Chart unavailable offline — the figures are in the table and KPIs on this page.

`; return; } try{charts[k]=new Chart(c,cfg);}catch(e){console.error('chart '+k+': '+e.message);} } const $=id=>document.getElementById(id); const money=n=>'$'+Math.round(n).toLocaleString(); const k$=n=>'$'+(n/1000).toFixed(1)+'K'; /* ==================== GLOSSARY ==================== */ const G={ /* --- rate drivers --- */ fdemand:['Forecast demand','How many people the model expects to shop for this unit type in your trade area over the next 30–60 days, from Marketplace search volume, website sessions and seasonal patterns.','Positive when demand is building, negative when it is fading. This is forward-looking — it is the driver that separates revenue management from reacting to last night’s occupancy.'], occband:['Occupancy band','The occupancy threshold this unit type currently sits in. Bands are 80 / 88 / 93% — above 93% you can push rate, 88–93% hold, 80–88% watch, below 80% the unit type needs demand not price.','High occupancy means scarcity, so the driver pushes price up. Low occupancy pushes it down.'], attrprem:['Attribute premium','The extra amount shoppers actually pay for a physical feature — climate control, drive-up access, ground floor, 24-hour access.','Measured from live Marketplace conversion at different prices, not inferred from your own rate history. If your price does not already include the premium, the agent adds it.'], season:['Seasonality','The month-of-year adjustment for this market, built from 24 months of demand history. Self-storage peaks in late spring and summer.','Positive heading into peak season, negative heading out of it.'], compos:['Competitive position','Where your current price sits relative to the aggregated market median for this unit type.','Below market pushes price up; above market pushes it down. Built from public rate data aggregated across the comp set — never an identifiable competitor’s rate.'], stale:['Unit staleness','How many days since a unit of this type last rented. A long gap means the price is not clearing the market.','Always a downward driver, and it grows the longer the unit sits. This is the agent’s check against over-pricing into an empty unit.'], scarcity:['Unit-mix scarcity','How few units of this type are left to rent. Two units left in a popular size is pricing power; thirty is not.','Positive when availability is scarce relative to demand.'], vacdepth:['Vacancy depth','The opposite of scarcity — how much unrented inventory of this type you are carrying. Deep vacancy is a strong signal the price is too high.','Always a downward driver.'], ranksup:['Rank suppression','Your Marketplace sort-order rank is low enough that shoppers are not seeing this unit type at all.','When this driver appears, price is not the binding constraint — visibility is. The agent will hold rate and send you to Demand Optimizer instead.'], dpool:['Demand pool','The count of shoppers currently in-market for this unit type in your trade area, across Marketplace, organic, and paid channels.','A large pool against high vacancy means you have a conversion or visibility problem, not a pricing problem.'], /* --- rate concepts --- */ street:['Street rate','The rate a brand-new tenant pays today. Also called the move-in rate.','This is what Rate Optimization changes. It only affects new move-ins — existing tenants keep paying their in-place rate until they turn over.'], inplace:['In-place rate','What your current tenants are actually paying. Almost always lower than the street rate, because rates rise over time but existing tenants only move up through rate increases.','The gap between in-place and street is what ECRI exists to close.'], webrate:['Web rate','The rate shown online or through Marketplace, usually set slightly below the standard rate to win price-sensitive online shoppers.','Setting web below standard is a conversion lever, not a discount — walk-ins still pay the standard rate.'], rackrate:['Standard / rack rate','The published list rate for a unit type before promotions or optimization.','The reference point everything else is measured against.'], rateindex:['Rate index','Your average rate as an index against the market median, where 100 = exactly at the median.','94 means you price 6% under the market. Below 100 persistently is unrealized rate; well above 100 with weak occupancy is over-pricing.'], pctile:['Percentile','The share of comparable facilities priced below you. The 44th percentile means 44% of the comp set is cheaper than you and 56% is more expensive.','Different from the position bar, which shows where your rate falls between the cheapest and most expensive comp. A skewed market can put those two numbers in different places.'], mktmed:['Market median','The middle rate for this unit type across the comp set — half the comps are above, half below.','Median rather than average, because a single very expensive facility should not drag the benchmark.'], compset:['Comp set','The group of competing