Learning from man or machine: Spatial aggregation and house price prediction
Working paper
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http://hdl.handle.net/11250/2499920Utgivelsesdato
2018Metadata
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- CLTS Working papers (HH) [118]
Sammendrag
House prices vary with location. At the same time the border between two neighboring
housing markets tends to be fuzzy. When we seek to explain or predict house
prices we need to correct for spatial price variation. A much used way is to include
neighborhood dummy variables. In general, it is not clear how to choose a spatial
subdivision in the vast space of all possible spatial aggregations. We take a biologically
inspired approach, where different spatial aggregations mutate and recombine
according to their explanatory power in a standard hedonic housing market model.
We find that the genetic algorithm consistently finds aggregations that outperform
conventional aggregation both in and out of sample. A comparison of best aggregations
of different runs of the genetic algorithm shows that even though they converge
to a similar high explanatory power, they tend to be genetically and economically
different. Differences tend to be largely confined to areas with few housing market
transactions.