durable consumption items whose costs rise when real estate values go up. Such goods would have land as a significant input factor. Assisted living care, for example, might be more affordable to homeowners than to renters because if house values and assisted living costs tend to rise at the same time, homeowners could usually sell their houses to pay for assisted living care.
Another way that owning a home might reduce the effect of housing market uncertainty is that it provides an option to move. Suppose an owner’s house price rose more than house prices elsewhere. He would be able to sell his house and move to other places that, perhaps, were previously unaffordable. If, however, his house price fell by more than house prices elsewhere, the owner would not have to move. Instead, he could stay put and consume just as much housing services as he always has. Only homeowners have the option to move and trade up their housing if market conditions allow without being forced to trade down their housing when market conditions are poor. Such homeowners would prefer house price uncertainty, as long as the uncertainty is relatively uncorrelated with house prices elsewhere.
One important factor complicates this analysis. So far, this article implicitly assumes that households’ incomes are independent of housing cost changes, so incomes do not necessarily go up when rents rise. If incomes and rents covaried, renting would be more favorable (Davidoff, 2006).
Rent uncertainty would be offset by income uncertainty, and together would reduce housing and nonhousing consumption volatility. If households had greater incomes when rents were higher, they not only could afford the higher rent, they would still have money left over for nonhousing consumption. In a sense, rent volatility could hedge income uncertainty, leading to less volatility in consumption overall. Davidoff (2006) considered the case where households are not liquidity constrained, and examines the correlation between total housing cost and lifetime income. In addition, if liquidity constraints are an issue, a positive covariance in annual incomes and rents would reduce renting risks relative to owning. Both cases can be exemplified by the idea of company towns. If housing demand and employees’ wages are driven by the productivity of the local factory, then rents in that location would be high when incomes were high, and being a renter would yield less volatility in both housing and nonhousing consumption. By contrast, an owner would have more nonhousing consumption volatility (because housing costs would be constant and income would be variable) and her house value would be lowest precisely when she would want to move away: when the factory was not doing well and she was laid off.
Income uncertainty also complicates the analysis of the risk of moving to a new city. A positive covariance between income subsequent to a move and house prices in the destination reduces moving risk. For example, if a household that moves to a city where home prices have gone up more than expected also earns more than expected, that household’s wealth is again higher (because of higher human capital wealth) precisely when housing is more expensive. The household need not own a home to obtain the benefit of income as a hedge for future housing costs. Indeed, depending on 114 Rental Housing Policy in the United States Understanding and Mitigating Rental Risk how much income changes offset changes in housing costs after a move, owning a home could overcompensate and create too much volatility. That is, if both home prices and incomes rise when a household moves to a location where housing costs rose more than expected, the household’s wealth might have gone up by more than necessary to cover the additional housing costs. The degree to which incomes and home prices might covary depends on things like the worker’s industry and that industry’s share of the local employment market. For example, if an industry is a large local employer and has a profitable year, it may pay employees more, and their good fortune could then be capitalized into home prices, generating a high correlation between income and home prices.
Direct evidence is lacking on the degree of covariance between incomes and house prices subsequent to a move. Paciorek and Sinai (2010) provided indirect evidence that income does not fully hedge housing cost uncertainty and that homeownership does provide an additional reduction in housing consumption volatility. They find that after netting out any income relationship, owners of homes that provide better hedges against future housing cost uncertainty have lower variability of housing consumption after a move. This result indicates that homeowners, on average, are not overcompensating for volatility in future housing costs. If they did overcompensate, hedged homeowners should experience increased housing volatility, not less.
Paying for Reduced Risk Households seem to recognize the value of the reduced risk that accompanies homeownership.
Several recent studies have found that in circumstances where homeownership provides a better hedge, households have higher housing demand. Sinai and Souleles (2005) showed that the likelihood of homeownership is higher when a household lives in a more volatile housing market and is less likely to move (for exogenous demographic reasons). In low-volatility housing markets neither renting nor owning generates much uncertainty. By contrast, in high-volatility housing markets, short horizon owners experience sizeable sale price risk whereas long horizon renters experience sizeable rent risk. Han (2010) showed that the home sale price risk effect reduces the quantity of housing purchased by homeowners who are more likely to move out of the local housing market.
Sinai and Souleles (2009) found that the reduction in demand for homeownership by short horizon households in high-volatility housing markets is mitigated for households that expect to move between highly covarying housing markets. For homeowners in short horizon households, the uncertainty about the sale price is a benefit because it reduces the uncertainty of the purchase price of their subsequent home. The benefit can be quite sizeable. Paciorek and Sinai (2009) estimated that, for households that move, the value of the lower variability in subsequent housing consumption is as much as 20 percent of their home price.
Importantly, households appear willing to pay a higher house price to avoid the higher volatility that accompanies renting. Sinai and Souleles (2005) provided empirical evidence that home prices capitalize a premium that increases with the amount of rent volatility avoided by owning. They find that a one standard deviation increase in the volatility of detrended real rent leads to a 0.18 to 0.62 increase in the price to rent ratio, or a 1.1- to 3.9-percent increase in prices (holding rents constant). Those home prices capitalize only the willingness to pay of the marginal homebuyer.
Within a housing market, then, inframarginal households value avoiding the risk of renting even more than the risk premium embedded into home prices. (And some households still rent because they are unwilling to pay the premium required to own.) Capacity for Volatility If renting delivers more risk than owning and households realize that fact (and the evidence that they have a higher demand for homeownership when renting is riskier suggests they do), why does popular opinion seem to perceive that homeownership is riskier than renting? One possibility is that renters have a greater capacity to absorb uncertainty in housing costs or incomes. The primary channel by which that happens is that renters spend a smaller fraction of their incomes or net worth on housing than owners, holding constant age and marital status. This fact can be seen in exhibit 3, which regresses a measure of annual log housing costs on log income and an indicator variable for a renting household, plus some controls. The first three columns use household-level data from the 1980, 1990, and 2000 U.S. Census. The last two columns use household data from the Survey of Consumer Finances in 2004. Annual housing costs for renters are defined as 12 months of rent.
Estimating annual housing costs for owners is tricky, because a house’s price is observed, but its rental value is not. In this article, rental value is imputed for owned houses in a couple of ways.
First, a hedonic model of rents is constructed using the data from the Census. The hedonic model is then applied to predict rental values for each of the homeowners. Second, a user cost model is applied, following the method of Poterba and Sinai (2008). The user cost (UC) is the sum of the annual after tax expenses (including the cost of capital) less the expected capital gain, which is the money the owner gets back by selling the home for more than what he paid, per dollar of house.
The two approaches are conceptually related. For a landlord, rent (R) plus the expected capital gain needs to yield the market return on his investment. For an owner, the annual cost plus the expected capital gain needs to deliver the same return. Thus, R should equal UC x P, when P is the price of the house. However, an important distinction remains between rent and user cost: Rent is a cash payment to landlords. User cost has a higher cash cost than rent but user cost is reduced— on paper—by any house price appreciation.