Land Use Regulation and the Price of Housing in a Suburban Wisconsin County

Journal of Housing Economics 8, 144–159 (1999) Article ID jhec.1999.0243, available online at http://www.idealibrary.com on

Land Use Regulation and the Price of Housing in a Suburban Wisconsin County* Richard K. Green School of Business, University of Wisconsin—Madison, Madison, Wisconsin 53706 Received July 21, 1998

INTRODUCTION Land use regulation generally increases the cost of housing. This should not be a controversial statement: land use regulation limits the supply of a commodity and therefore increases its price. Empirical work by Malpezzi (1996), Malpezzi and Green (1996), Malpezzi et al. (1998), and Bertaud and Renault (1997), among others, has confirmed this unsurprising fact. Still, it may be useful to describe the mechanism by which land use regulations affect housing costs, the distributional impacts of land use regulations, and the magnitude by which land use regulation affects prices in a particular market: Waukesha County, Wisconsin. Waukesha County, a place that is growing rapidly by Midwestern standards (and is indeed growing more rapidly than the United States as a whole), is a community that makes for an interesting case study. The county also has a large number of individual municipalities with a wide variety of land use regulatory practices, which shall be described below. The county’s rapid growth means that land use controls might be binding, and therefore have an impact on costs. The county’s myriad of governments gives us a natural experiment. The proximity of the many communities gives us some implicit geographical controls, while the variety of land use regulations might allow us to identify how they influence costs. The organization of this paper follows. We begin by describing theoretical predictions of the impact of a particular type of land use regulation—zoning—on house values. Because zoning is the most common method Waukesha governments use to regulate land use, a discussion of zoning is especially important. We examine zoning from the perspectives of its critics and supporters. We then describe the empirical model we use to determine the specific impact of zoning *Thanks are due to Stephen Malpezzi and Man Cho for comments. This paper was funded in part by Wisconsin Realtors Association and the Wisconsin Builders Association. 144 1051-1377/99 $30.00 Copyright q 1999 by Academic Press All rights of reproduction in any form reserved.

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on housing costs in Wisconsin. Next we describe the data we use. We then report our results and draw some conclusions.

WHAT DOES ZONING DO? Karkkainen (1994) divides the arguments of the supporters of zoning into two types, and the arguments of zoning’s detractors into four.1 While these divisions are certainly not exhaustive, they are useful for the purposes of this paper. Zoning’s supporters cite two justifications for its existence: to preserve home values and to prevent undue burden on a community’s tax base. Its detractors suggest: (1) that zoning benefits some households at the expense of others, and is therefore unfair; (2) that zoning introduces large transaction costs into the land use succession process; (3) that zoning is often used to exclude households from certain income and ethnic categories from living in the zoned communities; and (4) that zoning leads to allocative inefficiency in land use. Let us investigate each of these statements in turn. With respect to protecting property values, Karkkainen (1994) notes . . . zoning serves principally to protect property owners from the negative externalities of new developments. Without zoning (or some comparable system of land use regulation), residential property owners would face plummeting property values if a development with significant negative externalities—a junkyard or brick factory, for example—moved in next door. Moreover, the mere prospect that such a development could move in would tend to depress the value of residential property. The solution is to divide the municipality into zones so that industries are sited near other industries, commercial enterprises near other commercial enterprises, and residential properties with other residential properties . . . This rationale has some intuitive appeal, based on the real or imagined horrors of entirely unregulated development.

Karkkainen goes on to note the short supply of empirical evidence supporting this rationale. As for escaping tax burdens, the classic treatment is in Fischel (1985), who coined the term “fiscal zoning” to show how communities could push problems off to neighbors—and especially older neighbors with a large existing housing stock—through the judicious use of zoning. Consider, for instance, communities that impose minimum lot size requirements. These requirements put a floor on the cost of producing a housing unit, because they essentially make small houses infeasible. If these requirements are binding (i.e., prevent the construction of small houses that would otherwise be demanded in the marketplace), households with incomes below some threshold level will be fenced out of the community imposing the requirements. 1 Karkkainen correctly notes that the academic literature largely disapproves of zoning as a method for accomplishing the policy ends for which it is intended. The summary is contained in a Web journal.

