There are a lot of measures for error of estimation and the one you provided is a valid one. But since you are working with the sum of random variables, I suggest using normal distribution (supported by the Central Limit Theorem) and instead of calculating once the sum of sales in New York, you’ll have to repeat that algorithm (at least 30 times) including randomness in your selection.

With your sample of 30 "sum of sales" you can use Normal Distribution and not only calculate the Mean Square Error as a good estimator of error, but also calculate probabilities.

Another good news is most of statistical inference is developed for variables with normal distribution.

There are a lot of measures for error of estimation and the one you provided is a valid one. But since you are working with the sum of random variables, I suggest using normal distribution (supported by the Central Limit Theorem) and instead of calculating once the sum of sales in New York, you’ll have to repeat that algorithm (at least 30 times) including randomness in your selection.

With your sample of 30 "sum of sales" you can use Normal Distribution and not only calculate the Mean Square Error as a good estimator of error, but also calculate probabilities.

Another good news is most of statistical inference is developed for variables with normal distribution.