Neural Network adjusted Spatial Dynamic Factor Models for Real Estate Valuation

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This thesis concerns modeling residential real estate selling prices in a hedonic price model framework on a small spatial-temporal granularity. The research addresses the challenge of sparse spatial-temporal real estate data, i.e. many combinations of location and time with few or no transactions, by employing spatial dynamic factor models (SDFMs). Two types of SDFMs are employed: an SDFM with a 1D spatial structure based on the spatial random walk and an SDFM with a 2D spatial structure based on the Gaussian random field. To capture the information on the property characteristics, spatial dynamic factor models are combined with two different data-driven models, namely a neural network (NN) and an interpretable version of an NN, the local generalized linear model network (LGLMN). Both a Bayesian approach and an algorithmic approach are employed to estimate the models on both a PC and a high-performance computer (HPC). A simulation study is conducted to demonstrate the ability of an NN to capture linear and non-linear structures when combined with an SDFM and to show the ability of the LGLMN to replicate a linear structure. Furthermore, the models are evaluated on real transaction data from the municipality of Rotterdam. The findings demonstrate that the algorithmically estimated NN-adjusted SDFM based on the spatial random walk (NN-SRW-DFM) outperforms the other models in terms of accuracy with an out-of-sample MAPE of 0.128. Moreover, the results highlight a trade-off between accuracy, speed, and interpretability.