facilities a benchmark is built from. This one is 11 facilities in the Encinitas submarket, using publicly displayed rates only.','Must clear the cell thresholds before anything publishes: at least 5 distinct operators, at least 10 facilities, no operator over 20% of the weight.'], steady:['At steady state','The revenue difference once this unit type has fully turned over at the new rate — roughly 14 months at your current length of stay.','A street-rate change only reaches new move-ins, so month one is small. Steady state is the number that belongs in an annual plan.'], occafter:['Occupancy after','The occupancy the model expects once the new rate settles in. Raising price costs you a little occupancy; cutting it buys occupancy back.','This is why the agent compares total revenue rather than rate alone — a higher rate on fewer units can be worse.'], /* --- occupancy --- */ physocc:['Physical occupancy','The share of units that are rented, regardless of what they pay.','The number operators quote, but on its own it hides whether the rented units are priced well.'], econocc:['Economic occupancy','Rent actually collected as a share of what you would collect if every unit were full at street rate.','Always lower than physical occupancy. The gap between the two is the combined cost of vacancy, discounts and in-place rates lagging street.'], daysfill:['Days to fill','Average days a unit of this type sits vacant before it rents.','A rising number with flat demand is the earliest signal that price is above what the market will clear.'], avgstay:['Length of stay','How long the average tenant stays before moving out, derived from move-outs against occupied units.','Drives both lifetime value and how fast a street-rate change reaches the whole cohort.'], /* --- ECRI --- */ ecri:['ECRI','Existing Customer Rate Increase — raising the rate on tenants already in place.','The largest single lever on tenant lifetime value, and the lowest-risk one in this product because it uses only your own tenant data, never competitor or market data.'], cohort:['Cohort','A group of tenants banded by how long they have been with you. Tenure is the strongest predictor of both how far below street they sit and how they react to an increase.','Longer-tenure tenants are usually the most underpriced and the least likely to leave.'], gapstreet:['Gap to street','How far below the current street rate a cohort is paying.','A −20% gap means those tenants pay 20% less than a new tenant would today. That gap is the capture opportunity.'], acceptance:['Acceptance rate','The share of increases that stick — the tenant stays and pays the new rate.','Modeled from your own increase history. Acceptance falls as the increase gets larger, which is what bounds the recommendation.'], churnrisk:['Churn risk','The modeled probability a tenant moves out because of the increase, from tenure plus behavioral signals like autopay, payment history and gate activity.','Rises faster than linearly with the size of the increase.'], churnceil:['Churn ceiling','The maximum blended churn risk the agent is allowed to accept. Set at 2.5% here.','This is the guardrail that stops the recommendation, not the math. Revenue keeps rising past it — the ceiling is a deliberate choice to protect the tenant base.'], guardrail:['Guardrail','A hard limit the agent operates inside — maximum change per cycle, minimum months between increases, notice period, churn ceiling.','Anything that breaches a guardrail is held for human review rather than scheduled. Guardrails are also the compliance record.'], behsig:['Behavioral signals','Non-price data that predicts how sensitive a tenant is: autopay enrollment, payment history, gate access frequency, app usage, whether they accepted a prior increase.','A long-tenure tenant on autopay with clean payment history is far less price-sensitive than tenure alone would suggest.'], /* --- demand --- */ bidmult:['Bid multiple','What you pay for Marketplace placement, expressed as a multiple. 2.5× means you bid 2.5 times the baseline for a reservation.','The single input that drives sort-order rank, which is what actually determines how much demand you see.'], sortrank:['Sort-order rank','Where you appear in Marketplace search results. Rank 1 is the top listing.','The chain is bid → rank → reservations → move-ins. Rank is the lever that matters; the bid is just how you buy it.'], reservation:['Reservation','A shopper has picked your facility and reserved a unit, but has not yet moved in and started paying.','Roughly two-thirds of reservations convert to move-ins.'], movein:['Move-in','A tenant has signed, paid, and taken occupancy. This is the revenue event.','Move-ins divided by reservations is your reservation-to-move-in rate.'], cac:['CAC','Customer acquisition cost — total acquisition spend divided by move-ins.','Only meaningful against lifetime value. A rising CAC is fine as long as LTV:CAC stays healthy.'], ltv:['LTV','Tenant lifetime value — the total revenue a tenant generates over