input: Write a function to create a Linear Regression model in Python using sklearn that can predict housing prices given the living area and number of rooms. The function should take a training dataset as input, fit the model, and then return the model itself. Additionally, provide a test output using the given dataset.
  
  from sklearn import linear_model
  import numpy as np
  
  def create_housing_price_model(data, prices):
      X = np.array(data)
      y = np.array(prices)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model
  
  data = [[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]]
  prices = [75000, 43000, 59000, 32000, 85000]
  model = create_housing_price_model(data, prices)
  prediction = model.predict([[2200, 3]])
  assert round(prediction[0]) == 73600
output: def create_housing_price_model(data, prices):
      X = np.array(data)
      y = np.array(prices)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      return model