Mateusz Tomal (p) /Epainos Award Candidate

Vicente Royuela

In the current scientific literature, the topic of identifying determinants of residential rental prices is rarely undertaken. Therefore, this paper aimed to explore the factors influencing the formation of housing rents on the examples of Warsaw (Poland) and Amsterdam (Netherlands). A novel spatial autoregressive geographically weighted quantile regression (GWQR-SAR) was applied to achieve the research goal. GWQR-SAR takes into account both spatial autocorrelation of residential rents and heterogeneous influences of rent determinants across space and the price distribution. The study results indicated that rental housing prices in both cities are driven by a set of structural, locational and neighbourhood characteristics as well as by the spatial autoregressive term. However, the impact of the identified determinants varies in space and over the distribution of rental price. These new findings helped to propose valid policy suggestions for developing the private residential rental market in Warsaw and Amsterdam. Finally, the paper provided the R package GWQR implementing a geographically weighted quantile regression estimation.

Elena Semerikhova (p), Anastasia Blokhina

Vicente Royuela

The aim of the paper is to identify and establish empirical facts on the determinants of the real estate prices by analyzing spatial regional data, considering the price level of the region. We provide empirical analysis on the panel data set of 401 German regions for the period 2004 – 2020 taking into account their relative geographical location and prices. The main contribution of our paper is the analysis of determinants and spatial effects in housing prices, taking into account whether the region belong to high-prices or low-prices clusters using quantile regression analysis.

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