input: Write a function to create a Linear Regression model in Python that can predict the housing prices based on the living area and number of rooms. The function should take in the training data and return a trained model. The function should also include a way to make a prediction on new data.
  assert create_housing_price_model(data=[[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], prices=[75000, 43000, 59000, 32000, 85000])().predict([[2200, 3]])==79000.0
  assert create_housing_price_model(data=[[1800, 2.5], [1500, 2], [2200, 3.5]], prices=[50000, 35000, 70000])().predict([[1900, 3]])==57000.0
  assert create_housing_price_model(data=[[3000, 4], [2500, 3.5]], prices=[90000, 75000])().predict([[2700, 4]])==80000.0
  assert create_housing_price_model(data=[[], []], prices=[])().predict([[2100, 3]])==None
  assert create_housing_price_model(data=[[1400, 2], [2400, 3.5]], prices=[32000, 59000])().predict([[1800, 3]])==44000.0
output: def create_housing_price_model(data, prices):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      if not data or not prices:
          return lambda: None
      
      X = np.array(data)
      y = np.array(prices)
      
      model = LinearRegression()
      model.fit(X, y)
      
      return lambda: model