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Brueckner and Lai (1996) give perhaps the most elegant description of how zoning benefits some households at the expense of others. Specifically, using a Nash equilibrium model, they show how limits on development can benefit landowners living in a city that is in a position to control development; the costs of zoning are borne by renters and those living in a city (such as an old city) that cannot control development. The transactions cost argument is made by Fischel (1985), who shows that zoning prevents developers and communities from efficiently negotiating an outcome that might be desirable to both. Specifically, it precludes developers from making payments that might pay for the social costs of development and instead requires them to go through the cumbersome process of making campaign contributions to elected officials who might in turn support zoning laws that allow specific, different types of development. White (1978) shows how zoning can be used for fiscal or exclusionary purposes. Simply put, when a community puts in place zoning that prevents the construction of small, dense housing, it prevents low-income households from consuming housing within its boundaries. The mechanisms by which this happens are both obvious and not so obvious. First, because all new housing will be large, and therefore expensive, low-income households will not consume it. Second, by not allowing density, communities reduce the feasible number of housing units that can be constructed. As the filtering model (see, e.g., Olsen (1972)) shows, the construction of new housing units will allow older, less expensive units to be freed up in the housing market. The most important source of housing supply for low income people comes from the existing stock—the easier it is to build, the lower will be the pressure on the existing stock, and therefore the lower its cost will be. Ironically, the putative justification for exclusionary zoning—that it lowers the cost of providing government services for those already living in the city—may be an incorrect one. Work by Sternlieb and Hughes (1974) shows that high density developments often put less fiscal pressure on government services than low density developments. Finally, Fischel (1985) argues that zoning is allocatively inefficient. In principle, planners can do as well as the market at allocating resources if they are omniscient. However, we also know that in the absence of externalities, planners can perform no better than the market, and it is an empirical reality that we have yet to discover the omniscient planner. The presence of externalities, moreover, does not necessarily mean zoning is good policy. If planners identify the externality and estimate its costs, they can: (1) specifically regulate against allowing the externality or (2) impose a tax that internalizes the external cost. All of the above critiques have something in common with each other, and with the arguments in support of zoning—they all recognize that zoning increases the cost of housing. We now turn to developing an empirical model of the magnitude of zoning’s impact on house prices.

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AN EMPIRICAL MODEL OF ZONING AND HOUSE PRICES It is, of course, not sufficient to correlate zoning’s restrictiveness and house prices. While we hypothesize that restrictive zoning will ceteris paribus cause house prices to rise, all things are not equal across all communities, and many of these unequal things also affect house prices. Among the things that might cause house prices to rise are population pressures, rising incomes, and “spatial mismatch,” which we shall explain below. Other phenomena affecting prices include household composition (the share of married couple households and the share of households with children), education levels (which proxy well for lifetime, as opposed to contemporaneous income), and race. The effects of household composition and education are discussed in Green (1996) and Green and Hendershott (1996). A long literature shows that black households tend to pay more than whites for housing (see, e.g., Yinger (1978) and Galster (1992)). The interpretation of this result is generally that blacks have a greater taste for integration than whites, so that blacks are willing to buy housing even in the face of price discrimination. Population, income levels, spatial issues, households composition, education, and race all affect the demand for housing. The price at which housing is supplied is largely determined by three factors of production: land, labor, and materials. One of the benefits of studying a localized area such as Waukesha County is that it is fair to assert that it has something like a single labor and materials market. The land market would be uniform as well, were it not for the fact that different regulatory practices across jurisdictions mean that land is supplied differently across these jurisdictions. We may therefore now write expressions for the supply of and demand for housing in an area in the following manner: Qd 5 ad 1 g1dPd 1 b1dPopulation 1 b2dIncome 1 b3d Age 1 b4d HouseholdType 1 b5d Race 1 «1

(1d)

Qs 5 as 1 g1sPs 1 b1swages 1 b2smaterials 1 b3sland 1 «2. These are the structural equations for the demand for and supply of housing, respectively. Because in equilibrium the quantity demanded and supplied must equal each other, we may put (1d) equal to (1s) and get the reduced form equation P 5 p1 1 p2Population 1 p3Income 1 p4Age 1 p5Household Type 1 p6Race 1 p7Wages 1 p8Materials 1 p9Land 1 m.

(2)

The coefficients p have a mixture of the parameters from the equations (1d) and (1s). Because we only have information on rents and house prices, we are unable to separately identify the equations (1s) and (1d), but we can estimate

