Using home buyers’ revealed preferences to define the urban–rural fringe

The location of new homes defines the urban–rural fringe and determines many facets of the urban–rural interaction set in motion by construction of new homes in previously rural areas. Home, neighborhood and school district characteristics play a crucial role in determining the spatial location of new residential construction, which in turn defines the boundary and spatial extent of the urban–rural fringe. We develop and apply a spatial hedonic variant of the Blinder (J Hum Resour 8:436–455, 1973) and Oaxaca (Int Econ Rev 9:693–709, 1973) price decomposition to newer versus older home sales in the Columbus, Ohio metropolitan area during the year 2000. The preferences of buyers of newer homes are compared to those who purchased the nearest neighboring older home located in the same census block group, during the same year. Use of the nearest older home purchased in the same location represents a methodology to control for various neighborhood, social–economic-demographic and school district characteristics that influence home prices. Since newer homes reflect current preferences for home characteristics while older homes reflect past preferences for these characteristics, we use the price differentials between newer and older home sales in the Blinder–Oaxaca decomposition to assess the relative significance of various house characteristics to home buyers.

[1]  David M. Brasington,et al.  Department of Economics Working Paper Series Educational Outcomes and House Values: a Test of the Value-added Approach , 2022 .

[2]  Robust decomposition analysis of wage differentials , 2004 .

[3]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[4]  R. Oaxaca Male-Female Wage Differentials in Urban Labor Markets , 1973 .

[5]  J. LeSage Bayesian Estimation of Spatial Autoregressive Models , 1997 .

[6]  J. Geweke,et al.  Bayesian Treatment of the Independent Student- t Linear Model , 1993 .

[7]  A. Blinder Wage Discrimination: Reduced Form and Structural Estimates , 1973 .

[8]  Private Schools and the Willingness to Pay for Public Schooling , 2007, Education Finance and Policy.

[9]  Micah Altman,et al.  Numerical Issues in Statistical Computing for the Social Scientist , 2003 .

[10]  James P. LeSage,et al.  Models for Spatially Dependent Missing Data , 2004 .

[11]  Diane Hite,et al.  A Mixed Index Approach to Identifying Hedonic Price Models , 2006 .

[12]  Stephen B. Jarrell,et al.  Gender Wage Discrimination Bias? A Meta-Regression Analysis , 1998 .

[13]  James P. LeSage,et al.  Spatial Statistics and Real Estate , 2004 .

[14]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[15]  Calculation of approximate variances for wage decomposition differentials , 1998 .

[16]  Ronald L. Oaxaca,et al.  On discrimination and the decomposition of wage differentials , 1994 .

[17]  James P. LeSage Spatial Regression Models , 2004 .