their stay, including ancillaries like insurance and fees.','Here: average in-place rate plus ancillary revenue, multiplied by average length of stay.'], ltvcac:['LTV : CAC','Lifetime value divided by acquisition cost — how many dollars a tenant returns for every dollar spent acquiring them.','The agent will not recommend a bid that pushes this below 4×. High coverage is a signal to bid up, not to protect CAC.'], q2r:['Quote → reservation','The share of shoppers who ask for a price and then actually reserve.','The most price-sensitive step in the funnel. When this trails the market and every other step is normal, rate is the friction.'], bidgap:['Bid gap','The difference between what REITs bid for Marketplace placement and what independents bid.','REITs bid higher because they hold market intelligence independents lack. Surfacing that intelligence is how the gap closes.'], /* --- unit --- */ revsqft:['Revenue per square foot','Monthly rate divided by the unit’s footprint.','The right way to rank unit types. A 5×5 at $48 earns far more per foot than a 10×20 at $250 — ranking by occupancy alone hides that completely.'], cug:['Custom Unit Group','An operator-defined grouping of units that pricing is set against, rather than pricing every unit individually.','Real facilities have dozens of unit types once size and attributes multiply out; CUGs are how operators keep that manageable.'], unitmix:['Unit mix','The distribution of unit sizes and attributes across the facility.','A mix that does not match local demand shows up as some types permanently full and others permanently empty — a construction problem, not a pricing one.'], /* --- compliance --- */ cbsa:['CBSA','Core-Based Statistical Area — the U.S. Census geography for a city and its commuting area. 935 of them nationally.','Our default publishing tier. A metro median sits deliberately above the 3–5 mile trade ring where facilities actually compete, which is exactly why it cannot act as a price-coordination device.'], cellthr:['Cell thresholds','The tests a benchmark must pass before it can be shown: at least 5 distinct operators, at least 10 facilities, no single operator above 20% of the weight, and statistics only — never a rate.','If a cell fails, it rolls up one geographic tier and re-tests. The tier you see is the output of this test, not a setting anyone chose.'], tradering:['Trade ring','The 3–5 mile radius inside which self-storage facilities genuinely compete for the same tenant.','This is the relevant market for antitrust purposes, which is why we publish benchmarks at a deliberately broader geography.'], hubspoke:['Hub-and-spoke','The antitrust theory behind DOJ v. RealPage: the vendor is the hub, competing customers are the spokes, and a shared pricing algorithm is the rim connecting them.','No explicit agreement between competitors is required for liability. This is the specific shape we engineer out of the product.'], provenance:['Data provenance','Where every input to a recommendation came from, and whether it was the operator’s own data, public data, or Storable first-party demand.','Recorded per recommendation. Competitors’ current nonpublic rates are never used to price a rival.'], aggtier:['Aggregation tier','The geographic level a benchmark is published at, from submarket up through CBSA, state and region.','Always labeled on screen with its facility and operator count, so you know exactly what you are looking at.'], /* --- ROI --- */ verified:['Verified impact','The measured result of an applied recommendation, after it beat a baseline over the attribution window.','Recommendations that were applied but did not beat the baseline are excluded. We do not bank impact we cannot prove.'], attrwin:['Attribution window','The 30-day period after a change during which its effect is measured against a matched-facility and forecast baseline.','Long enough to see the effect, short enough that unrelated market shifts do not contaminate it.'], tokens:['Metered tokens','The agent’s compute cost — the model reasoning behind recommendations, forecasts and explanations, billed by usage.','Only reasoning that produced a recommendation is billable.'], payback:['Payback period','How long until cumulative verified impact exceeds cumulative agent cost.','Includes the ramp, since the agent produces few recommendations in its first days.'], runrate:['Run-rate','The recurring monthly value of a change once it has fully taken effect, as opposed to what it delivered in its first month.','The right basis for annual planning.'], revpau:['Revenue per available unit','Total revenue divided by all units, rented or not.','Captures rate and occupancy in one number, so it cannot be gamed by pricing high into empty units.'] }; function g(key,label){const e=G[key];if(!e)return label||key;return `${label||e[0]}`;} function gi(key){return `?`;} /* tooltip engine */ (function(){ const tip=$('tip');let cur=null; function show(el){ const k=el.getAttribute('data-g'),e=G[k];if(!e)return; tip.innerHTML=`

