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Risks and interrelationships of subdistrict house prices: the case of Amsterdam.

Alfred Larm TeyeJan de HaanMarja G Elsinga
Published in: Journal of housing and the built environment : HBE (2017)
This paper uses individual house transaction data from 1995 to 2014 in Amsterdam to explore the risks and interrelationships of the subdistrict house prices. Simple indicators suggest that house prices grow faster and are more risky in the central business district and its immediate surrounding areas than in the peripherals. Furthermore, we observe an over time decreasing intervariations between the subdistrict house price growth rates, whereas we find a lead-lag and house price causal flow from the more central to the peripheral subdistricts.
Keyphrases
  • human health
  • risk assessment
  • machine learning
  • big data
  • deep learning
  • data analysis