Self-Storage Demand Index (SDI) and the Future of Self-Storage Asset Demand
- Loan Analytics, LLC
- 1 day ago
- 32 min read
1. Overview of the Self-Storage Demand Index (SDI)
The Self-Storage Demand Index (SDI) is a metric designed to quantify local demand for self-storage space relative to supply. In essence, SDI distills various market factors – population growth and density, housing turnover, supply of competing facilities, and consumer behavior – into a single indicator of demand strength at a given location. According to industry data, self-storage usage has grown substantially in recent years, with U.S. household penetration reaching about 12.6% by 2025(up from roughly 10–11% pre-pandemic). This reflects structural drivers of demand: more Americans are renting storage units amid life transitions (moving, downsizing, divorce, etc.) and limited living space. The SDI was introduced (as described by CoStar’s market analytics) to harness these data points and predict two critical outcomes for a new or existing facility: (a) how quickly it will lease up to a stable occupancy level, and (b) what stabilized occupancy rate it can achieve and maintain.
Construction of SDI. While proprietary in detail, SDI is built on publicly observable inputs. It incorporates demographic trends (e.g. local population and job growth, median home values, renter ratios) alongside competitive supply metrics (existing rentable square footage per capita, new facilities under construction, and barriers to entry). For example, an area with below-average storage square footage per capita and growing households would score a high SDI, signaling unmet demand. Conversely, a locale with abundant storage space (relative to population) and flat growth would score low, indicating saturation. This approach addresses the classic feasibility yardstick: historically, the U.S. average is around 6–7 square feet of storage per person, so markets above that have often been deemed “oversupplied” while those below it are “undersupplied”. However, SDI goes a step further by layering on actual usage and absorption data – for instance, occupancy rates at existing facilities and rental rate trends. This is crucial because supply per capita alone can be misleading. Industry analysts note cases where a market had only ~6 sq. ft. per capita (normally undersupplied), yet facilities were only ~75% full – indicating demand was in fact soft despite the low supply. Thus, SDI attempts to capture latent demand (or lack thereof) that pure supply metrics might miss.
Intended purpose. The SDI provides investors and developers with a forward-looking gauge of self-storage demand at the location or submarket level. A high SDI suggests favorable conditions for new development or acquisition: one can expect faster lease-up times and higher stabilized occupancy, as the local customer base is deep enough to absorb added units. A low SDI serves as a warning sign that a new facility may struggle to fill units or reach projected occupancy, absent significant marketing or discounting. In practice, lenders and equity investors use SDI (alongside other due diligence) to inform underwriting assumptions. For example, if CoStar’s SDI for a proposed site predicts above-average demand, an underwriter might assume a quicker lease-up period (perhaps 18–24 months to stabilization), whereas a low SDI might warrant assuming a prolonged lease-up (36+ months) and additional interest/carry costs. Similarly, SDI influences the expected stabilization occupancy (e.g. 90% vs. 80%). It’s important to note that SDI is an index, often normalized (e.g. on a scale like 0–100 or relative to a national baseline of 100). The higher the index value, the stronger the demand relative to supply. For instance, a booming suburban corridor with rapid population inflows might score 120 (20% above the norm), while a city with flat growth and heavy new construction might score 80 or below. By aggregating multiple demand drivers, SDI offers a single signal that stakeholders can track over time and compare across markets.
In summary, SDI is effectively a composite demand forecast for self-storage. It marries data on who needs storage (demographics) and how much is available (supply) to predict performance outcomes. The Self Storage Association’s research underscores why such an index matters: Americans are renting more storage than ever (over 52,000 facilities nationwide totaling ~2.1 billion sq. ft., or 6.3 sq. ft. per person as of 2024), yet demand is highly localized. By translating local demand/supply dynamics into a score, SDI’s purpose is to guide investment decisions and development planning with a data-driven measure of market viability.
2. What Has Worked: SDI Successes in Predicting Performance (2020–2025)
In the past five years, the Self-Storage Demand Index has proven fairly effective in certain markets and asset profiles, accurately forecasting strong performance where underlying demand fundamentals aligned with its predictions. Several trends and examples illustrate where SDI “got it right” in predicting lease-up velocity and stabilized occupancy:
High-growth Sun Belt markets meeting pent-up demand. Many Sun Belt and Southeast markets that SDI flagged as high-demand in 2020–2022 experienced performance that validated those forecasts. The Southeast region in particular saw significant absorption of new supply in line with SDI’s predictions. For instance, cities like Atlanta, GA and Miami, FL – marked by growing populations and constrained housing space – exhibited robust self-storage uptake. CoStar’s index correctly anticipated that these areas’ rapidly expanding populations (and high in-migration) would translate to swift lease-ups. Indeed, industry reports note the Southeast’s booming population and trade activity “create significant demand for storage in the region”. Facilities opened in these markets around 2020–21 often leased up to stabilization within ~18–24 months, achieving occupancy levels in the 90%+ range, which SDI had signaled as attainable. The SDI’s positive read on Sun Belt demand was driven by factors like above-average household formation, high rates of housing moves, and lower existing storage penetration (sq.ft. per capita) in fast-growing suburban corridors. These elements led to SDI predictions that aligned well with actual outcomes – short lease-up durations and healthy stabilized occupancies.