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the reduced form. The reduced form does not allow us to interpret coefficients as elasticities (that is, will not predict the separate supply and demand response to changes in such things as land use regulation); it does, however, allow us to predict the impact on price of changes in the right hand side variables. Our empirical strategy, then, is to estimate (2). We estimate the effects of various zoning requirements (to be detailed below) on tenure, house prices, and rents, controlling for the other demand and supply variables. We specifically estimate the effect of zoning requirements on the homeownership rate, the natural logarithm of price, the natural logarithm of rent, and the share of houses in a community that sell for less than $75,000. The first two specifications allow us to determine the percentage changes in rents and house prices, respectively, given a change in zoning requirements. The last specification allows us to determine the extent to which zoning requirements constrain the ability of the market to provide “affordable” housing. The $75,000 eve seems a reasonable threshold for affordability. If we look at current mortgage market conditions, where the interest rate on a 30-year fixed rate mortgage is 8%, if a household makes a 10% down payment on a house, its principal and interest payment will be $495 per month. Assuming a property tax mill rate of 30 and a mortgage insurance premium of 0.5%, the household’s PITI payment will be $710 per month. Applying Fannie Mae underwriting criteria, this means a household needs income of a little over $30,000 to afford the $75,000 house. A two-income household with both earners in entry level manufacturing jobs would earn about this amount.

WHY WAUKESHA COUNTY? As already noted, Waukesha County gives us a good natural experiment on the impact of land use regulation on house prices. First, Waukesha County has been among the fastest growing in the Midwest over the past 30 years—its population grew from 231,365 in 1970 to 304,715 in 1990, an increase of nearly 32%. This compares with an 11% increase for Wisconsin and a 9% increase for the Midwest over this time period. Growth is necessary for land use controls to be binding. Should a community not grow, land use controls, no matter how stringent they might be, will be largely irrelevant: regulation cannot prevent housing construction that the market would not have undertaken to begin with. Second, Waukesha County is unusual in that it contains 37 municipalities within a very small area geographically. This means that many of the phenomena other than land use controls that affect house prices, such as labor and materials costs and transportation issues, are reasonably well controlled for. Indeed a potential criticism of the studies cited at the beginning of this paper is that they do not control sufficiently well for labor and materials prices. If land use regulation

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is correlated with the prices of these inputs, the reported effects of land use regulation on house prices in these studies might actually be reflecting these other costs. By staying within a single labor and products market, we may allay such concerns. Any variation in prices can therefore potentially be explained by differences in land use policy—although we must of course control for things such as income, race, and educational differences across jurisdictions. Finally, in 1992, Schuetz and White prepared a detailed study on land use controls in Waukesha County. The study is wonderfully painstaking in its collection of detailed information on the regulatory cost of providing housing in Waukesha County. But as Eqs. (1d) and (1s) demonstrate, costs2 are only one of many determinants of house prices. We therefore seek to extend Schuetz and White’s work by finding the extent to which higher costs do in fact get translated into higher house prices.

DATA Our data come from two sources: the Summary Tape Files of the 1990 Census of Population and Housing, and a study by Scheutz and White (1992) detailing the particulars of the zoning codes for the 39 municipalities in Waukesha County. From the summary tape files, we get data on households’ marital status, race, educational levels, income, average travel time to work and age distribution. We also get data on the number of households that have moved into an area over the period 1985 to 1990. From the Scheutz and White study, we get a variety of measures on zoning requirements for residential development, including street width, front set back, lot width, storm sewer, sanitary sewer, water, curb and gutter, and sidewalk requirements. We also learn whether new mobile home sitings are permitted in a community, and if so, whether, they require a conditional use permit. Note that we do not have data on labor costs or materials prices. This does not create a particularly serious problem in our analysis, because these costs are likely quite similar across municipalities in as concentrated an area as Waukesha County. The effects of labor and materials costs will therefore be reflected in the intercept term in Eq. (1s). The data from the census are available at the census tract level; the zoning data are available at the municipality level. We apply the appropriate zoning information to each census tract, so that we have a total of 160 observations in our original data set. Some tracts (43 to be specific), however, have no rental 2

Costs in this context refer to construction costs, which are quite distinct from prices. A comparison of national construction cost indexes to house price indexes drives this point home: they often move quite differently from year to year.

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RICHARD K. GREEN TABLE I Descriptive Statistics

Percentage , 9th grade Percentage . 9th grade but not HS graduate Percentage HS graduate Percentage with some college but no degree Percentage with associates degree Percentage where head of household is white Percentage married households Aggregate commute time to work Median household income (000) Average length of tenure Percentage , 25 Percentage . 25 and , 35 Percentage . 35 and , 45 Percentage . 45 and , 55 Mobile homes permitted Minimum required lot width Minimum required front setback Minimum required street width Curb and gutter required Sidewalk required Written subdivision standards Median rent Median price

Mean

Standard deviation

.025 .052 .210 .135 .052 .982 .725 10.8 41.4 .557 .265 .097 .171 .285 .206 100. 42.4 24.4 .544 .220 .950 555. 104,254

.027 .049 .105 .090 .034 .031 .200 6.0 19.6 .186 .103 .111 .117 .095 .406 25.6 10.0 12.2 .499 .416 .218 275. 54,173

