Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/213313
Authors: 
López, Fernando A.
Kholodilin, Konstantin A.
Year of Publication: 
2020
Series/Report no.: 
DIW Discussion Papers No. 1842
Abstract: 
Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric technique that automatizes the selection of non-linear terms in regression models. Non-linearities and spatial effects are natural characteristics in numerous spatial hedonic pricing models. In this paper, we propose using the MARS data-driven methodology combined with the Instrumental Variables method in order to account for potential non-linearities and spatial effects in hedonic models. Using a large data set of more than 6,000 dwellings in Hamburg and about 17,000 in St. Petersburg, we confirm the presence of both effects (non-linearities and spatial autocorrelation). The results also show that there is a non-linear effect of the prices of neighboring houses on the price of each house. High prices for neighboring houses have a lower impact on the house price than low prices of neighboring houses. Finally, an extensive Monte Carlo exercise evaluates the ability of MARS to incorporate the correct spatial spillover terms in spatial regression models simultaneously including at same time non-linear effects.
Subjects: 
Multivariate Adaptive Regression Spline
spatial regression models
hedonic models
housing prices
Hamburg
St. Petersburg
JEL: 
C4
C5
R1
Document Type: 
Working Paper

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