Housing Markets, Prices, and Policies
Abstract and Keywords
This article provides a detailed explanation of housing markets. Housing markets are different from other kinds of markets, in part because housing is not a commodity, and in part because when one buys (or rents) a house, one is also buying a set of neighborhood amenities, schools, transportation systems, and taxes. Housing markets are also heavily regulated. It discusses housing markets and policies and examines related issues, revealing why different cities have different housing markets and why housing markets are different within metropolitan areas. It also looks into measurement issues and house prices, rents and house prices and house price bubbles. Furthermore, it describes the U.S. housing policy, and concepts such as land-use regulation and housing; taxes and housing; and looks into the issues of segregation and discrimination.
Housing markets are different from other kinds of markets, in part because housing is not a commodity, and in part because when one buys (or rents) a house, one is also buying a set of neighborhood amenities, schools, transportation systems, and taxes. Markets are segmented across locations and across property types. This has important implications for interpreting house prices, about which I will say more later.
Housing markets are also heavily regulated. In the U.S. context, the most common form of regulation is zoning, but other countries have other panoplies of regulations, including rent control, floor-area ratios, limits on concentration of ownership, and so forth.
My discussion of housing markets and policies will briefly examine the following issues: (1) why different cities have different housing markets; (2) why housing markets are different within metropolitan areas; (3) measurement issues and house prices; (4) rents and house prices; (5) house price bubbles; (6) U.S. housing policy; (7) land-use regulation and housing; (8) taxes and housing; and (9) segregation and discrimination.
(p. 420) Housing Markets across Cities
Within the United States, the most expensive housing markets are an order of magnitude more expensive than the least expensive housing markets.1 To understand why this is the case, one must consider fundamentals underpinning house prices.
In equilibrium, house prices should equal depreciated replacement cost. Depreciated replacement cost has three components: improvements cost, land cost, and depreciation. All three vary from city to city.
The cost of building a standard house varies considerably from one market to the next. Average construction cost in the United States for a standard house is about $88 per square foot. In the Bay Area of California, costs are 30 to 40 percent higher than this; in Alabama, they are 20 to 30 percent lower.2 The reason is in part because of differences in labor cost, in part because of differences in code requirements (houses in California need to withstand earthquakes; those in Alabama do not), and in part because of differences in transportation costs for materials.
While differences in construction costs are important to explaining differences in house prices across markets, they pale in comparison with differences in land value. In many parts of the United States, raw land is nearly costless. For instance, farmland on the fringe of small cities in the Midwest sells for around $2,000 per acre, or about $.05 per square foot. On the other hand, land in Midtown Manhattan is worth somewhere in the neighborhood of $5,000 per square foot (although we should note that this amount includes land improvements, about which I will say more later).
Land values vary by a factor of 100,000 because of differences in land rents and in capital costs across markets. Large cities provide agglomeration economies that individuals value. In particular, larger cities have economies of scale and greater opportunities for specialization. Consider a physician who learns a difficult procedure that only 1/10 of 1 percent of the population will ever need. In a city of 50,000, only 50 people will ever need her services, which means she will only rarely get to use her skills. But in a city of 5 million, there will be 5,000 people who need her services, which means that she can (1) use her skill regularly and (2) amortize the capital cost of the equipment necessary to provide the service.
Glaeser (1998) reports a correlation between urban size and wages: as cities get bigger, people get paid more, presumably because they are more productive. Because people get paid more in larger cities, they are also willing to pay more for land (and thus housing) in those cities. Glaeser also notes, however, that the cost of living will (p. 421) change if the size of cities rises faster than wages, indicating that people get an amenity benefit out of living in cities. This is consistent with the idea that size allows for specialized consumer goods that in turn add to the amenity value of large cities.
It is worth noting, however, that there are some large cities in the United States, such as Houston, Dallas, and Phoenix, where land is much less valuable than it is in others, such as San Francisco, Los Angeles, New York, Boston, and Washington. Davis and Palumbo (2008) estimate the share of house value that is land across forty-six metropolitan areas. Currently, the land share of house value in Houston, Dallas, and Phoenix is less than 25 percent (although the housing bust has brought land values in Phoenix to anomalously low levels), while in Los Angeles, San Francisco, New York, Boston, and Washington it exceeds 50 percent.