Underserved secondary cities and suburbs absorbing new supply. The index also proved accurate in many secondary markets where it identified latent demand. For example, mid-sized cities in the Midwest and South with growing suburbs but historically few storage facilities saw SDI readings indicating room for new supply – and in practice, new developments in these areas filled up as hoped. Industry data show capital began flowing to non-core and secondary regions in this period, targeting under-penetrated markets. This was a response to SDI-type analyses highlighting that many secondary cities had below-average storage per capita but rising incomes and population. A prime example is the outperformance of locations like Nashville, TN or Raleigh, NC – their combination of population inflows and limited existing storage stock yielded high SDI scores. Actual performance bore this out: projects in such locales generally hit stabilization on schedule and achieved occupancies in the high-80s to low-90s percent. In other words, SDI successfully pinpointed “demand gap” markets where new facilities could quickly capture renters without oversaturating the area.
Aligning product mix with consumer demand. Another aspect of “what worked” is how SDI insights (combined with on-the-ground knowledge) helped operators tailor asset profiles (unit mix) to meet demand. Nationwide from 2020–2025, the most popular storage unit sizes were medium units (e.g. 10×10 and 10×15), which cater to a wide range of users (2–4 rooms of furniture, common for both individuals and businesses). Many developers, guided by demand studies, increased the construction of these in-demand unit sizes and were rewarded with strong uptake. Industry analyses confirm that 10×10 and 10×15 units “top the list for individuals and businesses,” and the sector has expanded supply of those sizes because they fit a broad range of needs. This alignment of unit mix with demand meant facilities in many markets achieved occupancy targets more easily – essentially, SDI’s granular look at demand by unit type (if available) helped avoid mismatches (e.g. building too many large units in a market of apartment dwellers who mainly need 5×10 or 10×10 spaces). A concrete example: in urbanizing areas where downsizing retirees and apartment renters drive demand, SDI correctly emphasized smaller climate-controlled units, and properties that emphasized those saw occupancy ramp up smoothly, validating the index’s guidance.
Operator behavior and revenue management boosting performance. The period 2020–2025 also illustrated that sophisticated operators in high-SDI markets could surpass performance expectations, thanks to strategic leasing practices. SDI might indicate strong demand, but the magnitude of success often depended on operator execution. Large self-storage REITs (Public Storage, Extra Space, CubeSmart, etc.) demonstrated this by using dynamic pricing and aggressive marketing to fully capitalize on demand. For example, teaser rates and move-in discounts were deployed to accelerate lease-up, followed by frequent rent increases on existing tenants once facilities reached high occupancy. CBRE Investment Management observes that the best operators “employ advanced data analytics around customer behavior to optimize discounts and teaser rents against long-term retention and income growth,” often imposing existing-customer rent increases (ECRIs) above 10% per year. In high-demand markets, this strategy led to exceptional rent growth and NOI, reinforcing SDI’s positive signals. Notably, facilities managed by top-tier operators achieved higher effective occupancies and rent levels than local averages. In 2024, for instance, same-store rents at REIT-operated facilities were up +1.2% year-over-year even as many non-REIT facilities saw slight declines – an indication that skilled operators can outperform even in normalized markets. Thus, SDI predictions “worked” best when paired with strong management: the index correctly pointed to fertile ground, and operators’ execution turned that demand into realized occupancy and revenue.
Case in point – resilient high-barrier markets. Some of the most dramatic validation of SDI came in land-constrained, high-barrier markets (often coastal cities). These markets (e.g. parts of California, the Northeast) have traditionally low self-storage supply per capita but steady demand from dense populations. SDI consistently identified them as safe bets for high stabilized occupancy. During 2020–2025, this held true: places like Los Angeles, New York City, and Boston saw occupancy remain very strong (often 90–95%) and rental rates even increased, despite economic ups and downs. In the Northeast and Mid-Atlantic, operators reported strong tenant retention and pricing power in recent years. The index had anticipated this, essentially flagging that high demand plus difficulty adding new supply would keep these markets tight. Indeed, even as the industry’s pandemic surge cooled off by 2024, infill urban markets with high SDI remained near capacity with minimal concessions. A vivid example is Los Angeles, where by late 2024 REIT-owned facilities were pushing rents aggressively (achieving ~6% YoY rent growth) thanks to high occupancy. SDI had signaled such markets would be “last to soften” due to their fundamental supply-demand imbalance, and that proved accurate – they remained top performers.
In summary, SDI has been effective in predicting performance when it correctly captures real demand drivers. High-SDI markets – from Sun Belt boomtowns to undersupplied secondary cities and dense coastal metros – generally saw faster lease-ups and higher stable occupancies in 2020–2025, validating acquisitions or developments in those areas. By highlighting favorable geographies, the index guided investors toward strong opportunities (often with lease-up periods under 2 years and stabilization in the 90% occupancy range). Moreover, aligning asset strategy (unit mix, operations) with the demand profile enhanced these successes. The past five years provided many “wins” where SDI’s foresight matched market reality, giving lenders and equity sponsors confidence in using the index as part of their toolkit.
3. What Hasn’t Worked: SDI Misfires and Market Overestimations
No predictive tool is perfect – and the Self-Storage Demand Index has seen notable misses, especially in the face of rapid market shifts and factors it only partially captures. Several areas stand out where SDI’s predictions did not pan out as expected, leading to overestimation of demand, slower lease-ups, or lower occupancy than pro forma:
Oversupply and saturation risks. The most common SDI “misfire” has been in markets that became oversaturated. In the early 2020s, buoyed by low interest rates and rosy demand projections, developers added significant new supply – roughly a 10% expansion of national inventory from 2020 to 2023. SDI models based on prior demand growth often did not fully anticipate how this wave of new facilities would strain occupancy. By 2024–2025, certain metro areas (particularly in the Sun Belt) had far more storage space than their populations could readily absorb, despite decent growth in demand. For example, markets in Texas, Florida, and parts of the Southwest saw a glut of new facilities. Sunbelt metros that SDI ranked favorably at first began to show cracks as supply outpaced demand, resulting in slower absorption and rent softness. CoStar data in mid-2025 noted that while some regions held firm, “oversupplied Sunbelt metros faced rent compression”. One striking case is Austin, TX – often cited for rapid growth, Austin’s SDI was high in theory, but developers overshot. By 2025 Austin’s storage inventory had grown so much that average rents fell –3.3% year-over-year, as owners cut prices to fill units. Occupancies in such overbuilt markets languished in the 80-85% range (or worse for new properties), well below the 90%+ that SDI might have forecast under more balanced conditions. The lesson is that SDI’s accuracy suffers when a market’s supply pipeline isn’t fully accounted for or suddenly surges – an index based on historical absorption can overestimate future occupancy if developers flood the market.
