Abstract
This chapter compares forecasts of the median
neighbourhood prices of residential single-family homes
in Cambridge, Massachusetts, using parametric and
nonparametric techniques. Prices are measured over time
(annually) and over space (by neighborhood). Modelling
variables characterised by space and time dynamics is
challenging. Multi-dimensional complexities due to
specification, aggregation, and measurement errors
thwart use of parametric modeling, and nonparametric
computational techniques (specifically genetic
programming and neural networks) may have the
advantage. To demonstrate their efficacy, forecasts of
the median prices are first obtained using a standard
statistical method: weighted least squares. Genetic
programming and neural networks are then used to
produce two other forecasts. Variables used in
modelling neighbourhood median home prices include
economic variables such as neighbourhood median income
and mortgage rate, as well as spatial variables that
quantify location. Two years out-of-sample forecasts
comparisons of median prices suggest that genetic
programming may have the edge.
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