There are at least three plausible reasons for the differences. The first is differences in land-use regulation. A number of recent papers (Glaeser, Gyourko, and Saks 2005; Green, Malpezzi, and Mayo 2005; Quigley and Raphael 2005) have found a strong relationship between the level of urban land regulation and land values. In the Texas cities, impediments to development are quite small, meaning that when prices rise just a little bit, developers have an incentive to build more houses, and nothing to prevent them from doing so. On the other hand, in San Jose and San Francisco, when prices reach a point where developers have an incentive to build, it takes them many years to get the entitlements necessary to do so. They also must pay hefty fees, which increase the cost of turning raw land into a finished lot.
The second cause of variations in land prices is differences in natural barriers. Kolko (2008) has written a paper exploring this issue in which he looks at the extent to which being in a coastal plain impedes the ability to develop and finds that it is quite substantial. While in principle, people can substitute out of one coastal place for another, the process for doing this is slow.3
Finally, some cities seem to have greater agglomeration than others: they certainly have more pronounced urban centers. Metropolitan New York, while containing several subcenters, is dominated by Manhattan; there is no analogue for this in Houston. Consequently, urban form within New York contributes to the presence of higher prices there than in Houston.
Depreciation also likely explains differences in house prices across cities—particularly economic depreciation. Depreciation comes in three flavors: physical, functional, and economic. Physical depreciation happens because things wear out as they age—it is what Congress is thinking of when it allows depreciation deductions for investment property and plant and equipment.
Functional depreciation happens when a component of a capital asset does not perform its function well by current standards. Think of a furnace that uses lots of energy and could be replaced by a more efficient model. It is possible that it could work as a furnace for years, but it still would be best replaced by something more efficient.
Finally, there is economic depreciation, which happens when the demand for something (like Detroit real estate) disappears. It is possible that houses in some (p. 422) cities have incurred economic depreciation because economic activity has disappeared. If this is true, values can fall below original construction cost and stay there for some time. We observe this in places such as Detroit, Cleveland, St. Louis, and Buffalo, but also recently in the exurbs of Los Angeles and Phoenix.
Housing Market Segmentation within Cities
Alonso (1964) showed that in well-functioning land markets, some land can be very expensive. In a famous paper, Alonso formalized the von Thűnen model of urban development and defined bid-rent. The insight is straightforward. Suppose two agricultural uses compete for land near a trade center. One use both produces greater revenue and has higher transportation costs per mile than the other. At the center of the city, the land use that provides the higher revenue will outbid all other uses. But because the high-revenue use has higher transport costs, as location moves away from the center of the city, it will eventually be outbid by the lower revenue use. This has implications for both the settlement of land uses and people. In the context of cities, production uses often generate greater revenue per unit of land than residential uses. Consequently, we often observe that central business districts are just that: areas in the center of metropolitan areas that contain many businesses and relatively few dwelling units. From the standpoint of housing policy, this implies that there are locations for which housing is not the most efficient use.
But bid-rent theory also predicts settlement patterns. Suppose a household can trade off location costs with transportation costs. Consider a low-income household whose budget set makes transportation spending difficult, if not impossible. Such a household will wish to live within walking distance of work and services and may be willing to bid more per unit of land area than a richer household. This seems counterintuitive, as many poor people living in the center of cities, whether in Indian slums or American inner cities, appear to reside in cheap housing. But it is only cheap because it is very dense and of very poor quality.
When we observe rent (whether formal or informal), we are not observing a price per se but rather a price multiplied by a quantity, where the quantity is housing quality and total land consumption. The urban poor appear to spend little relative to the rich for housing, but they actually spend more per unit of housing quality than the rich.
While this is distributionally obnoxious (we generally do not like it when the rich pay less for something than the poor), it is the natural outcome of a well-functioning land market. Under such circumstances, price signals are working well at allocating resources and therefore are best left undisturbed. The housing problem thus becomes a poverty problem—households do not have enough income to pay for transportation and therefore live in more comfort away from the center of the city—rather than a market failure problem. As I shall discuss later, the implication is that poverty is best addressed directly through income supplements.
(p. 423) This phenomenon can also contribute to differences in price levels in cities. Suppose some cities are more congested than others (i.e., people in some cities cover distances in less time than others). In cities with congestion, the relative value of the interior compared with the urban fringe is greater than in less congested cities. Capozza and Helsley (1989) show that as cities grow, and convenience becomes more valuable, both supply and demand elasticities of central places fall because the fringe becomes a less desirable substitute.
One also gets segmentation, as high-income people buy their way out of traffic and push low-income people to less convenient locations. Essentially the bid-rent functions of high-income people over take the bid-rent function of lower-income people—even those who are willing to live densely. This has produced substantial debates over “gentrification” in places such as Washington, D.C., and San Francisco; wealthy people have moved into these central cities, and consequently improved their health. But they have also pushed low-income renters outward.
