New KPI: Median Length of Vacancy

Summary:

• We created a new KPI: Median Length of Vacancy (MLOV)
• This KPI is valuable for recovering lost revenue when units sit unrented (and understanding why they sit unrented) and measuring performance across communities
• Read on to learn how MLOV is calculated and explore the financial impact! 

Introduction:

At Clarent, we strive to turn data into value for our customers in the senior living industry. We combine data from different sources into a single source of truth and calculate 150+ industry-specific KPIs to drive revenue, manage costs, and improve operations. 

We recently collaborated with clients on how to track (and fill) vacant units. Units that sit vacant for a long time can significantly impact both revenues and operational effectiveness. We conducted an in-depth study to gain insights into unit vacancy distribution across various communities. As a result, we developed a new KPI, Median Length of Vacancy (MLOV), to assist stakeholders in addressing these concerns and optimizing their operations.

Methodology:

MLOV is defined as: The median length of time (in days) a rentable unit is vacant before it is rented out

Our calculation utilized historic vacant periods of all rentable units and it can be analyzed for regions or individual communities.

During data processing, we carefully excluded units such as "Salon", "Waitlist Unit", "Physical Therapy Room", or "Rehab Room" that may be present in communities' rent rolls to prevent them from skewing the data.

We also recognized that several other factors can complicate the process, including privacy levels (single units vs shared units) and second residents. Our analysis is designed to handle these complications to provide a more accurate and meaningful MLOV calculation.

Findings:

One intriguing observation from our study is the considerable variation in MLOV between different communities. Across customers, we observed a significant spread in performance, with the slowest-renting communities taking up to 5 times longer to fill vacancies compared to the fastest-performing communities. 

Another interesting finding is that communities with higher MLOV tend to have a larger percentage of units that are vacant for an extended period. This correlation implies that an efficient approach to filling vacancies not only reduces the median duration of vacancies but also minimizes the occurrence of long-term vacant units. 

Calculating the Financial Impact

We compared 2 sample communities to demonstrate the financial impact of higher MLOV. In our scenario, here are the assumptions:

Community A and Community B have the same amount of move outs in a year

Community A: 

1. 96% of units take 25 days to fill (MLOV = 25)
2. 4% of vacant units take 1-3 years to fill

Community B

1. 92% of units take 45 days to fill (MLOV = 45)
2. 8% of vacant units take 1-3 years to fill

The Average Daily Rate for both Community A and B is $150

The MLOV for Community B is only slightly higher than Community A (45 days for Community B vs. 25 days for Community A). Here’s what we found:

• Community B is projected to experience a revenue loss of approximately $166,000 (221%) more than Community A at 20 move-outs, and nearly $241,100 (127%) at 35 move-outs. 
• Our analysis further reveals that, when faced with even larger disparities in MLOV (e.g., 25 vs. 125), the difference in revenue loss can rise to as much as one million dollars. 

Beyond MLOV:

MLOV can offer valuable insights that help operators maximize revenue by finding which communities have the most unrented units. This could be an opportunity to focus sales & marketing support to reduce MLOV and get move-ins! 

As always, it’s important to have the full context in any analysis. That’s where Clarent’s Library of 150+ KPIs come in handy! You can look at other KPIs like Actual Average Daily Rate and Average Length of Stay to get more context on what’s going on. 

To learn more about how to effectively use these and other KPIs for in-depth pricing analysis, we invite you to explore our blog post on Metrics that Matter: Pricing Deep Dive.