Green Space and Property Values
Although many of the benefits associated with public green spaces are seemingly obvious and easy to describe, they are often much harder to quantify. Green spaces in urban and suburban areas have typically been publicly provided amenities that have no set market price, but it has become increasingly common to evaluate them in terms of their monetary contributions to their surrounding communities. There exists a need, therefore, to convert the many assumptions regarding the inherent benefits of green space into objective, quantitative estimates of their worth (Nicholls 2005). Recent trends towards increased land development, particularly in urban areas, makes the ability to determine the economic values of public parks and green spaces important in order to ensure their existence and designation. Early literature supports the notion that green space causes an increase in property values because home-owners and renters are willing to pay more for the perceived benefits of being close to green space (Crompton 2001). However, more recent studies have been able to use the hedonic pricing analysis as a more accurate means of demonstrating the variable effects. Because green space offers many different benefits, such as environmental, recreational, transportation, aesthetic and health-related nature, no one method exists to measure all such benefits simultaneously (Nicholls 2005). On a similar note, not all green spaces are the same or provide the same amenities, and thus their impact on property value may vary. Therefore, this literature survey will discuss one essay that provides a foundation establishing the importance of a quantifiable measure for green space benefits on property value, and two studies that use regression analysis to measure the different variables that impact the perceived benefits of green spaces and subsequent property values.
Some city planners, urban developers, and governmental officials believe that development brings prosperity through enhanced tax revenues, and hence any land left open or undeveloped is considered a wasted asset. Furthermore, opponents of green spaces have identified several negative externalities, such as the invasion of the privacy of those residents whose properties directly adjoin greenways, concern regarding the numbers of strangers who will be passing through local neighborhoods, and fears of increased noise, littering, trespass, and vandalism (Nicholls 2005). All of these factors can (and in some studies have) decrease the generally believed positive impact that green spaces have on home values (Nicholls 2005). Crompton (2001) combats this perception through the establishment of the proximate principle. This principle suggests that the value of a specified amenity, like green space, is at least partially captured in the price of residential properties “proximate” to it (2001). If it is anticipated that properties or homes located near an open green space are considered desirable, the additional money that homebuyers and renters are willing to pay for this location represents a “capitalization” of the land into proximate property values (2001). With an increase in property value comes and increase in property taxes, and in some cases the additional taxes paid for all proximate properties may cover or even exceed the annual cost of acquiring, developing, and even maintaining the green space (2001). As such, many public parks were originally created with the hopes of their direct and indirect economic contributions to city tax revenues, Central Park in New York City being a prime example (2001). As a result, the impact that green spaces can have on the economic development of an area makes them an important factor of consideration in urban and suburban planning. Twenty out of thirty previous studies that Crompton (2001) discusses support the proximate principal; however, several other factors can influence the relationship between green space and property values, such as the various forms of desirability associated with green space and the physical characteristics of the green space itself.
Nicholls (2005) uses hedonic pricing to operationalize and measure Crompton’s proximate principle in a specific location and takes into account two different desirability interpretations of green space: aesthetic appeal and physical proximity. The greenbelt chosen for the study is the Barton Creek Greenbelt and Wilderness Park in Austin, Texas, along with three major residential bordering neighborhoods: Barton, Lost Creek, and Travis. The greenbelt is a 1,771-acre natural area located to the west of downtown, and includes 7.5 miles of multi-use trails, as well as various parking and restroom facilities (2005). Each neighborhood is examined separately since each contains a different set of locational amenities for inclusion in the hedonic model, but since properties were located within the same geographic sub-areas (such as school and tax zones) neighborhood and community variations were not investigated (2005). Sales price is the dependent variable, and the independent variables include three groups of property value influence: structural, locational, and environmental (2005). The value of the greenbelt is measured in three ways: aesthetic value, which is shown using two variables, direct adjacency to the greenbelt and view of the greenbelt; and physical proximity, which is represented by a continuous measurement of the distance between each property and the closest entrance to the greenbelt (2005).
