Forecasting Housing Prices under Different Submarket Assumptions
AbstractThis 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.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN with number 9689.
Date of creation: 2007
Date of revision:
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Clustering; Forecasting; Hedonic price; Housing Submarket; Demand and Price Analysis; C53; R21;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
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