Occupancy Rate

 

Occupancy rate is a way of measuring the Angel's stay, a measure we use internally to know how much revenue we loose to empty beds. How many beds are occupied on a given day? A day is the lowest measurement of time units. So the occupancy rate must be calculated per time measure per unit of stay. Unit of stay is a bed or a room. Room could be ambiguous as it can have multiple beds, so I'll stick with beds.

Calculating Occupancy Rate

An example explains this better. Assuming we have a house with 2 bedrooms: first room with just one bed (single occupancy) and the second room with two beds (double sharing room), if the first room is occupied and in the second room only one bed is occupied, i.e, 2 out of 3 beds are occupied, a simple occupancy rate calculation is 2/3*100 = 67%.

Bed Occupied?
Room 1 - Bed 1 Yes
Room 2 - Bed 1 Yes
Room 2 - Bed 2 No
All beds aren't equal, single occupancies are expensive as they are more private and exclusive. Occupancy rate varies if we consider price of a bed; it makes sense to consider the price because it determines how much revenue we make. Without that, having 2 double beds occupied yields same occupancy rate as having 1 single and 1 double bed occupied. Clearly, the second case yields more revenue than the first.

Bed Rate Occupied?
Room 1 - Bed 1 14,000 Yes
Room 2 - Bed 1 9,500 Yes
Room 2 - Bed 2 9,500 No

Occupancy Rate = (14000+9500)/(14000+9500+9500)*100 = 71%

Visualizing Occupancy Rates

After experimenting with many methods of visualizing occupancy rates, an yearly calendar chart is what we use now. It shows all the required data succinctly. I am more interested in knowing how occupancy rates vary over time. Maroon means none of the beds are occupied, it starts transitioning towards yellow for better occupancies and towards green for full occupancy rates. 100% occupancy is dark green. Following is the graph for a 3BHK in BTM Layout. Mouse over the cells to see its occupancy rate.

Trends

Blue box is when we commissioned the house. The initial few weeks are empty as we try to fill the house. Eventually every cell should become dark green, or at the very least some shade of green.

Some weeks remain somewhere in between pale yellow and light green. That's not good either. For example, the following house is struggling with pale green for about 4 months. Even initial 2 months weren't that great.

 

The following house is running at 100% after the first month, an ideal case. First month is reddish, but that's the same in almost all houses.

 

Another good character of the graph is that it expects fresh data, until today. The cells become maroon if there is no data for a day. I am happy with what these graphics tell me, in one glance I know how a house is doing.


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