${e[0]}

${e[1]}

${e[2]?`

${e[2]}

`:''}`; tip.classList.add('on');cur=el;place(el); } function place(el){ const r=el.getBoundingClientRect(),t=tip.getBoundingClientRect(); let x=r.left+r.width/2-t.width/2, y=r.top-t.height-9; if(y<8)y=r.bottom+9; if(x<8)x=8; if(x+t.width>innerWidth-8)x=innerWidth-t.width-8; tip.style.left=x+'px';tip.style.top=y+'px'; } document.addEventListener('mouseover',e=>{const el=e.target.closest('[data-g]');if(el&&el!==cur)show(el);}); document.addEventListener('mouseout',e=>{const el=e.target.closest('[data-g]');if(el){tip.classList.remove('on');cur=null;}}); document.addEventListener('scroll',()=>{if(cur)place(cur);},true); })(); /* ==================== CANONICAL DATA ==================== */ /* Single source of truth. Every facility total, occupancy figure, revenue number and rate delta below is DERIVED from this array — nothing is typed twice, so the tabs cannot drift out of sync. */ const UT=[ {k:'5x5',n:'5×5',sf:25,u:72,occ:93,cur:48,med:52,rec:54,uA:66,web:51,conf:84,stale:6,rng:[42,62],pctile:38, drv:[['occband','Occupancy band 93%',7],['compos','Below-market position',4.5],['scarcity','Unit-mix scarcity · 5 left',2.5],['fdemand','Forecast demand',2],['season','Seasonality',-1],['stale','Unit staleness 6 days',-2.5]], conv:[[46,74],[48,71],[52,64],[54,59],[58,48]], note:'Highest revenue per square foot in the facility at 93% occupancy, and still 8% under the market median. The cleanest raise you have.'}, {k:'5x10',n:'5×10',sf:50,u:104,occ:90,cur:68,med:71,rec:74,uA:92,web:70,conf:81,stale:11,rng:[58,86],pctile:41, drv:[['occband','Occupancy band 90%',5],['compos','Below-market position',3],['fdemand','Forecast demand',2],['scarcity','Unit-mix scarcity · 10 left',1.5],['season','Seasonality',-1],['stale','Unit staleness 11 days',-1.7]], conv:[[64,71],[68,66],[74,55],[79,42]], note:'Second-strongest revenue per square foot, 90% occupied and under market. Small and medium units are where the margin lives.'}, {k:'10x10',n:'10×10',sf:100,u:128,occ:76,cur:132,med:141,rec:132,uA:97,web:127,conf:79,stale:22,rng:[112,170],pctile:44, drv:[['compos','Below-market position',3.5],['fdemand','Forecast demand',2],['occband','Occupancy band 76%',-3.5],['stale','Unit staleness 22 days',-2]], conv:[[126,66],[132,59],[142,47],[152,34]], note:'Priced 6% under market — but only 76% occupied with 31 units empty. Below-market price and weak occupancy means price is not the constraint. The agent holds and points at demand.'}, {k:'c10',n:'10×10 climate',sf:100,u:64,occ:81,cur:132,med:141,rec:145,uA:51,web:139,conf:87,stale:18,rng:[118,172],pctile:44, drv:[['attrprem','Climate premium not captured',8],['compos','Below-market position',3],['fdemand','Forecast demand',2],['season','Seasonality',1],['occband','Occupancy band 81%',-1],['vacdepth','Vacancy depth · 12 available',-1.5],['stale','Unit staleness 18 days',-1.7]], conv:[[132,58],[139,52],[145,46],[152,37]], note:'Your climate units are priced identically to non-climate 10×10s, but live conversion shows shoppers pay an 8% premium for climate here. You are giving the attribute away.'}, {k:'10x15',n:'10×15',sf:150,u:60,occ:60,cur:186,med:179,rec:174,uA:43,web:169,conf:74,stale:34,rng:[158,206],pctile:58, drv:[['season','Seasonality',3.5],['fdemand','Forecast demand',2],['compos','Over-market position',-1.5],['stale','Unit staleness 34 days',-2.5],['vacdepth','Vacancy depth · 24 available',-3],['occband','Occupancy band 60%',-5]], conv:[[169,58],[174,52],[186,41],[196,31]], note:'Above market at 60% occupancy with 24 units empty and 34 days since the last rental. The price is not clearing.'}, {k:'10x20',n:'10×20',sf:200,u:52,occ:45,cur:250,med:228,rec:228,uA:30,web:222,conf:72,stale:47,rng:[196,272],pctile:74, drv:[['attrprem','Drive-up attribute premium',5],['fdemand','Forecast demand',1.5],['season','Seasonality',1.2],['vacdepth','Vacancy depth · 29 available',-2],['stale','Unit staleness 47 days',-3.5],['compos','Over-market position',-5],['occband','Occupancy band 45%',-6]], conv:[[218,54],[228,47],[240,34],[250,26]], note:'The 74th percentile on the unit type nobody wants — 29 empty, 47 days stale. This is the recommendation that goes down: the agent never protects a price at the cost of an empty unit.'