Measuring House Prices
House prices are difficult to measure at both the individual level and the aggregate level. Figure 18.1 illustrates the problem: it presents two house price indices for San Francisco: the California Association of Realtors (CAR) median house price series, and the Case-Shiller repeat sales series. Both series show price cycles, and the timing of the peaks and troughs is similar in both. But the size of the cycles varies substantially: the Case-Shiller series is more volatile than the CAR series.
The reason for the variance is that the two indices measure different things. Let us begin with the fact that the value for which a house sells is not really a price but essentially a price index. The introduction to this chapter makes this point: a house is a composite commodity, made up of bedrooms, bathrooms, living area, a lot with a set of characteristics, a neighborhood with a set of characteristics, perhaps a school of a certain quality level, and so forth. Each of these characteristics has its own price; when we observe a house sell at a value, it is the sum of the products of prices and characteristics.
Attempts to disentangle this are difficult enough, but almost all fail to take into account that different households value characteristics (and their interactions) differently. Rosen's (1974) well known article on hedonic pricing makes the point that the household that values a particular house and its combination of attributes most will make the highest bid for the house. Because any one house is unique (unlike, say, a bushel of wheat), it is impossible to identify how the market prices its individual characteristics. For this and other reasons, any attempt to measure house prices will be flawed.
Let us return to the two San Francisco indices. The CAR series is not really a price index at all but rather a measure of median house value. All it does is identify the house at the middle of the value distribution and report its prices. The series (p. 424) takes no account of whether the house is large or small, in the Northeast or Southwest, and so forth. It tends to be less volatile than the other series because as prices change, home buyers substitute. When prices rise, people are more likely to purchase small houses on the urban fringe; when prices fall, they buy larger houses toward the urban center. Consequently, the product of attributes and attribute prices attenuates movement relative to price alone.
The other price series attempts to be a pure price index. Case-Shiller uses a repeat sales methodology. This method of index building limits observations to houses that sell at least twice. For each such house, the percentage difference in sales price between the second sale and the first is used as data to build up price index values period by period. The idea is that by keeping structural characteristics and (more important) location constant, one can disentangle price effects from characteristics.
This is a reasonable methodology, but it suffers from some limitations. First, the population of houses that sell more than once within a period is not representative of the population of all houses in a market. Second, structures are modified across time: in a typical year, home improvements make up around 35 percent of new construction put in place, meaning that the second time a house is sold it is often a better house than it was the first time it was sold.4 On the other hand, some houses do not even get standard maintenance, which means that they are actually (p. 425) deteriorating over time. Consequently, while the repeat sales indices attempt to control for quality, and do so more successfully than the CAR median price index, they have their own limitations.
The other method for measuring house prices is the hedonic price index measure. Malpezzi, Chun, and Green (1998) measured differences in house prices across U.S. metropolitan areas by using U.S. Census data as the foundation for performing regressions for each metropolitan statistical area. The general form of a hedonic regression is
where X is a set of house characteristics and Z is a set of neighborhood characteristics. Because hedonic regressions are reduced form regressions (i.e., a mixture of supply and demand curves for various characteristics), economic theory does not guide their specification. Although no one has done a formal count of the most frequently estimated functional relationship between house characteristics and house prices, it is not unreasonable to expect that the form most commonly used is the semilog form:
This form is popular not because it is necessarily correct but because it is easy to interpret. The coefficients approximate the percentage change in house prices arising from a one-unit change in characteristics.
Halvorsen and Pollakowski (1981) and Meese and Wallace (1991) suggest letting the data speak when trying to relate the characteristics of houses to house values. But Malpezzi, Chun, and Green (1998) choose one functional form so that they can compare the impact of number of bedrooms, bathrooms, and so on, on an apples-to-apples basis across cities. They run the same model for every city and compare rents and prices (in 1980 and 1990s) for a U.S. house that contains average characteristics. They find that the most expensive housing market in the United States is about eight times more expensive than the least expensive. In contrast, the difference between the most expensive rental market and the least is a multiple of only four; this is substantial, but much smaller than the difference in house prices.
