Footnote 2 Contingent upon a matching of the expectations of supply and demand, a market is able to operate. The second component is the time it takes until a prospective buyer or tenant is willing to take the dwelling off the market and to pay the price. The first component is the introduction of the dwelling onto the market at a price Footnote 1 determined by the seller or landlord. On the residential real estate market, the process of selling or renting out a dwelling comprises of two essential components. Thereby, the location of the respective dwelling plays a major role for the development of the key features determining the matching process. When it comes to residential real estate-an asset class which is strongly linked to individual preferences of buyers and tenants as well as expectations of sellers and landlords-a matching of both sides may be even more difficult. 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. 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. 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.įinancial assets such as stocks and bonds are traded in tremendous volumes, turning over billions of dollars within seconds and with almost no spatial constraints. In Germany the largest share of landlords is the one of the so-called non-professional landlords. One of the most interesting implications is, that apparently a large part of the German population has developed into professional real estate investors. Moreover, in each of the analysed categories cluster 1 reveals a lower unemployment rate as well as a higher disposable income. 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. Furthermore, the clusters are interpreted from a geographic perspective. The clusters are then analysed by means of further economic and socioeconomic data in order to identify similarities. The dataset underlying this analysis comprises more than 4.5 million observations in 380 German regions from 2013 Q1 to 2018 Q4. 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. Quality- and spatial-adjusted price and liquidity indices are calculated separately for the investment and rental market on a regional basis. This paper analyses the highly under-researched German residential real estate market.
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