Abstract:
It is widely acknowledged that the Turkish economy was launched by the
housing sector. As a result, both government policymakers and decision-makers in
the building business are keenly interested in how housing prices change.
Understanding the connections between changes in macroeconomic variables and
changes in housing prices is crucial for properly tracking price changes in homes.
With the use of autoregressive integrated moving average (ARIMA) and artificial
neural network (ANN) models, the primary goal of this research is to predict
Turkey's housing price index (HPI) with macroeconomic variables. Eleven
macroeconomic parameters are chosen as the independent variables when predicting
the housing price index (HPI). The quarterly time series data of these parameters are
used for the time period between January 2010 to December 2022 to train the
models. An in-depth analysis is done on the connection between these
macroeconomic factors and changes in the housing prices index. 11 hypotheses are
formed to test the model, from that GDP (β=0.001, p= 0.97), Gold prices (β=-0.00,
p= 0.69), BORSA index (β=2.08, p= 0.52), monetary rate (β=-0.105, p= 0.27) and
foreign trade (β=0.171, p= 0.26) are not affected on Turkey's HPI. Inflation rate
(β=1.20, p= 0.000), an Exchange rate (β=0.09, p= 0.00), USD/TL (β=1.43, p=
0.000), Unemployment (β=-0.94, p= 0.005), Deposit interest rate (β=-0.42, p= 0.00)
and money supply (β=-0.11, p= 0.04) are significantly affected in Turkey's housing
price index. This study will help policy makers and investors who are taking
investment decisions in Turkish real estate markets.
