input: Write a function to create a linear regression model using the scikit-learn library that can predict housing prices based on the living area and number of rooms. The function should accept a dataset as input, where each entry is a tuple consisting of the living area and the number of rooms, and a corresponding list of housing prices. The function should return the model after fitting the data. Additionally, provide a test case where the model predicts the price for a house with a living area of 2200 square feet and 3 rooms.
  assert create_housing_model(data=[[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], prices=[75000, 43000, 59000, 32000, 85000]).predict([[2200, 3]])[0] == 61000.0
  true_case = create_housing_model(data=[[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], prices=[75000, 43000, 59000, 32000, 85000]).predict([[2200, 3]])[0]
  assert round(true_case, 2) == 61000.0
output: 
  def create_housing_model(data, prices):
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(data, prices)
      return model
  