input: 
  from typing import List
  
  def predict_housing_prices(data: List[List[float]], new_data: List[List[float]]) -> List[float]:
      """
      Create a linear regression model to predict housing prices based on living area and number of rooms.
      Given a list of training data where each sublist contains the living area and number of rooms,
      and a list of new data for which predictions are to be made, return the predicted prices.
      
      >>> predict_housing_prices([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [[2200, 3]])
      [51000.0]
      """
  
output:     import numpy as np
  
      X = np.array([[1, area, rooms] for area, rooms in data])
      y = np.array([area * 100 + rooms * 10000 for area, rooms in data])
  
      theta = np.linalg.inv(X.T @ X) @ X.T @ y
  
      predictions = []
      for new_point in new_data:
          area, rooms = new_point
          prediction = theta[0] + theta[1] * area + theta[2] * rooms
          predictions.append(prediction)
  
      return predictions