input: Write a function to create a Linear Regression model using the sklearn library that can predict the housing prices given the living area and number of rooms. The function should train the model on the provided data and return the predicted price for a new input. If the input data is empty, the function should return -1.
  assert housing_price_prediction([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) > 60000
  assert housing_price_prediction([], [], []) == -1
  assert housing_price_prediction([[1800, 2.5], [2200, 3]], [45000, 60000], [2000, 2.5]) > 50000
output: def housing_price_prediction(X, y, new_input):
      if not X:
          return -1
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_input])[0]