Sources. U.S. Census 1990 Summary Tape Files and Sheutz and White (1992).

housing, and they therefore cannot be used for analyzing rents. Other tracts (25) are devoid of households. Finally, three communities (Chenequa, Lac LaBelle, and Oconomowoc Lakes) have no subdivision regulations in their zoning codes. In the regressions described below, we put their values for such things as lot width requirements as zero. Because a zero lot width is not feasible, however, we also create a dummy variable for the three communities without subdivision standards to try to pick up minimum requirements for feasibility, among other things.3 Descriptive statistics for the census data are put forth in Table I. Before moving to results, we should describe one anomaly in the data. The summary tape files data are from 1990. The Sheutz and White study is from early 1992 and therefore almost certainly describes land use controls in place in

3 We ran regressions that exclude and include these towns—the coefficients did not change much from one version to the next. We only report regressions that include the towns.

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1991.4 We are therefore assuming in performing this analysis that zoning ordinances in Waukesha County remained fairly stable between 1990 and 1991. We could find no evidence that this was not true.

RESULTS The results of our ordinary-least-squares models explaining percentage of households that are homeowners, house prices, rents, and the percentage of owner-occupied houses that cost less than $75,000 are presented in Tables II through V respectively. Table II has results for three equations explaining the percentage of households in the census tract that are owners. The table contains three columns: the first includes the natural logarithm of price and rents and length of tenure as explanatory variables, the second excludes prices and rents, and the third excludes prices, rents, and length of tenure. The reason we offer results from three specifications is that we want to examine whether the endogeneity of explanatory variables is having an impact on coefficient values. Prices, rents, length of tenure, and tenure choice are almost certainly determined simultaneously and therefore the coefficient estimates in the first two columns are almost certainly biased. Ideally we would deal with this simultaneity issue by using two-stage least squares. Unfortunately, however, any variable that would help explain prices would also help explain tenure choice, so identifying a tenure choice equation that includes prices is impossible. We face exactly the same problem with length of tenure. Our strategy is therefore to estimate equations that include and exclude these variables. Should the coefficients on our explanatory variables of interest remain relatively stable, our endogeneity problem is an innocuous one. If, on the other hand, the coefficients change dramatically with specification changes, we must be cautious in making too much of our results. Note that several coefficients are statistically different from zero at the 95% level: education, marital status, and age. These results are consistent with those in Green (1996), Goodman (1988), and many other studies: indeed, married couples nationally have nearly twice the ownership rate of singles, and as household heads age beyond 50, the ownership rate rises to nearly 90%. Note that those with long tenures are also more likely to be owners. This again makes substantial sense: transients will not want to incur the transactions costs involved in owner-occupied housing, because they will not be able to amortize these costs. It is rather surprising that income is not statistically different from zero at the 95% percent level in any of the three equations. One would think that income 4

There are indeed places in the text that suggest that this is so.

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RICHARD K. GREEN TABLE II Regressions Explaining Share of Households That Are Owners

Intercept Percentage , 9th grade Percentage . 9th grade but not HS graduate Percentage HS graduate Percentage with some college but no degree Percentage with associates degree Percentage where head of household is black Percentage married households Average commute time to work Median household income (000) Average length of tenure Percentage , 25 Percentage . 25 and , 35 Percentage . 35 and , 45 Percentage . 45 and , 55 Mobile homes permitted Minimum required lot width Minimum required front setback Minimum required street width Curb and gutter required Sidewalk required Written subdivision standards Log median rent Log median price R2 Note. Standard errors in parentheses. N5111.

(1)

(2)

(3)

20.47 (1.13) 0.97 (0.44) 21.39 (0.43) 0.04 (0.22) 0.50 (0.41) 20.08 (0.56) 21.03 (0.43) 0.42 (0.12) 0.00 (0.00) 0.000 (0.000) 0.73 (0.11) 0.38 (0.26) 20.38 (0.26) 0.10 (0.28) 0.41 (0.25) 20.05 (0.03) 0.00 (0.00) 20.00 (0.00) 0.00 (0.00) 20.01 (0.02) 20.03 (0.03) 0.11 (0.10) 0.01 (0.04) 0.09 (0.08) 0.85

0.76 (0.43) 1.04 (0.43) 21.66 (0.36) 20.06 (0.20) 0.34 (0.38) 20.18 (0.55) 21.02 (0.43) 0.43 (0.11) 0.01 (0.00) 0.001 (0.001) 0.70 (0.10) 0.28 (0.24) 20.52 (0.24) 0.06 (0.28) 20.33 (0.24) 20.06 (0.02) 20.00 (0.00) 20.00 (0.00) 0.00 (0.00) 20.02 (0.02) 20.03 (0.03) 0.09 (0.08)

