input: Write a function to create a linear regression model in Python that predicts housing prices based on the living area and number of rooms. The function should take as input a list of data points and a list of corresponding housing prices, and return a trained model. The model should be capable of predicting new housing prices given the living area and number of rooms.
  
  def predict_housing_prices(data, prices):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model
  
  assert int(predict_housing_prices([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000]).predict([[2200, 3]])) == 67000
  assert int(predict_housing_prices([[1800, 3], [1500, 2.5], [2000, 3.5], [1300, 2], [2700, 4]], [70000, 38000, 52000, 28000, 78000]).predict([[2100, 3]]) == 64000)
  assert int(predict_housing_prices([[2100, 3], [1650, 2.5], [2300, 3.5], [1450, 2], [2900, 4]], [72000, 44000, 57000, 33000, 82000]).predict([[2200, 3]]) - 65000) < 500
output: def predict_housing_prices(data, prices):
      import numpy as np
      from sklearn import linear_model
  
      X = np.array(data)
      y = np.array(prices)
  
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model