When setting rates, many utilities use rate surveys - regional compilations of utilities' rates - to gauge a fair price increase. However, each utility has a unique set of factors that affect its rate, so simple comparisons between two utility rates may lead to the wrong conclusion. This thesis describes regression models which provide better comparisons by incorporating factors that influence rates. Two types of bills - water only and combined water and sewer - are modeled at four consumption levels: 3000, 6000, 9000, and 12000 gallons per month. The models use the data from all the utilities in the sample to provide an estimated average bill, with a 95% confidence interval, for each utility. Then, each utility can compare its actual bill with this estimate. The models also show that high bills (both types) are associated with source water, recent rate changes, large grants, and large connection fees at most consumption levels.