input: Write a function to create a Linear Regression model using `sklearn` that can predict housing prices based on the living area and number of rooms. The function should fit the model with the provided data and return the prediction for a new living area and number of rooms. Ensure the function handles the input data correctly and returns only the predicted price.
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==55000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1800, 2.5])==45000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2800, 4])==75000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1600, 2])==40000
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2600, 3.5])==65000
output: 
  from sklearn.linear_model import LinearRegression
  
  def predict_housing_price(X, y, new_data):
      model = LinearRegression()
      model.fit(X, y)
      return int(model.predict([new_data])[0])
  