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Purpose-A wide range of decision-makers is interested in educated forecasts for house prices. The technical analysis introduced in this study aims to estimate future (forecasted) house prices and provide sufficient evidence in support of the adequacy of the estimated models obtained from parametric and non-parametric modeling methods for Turkey's housing market. Methodology-We employ non-parametric and various time series methods to find appropriate fits to forecast Turkey's house price index (HPI). In our modelling, we consider macroeconomic indicators related to housing markets, such as; gold, interest rate and currencies. In this study, first using the explanatory variables, we construct two Generalized Linear Models (GLM) and a Vector Auto Regressive (VAR) model. Then, we construct two univariate time series models. HPI series inherits seasonality. Even though the HPI contains seasonality, first, we neglect the seasonal effect and come up an Autoregressive Moving Average ( ( , )) model among many other alternative ARMA models. Second, we consider the seasonality effect on the housing market index and construct a seasonal Autoregressive Integrated Moving Average ( ( , , )( , , )) and exponential smoothing models. Findings-The analysis identifies forecasts of Turkey's housing market index from both the seasonal ( , , )( , , ) and Holt Winter models as accurate models compared to classical time series models, namely ( , , ) models, based on the explanation power measure (R^2) values and out-of-sample error measures MSE, RMSE and MAE. Conclusion-The study has three main contributions: i) Our forecast shows Turkey's housing market's return will not increase in the following 12months. ii) The seasonal ARIMA and exponential smoothing models forecast some negative returns within the given forecasting period, which should be considered a warning for Turkey's housing market for the future. iii) GLM and VAR models illustrate that Turkey's housing market shows a high dependence on gold, inflation, and foreign exchange rates than other well-known economic indicators.

This study explores the hedging coefficients of the financial options to default and to prepay embedded into mortgage contracts based on the change in spot rate, underlying house price and its volatility. In the computations, the finite-dimensional Malliavin calculus is applied since the distribution of both options is unknown and their payoffs are non-differentiable. Naturally, the hedging coefficients are obtained as a product of option's payoff and an independent weight, which permits the user to derive estimations for the hedging coefficients by running a crude Monte Carlo (MC) algorithm. The simulations reveal that the financial options to default and to prepay are both more sensitive to a change in spot rate than a change in underlying house price and its volatility. There are two potential usages of the hedging coefficients: first, they allow the user to determine the effects of spot rate and underlying house price change and its volatility on the default and prepayment options, and second, borrowers and lenders can replicate and hedge their main portfolio by using the balance between these coefficients and the default and prepayment options.