Rents and Prices
Can an equilibrium model of housing explain such large differences in the variation of rents across markets relative to the variation in prices? The answer is perhaps. When housing markets are in equilibrium, the value of a house should just be equal to the present value of its rents. That is (p. 426)
Where T is the life of the asset and r is the discount rate. The ratio of rent to value at a particular time can vary across places if (1) there are different expectations about rent growth and (2) there are different discount rates. Cities that grow faster should see rents rise more quickly in convenient locations—one would expect rents to rise more in San Jose than in Detroit. The Gordon growth model shows why differences in rental growth rates produce different rent-to-price ratios. Suppose one has an asset with discount rate r and with cash flows that grow at rate g in perpetuity. Then the present discounted value of the asset is
Rearranging, this becomes
If CFt represents net rents, and Vt represents house prices, we may see how even for cities with equal discount rates, the rent-to-price ratio can vary with differences in expected rent growth.
At the same time, discount rates can vary because some cities are perceived as riskier than others, and after-tax discount rates can vary, even when before tax discount rates are the same. San Jose and El Paso provide a useful contrast. Median household income in Santa Clara County is $88,585, while in El Paso it is $36,519. Thus the median household in San Jose pays a higher marginal federal tax rate than its counterpart in El Paso. But San Jose is also located in a state with high state marginal tax rates (as of this writing, only New York and the District of Columbia exceed it), whereas Texas has no state income tax.
Because mortgage interest is deductible, and imputed rent is not taxed,5 the capital cost of owning a house is the before-tax discount rate for owning multiplied by one minus the marginal tax rate. For example, if one pays 8 percent interest on a mortgage and has a federal and state marginal tax rate of 30 percent, the after-tax interest paid is 8 percent multiplied by 70 percent, or 5.6 percent. The higher the marginal tax rate, the lower the after-tax cost of capital, holding interest rates constant.
Later I will discuss the tax advantages that owner-occupied housing has over renting. Suffice it to say that as a household's marginal tax rate goes up, its after-tax cost of capital for housing goes down. Consequently, the after-tax discount rate for (p. 427) housing in California is lower than it is in Texas and at least partially explains why house-price to income ratios are higher in California than in Texas.
At the moment, there seems to be little dispute that the United States (and other countries around the world) has suffered from housing bubbles: house prices that rose so rapidly in such unlikely places that it is hard, in retrospect, to arrive at an explanation for recent house price dynamics other than the presence of unreasonable expectations (irrational exuberance?) on the part of home buyers.
The housing and macroeconomics literature now feature debates about the source of the frenzy. Among the most popular explanations was the deterioration of lending standards: households with poor credit histories, undocumented incomes, and small down payments could for the first time purchase houses. This led to two channels for pushing up house prices: it rapidly increased demand, and it encouraged speculators to invest in housing.
In parts of the United States (especially along the coasts), land-use regulation inhibits supply responses to changes in demand. The literature has yet to produce convincing evidence as to why coastal regions have more land-use regulation than others, and this is an area worthy of future research. Consequently, in the short run, demand shocks show up in prices rather than new houses. Eventually, however, markets did respond to demand pressures, and more houses were built in the United States between 2002 and 2006 than in any previous four-year period since 1972–1976. Ultimately, homebuilders would build too many houses, which would lead to an unprecedented decline in house prices.
Perhaps as important as the change in the fundamental demand for housing (i.e., demand arising from increases in household formation and/or income) was a change in the speculative demand for housing. When investors could obtain a loan with little or no equity, they would lose little if prices fell but would gain much if prices rose. Data on the extent to which investors drove the housing market are poor, but some estimates place the investor share in 2005–2006 at as high as 30 percent.
Ultimately, the housing market worked like a Ponzi scheme—so long as there is someone willing to buy a house at a price higher than its previous sales price, the upward price dynamics continue. But once prices begin to fall, loans supported by home equity begin defaulting, which in turn harms the balance sheets of financial institutions. These institutions saw their capital erode, and they either failed or reduced their lending. House prices fell, which produced more default, and so began a downward spiral.
It is difficult to pinpoint the moment at which house prices began to deflate, but the first places where house prices began falling were Cleveland and Detroit, in 2006 and 2007, respectively.
(p. 428) Housing Policy in the United States
The first major housing policy in the United States was implemented during the Lincoln administration. The Homestead Act of 1862 had much in common with future American housing policies: its purpose had many nonhousing elements. In the case of the Homestead Act, the idea was to get people out of cities (reflecting a Jeffersonian-rooted antiurban bias)6 and to “settle” the West by displacing Native Americans with white Americans. It worked very well, and it did allow families to sustain themselves as farmers and to provide themselves reasonable housing.