}, {k:'rv',n:'RV / vehicle',sf:200,u:32,occ:34,cur:165,med:172,rec:165,uA:11,web:159,conf:69,stale:58,rng:[142,196],pctile:43, drv:[['dpool','Demand pool strong · ~25 in-market',5],['compos','Below-market position',3.5],['ranksup','Rank 5 suppresses visibility',2.5],['occband','Occupancy band 34%',-6],['stale','Unit staleness 58 days',-5]], conv:[[152,44],[165,37],[172,31],[182,22]], note:'34% occupancy with ~25 in-market shoppers and a below-market price. Cutting rate will not fix this — you are invisible at rank 5. This is a bid decision, not a pricing decision.'}]; /* derived helpers — all computed, never typed */ const occU=t=>Math.round(t.u*t.occ/100); const vacU=t=>t.u-occU(t); const dPct=t=>+((t.rec-t.cur)/t.cur*100).toFixed(1); const revNow=t=>occU(t)*t.cur; const revAfter=t=>t.uA*t.rec; const dRev=t=>t.rec===t.cur?0:revAfter(t)-revNow(t); const occAfterPct=t=>Math.round(t.uA/t.u*100); const revSqFt=t=>t.cur/t.sf; const F={ name:'Encinitas Self Storage',market:'San Diego–Carlsbad MSA', units:UT.reduce((s,t)=>s+t.u,0), occupied:UT.reduce((s,t)=>s+occU(t),0), vacant:UT.reduce((s,t)=>s+vacU(t),0), sqft:UT.reduce((s,t)=>s+t.u*t.sf,0), grossPot:UT.reduce((s,t)=>s+t.u*t.cur,0), /* every unit at street */ revAtStreet:UT.reduce((s,t)=>s+revNow(t),0), /* occupied at street */ rateAchv:0.931, /* in-place vs street */ ancillary:25, /* insurance + fees / tenant / mo */ compFacilities:11,compOperators:6, cbsaFacilities:148,cbsaOperators:34,cbsaOccMedian:81,cbsaDaysFill:17, targetOcc:85 }; F.physOcc=+(F.occupied/F.units*100).toFixed(1); F.actualRev=Math.round(F.revAtStreet*F.rateAchv); F.econOcc=+(F.actualRev/F.grossPot*100).toFixed(1); F.avgInPlace=+(F.actualRev/F.occupied).toFixed(2); F.avgStreetPerUnit=+(F.grossPot/F.units).toFixed(2); F.revPerSqFt=+(F.actualRev/F.sqft).toFixed(2); F.moveInsMo=28; F.avgStay=+(F.occupied/F.moveInsMo).toFixed(1); F.ltv=Math.round((F.avgInPlace+F.ancillary)*F.avgStay); F.targetOccupied=Math.round(F.units*F.targetOcc/100); F.netTenantsToTarget=F.targetOccupied-F.occupied; F.rateOpp=UT.reduce((s,t)=>s+dRev(t),0); F.occAfterAll=Math.round(UT.reduce((s,t)=>s+(t.rec===t.cur?occU(t):t.uA),0)/F.units*100); $('facMeta').innerHTML=`${F.market} · ${F.units} units · ${F.physOcc}% occupied`; /* ==================== ECRI DATA (tied to occupied units) ==================== */ const CEIL=2.5; const acc=p=>Math.max(60,Math.min(99,99-1.05*p)); const chn=p=>+(0.3+0.026*p*p).toFixed(3); /* cohort tenant counts sum to F.occupied; street = facility blended street rate */ const STREET_BLEND=Math.round(F.grossPot/F.units); const COH=[ {id:'c1',n:'> 24 mo',t:76,ip:96,risk:'Low',rc:'var(--good)',rec:9,last:'14 mo',elig:true}, {id:'c2',n:'18–24 mo',t:112,ip:103,risk:'Low',rc:'var(--good)',rec:7,last:'11 mo',elig:true}, {id:'c3',n:'12–18 mo',t:82,ip:109,risk:'Medium',rc:'var(--warn)',rec:6,last:'8 mo',elig:true}, {id:'c4',n:'6–12 mo',t:64,ip:114,risk:'Medium',rc:'var(--warn)',rec:4,last:'5 mo',elig:true}, {id:'c5',n:'3–6 mo',t:31,ip:119,risk:'High',rc:'var(--cooper)',rec:0,last:'—',elig:false}, {id:'c6',n:'< 3 mo',t:15,ip:STREET_BLEND,risk:'High',rc:'var(--cooper)',rec:0,last:'—',elig:false}]; COH.forEach(c=>{c.st=STREET_BLEND;c.gap=Math.round((c.ip-STREET_BLEND)/STREET_BLEND*100);}); let cohOn={c1:1,c2:1,c3:1,c4:1},useRec=true,globalPct=7; function ecriCalc(){ const sel=COH.filter(c=>cohOn[c.id]); let gross=0,tw=0,cw=0,tot=0; sel.forEach(c=>{const p=useRec?c.rec:globalPct;gross+=c.t*c.ip*p/100;tot+=c.t;tw+=c.t*p;cw+=c.t*chn(p);}); const avgP=tot?tw/tot:0,churn=tot?cw/tot:0,a=acc(avgP); return {sel,gross,net:gross*a/100,avgP,churn,a,tot}; } const ECRI_REC=(()=>{const s={...cohOn};let