An average daily rate (ADR) is a statistic that hoteliers use to measure how much revenue they generate per available room. The rate is calculated by dividing the total room revenue for a given period of time by the number of rooms available during that period. The ADR can be used to compare the performance of different hotels and track changes in a hotel's revenue over time.
To calculate the average daily rate, divide the total room revenue by the number of rooms available. For example, if a hotel has 100 rooms and generates $200,000 in room revenue over the course of a month, its ADR would be $200,000/100, or $2,000.
There are a number of factors that can affect a hotel's ADR. These include the location of the hotel, the types of rooms offered, the amenities provided, the seasonality of demand, and the overall economic conditions.
Average daily rates can be used in financial planning in a number of ways. For example, they can be used to forecast future room revenue, set budget targets, and compare the performance of different hotels.
There are a number of misconceptions about average daily rates. Some people believe that ADR is the same as the room rate when, in fact, it is a measure of revenue. Others mistakenly think that a high ADR means that a hotel is more expensive, when it may just be that the hotel is located in an expensive city or has a high demand.1
Businesses can make use of average daily rates in a number of ways. They can use them to track the performance of their own hotels or compare it to the performance of different hotels. Additionally, businesses can use ADR data to forecast future room revenue and set budget targets.
There are several risks associated with using average daily rates, including the risk of using outdated data or data from a limited sample size and the risk of making inaccurate comparisons.
To achieve the most accurate ADR, businesses should use data from multiple sources over a wide time period and for a variety of hotels. Additionally, businesses should be sure to adjust for inflation when comparing average daily rates over time.