The New York Tenement Commission of 1894 brought about the New York Tenement Act of 1901, whose purpose was to upgrade housing conditions for the poor. One of the motivations behind the act was public health—by improving conditions and reducing density, new regulations sought to limit communicable diseases.
The next major housing policies were those of the New Deal. The policies that had the longest-term effects were those that dealt with housing finance, for which many institutions were created: the Federal Housing Administration (FHA), which provided mortgage insurance for long-term mortgages, the Federal National Mortgage Association (which would become Fannie Mae), whose purpose was to use capital markets to fund mortgages; the Federal Home Loan Bank (which came into being during the Hoover administration); and the Federal Deposit Insurance Corporation. Before these institutions came into being, the long-term, self-amortizing fixed-payment mortgage barely existed in the United States; afterward, that instrument became ubiquitous and was essentially the only mortgage instrument in the United States until the late 1970s.
Among the most successful institutions (but also short-lived) was the Home Owners Loan Corporation, whose purpose was to fund defaulted mortgages. Alan Blinder notes:
The HOLC was established in June 1933 to help distressed families avert foreclosures by replacing mortgages that were in or near default with new ones that homeowners could afford. It did so by buying old mortgages from banks most of which were delighted to trade them in for safe government bonds and then issuing new loans to homeowners. The HOLC financed itself by borrowing from capital markets and the Treasury.The scale of the operation was impressive. Within two years, the HOLC received about 1.9 million (p. 429) applications from distressed homeowners and granted just over a million new mortgages. (Adjusting only for population growth, the corresponding mortgage figure today would be almost 2.5 million.) Nearly one of every five mortgages in America became owned by the HOLC. Its total lending over its lifetime amounted to $3.5 billion - a colossal sum equal to 5 percent of a year's gross domestic product at the time. (The corresponding figure today would be about $750 billion.)
As a public corporation chartered for a public purpose, the HOLC was a patient and even lenient lender. It tried to keep delinquent borrowers on track with debt counseling, budgeting help and even family meetings. But times were tough in the 1930s, and nearly 20 percent of the HOLC's borrowers defaulted anyway. So the corporation eventually acquired ownership of about 200,000 houses, nearly all of which were sold by 1944. The HOLC closed its books in 1951, or 15 years after its last 1936 mortgage was paid off, with a small profit. It was a heavy lift, but the incredible HOLC lifted it.7
The HOLC is worthy of current attention because it perhaps provides a model for how to escape from our current mortgage crisis. The other institutions were put in place largely in response to the financial crisis, and they help underpin the modern mortgage finance system, largely to the good until quite recently. The programs were not without critics, however. On the one hand, some critics maintained that government housing finance programs, and the FHA program in particular, discriminated against minorities, encouraged segregation, and helped capital flee from central cities.8
FHA documents from the 1930s indicate that the program did indeed discriminate against minorities; to the extent discrimination and segregation are connected, it enabled segregation as well. But it is not entirely clear whether the FHA program is responsible for flight to the suburbs; it is entirely possible that such flight would have occurred in the program's absence. Throughout the developed world, cities have been expanding for some time now, even in the absence of financial preferences.
Succeeding housing acts (such as the Housing Act of 1937 and the Housing Act of 1949) focused more on the housing stock than on housing finance. Specifically, the federal government subsidized local public housing and urban renewal. Although some public housing developments worked reasonably well, large-scale public housing in the United States was, understandably, deemed to be a disaster. In the first place, financial analysis of such housing showed it to be expensive and wasteful: some estimates suggest that public housing produces only about thirty-five cents of benefit for each dollar that is spent on it.9 Second, many large-scale public housing (p. 430) projects, such as Cabrini Green and Robert Taylor Homes in Chicago, Ida Wells in Detroit, and Pruitt-Igo in St. Louis, were ugly and dehumanizing. These developments were not necessarily a failure of public housing per se but rather of a particular kind of public housing design–one that was inspired by Le Corbusier and the modernist school.
The failure of public housing helped spur one of the most extraordinary experiments in social research: the Experimental Housing Allowance Program (EHAP). The purpose of this program, which operated in Green Bay, Wisconsin, and South Bend, Indiana, was to compare the efficiency and efficacy of demand subsidies relative to supply subsidies. Low-income households were given vouchers that would be sufficiently large that they would spend no more than 30 percent of their own income on what was known as “fair-market rent,” a measure of what a standard housing unit would fetch in the rental market.