Delayed absorption (longer lease-up times). Related to oversupply, many new facilities in 2022–2023 experienced slower lease-up velocity than projected, even when SDI had indicated strong demand. This became evident as interest rates rose and pandemic-era demand normalized. Industry observers noted that lease-up periods stretched longer, often 36+ months, in markets with heavy new competition. For instance, in some mid-sized cities of the Southeast where multiple projects opened concurrently, SDI had flagged robust population growth – but with 4–5 facilities leasing up at once, each one captured fewer move-ins per month than expected. A national report in 2025 pointed out that softer street rents were partly a result of “slower lease-up periods in newer builds” amid increased competition. This indicates SDI may have overestimated initial absorption pace when it didn’t fully anticipate the timing and clustering of new deliveries. The overshoot in demand forecasts meant developers had to offer steeper concessions and endure longer breakeven periods. In practical terms, many pro formas that assumed, say, 18–24 months to reach stabilization had to double the lease-up duration. This “miss” was especially evident in suburban markets where SDI identified demand but ignored that consumers had many new facilities to choose from, diluting each facility’s share. Essentially, the index was correct that demand existed, but not on the timeline predicted – absorption was simply spread thinner and slower due to concurrent supply, a nuance beyond the scope of a broad index.
Interest rate and economic headwinds reducing demand. The SDI also struggled to account for macroeconomic swings – notably the sharp rise in interest rates and cooling of the housing market in 2022–2023 – which dampened self-storage demand in ways not immediately reflected in the index. An important driver of storage usage is housing mobility (home sales, moving rates). When the Federal Reserve’s rate hikes drove 30-year mortgage rates up and home sales dropped, fewer people moved, directly translating to fewer storage rentals related to moves. By May 2025, U.S. home sales were over 6% lower year-over-year, a trend that put a drag on what had been red-hot storage demand. Many SDI projections based on prior years assumed demand would keep rising unabated (given demographic trends), but interest rate impacts created a demand “air pocket.” In effect, SDI overestimated demand in late 2022 through 2024 by not fully capturing that some traditional demand drivers (moving, new household formation) had slowed. The outcome: actual occupancies fell short of SDI forecasts in several markets. Nationally, occupancy pulled back from its pandemic peak (mid-90s%) to more normal levels. One data set from Storable shows overall industry occupancy was ~85% in mid-2024, down ~2.7 percentage points from the prior year. Many operators found that units weren’t leasing as easily as they expected in 2023, despite decent SDI scores – a reflection of demand temporarily stalling due to economic conditions. Similarly, SDI didn’t directly factor in inflation’s impact on consumers’ willingness to pay for storage; as inflation squeezed budgets in 2022, some marginal storage customers gave up their units, softening demand even in high-index areas. In summary, macroeconomic misalignment (high rates, slow home sales) revealed a gap in SDI’s predictive power, as the index didn’t dynamically adjust to these broader demand inhibitors.
Overestimation in fringe or low-population areas. There were instances where SDI signaled local demand that, in reality, proved thin. These usually involved tertiary markets or rural fringe areas. For example, an index might rate a small metro area as having “high unmet demand” if it showed few existing facilities and some population growth. In practice, some of those markets did not perform as the index predicted. Investors learned that not all square-foot-per-capita gaps equal real demand – sometimes the local culture or economic base doesn’t produce as many storage renters as the model expects. An anecdotal case: a small city with, say, 5 sq. ft. per capita (well below national average) might get a strong SDI reading. But if that city’s population has lower rental propensity or less propensity to use self-storage (perhaps due to larger average house sizes or a local storage shed tradition), new facilities might struggle. Industry experts warn that you must confirm demand on the ground: one noted that if per-capita supply is low but local facilities are only 75% occupied, “demand is soft… residents and businesses aren’t using the self-storage available”. A number of projects in tertiary markets had this experience – SDI essentially overestimated demand by not accounting for local usage habits. These misfires resulted in protracted lease-ups and occupancies topping out in the 70s or low 80s percent, far below initial targets.
High SDI but low operator performance (execution risk). Another subtle “what hasn’t worked” scenario is when SDI correctly identifies demand, but operators fail to capture it. In a few cases, SDI predicted high potential in a market, yet a particular facility underperformed due to competitive missteps or operational issues. For example, an independent operator might build in a high-demand city (per SDI) but choose a poor site location (tucked away with low visibility) or offer subpar management/service. Those facilities sometimes lagged in occupancy, effectively underperforming the SDI forecast. While this is not a flaw in the index per se, it highlights that SDI can only predict market demand, not an individual project’s execution. Thus, lenders saw instances where loans on new storage properties in “A” markets still ran into lease-up trouble because the project wasn’t competitive. This underscores that SDI’s effectiveness is limited if micro-location or quality issues hinder a property – an element outside the index’s scope.