0.32 (0.52) 0.95 (0.52) 21.50 (0.43) 0.26 (0.24) 0.14 (0.46) 0.68 (0.65) 20.39 (0.51) 0.61 (0.14) 0.00 (0.00) 0.001 (0.001)

0.85

0.77

0.14 (0.29) 20.21 (0.29) 20.38 (0.33) 0.57 (0.29) 20.07 (0.03) 20.00 (0.00) 20.00 (0.00) 0.00 (0.00) 20.04 (0.03) 20.07 (0.04) 0.19 (0.10)

153

REGULATION AND HOUSING COSTS TABLE III Regressions Explaining Natural Logarithm of Price

Intercept Percentage , 9th grade Percentage . 9th grade but not HS graduate Percentage HS graduate Percentage with some college but no degree Percentage with associates degree Percentage where head of household is black Percentage married households Average commute time to work Median household income (000) Average length of tenure Percentage , 25 Percentage . 25 and , 35 Percentage . 35 and , 45 Percentage . 45 and , 55 Mobile homes permitted Minimum required lot width (0) Minimum required front setback (0) Minimum required street width (0) Curb and gutter required Sidewalk required Written subdivision standards Tenure R2 Note. Standard errors in parentheses. N 5 134.

(1)

(2)

(3)

10.8 (0.7) 0.43 (0.62) 21.41 (0.40) 20.05 (0.26) 21.07 (0.44) 20.81 (0.58) 1.36 (0.69) 20.45 (0.16) 0.009 (0.002) 0.01 (0.00) 20.57 (0.13) 20.42 (0.26) 20.19 (0.31) 20.67 (0.26) 20.35 (0.28) 20.07 (0.04) 20.00 (0.01) 0.06 (0.03) 0.01 (0.01) 20.03 (0.03) 0.01 (0.05) 20.57 (0.14) 0.25 (0.12) 0.80

11.1 (0.7) 0.40 (0.63) 21.52 (0.41) 20.02 (0.26) 21.10 (0.44) 20.86 (0.59) 1.16 (0.69) 20.30 (0.14) 0.01 (0.00) 0.01 (0.00) 20.48 (0.12) 20.46 (0.26) 20.27 (0.31) 20.71 (0.25) 20.41 (0.28) 20.08 (0.04) 20.01 (0.01) 0.06 (0.03) 0.01 (0.01) 20.04 (0.03) 20.01 (0.05) 20.54 (0.14)

11.3 (0.7) 0.51 (0.67) 21.76 (0.43) 20.29 (0.27) 21.29 (0.47) 21.39 (0.61) 0.70 (0.72) 20.28 (0.15) 0.01 (0.00) 0.01 (0.00)

0.79

0.76

20.51 (0.28) 20.40 (0.33) 20.21 (0.23) 20.41 (0.30) 20.07 (0.04) 20.01 (0.01) 0.08 (0.04) 0.01 (0.02) 20.03 (0.04) 0.03 (0.05) 20.55 (0.15)

154

RICHARD K. GREEN TABLE IV Regressions Explaining Natural Logarithm of Rent

Intercept Percentage , 9th grade Percentage . 9th grade but not HS graduate Percentage HS graduate Percentage with some college but no degree Percentage with associates degree Percentage where head of household is black Percentage married households Average commute time to work Median household Income (000) Average length of tenure Percentage , 25 Percentage . 25 and , 35 Percentage . 35 and , 45 Percentage . 45 and , 55 Mobile homes permitted Minimum required lot width (0) Minimum required front setback (0) Minimum required street width (0) Curb and gutter required Sidewalk required Written subdivision standards Tenure R2 Note. Standard errors in parentheses. N 5 117.

(1)

(2)

(3)

5.24 (0.86) 22.01 (1.06) 0.50 (0.92) 0.12 (0.37) 0.68 (0.42) 0.64 (1.06) 1.96 (0.86) 0.09 (0.20) 20.01 (0.01) 0.00 (0.00) 20.63 (0.29) 20.85 (0.46) 20.93 (0.37) 21.34 (0.42) 21.12 (0.58) 0.02 (0.06) 20.00 (0.02) 0.03 (0.04) 20.01 (0.02) 0.10 (0.05) 20.02 (0.08) 20.14 (0.20) 0.20 (0.23) 0.41

