Liquidity and prices: a cluster analysis of the German residential real estate market
- PDF / 1,764,689 Bytes
- 36 Pages / 439.37 x 666.142 pts Page_size
- 15 Downloads / 218 Views
Liquidity and prices: A cluster analysis of the German residential real estate market Marcelo Cajias1 · Philipp Freudenreich2 Wolfgang Schäfers2
· Anna Freudenreich2 ·
© The Author(s) 2020
Abstract This paper analyses the highly under-researched German residential real estate market. Quality- and spatial-adjusted price and liquidity indices are calculated separately for the investment and rental market on a regional basis. Applying the “Partitioning Around Medoids (PAM)” clustering algorithm, the regions are clustered with respect to their price and liquidity development after the average silhouette method is applied to find the optimal number of clusters. The dataset underlying this analysis comprises more than 4.5 million observations in 380 German regions from 2013 Q1 to 2018 Q4. The clusters are then analysed by means of further economic and socioeconomic data in order to identify similarities. Furthermore, the clusters are interpreted from a geographic perspective. We find that the allocation to cluster 1 is always supported by higher growth rates in the variables, population, working population and real GDP, implying higher demand for space. Moreover, in each of the analysed categories cluster 1 reveals a lower unemployment rate as well as a higher disposable income. One of the most interesting implications is, that apparently a large part of the German population has developed into professional real estate investors. In Germany the largest share of landlords is the one of the so-called non-professional landlords. As the regions assigned to cluster 1, displaying the most significant price increase, seem to be chosen based on a very sophisticated market analysis by identifying the regions with the strongest fundamental data, it seems like the dominating market players have significantly increased their knowledge and approach for investing in residential real estate. Keywords Residential · Housing · Liquidity · Index · Time on market · GAMLSS · Cluster · Partitioning around medoids JEL Classification R2 · R21 · R3 · R32
The online version of this article (https://doi.org/10.1007/s11573-020-00990-2) contains supplementary material, which is available to authorized users. Extended author information available on the last page of the article
123
M. Cajias et al.
1 Introduction Financial assets such as stocks and bonds are traded in tremendous volumes, turning over billions of dollars within seconds and with almost no spatial constraints. By contrast, the transaction process of direct real estate is more complex, often consuming several months due to the heterogeneity of individual properties and market specific frictions. For example, larger participation-, search- and transaction-costs, as well as considerable asymmetric information impede a smooth match between buyer’s or tenant’s and seller’s or landlord’s price expectation within “short” time intervals. When it comes to residential real estate—an asset class which is strongly linked to individual preferences of buyers and tenants as well as expectation
Data Loading...
