Unbiasedness of SSE and regression SS

Consider the simple linear regression model $y_i = \beta_0 + \beta_1 x_i + \epsilon_i$, $i = 1,2,…,n$ ($n \ge 3$), where $\beta_0$ and $\beta_1$ are unknown parameters, $\epsilon_i$'s are uncorrelated random errors with mean 0 and finite variance $\sigma^2 > 0$. Let $\bar{y} = \frac1n \sum_{i=1}^n y_i$ and $\hat{y}_i = \hat\beta_0 + \hat\beta_1 x_i$, $i = 1,2,…,n$, where $\hat\beta_0$ and $\hat\beta_1$ represent least squares estimators of $\beta_0$ and $\beta_1$, respectively. Let $T_1 = \frac{1}{n-2} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$ and $T_2 = \sum_{i=1}^{n} (\hat{y}_i - \bar{y})^2$. Then which one of the following statements is true?