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This analysis compares two forecasting models, ARIMA and LSTM, for predicting housing prices, indicating LSTM's superior performance.
Due to high inflation, Türkiye has been struggling with high housing prices. This study compares two forecasting models: an econometric time-series model, ARIMA, and a machine learning algorithm, LSTM, in predicting housing prices in Istanbul. First, only the Central Bank’s quarterly average housing unit prices are used in both models. Second, two crucial macroeconomic variables, the mortgage loan interest rate and the inflation rate (as measured by the CPI), are added to the model. The results reveal that the forecast obtained from LSTM outperforms the ARIMA approach. This research fills a significant gap in the literature where the implementation of artificial intelligence in the housing industry is limited.
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Levent Sümer (2025) studied this question.
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