gross=0,tw=0,cw=0,tot=0; COH.filter(c=>c.elig).forEach(c=>{gross+=c.t*c.ip*c.rec/100;tot+=c.t;tw+=c.t*c.rec;cw+=c.t*chn(c.rec);}); const avgP=tw/tot,a=acc(avgP);return {gross,net:gross*a/100,avgP,churn:cw/tot,a,tot};})(); /* ==================== FUNNEL / DEMAND ==================== */ const FUN={searches:1240,leads:186,quotes:96,resv:41,moveins:F.moveInsMo, mktLead:15.8,mktQuote:53.0,mktResv:58.0,mktMove:70.1}; const CHAN=[['Marketplace',3360,14,'var(--good)','Efficient'],['Organic / website',1180,6,'var(--good)','Best return'], ['Paid search',3240,6,'var(--warn)','Watch'],['Other media',1100,2,'var(--cooper)','Reallocate']]; const MEDIA=CHAN.reduce((s,c)=>s+c[1],0); const CHAN_MI=CHAN.reduce((s,c)=>s+c[2],0); const BLENDED_CAC=Math.round(MEDIA/CHAN_MI); const MKT_CAC=Math.round(CHAN[0][1]/CHAN[0][2]); const AGENT_COST_MO=Math.round(F.units*0.55+150); /* bid model */ const BCURVE=[[2.0,7.0,180,1],[2.4,6.0,232,3],[2.5,5.0,MKT_CAC,2],[2.6,4.0,247,5],[2.7,3.0,255,9],[2.9,2.0,268,20],[3.1,1.0,300,24],[3.5,1.0,340,26]]; function interp(b){ if(b<=BCURVE[0][0])return BCURVE[0]; for(let i=1;i{const now=interp(2.5),rec=interp(2.9); const dResv=rec[3]-now[3], dMove=dResv*(FUN.moveins/FUN.resv); return {dResv,dMove,rev:Math.round(dMove*STREET_BLEND),cacNow:Math.round(now[2]),cacRec:Math.round(rec[2]),rankNow:now[1],rankRec:rec[1]};})(); /* conversion levers */ const LEVERS=[['web','Web rate −6%','+8.7 pts',8.7],['first','First month $1','+6.2 pts',6.2], ['hold','Hold unit 48h','+2.1 pts',2.1],['ari','Ari follow-up','+4.4 pts',4.4]]; let levOn={web:true}; function convCalc(){ const on=LEVERS.filter(l=>levOn[l[0]]),n=on.length; const dim=n>1?1-0.08*(n-1):1; const pts=on.reduce((s,l)=>s+l[3],0)*dim; const base=+(FUN.resv/FUN.quotes*100).toFixed(1); const conv=Math.min(base+pts,64); const resv=Math.round(FUN.quotes*conv/100); const mi=Math.round(resv*(FUN.moveins/FUN.resv)); const rate=levOn.web?Math.round(STREET_BLEND*0.94):STREET_BLEND; const rev=mi*rate-FUN.moveins*STREET_BLEND; return {n,dim,conv,base,resv,mi,rate,rev}; } const CONV_REC=(()=>{const save={...levOn};levOn={web:true};const r=convCalc();levOn=save;return r;})(); /* unit-mix conversion */ const MIXCONV=(()=>{const big=UT.find(t=>t.k==='10x20'),small=UT.find(t=>t.k==='10x10'); const nConv=8, newUnits=nConv*(big.sf/small.sf); const before=nConv*(big.occ/100)*big.cur; const after=newUnits*(small.occ/100)*small.cur; const capex=newUnits*2500; return {nConv,newUnits,before,after,delta:after-before,capex,payback:Math.round(capex/(after-before))};})(); /* ==================== NAV ==================== */ const ICO={bolt:'',chart:'', grid:'', check:'', shield:''}; const NAV=[ {g:'Decisions',hint:'What should I do?',icon:'bolt',items:[['overview','Overview',"What's my next best action?"], ['rate','Rate Optimization','What should each type price at?'],['unit','Unit Intelligence','Is my unit mix right?'], ['convert','Conversion','How do I convert more leads?'],['ecri','Lifetime Rate / ECRI','How do I grow the tenant?'], ['demand','Demand Optimizer','How do I get leads to my door?']]}, {g:'Insights',hint:'How am I doing?',icon:'chart',items:[['scorecard','Health Scorecard','All drivers at a glance'], ['i-occ','Occupancy','Physical & economic fill'],['i-rate','Rate & Volatility','Price level and price churn'], ['i-ecri','ECRI Capture','In-place vs. street gap'],['i-demand','Demand & Funnel','Rank, reservations, conversion'], ['i-market','Market Position','Where I sit in the market'],['i-cost','Cost & Spend','CAC, media, agent cost']]}, {g:'Portfolio',hint:'How do my sites compare?',icon:'grid',items:[['portfolio','Facility Comparison','Compare any set of sites']]}, {g:'Proof',hint:'Was it worth it?',icon:'check',items:[['roi','ROI & Token Value','Verified impact vs. cost']]}, {g:'Trust',hint:'Can I defend this?',icon:'shield',items:[['compliance','Compliance','What compliance means here']]}]; let collapsed={}; function renderNav(active){ $('navRoot').innerHTML=NAV.map((s,gi)=>{const has=s.items.some(i=>i[0]===active); return `

