The trade-off between private lots and public open space in subdivisions at the urban-rural fringe.

In many communities, particularly those on the urban-rural fringe, most housing is located in subdivisions. Increasingly, those developments are subject to "clustering" rules, in which houses must be located on a portion of the total land and the remainder is left as open space. In some communities the zoning law mandates clustering; in others, clustering is recommended but not required. This open space may be held as forests, put into recreation uses, or remain in agriculture as grazing or cropland. Proponents of clustering requirements argue that undeveloped areas convey value, not only to the residents of the subdivisions themselves, but also to the broader community by preserving the aesthetic and rural character of the community and improving environmental quality through habitat protection or water pollution reduction (Arendt 1992). In communities on the urban-rural fringe, clustering residential developments is one approach to maintaining an agricultural base and curbing sprawl. (1)

Open space may provide benefits to subdivision residents, but clustering means that those residents are living in a higher-density setting compared to conventional subdivisions. Although the external benefits from the preserved forest, recreation area, or other kind of open space may be positive, it is unclear whether those benefits offset the loss experienced by smaller lots and higher density. Several studies have examined the value of open space (see McConnell and Walls 2005), but few have focused on subdivision open space and the trade-off with private lot space. This trade-off is the focus of our study. We estimate a hedonic price model with data on subdivision house sales occurring between 1981 and 2001 in a county on the fringe of the Washington, DC, metropolitan area, Calvert County, Maryland. We examine how households value adjacency to open space and more open space in the subdivision as well as how readily they will be willing to trade off those amenities with their own private lot space.

We find that private acreage positively affects prices but so does subdivision open space. And, there is some evidence that subdivision open space does substitute for private lot size. In addition, having a lot that is adjacent to subdivision open space appears to enhance the value of a house, particularly if the open space is not too steeply sloped. However, we find no evidence of willingness to trade off one's own lot size for adjacency to the open space. We use the results of the estimated hedonic model to simulate the effects on prices of jointly increasing open space and reducing average lot size. We find average house prices are slightly lower with the clustering, particularly for lots not adjacent to open space.

Overview of Relevant Literature

McConnell and Walls (2005) review the extensive revealed and stated preference literature on valuing open space. Almost all of the revealed preference studies use a hedonic property value approach. These studies look at a range of different types of open space--natural areas, wetlands, parks, forested lands, and different kinds of farmland, and they have used different measures of open space, including distance to open space and the percentage of open space within a particular radius of a property.

In this brief review, we emphasize studies that focus on the value of open space amenities within and surrounding residential subdivisions. Mohammed (2006) compares the costs and prices of "conservation" subdivisions with conventional subdivisions. He finds conservation subdivisions to have a price premium of 12-16% and also finds development costs to be lower in conservation subdivisions. (2) Lacy (1990) uses a similar methodology. Neither of these studies controls for the many other factors that could explain price differences.

Peiser and Schwann (1993) analyze house prices in a Dallas subdivision that has publicly usable open space between houses and survey residents about their preferences. While the survey findings suggest that households place a high value on open space, the hedonic price analysis shows otherwise. Houses on the open space generally sold at a premium, but the effect was statistically insignificant and much smaller in magnitude than the effect of the size of the private lots themselves.

Thorsnes (2002) uses sales data from three subdivisions in Grand Rapids, Michigan, each bordering permanently preserved forested lands, to estimate hedonic price equations for both building lots and houses for each subdivision. He finds that lots next to the preserves sell at a 19-35% premium over the total lot price, but the benefits are very localized and do not extend even to parcels across the street. Using a random sample of subdivisions in five Maryland counties, Hardie, Lichtenberg, and Nickerson (2006) calculate the average peracre price of developed lots in 255 subdivisions and estimate this price as a function of subdivision characteristics, including: percentage of the subdivision area that must be planted in trees (by the state Forest Conservation Act). They find that the amount of land in forests has a positive effect on prices.

Patterson and Boyle (2002) focus on the value of a view. Using a hedonic estimation, they find that the visibility of forested areas has higher value than visibility of agricultural land. Kearney (2006) in a survey of residents of subdivisions with different configurations and different amounts and types of open space, also finds that the views from a home, and particularly views of forested areas, are strongly valued by residents.

There are also studies by urban planners who look at the environmental merits of clustering rules and conservation subdivisions. Berke et al. (2003) find that there are potential watershed protection benefits, and Kaplan, Austin, and Kaplan (2004) find that "natural features" of clustered subdivisions appear to be important to residents.

Data

Calvert County is located in southern Maryland, on the western shore of the Chesapeake Bay. It has 101 miles of shoreline, along the Chesapeake Bay and the Patuxent River to the east. The county has historically had an agricultural economy consisting of small villages and rural lands, but in the past twenty years, population growth has been high, and it has increasingly become part of the broad Baltimore-Washington metropolitan area.

Most of the housing growth in recent years has been in low-density suburban subdivisions in the residential and rural areas of the county. Figure 1 shows average lot sizes within the county during different time periods. (3) Although the average gross lot size, calculated as total subdivision acreage divided by the number of houses, has remained relatively high and constant over time, the average lot size net of open space has declined. This provides some indication of the extent to which clustering has been increasing in the county in recent years. Gross lot size trended up in the late 1990s due to the downzoning that occurred, but actual house lots continued to fall in size slightly, reflecting more open space in subdivisions.

In this study we limit the sample to subdivisions that had at least ten house sales over the study period, 1981-2001. This allows us to include 3,386 individual house sales within eighty-nine subdivisions. Table 1 provides summary statistics for house and subdivision level variables included in the model. The mean lot size is 1.5 acres and subdivisions are, on average, 134 acres, with a little over 20% of their land under easement as protected open space. The degree of clustering varies considerably over the sample; however, sixteen of the eighty-nine subdivisions have minimal open space (less than one acre), and twenty subdivisions have over 40% of their acreage in open space.

[FIGURE 1 OMITTED]

Econometric Model and Results

We assume households choose housing characteristics, location, and open space amenities so as to maximize utility. Under this assumption and a housing market in equilibrium, we can use the hedonic price model (see Rosen 1974) to examine consumer behavior with regard to housing choices. The hedonic price function can be specified as P = f(l, C, S, T, O), where P is the price of the property; l represents the lot size; C is a vector of structural characteristics (age, number of bathrooms, square footage, etc.) associated with the house; S is a vector of subdivision characteristics other than open space amenities; T represents a vector of accessibility measures; and O is a vector of open space attributes.

Since evidence from the literature suggests that the value of open space amenities to residents may vary by proximity or the type of open space (e.g., number of trees, usability, steepness), in our model we include three subdivision open space variables: open space acreage, a dummy for whether a house is adjacent to subdivision open space, and the percentage of subdivision open space that is in steep slopes. We also include interaction variables, which we discuss more below, as well as surrounding land-use variables, including adjacency to preserved agricultural land.