What Is the Average Daily Rate (ADR)?

The average daily rate (ADR) is a metric widely used in the hospitality industry to indicate the average revenue earned for an occupied room on a given day. The average daily rate is one of the key performance indicators (KPI) of the industry.

Another KPI metric is the occupancy rate, which when combined with the ADR, comprises revenue per available room (RevPAR), all of which are used to measure the operating performance of a lodging unit such as a hotel or motel.

Understanding the Average Daily Rate (ADR)

The average daily rate (ADR) shows how much revenue is made per room on average. The higher the ADR, the better. A rising ADR suggests that a hotel is increasing the money it’s making from renting out rooms. To increase the ADR, hotels should look into ways to boost price per room.

Hotel operators seek to increase ADR by focusing on pricing strategies. This includes upselling, cross-sale promotions, and complimentary offers such as free shuttle service to the local airport. The overall economy is a big factor in setting prices, with hotels and motels seeking to adjust room rates to match current demand.

To determine the operating performance of a lodging, the ADR can be measured against a hotel’s historical ADR to look for trends, such as seasonal impact or how certain promotions performed. It can also be used as a measure of relative performance since the metric can be compared to other hotels that have similar characteristics, such as size, clientele, and location. This helps to accurately price room rentals.

Calculating the Average Daily Rate (ADR)

The average daily rate is calculated by taking the average revenue earned from rooms and dividing it by the number of rooms sold. It excludes complimentary rooms and rooms occupied by staff.


$\text{Average Daily Rate} = \frac{\text{Rooms Revenue Earned}}{\text{Number of Rooms Sold}}$Average Daily Rate=Number of Rooms SoldRooms Revenue Earned​

Example of the Average Daily Rate (ADR)

If a hotel has $50,000 in room revenue and 500 rooms sold, the ADR would be $100 ($50,000/500). Rooms used for in-house use, such as those set aside for hotel employees and complimentary ones, are excluded from the calculation.

Real World Example

Consider Marriott International (MAR), a major publicly traded hotelier that reports ADR along with occupancy rate and RevPAR. For 2019, Marriott’s ADR increased by 2.1% from 2018 to $202.75 in North America. The occupancy rate was fairly static at 75.8%. Taking the ADR and multiplying it by the occupancy rate yields the RevPAR. In Marriott’s case, $202.75 times 75.8% equates to a RevPAR of $153.68, which was up 2.19% from 2018.

The Difference Between the Average Daily Rate (ADR) and Revenue Per Available Room (RevPAR)

The average daily rate (ADR) is needed to calculate the revenue per available room (RevPAR). The average daily rate tells a lodging company how much they make per room on average in a given day. Meanwhile, RevPAR measures a lodging’s ability to fill its available rooms at the average rate. If the occupancy rate is not at 100% and the RevPAR is below the ADR, a hotel operator knows that it can probably reduce the average price per room to help increase occupancy.

Limitations of Using the Average Daily Rate (ADR)

The ADR does not tell the complete story about a hotel’s revenue. For instance, it does not include the charges a lodging company may charge if a guest does not show up. The figure also does not subtract items such as commissions and rebates offered to customers if there is a problem. A property’s ADR may increase as a result of price increases, however, this provides limited information in isolation. Occupancy could have fallen, leaving overall revenue lower.

What Is Average Inventory?

Average inventory is a calculation that estimates the value or number of a particular good or set of goods during two or more specified time periods. Average inventory is the mean value of inventory within a certain time period, which may vary from the median value of the same data set, and is computed by averaging the starting and ending inventory values over a specified period.

Understanding Average Inventory

Inventory is the value of all the goods ready for sale or all of the raw materials to create those goods that are stored by a company. Successful inventory management is a key focal point for companies as it allows them to better manage their overall business in terms of sales, costs, and relationships with their suppliers.

Since two points do not always accurately represent changes in inventory over different time periods, average inventory is frequently calculated by using the number of points needed to more accurately reflect activities across a certain amount of time.

For instance, if a business was attempting to calculate the average inventory over the course of a fiscal year, it may be more accurate to use the inventory count from the end of each month, including the base month. The values associated with each point are added together and divided by the number of points, in this case, 13, to determine the average inventory.

The average inventory figures can be used as a point of comparison when looking at overall sales volume, allowing a business to track inventory losses that may have occurred due to theft or shrinkage, or due to damaged goods caused by mishandling. It also accounts for any perishable inventory that has expired.

The formula for average inventory can be expressed as follows:

Average Inventory = (Current Inventory + Previous Inventory) / Number of Periods

Average inventory is used often in ratio analysis; for instance, in calculating inventory turnover.

Moving Average Inventory

A company may choose to use a moving average inventory when it’s possible to maintain a perpetual inventory tracking system. This allows the business to adjust the values of the inventory items based on information from the last purchase.

Effectively, this helps compare inventory averages across multiple time periods by converting all pricing to the current market standard. This makes it similar to adjusting historical data based on the rate of inflation for more stable market items. It allows simpler comparisons on items that experience high levels of volatility.

Example of Average Inventory

A shoe company is interested in better managing its inventory. The current inventory in its warehouse is equal to $10,000. This is in line with the inventory for the three previous months, which were valued at $9,000, $8,500, and $12,000.

When calculating a three-month inventory average, the shoe company achieves the average by adding the current inventory of $10,000 to the previous three months of inventory, recorded as $9,000, $8,500 and $12,000, and dividing it by the number of data points, as follows:

Average Inventory = ($10,000 + $9,000 + $8,500 + $12,000) / 4

This results in an average inventory of $9,875 over the time period being examined.

What Is an Autoregressive Integrated Moving Average (ARIMA)?