Vouchers were controversial because there was a view that they simply gave subsidies to landlords, as opposed to public housing, where all the subsidies were directed at the tenants. The EHAP, however, showed that vouchers were a more efficient method for providing housing to low-income residents and also allowed households to consume higher-quality housing. While the experiment did not satisfy those who doubted the merits of vouchers (who argued, among other things, that the experiment took place in markets where housing was abundant and therefore were well suited to vouchers), it did substantially alter the terms of the debate about best practices in housing policy. A Democratic administration—that of Jimmy Carter—moved funding away from building public housing projects toward vouchers.
Vouchers are now the most important low-income housing program in the United States. The nation still has a production-based program—the Section 42 Low Income Housing Tax Credit program—which has built more than 1 million units over its history. The annual budget of the Section 8 program is $26 billion for fiscal year 2010; for the Section 42 program, the budget is $700 million per year for new projects.10 Since the financial calamity of 2008, however, not all tax credits have been used.
One could argue, however, that the most important housing policy in the United States comes from local governments. In particular, local governments have large impacts on housing via zoning policy, tax policy, and service provision.
Regulation and Multifamily Housing
Many municipalities in the United States are hostile to multifamily housing. One Los Angeles suburb—San Marino—is completely bereft of housing in excess of four (p. 431) units. This is not because land is inexpensive there—on the contrary, it is quite expensive by American standards. Rather, local zoning ordinances both explicitly and implicitly make multifamily housing nearly impossible to build legally. Most areas have bulk requirements that are incompatible with multifamily housing. Sometimes units under a certain size will be forbidden; sometimes there will be low floor-area ratio regulations. For example, if a community has a maximum floor-area ratio of 0.5 (which is to say that for each square foot of land a developer can build only one-half square foot of dwelling space), multistory, multitenanted buildings become impossible, unless a developer wishes to purchase large swaths of land.
But even when higher bulks are permitted, zoning codes are often written in a manner that makes it difficult to execute multifamily housing. A common feature of zoning codes is the conditional use permit. If multifamily housing is a conditional use, it will under normal conditions be permitted unless a constituency provides a compelling reason to a zoning commission that such housing would have a negative impact on surrounding uses. Examples of negative impacts might be noise, propensity for crime, trash, and diminution of property values.
It is the last of these that community groups will often use to attempt to block multifamily housing permits. A series of papers, however, have contradicted the assertion that multifamily housing causes surrounding property values to decline, although it is possible that those who oppose multifamily housing sincerely believe that it will cause the values of their homes to decline.11
Yet assertions of declining property value may be an excuse that allows people from expressing the real source of their opposition to multifamily housing: that in the United States, multifamily housing tends to be more affordable, and therefore more likely to be occupied by low-income people than is single-family housing. Among the fears about multifamily housing—and in particular multifamily rental housing—is that its tenants will demand more services of local governments, and pay fewer taxes, than residents of single-family housing. In a study of Plano, Texas, Vandell (1979) finds that the opposite is true: those who dwell in multifamily housing are less likely to have children, and therefore less likely to consume local services, than those who dwell in single-family housing.
Part of America's broad hostility to multifamily housing, however, may have nothing to do with attitudes toward property values or the poor but rather the continuing Jeffersonian creed steeped in the virtues of landownership. While the owner of a single-family suburban house is hardly a yeoman farmer, the housing form continues the mythology of the importance of owning one's own land.
America's suspiciousness of multitenanted housing—indeed of urbanity in general—is not unique. While some cultures embrace multifamily housing (the French, in Paris anyway, and the Chinese, in Hong Kong and Shanghai anyway), many others do not. Officials in South Asian countries are quite hostile to allowing large bulk, even though their cities are among the densest in the world.
(p. 432) Taxes and Housing
The Income Tax and Housing
The United States has an “accidental” tax policy with respect to owner-occupied housing.12 The mortgage interest deduction, probably the best-known federal tax policy with respect to housing, is a residual of the original 1913 Federal Income Tax Code, which permitted the deductibility of all consumer interest payments. With the Tax Reform Act of 1986 consumer interest deductions from income with the exception of the mortgage interest deduction were eliminated. The powerful lobbying interests of the National Association of Realtors, the National Association of Homebuilders, and the Mortgage Banking Association of America worked to make sure that Congress would not eliminate the mortgage interest deduction, even as it eliminated deductions for other types of consumer interest.