In summary, the SDI’s misses over 2020–2025 were chiefly due to overestimations of demand or underestimations of supply-side friction. Oversupplied markets saw stabilized occupancies lower and lease-ups slower than SDI predicted. Saturation, interest rate spikes, and local market idiosyncrasies all contributed to demand shortfalls versus forecast. These misfires taught investors and lenders to use SDI with caution and context – it’s a valuable indicator, but it must be tempered by on-the-ground intelligence. For example, by late 2024 many had realized that a high SDI score in a metro with 15 sq. ft. per capita in pipeline might be an overly rosy signal. Going forward, stakeholders are adjusting their expectations (e.g. underwriting longer lease-up periods in certain markets, even if SDI historically was bullish) to account for these realities. As we turn to the next five years, these lessons will shape a more nuanced use of SDI.
4. Five-Year Outlook (2025–2030): SDI’s Evolving Relevance and Market Projections
Looking ahead, the period 2025–2030 is poised to bring a more moderate, segmented growth phase for self-storage – one where the Self-Storage Demand Index remains relevant but will need to adapt to new dynamics. We project that SDI will continue to be a critical tool for forecasting demand, but its interpretation will be refined by lessons of the past cycle. Below, we outline the outlook across different regions and property types, and we incorporate quantitative lease-up expectations, sustainability trends, and technology integration that will shape self-storage demand.
Market behavior by region: Broadly, the U.S. self-storage market is entering a period of stabilization and normalization after the tumult of the pandemic and rapid supply growth. Demand drivers – population growth, migration, household formation – remain intact, but vary widely by region, leading to divergent regional outlooks:
Sun Belt and High-Growth Markets: The Sun Belt (Southeast and Southwest states) will likely continue to lead in absolute demand growth, thanks to ongoing migration and housing activity. However, these regions also carry a legacy of heavy supply addition from 2018–2024. Over the next five years, absorption of the existing surplus is expected to gradually improve fundamentals. We anticipate that by 2030, many currently oversupplied Sun Belt metros will have worked down their supply overhang, resulting in higher occupancies than today – though perhaps still a tad lower than the national average. SDI will remain relevant here as a gauge of whether demand (which is still growing as people move south and west) finally catches up to the big inventory. Lease-up timelines in Sun Belt markets are projected to remain longer than pre-2020 norms in the near term (often 30–36 months to stabilization), then shortening as the pipeline abates. Stabilized occupancies in places like Texas and Florida might hover around the high-80s (%) in the next year or two and rise toward ~90% by 2030 as the glut eases. These markets will see SDI gradually strengthen each year if new construction stays restrained – e.g., Public Storage forecasted only ~2.5% supply growth in 2025, a sharp drop from prior years. Continued in-migration (people and businesses) into states like Florida, Georgia, and Arizona will bolster demand. However, SDI users will watch for micro-markets within these regions that might get a second wave of building; certain “hot pockets” (e.g. suburban Tampa or Raleigh) still show robust rent growth and could attract new projects, which could reset the cycle of temporary oversupply. On the whole, though, expect Sun Belt SDI readings to moderate – not the extreme highs of 2020’s unmet demand, but solid demand indices reflecting steady absorption of space.
Coastal Gateway and High-Barrier Markets: Markets with high barriers to new construction (e.g. New York City, Boston, Los Angeles, San Francisco) are projected to remain supply-constrained and demand-rich. SDI will likely continue to rate these markets strongly, though their growth is slower. In fact, some coastal metros are seeing a renewed uptick in development interest as performance has proven resilient – for instance, New York and L.A. are noted to have robust rent trends that support new projects. But any new supply in these markets will be a drop in the bucket relative to pent-up demand. We expect lease-up periods in core urban areas to remain relatively short (around ~18–24 months) even in 2025–2030, given the scarcity of competition. Stabilized occupancy in high-barrier cities should stay very high (low to mid-90s%). In fact, many urban facilities operate near full (95%+) and use price increases to manage occupancy. Our outlook is that by 2030, these markets will still be the demand anchors of the industry – SDI’s role here is confirming that any facility that can be built or acquired in such areas will enjoy durable high occupancy (limited only by operator strategy, not lack of customers). One caveat: if macroeconomic conditions soften (e.g. population stagnation in the Northeast), demand growth might slow, but given how undersupplied many dense cities still are (some Northeastern metros have <4 sq. ft. per capita), even flat demand would keep occupancy high.
Heartland and Slower-Growth Regions: Parts of the Midwest and Northeast that have stagnant or declining populations will likely see flatter demand, and thus lower SDI scores, over the next five years. Many such markets (e.g. Rust Belt cities, rural areas) have not been in the development spotlight – some are even under-supplied on paper but with weak utilization. We expect modest improvements as the overall economy stabilizes; for example, if job growth picks up slightly or housing turnover improves, storage demand could tick up. However, SDI’s relevance in these areas may diminish, because demand changes will be incremental and more driven by local economic fortunes than broad trends. Lease-up for any new project in a slow-growth market will remain challenging – likely 3+ years to reach 85-90% occupancy, if at all, because the demand pool isn’t expanding much. Lenders will likely be very conservative in such regions, requiring higher pre-leasing or refraining from new construction loans. By 2030, we forecast that many of these markets will have similar occupancy levels as today (perhaps mid-80s percent on average), unless there is consolidation where larger operators acquire older facilities and improve operations (which could artificially boost reported occupancy by taking out some capacity or driving out unmotivated mom-and-pop pricing). In essence, SDI might signal “caution” consistently here – a flat demand index – reinforcing a strategy of focusing on operations and cost control rather than expecting demand-driven growth.
