Spatiotemporal analysis of German real-estate prices

Otto, Philipp; Schmid, Wolfgang

doi:10.1007/s00168-016-0789-y

Cited by 12 publications

(10 citation statements)

References 42 publications

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“…(), whereby the minimum length should at least be as large as the log of the number of observations (in the present case: 170), the BSADF only detects the first bubble episode, although in this case the BSADF test result is at odds with expert judgment and other empirical findings suggesting that real estate markets were rather anchored by their fundamentals in 2007–2008 [see e.g., Figure in Kajuth et al . () and Otto and Schmid ()]. Similarly, Chen and Funke () and Engsted et al .…”

Section: Validation and Robustness Checksmentioning

confidence: 89%

A Novel Housing Price Misalignment Indicator for Germany

Hertrich

2019

German Economic Review

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From 2014 until present, housing prices in Germany have been rising faster than consumer prices in all quarters except one, raising concerns about an excessive overheating of the housing market. To assess the vulnerability of the German housing market to a future realignment of prices or even a housing bust, this paper develops a housing price misalignment indicator that is composed of seven indicators, which are commonly associated with the fundamental value of residential property. An empirical application to the most recent data suggests that the German housing market exhibits an overvaluation of approximately 11%, where interest rate risk and a relatively advanced stage of the housing cycle are identified as the main factors fueling these imbalances, while a rather solid debt‐servicing capacity mitigates these imbalances since end‐2009.

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Section: Validation and Robustness Checksmentioning

confidence: 89%

A Novel Housing Price Misalignment Indicator for Germany

Hertrich

2019

German Economic Review

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“…Spatial effects taken into account in price and market activity models, especially in regional terms, may concern both spatial autocorrelation and spatial heterogeneity. Spatial autocorrelation is included in spatial autoregressive models (SAR) as well as spatial panel models [26][27][28], while spatial heterogeneity can be presented with geographically weighted regression models. The occurrence of spatial autocorrelation may also form the basis for the application of the eigenvector spatial filtering (ESF) approach, which is a certain alternative to SAR models.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

Cellmer

Cichulska

Bełej

2020

IJGI

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The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

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“…Nau and Bishai found that life expectancy within communities predicted increases in home price indexes [11]. Otto and Schmid analyzed real estate prices in Germany using spatiotemporal models and found that urban regions with higher population density and higher per-capita disposable income have higher land prices than rural areas, shocks in regional real estate prices "ripple out" and affect the whole economy, and population density had an increasing impact on real estate prices [12].…”

Section: Related Workmentioning

confidence: 99%

The impact of colleges and hospitals to local real estate markets

et al. 2019

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Home price is made of two parts: price of land and the cost of the house. Land value is derived from its location which often, especially in urban areas, accounts for the lion's share of overall home price. The value of the land is subject to the laws of supply and demand and in turn depends on the land's scarcity. Indeed, decoupling price of land from price of construction has been extensively researched [1, 2]. Many factors are baked into land price, including proximity to amenities and land's inherent quality (e.g., proximity to a shoreline, the mountains, etc.). Unique and somewhat subjective home characteristics like view as well as proximity to ocean, lake, etc. are known to influence home price [3]. Conversely, land price may be adversely affected by proximity to sources of noise and pollution (airports, major highways, etc.) [4, 5]. Unlike building material, labor and capital, land is a "finite, " or "non-renewable, " resource, often limited by stringent geographic and topographical constraints. Amenities pertain to proximity and accessibility to things like opportunities in employment, education, transportation, entertainment, retail, cultural, recreational, etc. This analysis focuses on universities and hospitals as "opportunity hubs, " which encapsulate "packaged amenities" in terms of those listed above. It studies the impact of these

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Spatiotemporal analysis of German real-estate prices

Cited by 12 publications

References 42 publications

A Novel Housing Price Misalignment Indicator for Germany

A Novel Housing Price Misalignment Indicator for Germany

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

The impact of colleges and hospitals to local real estate markets

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