自助仓储行业研究：推出搬家费率爬虫工具

Introducing our move-in ratescraper Below we discuss quarterly move-in rate estimates, theimportance of move-in rates as an indicator of demand, andour methodology. Back-testing indicates our scraper isdirectionally accurate and we discuss the nuances ofinterpreting the data. CUBEOVERWEIGHTU.S. REITsNEUTRALPrice TargetUSD 46.00Price (08-Jul-26)USD 39.94Potential Upside/Downside+15.2%Source: Bloomberg, Barclays Research EXROVERWEIGHTU.S. REITsNEUTRALPrice TargetUSD 172.00Price (08-Jul-26)USD 143.99Potential Upside/Downside+19.5%Source: Bloomberg, Barclays Research Forecasts Scraper dataOur forecasts are based on a linear model using historicalmove-in rate growth in conjunction with scraped data fromeach company's website. For CUBE, we estimate thatmove-in rates increased +2.2% y/y in 2Q26, acceleratingfrom +0.9% reported in 1Q26. For EXR, we estimate that move-in rates increased +0.7% y/y in2Q26, slightlysofterthan the +2.4% y/y reported in 1Q26. For PSA, we estimate that move-inrates declined -3.5% 2Q26 vs. the -2.4% y/y reported in 1Q26. PSAEQUAL WEIGHTU.S. REITsNEUTRALPrice TargetUSD 349.00Price (08-Jul-26)USD 320.77Potential Upside/Downside+8.8%Source: Bloomberg, Barclays Research The "Barclays estimates" in the charts below reflect our models' output based on the scraperdata that was available at the time. These estimates correct for certain biases in the scraperdata including unit mix and frequency of data collection. We've also attached a spreadsheetwith the raw data. U.S. REITsBrendan Lynch, CFA+1 212 526 9428brendan.lynch@barclays.comBCI, US Annabelle Ayer+1 212 526 7387annabelle.ayer@barclays.comBCI, US Eileen Gao+1 212 526 7836eileen.gao@barclays.comBCI, US Data Science & Applied AITroy Li(iv)+1 212 526 1825troy.li@barclays.comBCI, US Importance of move-in rates Move-in rates provide real-time insight into supply/demand dynamics.As self-storageoperators adjust prices for each available unit daily, scraped move-in rates shed light on currentpricing trends. Further, these are a leading indicator of future same-store revenue growth. Dueto factors such as churn, occupancy gain/loss, and existing customer rate increases (ECRI), aninflection in move-in rates typically precedes an inflection in SSRev by about six months. Higher move-in rates also enable higher ECRIs(existing customer rate increases). If thespread between move-in rates vs. in-place rates becomes too wide, existing tenants becomeless willing to accept rate increases – customers could move to a cheaper unit in a competingfacility (assuming local pricing dynamics are consistent among operators). Recent roll-downs ofexiting customer rates vs. move-in rates of 30%-40% are the widest in years. High-teens are amore reasonable spread for sustainable same-store revenue growth, in our view. However, move-in rates are only part of the equation.Occupancy, churn, and ECRIs are alsocritical components of same-store revenue growth. •Occupancy is equally as important as pricing for revenue growth. Operators are generally•agnostic between renting more units at a lower price vs. fewer units at a higher price so longas total revenue is maximized. Both occupancy and rate reflect changes in the broadersupply/demand environment. •Reducing churn is a key element of improving same-store growth because in-place customers•are almost always paying higher rents than their replacements would. Based on customer exit surveys, churn due to price is relatively low as companies can delay or decrease priceincreases for customers that complain. Roughly 15%-20% of customers churn per quarter. •ECRIs have gained importance as customers increasingly rent digitally and as our coverage's•data collection and pricing algorithms have become more sophisticated. Online rentalsenable customers to more easily compare pricing across facilities, putting downwardpressure on move-in rates. As a result, operators use ECRIs to recover rent from the lowerinitial rent. The frequency and magnitude are important levers to maximize revenue. Dataand technology enable operators to be more dynamic with ECRIs and better predict howmuch they can increase rent without causing the customer to move-out. Years ago, the firstrate increase might comeafter12 months and reflect inflation-plus. Now the first rateincrease commonly comes in month five, and could be 30%+, with a second increase comingwithin another six months. Differencesin reported metrics vs. scraped data 1) Companies' reported move-in rates are based on actual rentals vs. scraped data whichreflects advertised prices.This leads to severaldifferences,including: •Self-storage operators usedifferingpricing structures fordifferenttypes of rentals, but•scrapers only collect data for online customers. Roughly one-third of rentals are walk-ins, sothose are not reflected in online rates (walk-in rentals are generally higher than the websiterate, because comparability is moredifficult).Scrapers also typically exclude otheroff-site