Ayan Ebubekir, Eken Süleyman
Department of Business Administration, Kocaeli University, 41001 İzmit, Turkey.
Department of Information Systems Engineering, Kocaeli University, 41001 Izmit, Turkey.
Soft comput. 2021;25(12):7957-7973. doi: 10.1007/s00500-021-05677-6. Epub 2021 Mar 10.
Since the early 2000s, there has been a long-term price increase trend in the Istanbul housing market, 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.
自21世纪初以来，伊斯坦布尔房地产市场一直呈现长期价格上涨趋势，这种情况也引发了价格泡沫的猜测。自2018年下半年出现经济放缓以来，房地产行业面临着大量未售出的住房库存，尤其是伊斯坦布尔市场，房价泡沫的猜测更是甚嚣尘上。在此期间，政府通过国有银行开展了住房贷款利率下调活动，以刺激住房需求，从而避免了房地产市场可能出现的崩溃。另一方面，由于需求受到刺激，在此期间房价持续上涨。在本文中，我们使用长短期记忆自动编码器模型，对2007 - 2019年期间伊斯坦布尔房地产市场选定区域进行了价格泡沫研究。第一次对月度数据的分析使用了房价指数、房租指数、消费价格指数、股票市场指数、政府债务证券回报率、美元/土耳其里拉汇率、伊斯坦布尔证券交易所价格指数、月度存款利率、抵押贷款利率和消费者信心指数，第二次对季度数据的分析则是在之前的数据集基础上增加了建筑施工成本指数和国内生产总值数据。在第一次分析中，泡沫形成在区域和周期上存在差异，并且在2019年底一些地区泡沫消失，而在第二次分析中，房地产泡沫形成表现得更为普遍和持续。实验结果表明，长短期记忆自动编码器模型可有效用于检测房地产泡沫。