The results of Nicholls’ hedonic analysis show that adjacency to the greenbelt produced significant property value premiums in two of three neighborhoods (Barton and Travis), but in no case did visual or physical access to a greenway have a significant negative impact on surrounding property prices (2005). The lack of positive impact of greenbelt adjacency in the Lost Creek area may be a result of the dramatic topography and dense vegetation that dominates the area. Lost Creek homes directly adjacent to the greenbelt are typically located on the edges of deep, thickly vegetated ravines that lack recreational access or nice views (2005). Conversely, homes located farther away from the greenbelt boundary on a higher elevation level have widespread views of both Austin and the greenbelt, but this view often includes a high voltage power line (2005). Although proximity to a power line is usually seen to have negative or neutral impact on property values, in this case the result could be that the beauty of the green space in the majority of the view offsets the interference of the power line into a part of it (2005). The finding of significant positive impacts of greenbelt adjacency in the other two neighborhoods supports this argument that physical characteristics may be influential (2005). In both the Barton and Travis areas, the topography is less steep and the vegetation is less dense, which might provide more obvious visual benefits (2005).
While the Lost Creek area did demonstrate the expected relationship of a decline in property value with increased distance from the closest greenbelt entrance ($3.97 decrease with each foot from the nearest entrance), in Barton and Travis the coefficient on the distance variable appeared insignificant (2005). An explanation for the Travis area isn’t clear, but for Barton this could be a result of the neighborhood’s distance to the bridge to downtown Austin. Being the closest of the three neighborhoods to downtown, it is possible that Barton homeowners tend to be work downtown and enjoy walking or biking to work, making the distance to downtown an important element (2005). Moreover, the Barton neighborhood enjoys easy access to many green spaces besides the Barton Creek Greenbelt and Wilderness Park, weakening the value of proximity to this specific amenity (2005). The city of Austin is known for its many open space amenities and downtown with several outdoor recreational opportunities (2005). While this analysis does emphasize the influences that variables such as topography, vegetation, and use patterns may have on the value of a green space amenity to local residents, there are other important variables that have not been accounted for, such as the type of green space.
A study conducted by Anderson and West (2006) uses home transaction data from the Minneapolis–St. Paul metropolitan area to analyze the relationship between the proximity to several different types of green spaces and property values. As suggested by Crompton (2001), the type and purpose of green space is an important factor to take into consideration. Anderson and West (2006) analyze several types of green spaces, including neighborhood parks, special parks, golf courses, and cemeteries. Special parks are defined as national, state, and regional parks, arboretums, nature centers, natural areas, and wildlife refuges, in order to differentiate them from neighborhood parks, which are generally more urbanized and provide fewer recreational opportunities and natural amenities (2006). Furthermore, their hedonic analysis differs significantly from Nicholls (2005) in that it allows the effects of proximity to depend a completely different set of variables, including population density, income, crime, age of the population, and distance to the central business district. In addition, they control for neighborhood characteristics and potential omitted spatial variables using local fixed effects.
The most significant from the analysis were in relation to population density, distance to CBD, income, and crime rates. The effect of green space on sales price depends on a home’s location and neighborhood characteristics. On a broader scale, Anderson and West (2006) find that urban residents in more densely populated neighborhoods located near the CBD place a higher value on the proximity to green space than suburban residents located further away from the CBD and in less densely populated areas: in neighborhoods that are twice as dense on average, the amenity value of proximity to neighborhood parks is nearly three times higher than average, while the amenity value of special parks is two-thirds higher (2006). This finding suggests that estimates of green space benefits for the average home in a metropolitan area will over/under-estimate the values of properties in particular neighborhoods. Consequently, conclusions from studies analyzing city preferences should not be used to draw implications for suburban planning. Additional results from the Anderson and West (2006) analysis highlight the effect of income on green space and home values. In neighborhoods that are twice as wealthy on average, the amenity value of neighborhood parks is more than four times higher than average, while the amenity value of special parks is more than two times higher (2006). Crime rates also proved to be a significant factor impacting green space values, in fact the amenity value of proximity to neighborhood and special parks rises with crime rates, so it appears that both types of parks act as buffers against the negative effects of crime (2006). Although conclusions based on the other previously mentioned variables were also realized from this study, they were not as significant as the four discussed above.
