Abstract: We estimate nonmarket values for natural views in an urban setting. These views contain the aesthetics of natural areas commonly found in public parks and open space, and offer an aspect of property valuation that previous research is unable to disentangle from proximity to parks and open space. We incorporate machine learning techniques on google street view images to identify natural views in an urban setting. We find positive capitalization rates associated with household views of park-like properties. Estimates are robust to a variety of specifications, including models that are identified off of new developments on neighboring properties and falsification tests that help to rule out the effect of a broader neighborhood environment. From a policy perspective, our results inform as to the optimal size, location, and shape of open space. Furthermore, machine learning methods used in the construction of our view variable provide a potentially powerful tool for other nonmarket valuation studies.
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Abstract: This paper analyzes the inefficiency of majority-rule voting in making siting decisions for noxious facilities, such as waste treatment facilities, landfills, or nuclear waste repositories. In particular, we demonstrate in a general context that a majority-rule voting process leads localities to make decisions that impose aggregate costs that are larger than aggregate benefits. We develop a robust model to establish the prevalence of such inefficiencies and demonstrate the mechanisms that exacerbate or mitigate them. The model illustrates how the spatial distribution of the population and the severity of disamenity costs can generate outcomes that make the entire locality worse off. Based on these properties, we provide a feasible remedy. Analysis of U.S. census data illustrates the magnitude of potential inefficiencies that arise with simple majority-rule decisions. Such losses can be mitigated using our model, which easily captures the aggregate disamenity cost and can consequently recommend the required voting supermajority to ensure efficiency.
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Abstract: This study explores the impact of bicycle-sharing infrastructure on urban transportation. We estimate a causal effect of the Capital Bikeshare on traffic congestion in the metropolitan Washington, D.C., area. We exploit a unique traffic dataset that is finely defined on a spatial and temporal scale. Our approach examines within-city commuting decisions as opposed to traffic patterns on major thruways. Empirical results suggest that the availability of a bikeshare reduces traffic congestion upwards of 4% within a neighborhood. In addition, we estimate heterogeneous treatment effects using panel quantile regression. Results indicate that the congestion-reducing impact of bikeshares is concentrated in highly congested areas.
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Abstract: This paper develops cost of living (COL) indices that vary across space. While conventional indices adjust for differences in prices, the COL defined here also reflects access to public goods. This analysis relies on the structure of a residential sorting model to estimate the COL index for each of 226 metropolitan statistical areas (MSAs) in 1990 and 2000 in the United States. empirical results show significant differences in the COL across the spatial landscape. This paper focuses on the Gini coefficient as a measure of inequality to demonstrate the distribution of public goods across the population.
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Abstract: We examine the interconnectedness of demand for regionally and locally varying public goods using a residential sorting model. We propose a version of the model that describes household choices at the city (MSA) level and, conditional on city, the neighborhood (census tract) level. We use a two-stage budgeting argument to develop an empirically feasible sorting model that allows us to estimate preferences for regionally varying air quality while accounting for sorting at the local level. Our conceptual and empirical approach nests previous sorting models as special cases, allowing us to assess the importance of accounting for multiple spatial scales in our predictions for the cost of air pollution. Furthermore our preferred specification connects the city and neighborhood sorting margins to the upper and lower elements of a nested logit model, thereby establishing a useful correspondence between two stage budgeting and nested logit estimation. Empirically we find that estimates from a conventional model of sorting across MSAs imply a smaller marginal willingness to pay for air quality than estimates from our proposed model. We discuss how the difference is attributable in part to the omitted variable problems arising when tract level sorting is ignored.
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Abstract: This research quantifies the property value benefits of National Wildlife Refuges near urban areas on the eastern coast of the U.S.A. Our approach is made possible through access to confidential U.S. Census data identifying property values surrounding all refuges with high geographic resolution. Results from hedonic property value models suggest that the amenity values of refuges located near urbanized areas are capitalized into the value of homes in very close proximity, averaging $11 million per refuge. These capitalized values add directly to the local tax base and are considerable complements to the annual economic value created by the refuge system.
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Abstract: The hedonic housing model provides a theoretical framework that connects observed housing prices with nonmarket values for environmental or other location-specific goods, but the precise functional relationship is often unknown. Only with particular underlying preference structures do we see functional forms of the hedonic price function that conform to functions that can be estimated with linear regression techniques. Still, empirical research typically approximates the hedonic price function using transformed variables in a linear regression. We explore the use of more flexible estimation techniques, including spline and nonparametric regression, to better capture the form of the hedonic price function. Our empirical analysis uses housing transactions and public park space in Denver, CO to demonstrate potential misspecification in linear regression models and to demonstrate how spline and nonparametric regression provide a means of overcoming this issue. Results indicate economically significant differences in estimates for the marginal willingness to pay for public parks when relying on these more flexible approaches.
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