Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach

Teye, Alfred Larm; Ahelegbey, Daniel Felix

doi:10.1016/j.regsciurbeco.2017.04.005

Cited by 33 publications

(20 citation statements)

References 48 publications

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“…US studies include Miller (2010, 2012), Holmes et al (2011), Barros et al (2012), and Chiang and Tsai (2016). Teye et al (2017) find ripple effects from Amsterdam. Cameron et al (2006b) follow the theoretical framework set out in Eq.…”

Section: A Framework For Regional House Price Modelling: the Case Of mentioning

confidence: 99%

Hot Property

Nijskens¹,

Lohuis²,

Hilbers³

et al. 2019

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Section: A Framework For Regional House Price Modelling: the Case Of mentioning

confidence: 99%

Hot Property

Nijskens¹,

Lohuis²,

Hilbers³

et al. 2019

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“…Teye and Ahelegbey used the Bayesian Graphical Vector Autoregression to research the spatial-temporal relationship between house prices in twelve provinces of the Netherlands. The result shows the house price diffusion patterns in the Netherlands and the patterns [24]. Che-Yu Lee used recurrent convolutional neural networks (RCN) to predict stock price.…”

Section: Related Workmentioning

confidence: 99%

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng

Xie

et al. 2019

J Wireless Com Network

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The data collected by the wireless sensor nodes often has some spatial or temporal redundancy, and the redundant data impose unnecessary burdens on both the nodes and networks. Data prediction is helpful to improve data quality and reduce the unnecessary data transmission. However, the current data prediction methods of wireless sensor networks seldom consider how to utilize the spatial-temporal correlation among the sensory data. This paper has proposed a new data prediction method multi-node multi-feature (MNMF) based on bidirectional long short-term memory (LSTM) network. Firstly, the data quality is improved by quartile method and wavelet threshold denoising. Then, the bidirectional LSTM network is used to extract and learn the abstract features of sensory data. Finally, the abstract features are used in the data prediction by adopting the merge layer of the neural network. The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators.

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“…Teye and Ahelegbey (2017) recently studied the house price diffusion process between the Dutch regional housing markets but did not specifically consider the Amsterdam effect. Pollakowski and Ray (1997), argued that the ripple effect may occur between regions that are economically related, although they need not necessarily border each other.…”

Section: The Empirical Literaturementioning

confidence: 99%

Amsterdam house price ripple effects in The Netherlands

Teye

Knoppel

Haan

et al. 2017

JERER

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Purpose -This paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.Design/methodology/approach -The paper considers the ripple effect as a lead-lag effect and a longrun convergence between the Amsterdam and regional house prices. Using the real house prices for secondhand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.Findings -The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1753-9269.htm Research limitations/implications -The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.Originality/value -This paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors' knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

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Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach

Cited by 33 publications

References 48 publications

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Data prediction model in wireless sensor networks based on bidirectional LSTM

Amsterdam house price ripple effects in The Netherlands

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