Market Data
Insights derived from publicly available market data delivered via HelloData.
Market Retention
How BubbleGum BI estimates retention rates across your properties and competitive set using publicly available market data delivered via HelloData.
Market retention estimates the percentage of expiring leases that resulted in renewals versus turnovers. It answers: of the leases expected to expire in a given month, how many tenants stayed? Because market data only records when a unit is leased to a new tenant, renewals are inferred from the absence of turnover rather than observed directly.
Why It's an Estimate
Market data captures turnovers, not renewals
Market data creates a record when a unit exits the market — meaning a new tenant signed a lease. If an existing tenant renews, nothing happens in the data: the unit never comes back on market, so no record is created. We infer renewals from the absence of turnover.
- A unit that turns over generates a new lease record — this is a confirmed event
- A unit where the tenant renews generates no record — we infer this from the gap
- The metric is most reliable when aggregated across many units and properties
Lease Classification
How each lease record is categorized
Turned Over
The unit has a subsequent lease record in the data. This means the unit came back on market and was leased to someone new. This is a confirmed turnover regardless of when the original lease was expected to expire — tenants can break leases early.
Assumed Renewed
The unit has no subsequent lease record, and the expected expiration date has already passed. Since the unit never came back on market, we infer the tenant renewed.
Not Yet Expired
The unit has no subsequent lease record, but the expected expiration date is still in the future. This lease cannot be classified yet and is excluded from the metric.
Long-Term Lease Adjustment
Accounting for multiple renewal cycles within a single lease term
A lease term longer than 15 months likely represents multiple renewal cycles where the tenant renewed one or more times before eventually turning over (or still being in place). We account for this by counting multiple expiration cycles per lease.
| Lease Term | Expected Expirations | Inferred Renewals |
|---|---|---|
| 12 months | 1 | 0 |
| 18 months | 2 | 1 |
| 27 months | 3 | 2 |
Formula: CEIL(lease_term / 12) expirations, and CEIL(lease_term / 12) - 1 inferred renewals from the long-term portion alone. If the lease is also classified as Assumed Renewed, it receives an additional renewal.
Calculating the Rate
How expiration and renewal counts become a retention rate
The underlying data outputs raw counts per property per month: expected_expirations and expected_renewals. To get a retention rate, sum both columns over whatever scope you want and divide:
- Slice by a single property, a competitive set, or your full portfolio
- Aggregate by month, quarter, or trailing 12 months depending on the analysis
- Compare your owned properties against comps to see if you're retaining at, above, or below market
Data Requirements
What must be present in the source data for a lease to be included
- Lease term must be present and greater than zero — leases without a term are excluded
- Exit market date (when the lease was signed) must be present
- Only active properties and their defined comparables are included
Important Caveat
Understanding what this metric can and cannot tell you
Market retention is a market-level proxy, not true tenant retention. The underlying data operates at the unit level — it can tell us whether a unit came back on market, but not whether the same person renewed.
- A tenant transferring to a different unit within the same property appears as a turnover in this metric
- The signal is most reliable when aggregated across many units and properties (law of large numbers)
- For true tenant-level retention from your own properties, use the PMS-derived renewal metrics in the Renewals category