Most multifamily data insights fail for the same reason: every property, manager, and system defines the metric a little differently, so the numbers cannot be compared. You end up with a stack of reports and no way to tell whether a property is performing well, badly, or indifferently. Here are the 23 multifamily data insights worth standardizing across an apartment portfolio this year, with the formula behind each one.
One of the best films of 1989, “Heathers,” provided a dark view of high-school life. The popular girls ruled the school. Their “lunchtime poll” question was far from empirical — “If you inherit five million dollars the same day aliens tell the earth they’re blowing us up in two days, what would you do?” Still, it showed how responses to open-ended questions vary wildly depending upon a multitude of factors.
Data is funny that way. Unstructured data is interesting, but it does not necessarily provide sufficient guardrails to allow for any comparative analysis. There is often little context within which to determine whether something is good, bad, or indifferent. Most multifamily operators like to look at things slightly differently, which is great. It also makes apples-to-apples comparison genuinely hard.
The cost shows up the moment a portfolio has more than one property management company, more than one property management system, or more than one reporting template. Occupancy gets computed on a different denominator. Renewals get counted on a different trigger date. Concessions are netted against rent in one ledger and booked as an expense in another. The result is multifamily data insights that look authoritative and cannot be added together.
Standardizing the definition first — then the calculation, then the report — is what turns a pile of exports into portfolio-level benchmarking that anyone will actually trust.
What follows is our list of 23 multifamily data insights for efficiently operating apartment properties, grouped into eight categories. Happy New Year, and here is to a data-driven 2023 for everyone.
The first six multifamily data insights answer two questions at once: are the units full, and are they full at the right price? Physical occupancy alone answers neither. That is why these six are the core of any leasing scorecard.
The percentage of residents that choose to stay when their lease expires. It is an indicator of whether the property is being successful at keeping residents. Formula: (renewing residents at the end of the period ÷ total residents at the beginning of the period) × 100 = % retention rate.
Physical occupancy shows whether a property is physically occupied with residents. Economic occupancy shows the percentage of actual rents collected compared to what could be collected if the building was fully leased at market rents. It indicates whether the property is keeping rents close to market and what kind of concessions are being provided. In essence: are the units being filled and priced to their full potential? Formula: total rent actually collected ÷ gross potential rent = % economic occupancy.
The leased percentage shows the actual availability at the property. A unit can be vacant but have a lease in place. So the leased percentage is a better indicator of the real state of leasing efforts and the property’s true exposure to vacancy. Formula: (units with a lease in place, whether occupied or not, + offline units) ÷ total unit count = % leased units.
An indicator of whether new leases are renting above or below the average rents in place. It shows whether rents to new residents are trending up or down. Formula: total of all rents in place ÷ total rent of all newly signed leases during a set period = % new lease trade-out.
An indicator of whether renewed leases are renting above or below the average rents in place. It shows whether renewal rents are trending up or down. Read alongside the new lease trade-out, it tells you whether renewals are priced against the same market as new leases. Formula: total of all rents in place ÷ total rent of all renewed leases during a set period = % renewal lease trade-out.
An indicator of whether rents are being paid in a timely manner. It shows the percentage of total rent revenue that is delinquent. Formula: (delinquent rent payments during a set period ÷ total rents in place during the same period) × 100 = % delinquency rate.
Financial multifamily data insights track direction rather than level. A single month’s revenue tells you very little. The rate at which revenue and expenses move relative to each other tells you where net operating income is heading.
An indicator of whether the property is increasing or decreasing the amount of revenue collected over a period of time. Formula: ((revenue during one period, say February, − revenue during an earlier period, say January) ÷ revenue during the earlier period) × 100 = % revenue growth.
An indicator of whether the property is increasing or decreasing the amount of expenses over a period of time. Formula: (expenses during one period, say Q2, − expenses during an earlier period, say Q1) ÷ revenue during the earlier period × 100 = % expense growth.
A measurement of property health. It shows whether the amount of revenue going to pay expenses is increasing or decreasing. Formula: (total expenses ÷ total revenue) × 100 = % expense ratio.
These multifamily data insights borrow from subscription economics, and deliberately so. A lease is a recurring-revenue relationship with an acquisition cost, a lifetime value, and a churn reason. Treating it that way exposes decisions that a rent roll alone will never surface.
Shares the amount of an average resident’s income that is going towards rent payments. The Department of Housing and Urban Development recommends a rent-to-income ratio of less than 30%. In many major cities the average rents greatly exceed the average incomes needed to hit the 30% benchmark (SmartAsset Rent-to-Income Market Study, 2022). Formula: (total annual rent payments ÷ total annual income reported by the resident) × 100 = % rent to income ratio.
Shows the amount of marketing dollars a property spends to acquire a new resident. To make it more useful, add the average cost to turn a unit, plus the average revenue collected per day times the average days on market. That shows how much it costs to fill a vacant unit. It also shows how much rent premium is required to break even on a new lease at market versus the renewal rate on a currently rented unit. Formula: (average marketing spend in a month + average leasing commissions in a month) ÷ new leases signed in a month = average monthly cost to acquire a new resident.
Shows the average revenue a resident generates over time at a property. It feeds a little-used but very interesting benchmark: the LTV : CRA ratio. Taken from the LTV : cost of customer acquisition ratio that online companies use, it shows how much revenue the property generates from a resident relative to the amount spent to acquire that resident. Use average residency rather than average lease term, which may not take renewals into consideration. Formula: (total average rent collected + total average other revenue collected) × average resident tenancy in months = lifetime value of a resident.