While the findings of Nicholls (2005) and Anderson and West (2006) focus on distinctly different green space areas (one being more urban than the other), they both provide quantitative measures to unravel the many factors impacting the proximate principle established by Crompton (2001). As the decentralization of cities continues throughout the 21st century and cities keep growing at their peripheries, the tradeoff between developing and preserving green space becomes an increasingly important debate. Although development can help fulfill a population’s needs for additional housing and commercial space as well as increase tax base revenue, green spaces provide a number of benefits, many of which have been discussed throughout this survey. Understanding the impact that green space has on property value will not only help regional developers and government officials make better decisions regarding the provision, design, zoning, and use of these public goods, but also help the creation and development of better homes and more desirable communities.
Anderson, Soren T. and West, Sarah E. “Open space, residential property values, and spatial context.” Regional Science and Urban Economics 36 (2006): 773–789. Web. http://www.macalester.edu/~wests/AndersonWestRSUE.pdf
Crompton, John L. “The Impact of Parks on Property Values: A Review of Empirical Evidence.” Journal of Leisure Research 33.1 (2001): 1-31. Web. http://www.actrees.org/files/Research/parks_on_property_values.pdf
Nicholls, Sarah. “The Impact of Greenways on Property Values: Evidence from Austin, Texas.” Journal of Leisure Research 37.3 (2005): 321-341. Web.
A fascinating, age-old U.S. public policy question surrounds the relationship between educational outcomes and segregation (e.g., income, racial). Over the years, growing economic literature continues to add nuanced perspectives to the issue of the interaction between school finances and the makeup of local communities. Social science researchers emphasize the importance of studying residential segregation, whether by income or race-ethnic groups, because of potential neighborhood effects on the long run educational outlook for young students. Since public education in the U.S. is largely financed by local property taxes, there is a large disparity between funding for schools in different communities.
Thomas Nechyba, “School finance, spatial income segregation, and the nature of communities”:
Although many education policy workers focus almost solely on the impacts of disparate per pupil expenditures across schools, a large body of economic research shows strong evidence that there are broader factors affecting educational inequities, and vice versa. For instance, since public school systems are based in local districts, residential segregation by income is more likely to occur—and over time this greater residential segregation feeds even larger inequalities, thus leading to a relentless cycle. Differences in education quality are capitalized into housing prices (i.e., property values often further decline in poor areas with inadequate schools).
Nechyba (2003) questions why the contemporary educational inequalities discussion is so restricted to per pupil spending gaps; rather, he calls for a more general examination of different school finance institutions and their various equilibrium effects. Thus, the paper sets up a structural model representing a decentralized economy in which households select their place of residence, where to send kids for education, and the degree of support to provide public schools; the model includes the most causal potential factors leading to income segregation. Using observations from New Jersey districts, Nechyba (2003) adjusts the underlying structural parameters until his model simulates realistic features from the data. Then, holding these parameters constant, policy simulations can be conducted. The framework accounts for a couple main sources of residential segregation by income: 1) different neighborhoods are historically endowed with disparate housing quality and neighborhood characteristics; 2) places of residence affect a child’s school quality since public education systems require households to live within exogenously outlined boundaries. However, the inclusion of private schools into this model complicates matters because households that send their children to private educations care less about public school quality near their homes; alternatively, they would be even motivated to live in a subpar public school district with lower housing costs. Since private school households are often wealthier, this counterintuitive phenomenon pushes the local economy toward income desegregation.
Subsequently, Nechyba (2003) use the structural model to find that state financing of school districts indeed dampens residential income segregation in an area.  This effect is expected because school systems that are purely locally financed give wealthier households motivation to segregate by income and thus shape better schools. However, two less intuitive findings are that the existence of a private school market leads to considerable residential income desegregation, and that in the presence of private schools, the existence of public schools actually tends to lower residential segregation—even though, by themselves, public school systems are associated with higher spatial segregation. However, a notable caveat to this study is that the model equilibrium overestimates the movement of private school households into poorer neighborhoods simply because the structural model does not account for non-school characteristics of such communities.