In the popular press, the mortgage interest deduction is often characterized as being the principal tax benefit accruing to homeowners. This is certainly not correct. First, fewer than 50 percent of homeowners itemize on their tax returns; the remainder use the standard deduction. This is because the value of itemized deductions for low- to moderate-income homeowners in states with low marginal tax rates will almost certainly be less than the value of the standard deduction, which in 2003 was $9,500 for married couples filing jointly. Also, according to tabulations from the Survey of Consumer Finances, many households with elderly heads own their homes entirely with equity (i.e., do not have mortgages), and for these households, the mortgage interest deduction has no value.
Second, even for those who do itemize, the mortgage interest deduction does not necessarily produce the largest tax benefit arising from owning. The imputed rent that households earn from their owner-occupied housing (i.e., the rents that households are not required to pay anyone else because they own) goes untaxed. This rent is therefore favored relative to most other types of income, including ordinary income, taxable bond income, dividend income, and capital gains income, which, while favored and deferrable, are still generally taxed. The average loan-to-value ratio in the United States is less than 50 percent. Thus, even if all owners with mortgages itemized on their tax returns, the value of the nontaxation of imputed rent would be larger than that of the mortgage interest deduction.
One could argue that the effect of the mortgage interest deduction combined with the nontaxation of imputed rent puts debt on a level playing field with equity as a way to finance housing. This contrasts with the tax treatment of corporate income, where interest is deductible and the opportunity cost of equity capital is not. Many analysts have shown that the U.S. tax system encourages corporations to take on debt. Capozza, Green and Hendershott (1996) have shown how the (p. 433) combination of nontaxation of imputed rent and the absence of a mortgage interest deduction would discourage households from financing housing with debt. In Australia, imputed rent is not taxed and mortgage interest is not deductible, and households there generally pay off their mortgages more quickly than in the United States.
The Property Tax and Housing
When households choose a particular dwelling, they are choosing a large number of other things as well, including neighborhood amenities and a basket of government services. Fischel (2005), among others, has argued that the property tax is a benefits tax, and as such aligns the interests of taxpayers and government officials well. Owners of property may be considered shareholders in their communities. When government officials provide services at reasonable tax prices, property values rise; when they do the converse, property values fall. Consequently, property owners receive a tangible signal about the quality of their governance; this signal can be the foundation for rewarding or punishing officials.
But property tax–based local finance can also produce inequalities, and in particular can produce paths that worsen these inequalities. Consider a distressed city with low property values per person. In order to provide basic services, the city will need to charge very high property tax rates. This in turn could lead businesses (which typically pay more in taxes than they use in services) to migrate away, which in turn reduces the tax base, which in turn worsens services. This leads to an un-virtuous cycle.
States such as Michigan and California have attempted to bring greater equality to municipal and school finance by collecting income and sales taxes at the state level and then sending the funds to school districts and municipalities for service provision. While this levels the playing field, it also breaks the link between government services and their cost.
Segregation, Discrimination, and Spatial Mismatch
One cannot think about U.S. housing markets and policy without considering the role of discrimination. As I have already noted, federal housing finance policy at one point explicitly discriminated against certain types of neighborhoods.
Cities in the United States, while becoming less segregated, are still highly segregated. A common measure of segregation is the dissimilarity index, which measures the relative separation or integration of groups across all neighborhoods of a city or metropolitan area. If a city's white-black dissimilarity index (p. 434) were 65, that would mean that 65 percent of white people would need to move to another neighborhood to make whites and blacks evenly distributed across all neighborhoods.
According to Frey and Myers's (2005) analysis of the 2000 census, the dissimilarity index for U.S. cities ranges from 31.7 in Jacksonville, North Carolina, to 87.9 percent in Gary, Indiana. The twenty most segregated cities in the United States in 2000 were all in the Midwest and Northeast, while the least segregated cities were scattered around the country, although none of them were in the Northeast. It is interesting to note that six of the twenty least segregated metropolitan areas are in college towns. The vast majority of American metropolitan areas have dissimilarity indices in excess of 60. If segregation were a “natural” phenomenon, it might not be something to worry about. But it is almost certainly the result of the legacy of discrimination, and of path dependence arising from that legacy. As I have already discussed, government programs, and in particular FHA programs, discriminated against neighborhoods based on racial and ethnic attributes. And as I shall discuss later, as much as we might like to think we have put discrimination in housing markets behind us, the evidence suggests that we have not.
The literature on the connection between discrimination and segregation is not well settled. Kelly Derango (2001) makes an argument that matched-pair studies, about which I will say more later, show that more discrimination takes place in the rental market than the owner market; he also notes that renters live in more segregated neighborhoods than do owners. He thus draws a link between the two, arguing that the presence of segregation accompanies the presence of discrimination.
