http://www.bis.org/cbhub/list/author/author_11456/index.rss Research hub papers by author John M. Clapp en http://research.stlouisfed.org/wp/2014/2014-050.pdf St Louis Fed Working Papers by Jeffrey P. Cohen, Cletus C. Coughlin, and John M. Clapp Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels 2014-12-13T06:19:59Z We estimate location values for single family houses by local polynomial regressions (LPR), a semi-parametric procedure, using a standard housing price and characteristics dataset. As a logical extension of the LPR method, we interpolate land values for every property in every year and validate the accuracy of the interpolated estimates with an out-of-sample forecasting approach using Denver sales during 2003 through 2010. We also compare the LPR and OLS models out-of-sample and determine that the LPR model is more efficient at predicting location values. In a balanced panel application, we use GMM estimation to examine how the location value estimates are affected by airport infrastructure investments. Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels 2014-12-13T06:19:59Z Abstract http://research.stlouisfed.org/wp/more/2014-050 Full text http://research.stlouisfed.org/wp/more/2014-050/ Cletus C. Coughlin John M. Clapp Jeffrey P. Cohen Jeffrey P. Cohen, Cletus C. Coughlin, and John M. Clapp 2014-12 St Louis Fed Working Papers C14 H41 H54 R51 R53 http://research.stlouisfed.org/wp/more/2014-050/ St Louis Fed Working Papers by Jeffrey P. Cohen, Cletus C. Coughlin and John M. Clapp Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels 2014-12-13T06:19:59Z We estimate location values for single family houses by local polynomial regressions (LPR), a semi-parametric procedure, using a standard housing price and characteristics dataset. As a logical extension of the LPR method, we interpolate land values for every property in every year and validate the accuracy of the interpolated estimates with an out-of-sample forecasting approach using Denver sales during 2003 through 2010. We also compare the LPR and OLS models out-of-sample and determine that the LPR model is more efficient at predicting location values. In a balanced panel application, we use GMM estimation to examine how the location value estimates are affected by airport infrastructure investments. Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels 2014-12-13T06:19:59Z Abstract http://research.stlouisfed.org/wp/more/2014-050 Full text http://research.stlouisfed.org/wp/2014/2014-050.pdf Cletus C. Coughlin John M. Clapp Jeffrey P. Cohen Jeffrey P. Cohen, Cletus C. Coughlin and John M. Clapp 2014-12 St Louis Fed Working Papers C14 H41 H54 R51 R53