input: Write a function that creates a Linear Regression model to predict housing prices based on the living area and number of rooms. The function should take a list of lists where each sublist contains the living area and number of rooms, and another list containing the corresponding prices. The function should return a trained model and a predicted price for a new data point.
  
  def predict_housing_price(data, prices, new_data):
      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)
      
      prediction = model.predict([new_data])
      return model, prediction[0]
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])[1] > 50000 and predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])[1] < 70000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1800, 3])[1] > 40000 and predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1800, 3])[1] < 60000
output: def predict_housing_price(data, prices, new_data):
      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)
      
      prediction = model.predict([new_data])
      return model, prediction[0]