Data/hora: 02/06/2024 13:34:22

Provedor de dados: 	31	
País: 	United States	
Título: 	Forecasting Housing Prices under Different Submarket Assumptions	
Autores: 	Chen,   Zhuo; Cho,   Seong-Hoon; Poudyal,   Neelam C.; Roberts,   Roland K.	
Data: 	2007-06-27	
Ano: 	2007	
Palavras-chave: 	Clustering; Forecasting; Hedonic price; Housing Submarket; Demand and Price Analysis; C53; R21.	
Resumo: 	This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategies are more accurate and efficient than models that are spatially aggregated, or with submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction in an urban setting and surrounding areas in that forecasting models based on expert knowledge of market structure or public school quality and simple model combining techniques may outperform the models using more sophisticated statistical techniques.	
Tipo: 	Conference Paper or Presentation	
Idioma: 	Inglês	
Identificador: 	26225

http://purl.umn.edu/9689	
Editor: 	AgEcon Search	
Relação: 	American Agricultural Economics Association&gt;2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon

Selected Paper 174463	
Formato: 	35

application/pdf