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The global electric vehicle (EV) market has been experiencing an impressive growth in recent times. Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we apply machine learning techniques on EV stated preference survey data to predict EV adoption using attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models' low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor's contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs. [ABSTRACT FROM AUTHOR]

As on-demand short-term rentals (STRs) grow popular with the rise of sharing platforms like Airbnb, regulations for the STR market have become the center of a debate among policymakers, housing interest groups, the hotel and lodging industry, and STR platforms. Washington, DC, the nation's capital and one of the most popular tourist destinations in the United States, is on the front lines of legalizing and regulating the STR business. With the heated policy debate over whether STRs disrupt the rental housing market in DC, a concrete discussion about what STRs impose on the owner housing market is left out. Using web-scraped data from Airbnb and property-level data from the city, I investigated the net impact of STRs on single-family property prices through a series of hedonic analyses. The results suggest that having Airbnb establishments in the neighborhood can significantly inflate property prices. Because of the uneven spatial market penetration of STRs, such price impact could inequitably affect low-income homebuyers and add another hurdle to resolving the housing affordability issue faced by policymakers in Washington, DC. [ABSTRACT FROM AUTHOR]

Abstract: Oregon is known for its strict Urban Growth Boundary (UGB) policies. While UGBs are designed to accommodate land supply for 20 years of growth, land within UGBs must be annexed into city limits before it can be developed at urban densities and serviced. In Oregon, cities use a variety of municipal annexation and voter‐approved annexation policies (VAAPs), providing an opportunity to study how different annexation policies affect land and housing markets, and affect urban density. Previous research on annexation has not considered how annexation policy influences housing values. This paper examines how VAAPs impact land growth, housing development, and density at the city level. It also examines how VAAPs impact housing values. Based on city‐level and tax‐lot‐level statistical analyses from 107 cities outside Portland Metro area, the results suggest that VAAPs negatively impact the availability of developable land within city limits. VAAPs also positively impact residential density and housing value. Lastly, VAAPs inequitably affect housing value between relatively high‐value housing and relatively low‐value housing, posing economic equity impacts for lower value housing. These findings provide important lessons for Oregon and other states. While VAAPs may increase residential density in cities, the policies may exacerbate affordability problems. [ABSTRACT FROM AUTHOR]