Regional Summary and Saturation Outlook: The table below summarizes an outlook comparison by market type:
Table: Projected supply, lease-up, and occupancy trends by 2030 for different market types. High-growth Sun Belt areas currently have above-average saturation (often >10 sq.ft. per person) and thus slower lease-ups, but should improve by 2030 as demand catches up. Balanced secondary markets track the national norms. High-barrier coastal markets with undersupply see quick lease-ups and sustained high occupancy.
These projections assume a baseline economic scenario of moderate growth and no major shocks. If a recession occurs in the late 2020s, even strong markets could see a temporary dip in occupancy (as some tenants consolidate space to save money), but historically storage demand has been resilient in downturns – often termed “recession-proof” – because people use storage in both good times and bad. For example, during economic downturns, rental rates might plateau but occupancy often holds fairly well. SDI’s forecasting in 2025–2030 will likely incorporate such resilience factors, albeit with conservative bias after the recent cycle’s surprises.
Property type outlook: The self-storage sector itself is evolving, and demand will vary by property subtype and features:
Climate-controlled, multi-story facilities: These have become the norm in many urban/suburban areas and will continue to be in high demand, especially in regions with extreme weather or where space is at a premium. SDI for climate-controlled units is expected to remain robust; customers have shown willingness to pay a premium for climate control, and in some markets (South/Southwest) it’s practically a requirement. Over the next five years, we foresee climate-controlled facilities maintaining higher occupancies and rent growth than non-climate facilities in the same market. Indeed, recent data shows climate-controlled units led rent growth in 2025 (e.g. +0.8% YoY nationally, versus slight declines for non-CC units). Thus, SDI in 2030 will likely weight climate-controlled demand strongly when scoring top markets. In practice, investors will gravitate toward acquisitions of modern, multi-story climate facilities (often in infill or dense suburban locations), as these will have the widest tenant base (households and businesses) and the most pricing power.
Drive-up, non-climate facilities: These will continue to serve rural areas, smaller towns, and value-conscious customers. Their demand outlook is stable but not high-growth. SDI might show flat or low-demand growth for this subtype except where population is expanding and land is cheap (because new supply can easily be added). Occupancy at legacy drive-up facilities may stay in the 80s percentile in many markets, unless an operator upgrades or repurposes them. One trend to watch: some older drive-up facilities in oversupplied metros could struggle (lower SDI) and become acquisition targets for conversion to other uses or major upgrades. By 2030, we anticipate fewer purely non-climate projects being built – most new developments will include at least partial climate-controlled units, due both to consumer preference and the need to compete. SDI will reflect this as well: demand scores in warm climates, for example, will more heavily favor climate-controlled supply, effectively disadvantaging any new non-climate-only projects.
Vehicle and specialty storage: Demand for RV/boat storage and specialized storage (wine storage, high-security vaults, etc.) is a niche that’s expected to grow. Demographic trends (retiring Boomers with RVs, increase in boat ownership, etc.) support higher demand for vehicle storage. Some markets have seen explosive need for boat/RV storage and related amenities. While SDI as commonly discussed focuses on the broad self-storage demand, sophisticated investors will parse demand at this granular level too. We might see micro-indices or sub-indicators for vehicle storage demand emerging. For example, areas in the Sun Belt or near recreational lakes may have an outsized need for covered RV/boat storage. These often occupy more land and have different economics, but can be very lucrative if demand is met. Through 2030, more facilities will integrate some portion of outdoor or large-unit vehicle storage, capturing this demand. Likewise, business storage needs (for inventory, files, etc.) are rising in urban areas where small businesses lack space – SDI may start to incorporate commercial demand factors (like number of small businesses in an area). Facilities offering climate-controlled units for inventory storage, e-commerce stock, medical or pharmaceutical supplies should see strong utilization. The IBISWorld analysis points out that many online retailers and businesses now rent storage units as a cost-effective mini-warehouse, a trend likely to continue. So, the outlook is favorable for properties that can cater to these segments (e.g. via drive-in units, 24/7 access, climate control, and security for high-value items).
Technology integration: By 2030, technology will be even more deeply woven into self-storage operations, and this will affect demand in two ways: enhancing customer experience (thus drawing more demand) and improving operator efficiency (thus sustaining supply growth without sacrificing service). The ongoing trend of “smart” storage facilities will accelerate. For example, mobile apps for unit access, digital leasing, and remote monitoring are quickly becoming standard. Extra Space Storage’s recent partnership to implement mobile access (digital key via smartphone) nationwide is a bellwether. Field trials have shown 99.8% reliability for these smart-lock systems. This tech not only appeals to tech-savvy customers (who prefer the convenience of phone-enabled entry and account management) but also reduces the need for on-site staffing, enabling unmanned or semi-unmanned facilities in more locations. Over the next five years, SDI might indirectly factor in tech integration by recognizing that facilities with superior tech and automation can capture demand more effectively (customers might choose a high-tech facility over a dated one, all else equal).
Additionally, dynamic pricing algorithms and revenue management software will grow more sophisticated. We expect the major operators to fine-tune yield management on a per-unit basis, similar to airlines and hotels. Already, larger operators are “refining dynamic pricing and unit-level revenue management” to optimize occupancy vs. rate. By 2030, even mid-size operators will likely use such tools (often licensed from technology vendors). This means rental rates will adjust more quickly to demand signals, and SDI could even be used in real-time pricing: for instance, if SDI indicates rising demand in a submarket, algorithms may proactively raise rents or reduce discounts. For consumers, this might mean more price fluctuation, but for the industry it means efficiently balancing occupancy. Importantly, technology may allow operators to maintain slightly lower physical occupancy (e.g. 88-90% instead of 95%) but at much higher rates, maximizing revenue. One recent analysis found that the sweet spot for pricing is around 91–95% occupancy; beyond 95%, facilities might be leaving money on the table by not raising rents. Advanced analytics will help identify those opportunities.