5.28 (0.86) 21.78 (1.02) 0.18 (0.85) 0.14 (0.37) 0.68 (0.42) 0.80 (1.04) 1.91 (0.86) 0.15 (0.19) 20.01 (0.01) 0.00 (0.00) 20.50 (0.25) 20.79 (0.45) 21.03 (0.36) 21.47 (0.40) 21.06 (0.57) 0.03 (0.04) 20.00 (0.02) 0.05 (0.05) 0.00 (0.02) 0.09 (0.05) 20.02 (0.08) 20.27 (0.22)

5.63 (0.86) 20.83 (1.03) 20.02 (0.86) 20.10 (0.36) 0.79 (0.43) 0.10 (1.00) 1.42 (0.84) 0.04 (0.18) 20.01 (0.01) 0.00 (0.00)

0.40

0.38

20.69 (0.46) 21.07 (0.38) 21.11 (0.36) 21.23 (0.36) 0.02 (0.06) 20.01 (0.01) 0.05 (0.04) 20.00 (0.02) 0.12 (0.05) 20.01 (0.07) 20.21 (0.21)

155

REGULATION AND HOUSING COSTS TABLE V Regressions Explaining Share of Houses Valued at Less then $75,000

Intercept Percentage , 9th grade Percentage . 9th grade but not HS graduate Percentage HS graduate Percentage with some college but no degree Percentage with associates degree Percentage where head of household is black Percentage married households Average commute time to work Median household income (000) Average length of tenure Percentage , 25 Percentage . 25 and , 35 Percentage . 35 and , 45 Percentage . 45 and , 55 Mobile homes permitted Minimum required lot width (0) Minimum required front setback (0) Minimum required street width (0) Curb and gutter required Sidewalk required Written subdivision standards Tenure R2

(1)

(2)

(3)

(4)

(5)

(6)

0.83 (0.65) 20.31 (0.60) 0.79 (0.39) 20.36 (0.25) 0.82 (0.42) 0.39 (0.57) 20.60 (0.67) 0.03 (0.16) 20.00 (0.00) 20.01 (0.00) 0.39 (0.12) 0.23 (0.25) 20.35 (0.30) 0.27 (0.24) 20.40 (0.27) 0.08 (0.04) 20.00 (0.10) 20.03 (0.03) 20.04 (0.01) 0.01 (0.03) 20.00 (0.05) 0.10 (0.13) 0.12 (0.12) 0.48

0.94 (0.64) 20.32 (0.60) 0.73 (0.39) 20.35 (0.25) 0.80 (0.42) 0.36 (0.58) 20.69 (0.66) 0.04 (0.14) 20.00 (0.00) 20.01 (0.00) 0.43 (0.12) 0.22 (0.25) 20.39 (0.30) 0.26 (0.24) 20.43 (0.27) 0.07 (0.04) 20.00 (0.10) 20.03 (0.03) 20.03 (0.01) 0.00 (0.03) 20.01 (0.05) 0.11 (0.13)

0.72 (0.67) 20.41 (0.63) 0.95 (0.41) 20.11 (0.26) 0.97 (0.44) 0.83 (0.58) 20.29 (0.69) 0.03 (0.14) 0.00 (0.00) 20.01 (0.00)

0.82 (0.64) 20.30 (0.60) 0.78 (0.39) 20.36 (0.25) 0.82 (0.42) 0.37 (0.56) 20.58 (0.66) 0.03 (0.16) 20.00 (0.00) 20.01 (0.00) 0.39 (0.12) 0.23 (0.25) 20.35 (0.30) 0.28 (0.24) 20.40 (0.27) 0.08 (0.04)

0.93 (0.64) 20.32 (0.60) 0.73 (0.39) 20.35 (0.25) 0.80 (0.42) 0.35 (0.56) 20.68 (0.65) 0.04 (0.14) 20.00 (0.00) 20.01 (0.00) 0.43 (0.12) 0.22 (0.25) 20.38 (0.29) 0.26 (0.24) 20.43 (0.27) 0.07 (0.04)

0.73 (0.66) 20.42 (0.63) 0.95 (0.40) 20.11 (0.26) 0.97 (0.44) 0.84 (0.57) 20.30 (0.68) 0.02 (0.14) 0.00 (0.00) 20.01 (0.00)

20.04 (0.02) 20.04 (0.01) 0.00 (0.03) 20.01 (0.05) 0.11 (0.13)

20.04 (0.02) 20.03 (0.01) 0.01 (0.03) 20.04 (0.05) 0.11 (0.14)

0.48

0.42

20.04 (0.02) 20.04 (0.01) 0.01 (0.03) 0.00 (0.05) 0.10 (0.13) 0.12 (0.12) 0.48

0.48

0.42

Note. Standard errors in parentheses. N 5 134.