${s.items.map(([id,n,sub])=> ``).join('')}

`;}).join(''); } function togGrp(i){collapsed[i]=!collapsed[i];renderNav(cur);} /* ==================== ACTIONS (all impacts computed) ==================== */ const ACTIONS=[ {id:'rate',mod:'Rate Optimization',color:CLR.midnight,deep:'rate', name:`Apply recommended rates across ${UT.filter(t=>dPct(t)!==0).length} of ${UT.length} unit types`, ctx:`3 raises, 2 reductions, 2 holds · led by the uncaptured climate premium`, impact:money(F.rateOpp),impactk:`per month at steady state`,conf:83,status:'new', drivers:[['Types priced under market',`${UT.filter(t=>t.curt.k==='10x20')))} / mo from a price cut`,'var(--good)'], ['Climate premium not captured','10×10 Climate priced identically to non-climate','var(--sedona)'], ['Occupancy effect modeled','Facility moves '+F.physOcc+'% → '+F.occAfterAll+'% if all applied','var(--lake)'], ['Two holds','10×10 nets to zero · RV is a rank problem, not price','var(--muted)']], forecast:[['Δ revenue / mo',money(F.rateOpp)],['Annualized',k$(F.rateOpp*12)],['Occupancy after',F.occAfterAll+'%'],['Types changed',UT.filter(t=>dPct(t)!==0).length+' of '+UT.length]], why:'A street-rate change only reaches new move-ins, so the figure shown is the steady-state difference once each type has turned over. Attribute premiums are learned from live Marketplace conversion rather than FMS history — which is how the agent catches a climate premium the operator is giving away.'}, {id:'ecri',mod:'Lifetime Rate / ECRI',color:CLR.seaweed,deep:'ecri', name:'Apply the increase schedule across 4 eligible cohorts', ctx:`${ECRI_REC.tot} of ${F.occupied} tenants eligible · avg gap ${Math.round((F.avgInPlace-STREET_BLEND)/STREET_BLEND*100)}% to street · +4% to +9% by cohort`, impact:money(ECRI_REC.net),impactk:`per month · ${k$(ECRI_REC.net*12)} annualized`,conf:92,status:'new', drivers:[['In-place vs. street gap','−20% on the >24 mo cohort, −11% blended','var(--sedona)'], ['Predicted churn risk',ECRI_REC.churn.toFixed(2)+'% blended — inside the '+CEIL+'% ceiling','var(--good)'], ['Behavioral signals','Autopay · payment history · gate activity',''], ['Guardrail check','≤20% / 30 days · ≥6 mo between increases — passes','var(--lake)'], ['Acceptance forecast',ECRI_REC.a.toFixed(1)+'% from your own increase history','var(--lake)']], forecast:[['Gross / mo',money(ECRI_REC.gross)],['Acceptance-adjusted',money(ECRI_REC.net)],['Annualized',k$(ECRI_REC.net*12)],['Blended churn',ECRI_REC.churn.toFixed(2)+'%']], why:'ECRI compounds over a '+F.avgStay+'-month average stay and runs entirely on the operator’s own tenant data — the biggest lever on lifetime value and the lowest antitrust exposure in the product.'}, {id:'demand',mod:'Demand Optimizer · Bid',color:CLR.lake,deep:'demand', name:'Raise Marketplace bid 2.5× → 2.9×', ctx:`Encinitas · sort-order rank ${BID_REC.rankNow} → ${BID_REC.rankRec}`, impact:money(BID_REC.rev),impactk:`per month · +${Math.round(BID_REC.dMove)} tenants`,conf:82,status:'new', drivers:[['Current rank','5 — below the fold in SoCal results','var(--sedona)'], ['CAC at 2.9× bid','$'+BID_REC.cacRec+' vs. LTV $'+F.ltv.toLocaleString()+' = '+(F.ltv/BID_REC.cacRec).toFixed(1)+'×','var(--good)'], ['Demand unlocked','+'+BID_REC.dResv+' reservations → +'+Math.round(BID_REC.dMove)+' move-ins',''], ['LTV : CAC guardrail','Accretive — the agent never buys demand below 4×','var(--lake)']], forecast:[['Rank',BID_REC.rankNow+' → '+BID_REC.rankRec],['Reservations / mo',FUN.resv+' → '+(FUN.resv+BID_REC.dResv)],['Incremental tenants','+'+Math.round(BID_REC.dMove)],['CAC','$'+BID_REC.cacNow+' → $'+BID_REC.cacRec]], why:'Bid optimization is live today and is the front door to the funnel — it gets the lead to the door so rate management can convert and grow it.'