input: Write a function to create a Linear Regression model in Python that can predict housing prices based on the living area and number of rooms. The function should take input data containing living area and room numbers, and output data containing corresponding housing prices. It should then fit the model and make a prediction for a new data point.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 67500.0
  assert predict_housing_price([[1800, 3], [2500, 4], [1500, 2]], [50000, 70000, 40000], [1900, 2.5]) == 52000.0
output: def predict_housing_price(X, y, new_data):
      from sklearn.linear_model import LinearRegression
      model = LinearRegression()
      model.fit(X, y)
      return model.predict([new_data])[0]