Raquel Fernandez and Richard Rogerson, “Keeping people out: income distribution, zoning, and the quality of public education”:
Meanwhile, Fernandez and Rogerson (1993) investigate the effects of community zoning regulations on per pupil spending, particularly with respect to property taxes and the formation of communities with average income differences. The analysis relies on simulations using a two-community model, where each community determines tax rates by majority vote, and households can choose their place of residence. Without zoning, the equilibrium of this model results in a poor neighborhood with a low tax rate (i.e., low per pupil expenditure) and a wealthier community with a higher tax rate. The study found that the existence of zoning regulations, meaning that households have to purchase a minimum level of housing in order to live in a predetermined area, is associated with the wealthy community decreasing in size and becoming richer, while the poorer neighborhood grows larger. The increased exclusivity of the rich neighborhood causes the lowest income households of that neighborhood to enter a poorer one, thus raising average income in both areas. As a result, the poorer community sees greater per pupil spending, but the change in education quality across the two areas is ambiguous.
Sarah Reber, “School desegregation and educational attainment for blacks”:
Although the first two studies mentioned did not delve into racial segregation, it is important to gauge the relationship between segregation and educational attainment for certain race-ethnic groups. Reber (2007) looks into the effects of the desegregation process after the Supreme Court’s 1954 Brown v. Board of Education decision, specifically focusing on schools in Louisiana. Previous studies showed that the desegregation policy had two effects: 1) increasing black students’ exposure to white peers in school and 2) raising the level of federal and state funding such that the average spending in predominantly black schools increased. Given that desegregation essentially eliminated disparities in student-teacher ratios for black and white students within previously segregated districts, Reber (2007) sought to determine whether greater educational attainment by blacks following desegregation resulted more from greater exposure to white peers or increased funding.
The simplest specification in this paper is a univariate regression that looks for an association between a county’s initial share of black enrollment and change in educational attainment from before and after desegregation; in this model, the dependent variable of interest is mean attainment for 1970-1975, minus the average attainment for 1960-1965. Later, the paper considers metrics such as the 12th grade continuation rate and adds controls for other characteristics such as change in county’s employment. These regressions on high school grade continuation and graduation rates indicate that greater educational attainment increased more for black students in districts with higher rates of black enrollment following desegregation—thus implying that, following desegregation, increased funding played a larger role than higher exposure to white students in raising attainment.
Growing literature contribute to U.S. policies designed to match educational opportunities of students coming from vastly different racial and socio-economic backgrounds. Whether through investigating the means by which certain communities are able to make use of taxes and zoning as instruments to keep specific segments of the population out of a school district, or by evaluating the effect of private schooling on income segregation, valuable insights can be drawn from these types of analyses. A thorough look at the institutional set-up of education can reveal the role that segregation and various school finance mechanisms play in long-run inequality.
Raquel Fernandez and Richard Rogerson, 1997, “Keeping people out: income distribution, zoning, and the quality of public education,” International Economic Review 38(1): 23-42.
Sarah Reber, 2007, “School desegregation and educational attainment for blacks,” Cambridge, MA: NBER working paper 13193.
Thomas Nechyba, 2003. “School Finance, Spatial Income Segregation and the Nature of Communities,” Journal of Urban Economics 54(1), 61-88, July.
 Thomas Nechyba, School finance, spatial income segregation, and the nature of communities, 66: It is notable that although this analysis focuses on income segregation, it can apply to problems involving racial segregation as well, “only to the extent that such segregation is driven by income differences.”
 Nechyba, 65
 Nechyba, 74: “While it might be expected that state financing will lead to less segregation than local financing, the relatively small magnitude of this effect compared to the huge effect of private schools is surprising, as is the different effect of public schools in a world with and without private school markets.”
 Raquel Fernandez and Richard Rogerson, “Keeping people out: Income distribution, zoning, and the quality of public education,” 1
 Fernandez and Rogerson, 32
 Sarah J. Reber, “School desegregation and educational attainment for blacks,” 3
 Reber, 6: To prevent white flight, schools with larger proportions of black students received more funding to “level up to the levels previously experienced only in the white schools”