Search

This study aims to investigate the spatial and temporal dynamics of housing prices associated with the 2007 U.S. housing-market bubble in Midwest Pinellas County, Florida. Two methods were used to examine the spatial and temporal dynamic of price levels: housing characteristic influence estimation and a hedonic modeling approach. Two consistent spatial patterns emerged in the estimated coefficients associated with various housing characteristics, with definitive changes occurring at 600 m and 2200 m from the coast. These changes suggested the existence of three geographical submarkets: a coastal, an intermediate or transitional, and an inland submarket. Three temporal stages were observed from the coefficient trends associated with various housing characteristics: 2002–2005 (bubble-growing), 2006–2008 (bubble-burst), and 2009–2011 (post-bubble). The hedonic models demonstrated a complexity in the determinant of housing prices during the housing peak-bubble period. The inclusion of submarket dummy variables in the hedonic models improved the amount of explained variation. The R 2 values of the hedonic models from 2002 through 2008, followed by a decrease after the bubble-burst period. The pre-bubble-burst trend in R 2 suggests that predictable market forces were at work; partly driven by irrational expectations of housing buyers and the perceptions of run-away housing prices during the growth phase of the bubble. [ABSTRACT FROM AUTHOR]

The increasing number of natural hazards in coastal tourism destinations has negatively affected their local lodging industries. The recent boom in shared accommodation in coastal destinations is also under threat due to increasing sea levels and extreme weather events. Thus, estimating the economic impact of natural hazards on shared accommodation is a critical prerequisite for effective tourism destination crisis management. This study aimed to estimate the economic impact of natural hazards on shared accommodation. To achieve this purpose, HAZards U.S. MultiHazard (HAZUS-MH) hurricane/flood models were employed in a case study of Hurricane Irma and 822 Airbnb properties in Collier County, Florida, for 2017. The estimated direct combined losses from wind gusts and storm surge flooding were $22, 683, 054, and the indirect losses to rental income were as high as $19,120 per day. This estimation method can help individual owners and local government managers predict problems related to natural hazards and more effectively prepare for future hazardous events in coastal tourism destinations. • This study estimated the economic impact of Hurricane Irma on shared accommodation. • HAZUS-MH hurricane and flood models were employed. • The estimated direct losses of Airbnb properties were $22, 683, 054 • The estimated indirect losses of Airbnb properties were up to $19,120 per day. • Findings are helpful to prepare for future hazardous events in coastal tourism destinations. [ABSTRACT FROM AUTHOR]