input: Write a function to create a Linear Regression model in Python that can predict housing prices based on the living area and number of rooms. The function should train the model using the provided data and return the prediction for a new data point. If the input data is empty, the function should raise a ValueError.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 68000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5]], [75000, 43000, 59000], [2200, 3]) == 68000
  assert predict_housing_price([], [], [2200, 3]) == raises ValueError
output: def predict_housing_price(living_area, prices, new_data):
      if not living_area:
          raise ValueError("Input data is empty")
      
      import numpy as np
      from sklearn.linear_model import LinearRegression
      
      X = np.array(living_area)
      y = np.array(prices)
      
      model = LinearRegression()
      model.fit(X, y)
      
      return int(model.predict(np.array(new_data).reshape(1, -1)))