Sustainability impacts: Sustainability will play a growing role in self-storage development and operations through 2030, influenced by both environmental factors and investor preferences. On one hand, climate change and weather events are directly impacting demand patterns. Regions prone to hurricanes, wildfires, or flooding may see short-term spikes in demand (e.g. after a disaster, many people need storage for their belongings). For instance, the hurricane effect in Florida has contributed to strong demand in markets like Tampa, as residents use storage during displacement or to protect valuables. Over five years, we might see SDI models incorporate climate risk data – areas with frequent disruptions might have more volatile (but sometimes higher) demand. On the other hand, climate change is also causing migration: some populations are moving away from high-risk areas, potentially reducing long-term demand there (for example, if parts of coastal Louisiana or inland California lose population due to climate or insurance costs, storage demand will follow suit). Thus, SDI outlook must be nuanced: some high-demand areas today could see demand stagnate by 2030 if climate pressures mount, whereas other regions (upper Midwest, Mountain West) might gain population (and storage demand) as climate refuges.
From an ESG perspective, investors and REITs are pushing for greener operations, which can have indirect demand effects. Extra Space and other leading operators have invested heavily in solar panels, energy efficiency, and sustainable practices. As of 2022, Extra Space had solar installations at over 55% of its owned facilities, significantly cutting grid electricity use. By 2030, it’s conceivable that a majority of large storage facilities will have solar roofs, LED lighting, smart HVAC controls, and even battery storage. These measures reduce operating costs (which helps keep rents competitive) and appeal to environmentally conscious customers and investors. Sustainable design (such as adaptive reuse of existing buildings into storage, rather than ground-up new builds) may also become more common, particularly in urban areas. We don’t expect customers to choose a storage facility solely for its solar panels, but a commitment to sustainability could bolster a brand’s reputation, indirectly supporting demand.
Finally, energy efficiency and climate control advances will allow climate-controlled facilities to operate with a smaller carbon footprint, possibly making climate-controlled space cheaper to offer. If electricity costs are mitigated by solar, operators can maintain climate settings without huge expenses. This might encourage an even greater share of units being climate-controlled, which aligns with demand trending that way (especially as climate change makes temperatures more extreme).
In summary, 2025–2030 will likely be a period of steady but segmented growth for self-storage. SDI will remain a key indicator, but users will interpret it with more sophistication, accounting for regional saturation, tech-enabled operational gains, and sustainability factors. We anticipate national occupancy hovering around 90% for stabilized facilities (barring economic upheaval), with lease-up times gradually converging to around 24 months in most markets as supply and demand find equilibrium. Markets that are today struggling (oversupplied) should see improvement as construction has throttled back – e.g., new supply in 2025 is projected at only ~20 million sq.ft., a sharp drop from 2024’s 59 million sq.ft. This pullback in development is a healthy sign for the next five years. By 2030, the industry will be more analytics-driven, technologically advanced, and sustainability-minded, and SDI’s role will be as a refined signal that incorporates these dimensions to guide investment strategy.
5. Implications for Lenders and Private Equity Investors
For U.S.-based lenders and private equity (PE) investors in self-storage, the evolving dynamics of SDI and market demand carry several important implications. This final section translates the above analysis into actionable considerations for underwriting, acquisitions, risk management, and strategy segmentation:
Using SDI to inform underwriting: Lenders and investors should use SDI as a quantitative compass when evaluating deals – but with prudent adjustments. An SDI-informed underwriting strategy means aligning pro forma assumptions (lease-up period, rental growth, stabilization timing) with the demand signal the index provides. For example, if a particular submarket’s SDI is high (indicating robust unmet demand), an underwriter might get comfortable with a shorter interest reserve on a construction loan or a quicker ramp-up in the cash flow model. Conversely, if SDI is low or middling, it’s wise to underwrite more conservatively – assuming longer lease-up, more free rent/concessions, and perhaps requiring higher debt-service coverage initially. Crucially, lenders should stress test deals for scenarios where SDI might be overly optimistic. This could involve asking: What if lease-up takes 2x as long as the SDI baseline suggests? Can the project/service the debt in that case? As we saw, some markets needed 36+ months to stabilize rather than the 18–24 months originally forecast. Incorporating those lessons, underwriters can build in extra cushion. Additionally, SDI can guide market selection during underwriting due diligence. If an investor is considering a portfolio acquisition across multiple cities, they might weight their pricing or required cap rates based on each market’s SDI – paying a premium (and accepting tighter spreads) in high-SDI markets with proven demand, while insisting on discounts or higher yields in markets where SDI is weaker (reflecting higher risk). In effect, SDI becomes one input to risk-based pricing.
It’s also worth noting that occupancy and rent assumptions in underwriting should mirror SDI’s outlook. For instance, if SDI suggests a stabilized occupancy of ~88% in a given area (perhaps due to slightly high supply), underwriters should not assume 95% occupancy at stabilization simply because that’s industry “peak” – that mismatch could lead to overestimation of NOI. Historical data shows that a market where storage occupancies are lingering around 75–80% signals weak demand that won’t support aggressive rent increases or quick fills. As such, credit officers and investment committees will scrutinize whether the business plan’s assumptions align with demand reality indicated by SDI and actual comps. Deals with SDI-aligned underwriting (realistic occupancy and absorption matching the index) are more likely to withstand diligence and perform as expected.
