Detection of Price Bubbles in Istanbul Housing Market using LSTM Autoencoders: A District-based Approach

Ayan E. B., Eken S.

Soft Computing, vol.25, no.12, pp.7959-7973, 2021 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 25 Issue: 12
  • Publication Date: 2021
  • Journal Name: Soft Computing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.7959-7973
  • Kocaeli University Affiliated: Yes


Since the early 2000s, there has been a long-term price increase trend in the Istanbul housingmarket, and this situation also has

led to price bubble speculations. Since the housing sector was caught with a high level of unsold housing stock to the economic

slowdown emerging in the second half of 2018, housing price bubble speculations have increased even more, especially for

the Istanbul market. In this period, housing loan interest reduction campaigns were implemented by the government through

state banks to stimulate the housing demand, and a probable collapse in the housing market was prevented. On the other

hand, house prices continued to rise during this period due to the stimulated demand. In this paper, we perform a price bubble

research on the selected districts in the Istanbul housing market over the 2007–2019 period using LSTM autoencoder model.

The first analysis on monthly data is performed by using housing price index, housing rent index, consumer prices index,

stock market index, return on government debt securities, USD/TRY exchange rates, BIST price index, monthly deposit

interest rates, mortgage interest rates and consumer confidence index, and the second analysis on quarterly data is carried out

by adding building construction cost index and GDP data to the previous dataset. In the first analysis, the bubble formations

differ regionally and periodically and disappeared toward the end of 2019 in some districts, while in the second analysis,

the housing bubble formations have a more common and continuous appearance. Experimental results show that LSTM

autoencoder model can be used to detect housing bubbles effectively.