Conference Agenda

Federal and state-owned public lands occupy a significant proportion of many U.S. urban regions, contributing to two highly-valued, nontradable goods: viewscapes and outdoor recreational opportunities. Yet, public lands have not been adequately addressed in the urban quality-of-life (QOL) literature. We argue that much of the previous literature has (1) used a geographic level of aggregation that has not been extensive enough to capture all the public lands that influence wage and housing equilibria, (2) often defined public lands measures too narrowly and has excluded over 50% of public land influencing hedonic equilibria, and (3) often failed to control for public lands endogeneity when quasi-experimental techniques could not be applied. Using GIS and Census data for urban areas of the United States, along with instruments rooted in the 19th century land disposal policies, we estimate how publicly owned federal and state lands contribute to urban QOL. Our preferred specifications are based on Bureau of Census defined Combined Statistical Areas that are linear in public land variables, and the results are robust regardless of geographic aggregation and model specification. Welfare calculations indicate that annual urban household willingness-to-pay estimate for a one percent increase in public land ranges between $80.58 and $130.29. Our model can be used to evaluate the welfare consequences of proposed land exchanges and other changes in land ownership or management.

Using an incentivized survey experiment with about 10,000 German households, I estimate the willingness to pay for programmable heating thermostats. Specifically, I examine whether a low adoption rate and thus seemingly low valuation of this digital technology which can support private households in achieving energy savings is driven by information asymmetries. To this end, I randomly provide households with either the private good features of the thermostats, i.e. the reduced energy consumption and thus energy cost, or the public good features, that is a reduction in carbon emissions. The results suggest that informing households about the potential cost savings does not significantly increase willingness to pay (WTP), whereas informing households about potential emission reductions significantly increases average WTP by about EUR 6. Yet, I find evidence for heterogeneous treatment effects, with the private good information treatment leading to a significantly higher WTP for larger households and households living in rather new buildings. Finally, I do not find evidence that factors like risk aversion, technology acceptance, craftsmanship or data privacy significantly influence households’ WTP.

Germany’s energy system is in the midst of a massive transformation: The last three nuclear plants went off-line in 2023, coal power is slated for nixing by 2038 at the latest, while renewable electricity generation facilities are further expanding substantially. The reverberations of these changes are of key policy importance given the centrality of the electricity sector to the economy. Previous studies using hedonic price models to quantify how surrounding house values are impacted by changes in energy infrastructure often focus on the opening or closing of just a single type of facility. In this study, we consider the entire electricity production portfolio, adopting a spatial difference-in-differences approach to assess the impact of different types of facility openings and closures simultaneously under a unified modeling framework. To this end, we leverage an extensive geo-referenced data set of house sale advertisements from 2008 to 2019 containing almost 2.4 billion observations on asking prices and property characteristics. We find that the opening of both wind and coal power facilities is associated with significant discounts on surrounding house values, with the discount of coal power plant openings being of much larger magnitude. Somewhat surprisingly, we also find evidence that closures of coal power plants are associated with reduced house prices, a possible result of economic channels, such as reduced employment in the aftermath of a large plant closure. Our results highlight potentially important distributional implications that warrant consideration in ensuring a just energy transition.

In our research, we employ the hedonic pricing method and the difference in

differences approach to examine the economic impact of urban green spaces on

housing prices in Tehran, the capital of Iran. The hedonic pricing method estimates housing prices as a function of housing characteristics, neighborhood, and

environmental variables. We contribute to the literature by enhancing our model

with a refined set of variables, improved model specifications, and incorporating spatial correlation considerations among observations. Our commitment to

a thorough investigation extends to assessing the unique effects of park type,

size, and geographical locations. Additionally, we delve into the distributional

impacts by considering the demographic characteristics of households in the regions, with a particular focus on their income levels. We employ GIS software

to analyze the distances of houses in specific regions from local amenities. our

results indicate a positive relationship between housing prices and proximity to

parks of all sizes in affluent regions, while green spaces may lower house prices

in less affluent regions, especially the large ones. Housing inflation further moderates this effect, indicating that as housing prices rise, the positive impact of

parks diminishes. Additionally, using the difference in differences method, our

study reveals that the construction of a midsize park in rich regions results in a

3% - 5% increase in neighboring house prices. These findings suggest wealthier

individuals invest more in environmental amenities, classifying green spaces as

luxury goods.