0.26 (0.26) 20.27 (0.31) 20.18 (0.22) 20.42 (0.28) 0.06 (0.04) 0.00 (0.10) 20.04 (0.03) 20.03 (0.01) 0.01 (0.03) 20.04 (0.05) 0.26 (0.26)

0.26 (0.26) 20.28 (0.31) 20.18 (0.22) 20.43 (0.28) 0.06 (0.04)

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would be an especially important determinant of tenure, particularly after one controlled for age and marital status. Perhaps education, which proxies for permanent income, is dominating the effect of contemporaneous income. We include six regulatory variables in all three specifications: whether mobile homes are a permitted use within a community, minimum lot width requirements for subdividers, minimum frontage setback requirements, minimum street width requirements, whether sidewalks are required, and whether curb and gutters are required. Finally, there is a variable that takes a value of one if the community has any written standards at all—this is to control for the three communities that have no standards. Only two regulatory variables seem ever to have an effect on tenure: sidewalk requirements and mobile homes as a permitted use. Communities that require sidewalks have lower ownership rates in the specification that does not include prices, rents, and length of tenure, while communities that forbid mobile homes have higher homeownership rates. Here it is likely that simultaneity issues are confounding our results: areas with high levels of homeownership are more likely to object to mobile homes. Thus forbidding mobile homes does not likely cause a higher ownership rate but is rather caused by a higher ownership rate. Our regressions explaining house prices get stronger effects from the regulatory variables than the regressions explaining tenure (see Table 3). First note that in all three specifications, education, income, marital status, and age are all significantly different from zero at the 95% level of confidence. Race is significant at the 95% level once and is significant at the 90% level once. When census tracts have large numbers of long-term stayers, prices also tend to be lower. The similarity of the coefficients across specifications suggests that endogeneity bias is not a severe problem in these regressions. Two of the regulatory variables in these regressions are significant at the 90% level of confidence (and under the third specification are significant at the 95% percent level): whether mobile homes are permitted uses, and the required number of frontage feet. These regulations tend to make house prices higher. Depending on the specification, forbidding mobile homes pushes up prices on average by between 7.1 and 8.5%. This actually shows that forbidding mobile homes performs as advertised: the whole rationale behind the regulation is, after all, to keep up values. But this manifestly has an impact on housing affordability. As for frontage requirements, the regression shows that, on average, each additional 10 feet of frontage that is required drives up the price of a house by 6.1 to 7.8%. The equations explaining rent do not perform as well as the others: this is consistent with past literature (see Table IV). The problem with attempting to explain rent is that rents tend to have far less variation than prices.5 This is 5 Capozza et al. (1996) show how nationally the coefficient of variation on rents is smaller than the coefficient of variation on house prices.

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because the distribution of rents tends to be truncated on both the bottom (because of regulatory requirements and subsidies) and the top (because once people reach a certain income level, the tax benefits of owning are so large that they will be unwilling to rent under almost all circumstances). The only demographic and economic variables that have a large impact on rent across all three specifications are the age variables. Yet one regulatory variable—a subdivision requirement for curbs and gutters—does have an association with rents. The results suggest that rents are between 10 and 12% higher in the presence of these regulations than they would be in their absence. Finally, perhaps the most important models from a policy standpoint are those that explain the share of owner-occupied houses in a census tract that are affordable, where our definition of an affordable house is one valued at less than $75,000.6 We added a few more specifications to this model: we estimated the model including minimum lot width and frontage requirements separately and jointly (see Table V). In these models, both education (permanent income) and current income affect the share of homes that are valued at less than $75,000. Surprisingly, the effect of education is not monotonic: for instance, those with some college education tend to live in tracts with more affordable owner-occupied houses than those with high school diplomas and no further education. But it is the regulatory variables that perform especially well in these regressions. Forbidding mobile homes reduces the share of houses that are affordable by between 6 and 8 percentage points. Each 10 feet of required street width reduces the share by 3 to 4 percentage points. When estimated alone (i.e., without minimum required lot width), each 10 feet of minimum required frontage reduces the share of houses that are affordable by 3 to 4 percentage points. Given that the average share of houses in each tract that are affordable is 16%, these numbers are quite large: 10 feet of minimum frontage requirements will reduce the share of housing valued at less than $75,000 by an average of one-fourth. Regressions that just contain minimum lot width as a variable produce similar results, although they are a bit less strong. When we attempt to use both variables at one time, neither shows up as significant. This is not because they are not important, but rather because they are highly correlated with each other, and therefore, it is difficult to disentangle one from the other when they are used simultaneously. Because both variables are regulatory variables of a very similar sort, it is almost certainly reasonable to infer that one can stand in for both.

