{"id":{"repo_id":"central-wash","oai_identifier":"oai:digitalcommons.cwu.edu:etd-1190"},"canonical_url":"https://search.dev.ndltd.org/etd/central-wash/oai:digitalcommons.cwu.edu:etd-1190","repository":{"repo_id":"central-wash","name":"Central Washington University","base_url":"https://digitalcommons.cwu.edu/do/oai/"},"display":{"title":"The Economic Impacts of Forest Pathogens in Washington State: A Hedonic Approach","abstract":"An increase in the incidence of forest pathogens in the Western US has created new resource management issues. In this research I employ a dataset of 170,141 housing transactions in twelve Western Washington counties to quantify the impacts of parasitic forest damage on the proxy real estate market. Specifically, I estimate a set of hedonic fixed effects models to control for omitted variable bias and spatial autocorrelation. 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In this research I employ a dataset of 170,141 housing transactions in twelve Western Washington counties to quantify the impacts of parasitic forest damage on the proxy real estate market. Specifically, I estimate a set of hedonic fixed effects models to control for omitted variable bias and spatial autocorrelation. Results show statistically significant impacts on property values in the presence of species specific and aggregate defoliation, suggesting new information for forestry management and policy."]},{"key":"dc:title","label":"Title","values":["The Economic Impacts of Forest Pathogens in Washington State: A Hedonic Approach"]}]}],"canonical_facts":{"dc:contributor":["Toni Sipic","Charles Wassell, Jr.","Mathew Novak"],"dc:creator":["Blair, Logan"],"dc:date.available":["2015-08-17T07:00:00Z"],"dc:description.abstract":["An increase in the incidence of forest pathogens in the Western US has created new resource management issues. In this research I employ a dataset of 170,141 housing transactions in twelve Western Washington counties to quantify the impacts of parasitic forest damage on the proxy real estate market. Specifically, I estimate a set of hedonic fixed effects models to control for omitted variable bias and spatial autocorrelation. 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