An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. 

A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.

Understanding Autoregressive Integrated Moving Average (ARIMA)

An autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables. The model’s goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values.

An ARIMA model can be understood by outlining each of its components as follows:

• Autoregression (AR): refers to a model that shows a changing variable that regresses on its own lagged, or prior, values.
• Integrated (I): represents the differencing of raw observations to allow the time series to become stationary (i.e., data values are replaced by the difference between the data values and the previous values).
• Moving average (MA):  incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.

ARIMA Parameters

Each component in ARIMA functions as a parameter with a standard notation. For ARIMA models, a standard notation would be ARIMA with p, d, and q, where integer values substitute for the parameters to indicate the type of ARIMA model used. The parameters can be defined as:

• p: the number of lag observations in the model, also known as the lag order.
• d: the number of times the raw observations are differenced; also known as the degree of differencing.
• q: the size of the moving average window, also known as the order of the moving average.

For example, a linear regression model includes the number and type of terms. A value of zero (0), which can be used as a parameter, would mean that particular component should not be used in the model. This way, the ARIMA model can be constructed to perform the function of an ARMA model, or even simple AR, I, or MA models.

ARIMA and Stationary Data

In an autoregressive integrated moving average model, the data are differenced in order to make it stationary. A model that shows stationarity is one that shows there is constancy to the data over time. Most economic and market data show trends, so the purpose of differencing is to remove any trends or seasonal structures. 

Seasonality, or when data show regular and predictable patterns that repeat over a calendar year, could negatively affect the regression model. If a trend appears and stationarity is not evident, many of the computations throughout the process cannot be made and produce the intended results.

How to Build an ARIMA Model

To begin building an ARIMA model for an investment, you download as much of the price data as you can. Once you’ve identified the trends for the data, you identify the lowest order of differencing (d) by observing the autocorrelations. If the lag-1 autocorrelation is zero or negative, the series is already differenced. You may need to difference the series more if the lag-1 is higher than zero.

Next, determine the order of regression (p) and order of moving average (q) by comparing autocorrelations and partial autocorrelations. Once you have the information you need, you can choose the model you’ll use.

Pros and Cons of ARIMA

ARIMA models have strong points and are good at forecasting based on past circumstances, but there are more reasons to be cautious when using ARIMA. In stark contrast to investing disclaimers that state “past performance is not an indicator of future performance…,” ARIMA models assume that past values have some residual effect on current or future values and use data from the past to forecast future events.

The following table lists other ARIMA traits that demonstrate good and bad characteristics.

The Bottom Line

The ARIMA model is used as a forecasting tool to predict how something will act in the future based on past performance. It is used in technical analysis to predict an asset’s future performance.

ARIMA modeling is generally inadequate for long-term forecastings, such as more than six months ahead, because it uses past data and parameters that are influenced by human thinking. For this reason, it is best used with other technical analysis tools to get a clearer picture of an asset’s performance.

What Is Average Propensity to Consume?

Average propensity to consume (APC) measures the percentage of income that is spent rather than saved. This may be calculated by a single individual who wants to know where the money is going or by an economist who wants to track the spending and saving habits of an entire nation.

In either case, the propensity to consume can be determined by dividing average household consumption, or spending, by average household income, or earnings.

Understanding Average Propensity to Consume

From the broader economic view, a high average propensity to consume is generally good for the economy. When the average propensity to consume is high, consumers are saving less and spending more on goods or services. This increased demand drives economic growth, business expansion, and broad employment.

Low-income households are often seen as having a higher average propensity to consume than high-income households. Low-income households may be forced to spend their entire income on necessities with minimal disposable income remaining to save. Alternatively, high-income households with higher cash flow after their necessities are met typically have a relatively lower average propensity to consume.

Economists often gauge economy forecasts on actions by the middle-income households. The spending and savings patterns of this demographic often indicate a degree of confidence or pessimism about their own personal financial situations and the economy as a whole.

Propensity to Consume vs. Propensity to Save

The sum of the average propensity to consume and the average propensity to save is always equivalent to one. A household or a nation must either spend or save all of its income.

The inverse of the average propensity to consume is the average propensity to save (APS). That figure is simply the total of income minus spending. The result is known as the savings ratio.

Notably, the savings ratio is normally based on its percentage of disposable income, or after-tax income. An individual determining personal propensities to consume and save should probably use the disposable income figure as well for a more realistic measure.

Example of Average Propensity to Consume

Assume a nation’s economy has a gross domestic product (GDP) equivalent to its disposable income of $500 billion for the previous year. The total savings of the economy was $300 billion, and the rest was spent on goods and services.

The nation’s APS is calculated to be 0.60, or $300 billion/$500 billion. This indicates the economy allocated 60% of its disposable income to savings. The average propensity to consume is calculated to be 0.40, or (1 – 0.60). Therefore, the nation spent 40% of its GDP on goods and services.

APS can include saving for retirement, a home purchase, and other long-term investments. As such, it can be a proxy for national financial health.

Special Considerations

The marginal propensity to consume (MPC) is a related concept. It measures the change in the average propensity to consume.

Assume that the nation in the previous example increased its GDP to $700 billion and its consumption of goods and services rose to $375 billion. The economy’s average propensity to consume increased to 53.57%.

The nation’s consumption increased from $200 billion to $375 billion. Alternatively, the nation’s GDP increased from $500 billion to $700 billion. The nation’s marginal propensity to consume is 87.5% ($375 billion – $200 billion) / ($700 billion – $500 billion). The marginal propensity measures the directional trend of how an entity is utilizing its money. In this case, 87.5% of new growth was further consumed.