One type of evidence that discrimination takes place in housing is differences in amounts paid for housing: the presence of discrimination might lead one racial group to pay more for housing than another. John Kain and John Quigley (1975) used regression to find that, in the presence of controls, blacks paid more for housing than did whites. James Follain and Stephen Malpezzi (1981) used similar methods and found that blacks paid less. Part of the issue explaining the different outcomes might have been unobserved neighborhood characteristics.
Daniel Chambers (1992) carefully measured neighborhood characteristics in a Chicago study in the early 1990s. He found that Chicago's black neighborhoods had lower prices than white neighborhoods, after accounting for a wide variety of structural and other neighborhood characteristics. He also found that within white neighborhoods, blacks paid no more than whites for housing.13 Katherine Kiel and Jeffrey Zabel (1996) looked at other cities and agreed with Chambers's conclusions. On the other hand, direct measures of discrimination confirm that it still exists. The U.S. Department of Housing and Urban Development funded three major studies of housing markets with paired testers. The earliest of these studies, Wienk (1979), (p. 435) focused primarily on blacks and whites and tended to find that real estate brokers treated whites and blacks substantially differently. Real estate brokers are more likely to show whites looking for housing more units and units in better neighborhoods than they do blacks. In particular, upon first meeting testers, brokers would show more options to whites than to blacks.
A study from the early 1990s (Turner, Struyk and Yinger 1991) examined the prevalence of discrimination against Hispanics as well as blacks. The bottom line of its findings was that discrimination was still pervasive in housing markets, but less so than in the past. Turner et.al. (2002) showed again that with respect to discrimination, things were improving but were hardly good.
Discrimination and segregation may have economic consequences. John Kain (1968) began an important literature on the spatial mismatch hypothesis, which posited the following chain of reasoning: discrimination produces segregation, segregation reduces access to jobs, and reduced access to jobs harms employment opportunities.
Since Kain's seminal paper, there has been some argument as to whether he got all the linkages correct. Even Kain himself found that blacks, while living in segregated neighborhoods, live no farther from jobs than whites. For example, it may be possible that integration leads to greater tolerance,14 which in turn leads to better opportunities for black employment.
One could argue that housing policy in the United States has been the product of a series of historical accidents that have little to do with housing per se. America's strong policy preference for single-family houses may be the result of the Jeffersonian agrarian tradition, a tradition that was reinforced by the Homestead Act.
Housing finance, as we know it, arose out of the crisis of the Great Depression. Its main purpose was arguably not to stimulate housing but rather to stabilize the banking system. Tax policy favoring housing is also at least in part an accident: a residual of the original Federal Income Tax Code of 1913, which allowed all consumer interest to be deducted.
Perhaps the lesson that arises from this is that it is important to have a coherent housing policy based on how the country feels about housing as a priority. This does not necessarily mean that housing should be favored relative to other capital goods; nor does it necessarily mean that it should be a social priority relative to, say, education and health care. But it is important to consider explicitly housing's role and to make policy based upon the explicit consideration.
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(1.) The median price of a house in Manhattan is more than ten times as expensive as the median price in many regions of the country, including parts of the Great Plains and the Deep South.
(2.) A number of cost estimation services provide data on construction costs at localized levels. These estimates are from R. S. Means, http://www.rsmeans.com/consulting/WP_predictive.asp (accessed March 5, 2010) and the National Association of Home Builders, http://www.nahb.org/fileUpload_details.aspx?contentID=80051 (accessed March 5, 2010).
(3.) Jed Kolko, presentation at Lusk Center for Real Estate Seminar, October 3, 2008.
(4.) US Census. Value of new Construction put in Place. (http://www.census.gov/compendia/statab/cats/construction_housing/construction_indices_and_value.html accessed March 5, 2010).
(5.) Imputed rent is essentially the rent pays oneself for a house she owns and lives in.
(6.) Jefferson's views on cities can be summarized in one quote: “I think our governments will remain virtuous for many centuries; as long as they are chiefly agricultural; and this will be as long as there shall be vacant lands in any part of America. When they get piled upon one another in large cities, as in Europe, they will become corrupt as in Europe.”
(7.) http://www.nytimes.com/2008/02/24/business/24view.html?pagewanted=print (accessed March 8, 2010).
(10.) http://www.hud.gov/budgetsummary2010/fy10budget.pdf (accessed March 5, 2010).