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 fit the model using provided data and then return the predicted price for a given input. If the input data is empty, return None.
  
  def predict_housing_price(data, price, new_data):
      if not data:
          return None
      import numpy as np
      from sklearn import linear_model
      X = np.array(data)
      y = np.array(price)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict([new_data])
      return 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])==67000.0
  assert predict_housing_price([], [], [2200, 3])==None
  assert predict_housing_price([[2000, 3], [1600, 2.5]], [75000, 43000], [1800, 3])==64500.0
output: 
  def predict_housing_price(data, price, new_data):
      if not data:
          return None
      import numpy as np
      from sklearn import linear_model
      X = np.array(data)
      y = np.array(price)
      model = linear_model.LinearRegression()
      model.fit(X, y)
      prediction = model.predict([new_data])
      return prediction[0]
  