SDI as a timing signal for acquisitions: Private equity investors can leverage SDI trends to optimize when to enter or exit a market. A rising SDI in a market (especially if it’s coupled with limited new supply) can be a green light to acquire assets or start developments before rent growth accelerates. Essentially, SDI can serve as a leading indicator – if the index is improving quarter over quarter, it suggests demand is building (or supply easing), so an acquisition at that stage could allow the investor to ride the upswing of occupancy or rents. Conversely, if SDI begins to decline in a market (e.g., due to a spate of new openings or economic slowing), it might be a cue to hold off on new acquisitions or even consider selling a marginal asset before performance deteriorates.
For example, a PE fund might monitor SDI across its target markets and notice that a city’s score has fallen from, say, 105 to 95 over a year due to an uptick in supply. That could influence them to pause acquisitions there or demand a price reduction reflecting potential softer income. On the flip side, if another market’s SDI jumps from 90 to 110 after a big demographic gain and no new projects – an indicator that occupancy will tighten – they might fast-track acquisition efforts in that market, anticipating they can underwrite rent growth more confidently. Several investors also use SDI to identify seasonal entry points: storage demand often has seasonality (peaks in summer moving season). If SDI (and occupancy data) is expected to hit a low at a certain time of year, an acquirer could negotiate purchase then, with the knowledge that performance will naturally uptick in the following season. Overall, by treating SDI as an analog to an economic barometer, investors can better time their capital deployment.
Furthermore, SDI can help in deciding development vs. acquisition timing. If SDI indicates demand will be strong 2-3 years out in a certain under-supplied submarket, a developer might start a project now (since that’s roughly the timeframe to deliver a new facility), confident that by opening, the demand will be there. If lenders see that a proposed construction’s delivery will coincide with an SDI peak or rising phase, they may view the deal more favorably, since lease-up risk is mitigated by timing the market cycle.
Identifying over-supply pockets and tenant churn risk: One of the valuable uses of SDI for risk management is pinpointing where local pockets of oversupply exist or are forming – these are areas prone to price wars, high tenant churn, and weaker financial performance. Lenders, especially those with portfolios of storage loans, can use SDI heatmaps to monitor their exposure. For instance, if a particular city or submarket shows an SDI drop (indicating worsening supply/demand balance), that could presage problems like tenant churn (tenants moving out due to new competitor discounts, etc.) and vacancy pressure for facilities in that area. In practice, a lender might flag loans in markets with SDI below a certain threshold for closer watch or require sponsors to provide extra updates. Similarly, PE owners can allocate resources (marketing dollars, retention programs) to facilities in markets where SDI is weak, knowing those properties are at greater risk of move-outs and rent concessions.
SDI also helps investors avoid overpaying for assets in saturated markets. If a seller pitches a self-storage property based on trailing 12-month stabilized occupancy, but SDI data shows multiple new projects will come online nearby (dropping the local demand index), the buyer can anticipate that occupancy or rates may fall. That insight allows them to negotiate price or walk away. We saw earlier that oversupplied markets like parts of the Sun Belt have experienced rent declines and higher operating costs – clearly not ideal for owners. SDI essentially provides a heads-up on such conditions. In underwriting, one might incorporate higher vacancy loss or marketing expense for a low-SDI market to reflect the likely churn and competition.
For tenant churn specifically, a robust SDI (strong demand) often means tenants stick around longer and absorb rent hikes (because alternatives are few or equally full). In low-demand environments, tenants can be fickle – they might shop around for better deals or simply vacate if rates rise. Metrics back this: high occupancy (90%+) markets give operators pricing power, whereas markets at 75% occupancy see operators “fight for every customer” with discounts. Lenders and owners should thus correlate SDI with expected tenant retention. High churn risk (from oversupply) will affect cash flow stability and potentially loan default risk or investment return volatility, and should be factored into covenants or reserves.
Risk-profile segmentation (Core vs. Value-Add vs. Opportunistic): Self-storage investors typically classify deals as core (stable income, low risk), value-add (some upside via improvements/lease-up), or opportunistic (development or heavy lease-up risk). SDI can be a key tool in segmenting and strategizing across these risk profiles:
Core investments: For core assets – typically stabilized properties in top-tier markets – SDI can help confirm that the market’s demand fundamentals will support continued high occupancy and steady rent growth. Lenders and core buyers will prefer markets with high SDI and low volatility for core deals. These are often the dense, undersupplied areas or places with very durable demand drivers (e.g. a city with multiple universities, constant housing turnover, etc.). A core acquisition in a market where SDI is consistently, say, 110+ and has minimal new supply pipeline is a relatively safe bet: underwritten cash flows are likely to be met or exceeded. As Trepp’s analysis noted, “stabilized assets in dense, demographically resilient markets are likely to outperform” – essentially the profile of core assets and precisely the markets where SDI would be strong. Lenders might offer more favorable terms (higher leverage, lower rates) for such deals, viewing them almost like bond substitutes due to reliable occupancy. The implication is that core investors can lean on SDI to validate long-term hold assumptions (low vacancy, moderate annual rent bumps) and to screen out assets that might look core-like now but are in weakening markets.
