Exploring the spatial variation in quality-adjusted rental prices and identifying hot spots in Berlin’s residential property market

Meulen, Philipp an de; Mitze, Timo

doi:10.1080/21681376.2014.981577

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…There is clear evidence that many socio‐economic, geographic and demographic data used in the empirical analysis of regional science are characterised by the presence of spatial heterogeneity and spatial clustering. This can be found in some studies of the real estate market (e.g., Baumont, 2009; an de Meulen & Mitze, 2014; Cellmer et al, 2020; Wang et al, 2019), the location and/or economic performance of firms (e.g., Nilsson & Smirnov, 2017; Nilsson et al, 2019), the spatial distribution of poverty (e.g., Curtis et al, 2019; Oteng‐Abayie et al, 2022), local employment (e.g., Bradley et al, 2020), international migration (e.g., Hierro et al, 2013) and the regional distribution of per capita GDP, (e.g., Le Gallo & Ertur, 2003), among others. The inclusion of such information about the natural clustering of the data is captured through the inclusion in the weights of similarities or dissimilarities within neighbourhoods and between neighbourhoods in the predictor using either the local Moran's index or similarity measures between neighbourhoods via medians.…”

Section: Introductionmentioning

confidence: 63%

Nonparametric prediction for univariate spatial data: Methods and applications

Arancibia

Llop

Lovatto

2023

Papers in Regional Science

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We introduce five nonparametric kriging-type predictors for spatial data where only the variable of interest, without covariates, is recorded. The proposed methods seek to fully exploit the information contained in the spatial closeness and also in the similarity between neighbourhoods of the variable of interest. This is managed using different combinations of kernels (one or two kernels), and different combinations of distances (multiplicative and additive). The good performance of the proposed methods is shown via simulation studies and housing price prediction applications.

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Section: Introductionmentioning

confidence: 63%

Nonparametric prediction for univariate spatial data: Methods and applications

Arancibia

Llop

Lovatto

2023

Papers in Regional Science

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“…Non-significant is the final group which indicates no significant spatial autocorrelation in the distribution of values. LISA statistics are useful for identifying clusters in the spatial arrangement of a variable, they have commonly been applied to house prices 58,59 .…”

Section: Lisa Analysismentioning

confidence: 99%

Intra-urban house prices in Madrid following the financial crisis: an exploration of spatial inequality

Kenyon,

Arribas-Bel,

Robinson

et al. 2024

npj Urban Sustain

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In this paper we explore the temporal dynamics of spatial inequality in housing prices for Madrid, the capital city of Spain. Spatial inequalities are a concerning feature of urban areas across the globe. It has been suggested that within cities housing prices are becoming more geographically unequal over time, particularly since the 2008 housing market crash. However, more evidence is needed at the intra-urban level to understand neighbourhood house price differences in large urban areas. Changes are analysed during a key period of the housing market bust (2010–2015) and boom (2016–2019), using data from a major housing listing portal. Fine grain space-time analysis of the distribution of housing prices supports an increase in spatial inequality and polarisation at the neighbourhood level. Two spatially differentiated housing sub-markets of high- and low-priced housing are identified. The persistence and growth of spatial house price inequality has important societal implications for the wealth gap and segregation of rich and poor in cities.

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Liquidity and prices: a cluster analysis of the German residential real estate market

Cajias¹,

Freudenreich

et al. 2020

J Bus Econ

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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.

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Exploring the spatial variation in quality-adjusted rental prices and identifying hot spots in Berlin’s residential property market

Cited by 6 publications

References 17 publications

Nonparametric prediction for univariate spatial data: Methods and applications

Nonparametric prediction for univariate spatial data: Methods and applications

Intra-urban house prices in Madrid following the financial crisis: an exploration of spatial inequality

Liquidity and prices: a cluster analysis of the German residential real estate market

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