Shows the reasons that residents are moving out. These can often be bucketed into controllable reasons, such as maintenance orders taking too long, and uncontrollable reasons, such as a lost job. That split shows whether more can be done to retain existing residents and reduce move outs. Formula: residents moving out for a particular reason ÷ total residents moving out = move out reason percentage.
Turnover is the one operating cycle that touches marketing, maintenance, and accounting at the same time. The turnover and maintenance multifamily data insights measure it in three separate pieces: how long the unit is exposed, how long the turn takes, and what the turn costs. That is what makes the total addressable.
A marketing metric showing how long it takes the manager to market an available unit and get a new lease in place. It varies based upon the unit type and available inventory at a property, and informs the pricing strategy for each unit. Formula: total average days of units in the status of occupied with notice to vacate + vacant = average days on market.
A maintenance metric showing how long it takes the maintenance team to complete repairs on a newly vacant unit and get it ready for a new resident. In low-vacancy markets, average turn time may highlight an opportunity to increase staffing so units become available more quickly. It may also point to prioritizing a particular unit type based upon availability and demand. Formula: total average days from a resident moving out to the unit changing to a status ready for a new resident = average turn time.
Shows the average amount of money it takes to get a unit ready to rent. This number can be used to set budgets and identify ways to reduce the cost. That has a significant impact on net operating income, particularly in buildings with a large number of units. Formula: average money spent on unit turns in a month, covering paint, flooring, cleaning and repairs, ÷ average units turned in a month = average cost to turn.
Shows how long it takes to complete a work order request on average. Depending upon the size of the property, most work orders are assigned a priority such as low, medium, or high. Work order completion times may impact resident satisfaction and retention, identify staffing issues, and inform whether larger capital improvement expenses may be on the horizon. Formula: the average response time from start date to completion date for work orders, by category of work order.
Three marketing multifamily data insights, measured together, locate the leak. A healthy lead volume with weak tours points at lead quality or follow-up speed. Strong tours with weak applications point at pricing or product. Strong applications with weak leases point at qualification criteria or prospect intent.
Shows the percentage of leads that convert to signed leases. An owner or manager can then budget marketing spend and keep enough leads in the pipeline to ensure a full building. Formula: leads in a period ÷ signed leases in a period = lead to lease conversion ratio.
Shows how many tours, or site visits, on average result in a signed lease. Conversion of tours to leases by leasing agent can also provide insight into the performance of team members. Looking at it monthly over several years can identify periods with greater or fewer tours that may require more marketing or sales effort. Formula: tours in a period ÷ signed leases = tour to lease conversion ratio.
Shows the quality of applicants. Low application to lease conversion might mean applicants were approved but did not sign, so they were not truly interested. Or it might mean applications were rejected, so they were not qualified to become a resident. Formula: applications in a period ÷ signed leases = application to lease conversion ratio.
The last three multifamily data insights point forward rather than back. One measures a commitment the industry is still learning to standardize. Two forecast what the rent roll will look like before it gets there.
A way to track whether there has been an impact to the amount of energy usage at a property. Much has been said and written about the lack of consistent and uniform ESG standards in commercial real estate. Residents, investors, and employees also care that owners and managers commit to ESG initiatives. Making commitments to ESG improvements, setting goals, and tracking incremental progress with easy-to-measure and easy-to-share insights can make ESG part of a property’s operational culture. Formula: [per building energy cost during one period, say Q1 2023, − per building energy cost during an earlier period, say Q1 2022] ÷ total building energy cost during the earlier period × 100 = % energy savings.
This insight takes the notice to vacate — future move outs — from the total leased unit count in future periods. That count already includes notice-to-vacate units that have been re-leased. The result shows whether the property is trending up or down over the next one to three months. Formula: current leased unit count − notice to vacate count over a 30, 60, or 90 day period + newly signed leases = projected lease trend.
There are less sophisticated ways of modeling the optimal lease length for a particular unit. Our company uses a machine learning algorithm instead. It considers a property’s average days on market for a unit type (demand), the number of that unit type available (supply), and the scheduled rental rate. From those it generates the likely yield premium over the standard 12-month term. Formula: projected net return premium over a 12-month lease for a particular unit, based upon average days on market for that unit type and the number of units available for that unit type.
A list of formulas is not an analytics program. Turning these multifamily data insights into something a portfolio can act on takes three things, in order.
If you adopt only a handful this year, start with economic occupancy, both trade-out percentages, delinquency, and average days on market. Those five describe the revenue side of the business almost completely. They are also the ones most often computed inconsistently between managers. Once they are stable, a consistent portfolio reporting layer can carry the rest without adding headcount.
The multifamily data insights worth tracking fall into eight categories: leasing, financial, resident, turnover, maintenance, marketing, ESG, and forward projection. At minimum that means resident retention, economic occupancy, leased percentage, new and renewal trade-out, delinquency, revenue and expense growth, expense ratio, and average days on market. The category structure matters as much as the individual metric, because it keeps leasing performance, cost performance, and resident behavior from being conflated.
Economic occupancy, the two trade-out percentages, and delinquency give the clearest read on whether units are filled and priced to their full potential. On the cost side, expense ratio and average cost to turn have the most direct effect on net operating income, particularly in buildings with a large number of units. Retention sits above all of these multifamily data insights, because every avoided turn removes a marketing cost, a turn cost, and a period of vacancy at once.
Benchmarking multifamily data insights requires a shared definition and a shared calculation before any comparison is meaningful, because unstructured data does not provide sufficient guardrails for comparative analysis. Standardize how each metric is computed, normalize the underlying property management system data to one model, then compare property to property, manager to manager, and period to period. Without that step, differences between properties usually reflect differences in bookkeeping rather than differences in performance.
These 23 insights only compare across a portfolio when the underlying data has been standardized first — that foundation is what we build.
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