Value-Add investments: These often involve buying an underperforming facility (maybe occupancy 60–70%) and bringing it up to market performance. Here, SDI is crucial to distinguish whether underperformance is due to poor management (which can be fixed) or fundamentally weak demand (which is a much tougher fix). If SDI is strong for that market, a value-add investor can be confident that with better operations (marketing, security upgrades, perhaps expansion or climate-adding), the asset can lease up to the 90% range. We saw earlier that some facilities lag simply because of operator execution despite strong markets. PE groups in the storage space routinely look for those mismanaged “mom and pop” facilities in high-demand locales – SDI helps pinpoint markets where such opportunities exist by highlighting strong demand not fully tapped by existing competitors. On the other hand, if SDI is low, a half-empty facility may be a value trap – no matter what the new owner does, they might never get above, say, 80% occupancy because the customers just aren’t there. So SDI can make or break the case for a value-add acquisition. From a risk perspective, lenders financing value-add plays will likely require evidence via SDI or similar studies that demand can support the occupancy gains in the business plan. This could be part of the feasibility/market study presented.
Value-add deals will also consider unit mix changes or expansion – SDI by unit type can inform those decisions. For example, if demand for climate-controlled 10x10s is high (index-wise) but the current facility has mostly drive-up 5x5s, the value-add strategy might involve converting or reconfiguring units to match demand. In sum, SDI guides where the upside is real versus where a facility is underperforming for structural reasons.
Opportunistic (development/new lease-up) investments: These are the highest risk and where SDI is perhaps most directly applied. When taking on a ground-up development or a major expansion, investors and lenders will look to SDI to justify that the market can absorb new supply on the timeframe needed. As discussed, many opportunistic deals in early 2020s faltered in oversupplied markets – a scenario to avoid going forward. Thus, an opportunistic project really needs a bullish SDI and ideally a forecast of improvement (like a growing population, a closure of a competitor, etc.) by the time the project delivers. Equity investors might target, for instance, a fast-growing suburban corridor with a current SDI of 120 and projected to remain high because no other facility is in a 5-mile radius – a classic development sweet spot. They would then structure the deal knowing lease-up still carries risk, but at least the underlying demand is there. Lenders for such projects will likely require lower leverage and strong guarantees unless the SDI and feasibility analysis are overwhelmingly positive.
Opportunistic funds also sometimes intentionally go into somewhat oversupplied markets but at the bottom of the cycle – e.g., building when everyone else has stopped, betting on the long-term. In those cases, SDI might be low at the moment but projected to rise as the market normalizes. It’s a risky play: essentially contrarian development. Only very experienced sponsors with strong capitalization would do this, and they’d price the risk accordingly (higher return requirement). As Trepp’s commentary suggests, newer deliveries in saturated regions face valuation and leasing pressure, meaning opportunistic investors should demand a significant discount or avoid those scenarios. It reinforces the notion that just because you can build it doesn’t mean they will come – SDI must indicate they will come, or the project likely won’t meet pro forma.
In terms of portfolio strategy, PE firms will use SDI to balance their holdings across these profiles. For example, they might keep a core asset in a top market for stability (SDI very high, low risk), have a few value-add deals in up-and-coming mid-tier cities (SDI good and improving), and maybe one or two developments in pipeline in clearly under-supplied pockets. If SDI data starts to warn that one segment (say, developments in the Southeast) is becoming riskier due to falling demand index, they can pivot to focus more on acquisitions or on different regions.
Incorporating SDI into risk monitoring: Both lenders and equity owners should integrate SDI into ongoing asset management and risk monitoring. For lenders, this could mean tracking the SDI (and underlying metrics like occupancy, rent growth) for each market where they have exposure, perhaps as part of quarterly loan reviews. If a loan is in a market where SDI drops significantly (due to a new competitor or local economic changes), that loan might be flagged for closer attention – maybe an updated appraisal or borrower discussion is warranted. For investors, if a market’s SDI deteriorates, they might accelerate capital improvements or step up advertising to defend market share, or conversely, consider selling if they believe the trend is secular.
Identifying market exit points: We touched on timing entry, but similarly for exits – a sophisticated owner might decide to sell an asset when the SDI is near peak and before any forecasted softening. This way, they sell on strong trailing numbers and a rosy market narrative. The buyer, not looking at SDI, might pay based on current occupancy/rent, not realizing a flood of supply is coming. In essence, SDI can give savvy investors an informational edge to sell high, buy low in the self-storage cycle relative to less informed players.
Finally, communication with capital partners: Many lenders and institutional investors (like pension funds investing in a storage fund) are increasingly data-driven. Sponsoring a deal with clear SDI-supported evidence can increase confidence. Sponsors should be prepared to discuss how SDI informs their strategy – e.g., “We’re focusing on these five MSAs because their demand index is 120 and supply pipeline under 2% of inventory, indicating room for growth”. Such a rationale resonates well. Likewise, noting that “we are steering clear of City X for now, given its SDI fell below 90 due to oversupply” demonstrates prudent risk management.
In conclusion, for lenders and private equity players, the SDI is a powerful signal – but not a sole answer. It should be used as part of a mosaic of information. When employed wisely, SDI can enhance underwriting discipline, sharpen market timing, and improve risk-adjusted returns by avoiding pitfalls of oversupply and anticipating demand shifts. The key implications boil down to this: align your strategy with the demand realities SDI reveals, but always overlay judgment and local insight. Those who heed the index – yet remain agile to adjust when reality deviates – will be best positioned to capitalize on the self-storage sector’s opportunities while mitigating its risks. As the self-storage industry moves further into a data-driven era, SDI (and similar analytics) will become ever more integral in separating the winners from the also-rans in lending and investing.
Sources:
Loan Analytics database.
SpareFoot Storage Beat – Self-Storage Industry Statistics (2025 update).
Multi-Housing News & Yardi Matrix – Self Storage Market Trends (2025).
StorTrack Data Snapshot (2025).
Inside Self-Storage Insights.
CBRE Investment Mgmt. “Self Storage Investing: Unpacking a Sector” (Nov 2024).
TreppTalk – Self-Storage Market Outlook (Sept 2025)