6 Property values are owners’ estimates of these values. Goodman and Ittner (1992), among others show that while these estimates tend to be biased, the bias is not correlated with variables that might explain value. Therefore regressions using these owners’ estimates will have unbiased coefficients on all terms but the intercept.

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CONCLUSION It is striking that both the magnitudes of the coefficients and their significance levels are stronger in the “affordable” share regressions than they are in the price regressions. This outcome is consistent with the nonlinear nature of land use constraints on house prices. Once land use regulations cease to be binding, as they likely will be for households earning in excess of, say $150,000, they will not have an effect on prices. But as we moved down the price scale, land use constraints become more likely to be binding, and therefore will have a greater impact on value. This underscores an unpleasant fact about land use regulations: they tend to fall more heavily on lower income households than they do on anyone else. The discussion about what zoning does suggests that this should not be surprising. Part of the point of zoning, after all, is often to exclude households from certain socioeconomic classes. At the very least, zoning seeks to keep fiscal problems at bay. By reducing the stock of affordable housing, communities perhaps seek to immunize themselves from social spending. At the same time, any reduction in the availability of affordable housing becomes particularly problematic in a place such as Waukesha County. The county has become a major center for entry-level jobs, but as we, as well as many others (including Scheutz and White) have demonstrated, the county does not have an abundant supply of low cost housing for entry level workers to reside in. Any further tightening of zoning restrictions would almost certainly make matters worse. REFERENCES Bertaud, A., and Renaud, B. (1997). “Socialist Cities without Land Markets,” J. Urban Econ. 41, 137–151. Brueckner, J. K., and Lai, F. C. (1996). “Urban Growth Controls with Resident Landowners.” Regional Sci. Urban Econ. 26(2), 125–43. Capozza, D., Green, R. K., and Hendershott, P. H. (1996). ‘‘Taxes, Mortgage Borrowing and Residential Land Prices,” in Economic Effects of Fundamental Tax Reform (Aaron and Gale, Ed.). Washington: Brookings. Fischel, W. A. (1985). The Economics of Zoning Laws: A Property Rights Approach to American Land Use Controls Baltimore: Johns Hopkins. Galster, G. (1992). “Research on Discrimination in Housing and Mortgage Markets: Assessment and Future Directions.” Housing Policy Debate 3(2), 639–83. Goodman, A. (1988). “An Econometric Model of Housing Price, Permanent Income, Tenure Choice, and Housing Demand,” J. Urban Econ. 23, 357–353. Goodman, J. C., and Ittner, J. (1992). “The Accuracy of Homeowners’ Estimates of House Value,” J. Housing Econ. 2, 327–353. Green, R. K. (1996). “Should the Stagnant Homeownership Rate Be a Source of Concern?” Regional Sci. Urban Econ. 26(3–4), 337–368.

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Green, R. K., and Hendershott, P. H. (1996). “Age, Housing Demand, and Real House Prices,” Regional Sci. Urban Econ. 26(5), 465–480. Karkainnen, B. C. (1994). “Zoning: A Reply to the Critics.” J. Land Use Environmental Law 10(1), 1–45. Malpezzi, S. (1996). “Housing Prices, Externalities, and Regulation in U.S. Metropolitan Areas.” J. Housing Res. 7(2), 209–241. Malpezzi, S., and Green, R. K. (1996). “What Has Happened to the Bottom of the US Housing Market?” Urban Stud. 33(10), 1807–1820. Malpezzi, S., Green, R. K., and Chun, G. (1998). “New Place-to-Place Price Indices for U.S. Metropolitan Areas, and Their Determinants: An Application of Urban Indicators.” Real Estate Econ., in press. Olsen, E. (1969). “A Competitive Theory of the Housing Market.” Am. Econ. Rev. 59(4), 612–622. Scheutz, M. K., and White, S. (1992). “Identifying and Mitigating Local Regulatory Barriers to Affordable Housing in Waukesha County, Wisconsin.” Urban Research Center, University of Wisconsin—Milwaukee. Sternlieb, G., and Hughes, J. W. (1974). “Neighborhood Dynamics and Government Policy,” Am. Real Estate Urban Econ. Assoc. J. 2(2), 7–23. White, M. J. (1978). ‘‘Job Suburbanigahar, Young, and NeWelfore of Urban Minority Groups,’’ J. Urban Econ. 5(2), 219–240. Yinger, J. (1978). “The Black–White Price Differential in Housing: Some Further Evidence.” Land Econ. 54(2), 187–206.