}, {id:'unit',mod:'Unit Intelligence',color:CLR.aqua,deep:'unit', name:`Convert ${MIXCONV.nConv} × 10×20 into ${MIXCONV.newUnits} × 10×10`, ctx:`10×20 at 45% occupancy · 29 units empty · weakest revenue per sq ft`, impact:money(MIXCONV.delta),impactk:`per month · but ${MIXCONV.payback}-month payback`,conf:61,status:'review', drivers:[['10×20 revenue / sq ft','$'+revSqFt(UT.find(t=>t.k==='10x20')).toFixed(2)+' at 45% occupancy','var(--sedona)'], ['10×10 demand','~31 in-market, 31 vacant — demand exists','var(--good)'], ['Mix imbalance','Oversupplied large, undersupplied small',''], ['Capex required','~'+money(MIXCONV.capex)+' to partition','var(--sedona)'], ['Payback',MIXCONV.payback+' months — above the 36-month threshold','var(--cooper)']], forecast:[['Δ revenue / mo',money(MIXCONV.delta)],['Capex',money(MIXCONV.capex)],['Payback',MIXCONV.payback+' mo'],['Recommendation','Defer']], why:'The agent surfaces this as an option and then flags it against your capex threshold rather than recommending it. Positive return, but the payback is too long to fund ahead of the pricing moves above — worth revisiting if 10×20 demand stays weak.'}, {id:'convert',mod:'Conversion Intelligence',color:CLR.sunset,deep:'convert', name:'Cut web rate −6% facility-wide for 48 hours', ctx:`Quote→reservation leaking at ${(FUN.resv/FUN.quotes*100).toFixed(1)}% vs. ${FUN.mktResv}% market`, impact:money(CONV_REC.rev),impactk:`per month net · +${CONV_REC.mi-FUN.moveins} move-ins`,conf:74,status:'new', drivers:[['The leak','Quote→reservation '+(FUN.resv/FUN.quotes*100).toFixed(1)+'% vs. '+FUN.mktResv+'% market','var(--sedona)'], ['Elasticity','−6% web rate buys +8.7 points of conversion','var(--good)'], ['Volume vs. rate','+'+(CONV_REC.mi-FUN.moveins)+' move-ins at $'+CONV_REC.rate+' beats '+FUN.moveins+' at $'+STREET_BLEND,'var(--lake)'], ['CAC vs. LTV','Still well under LTV — the trade works','var(--lake)']], forecast:[['Conversion',(FUN.resv/FUN.quotes*100).toFixed(1)+'% → '+CONV_REC.conv.toFixed(1)+'%'],['Move-ins / mo',FUN.moveins+' → '+CONV_REC.mi],['Web rate','$'+STREET_BLEND+' → $'+CONV_REC.rate],['Net revenue / mo',money(CONV_REC.rev)]], why:'Rate and conversion are one decision — lower rate, more tenants, higher total revenue. The trade only works when you can see both sides at once, and it stops working the moment CAC crosses LTV.'}].sort((a,b)=>parseFloat(b.impact.replace(/[$,]/g,''))-parseFloat(a.impact.replace(/[$,]/g,''))); function statusChip(s){const m={new:['new','New'],review:['review','Review'],verified:['verified','Verified +'],applied:['applied','Applied']};const[c,t]=m[s]||m.new;return `${t}`;} function dhead(mod,q,intro,xref){ return `

Decisions · ${mod}

${q}

${intro}

${xref?`

`:''}`;} /* ==================== OVERVIEW ==================== */ function pageOverview(){ const total=ACTIONS.reduce((s,a)=>s+parseFloat(a.impact.replace(/[$,]/g,'')),0); const a=ACTIONS[0]; return `

Decisions · Overview

What is my single next best action?

The agent watches demand, pricing, competition and your tenant base, then ranks every move it could make by dollar impact. Approve, adjust, or open the full report behind any recommendation. Hover any underlined term or ? for a definition.

⚡ Single next best action

${a.name}

${a.mod} — ${a.ctx}

${a.impact}

${a.impactk}

${a.conf}%

confidence

${UT.filter(t=>dPct(t)!==0).length}

unit types affected

Where this facility stands

${g('physocc','Physical occupancy')}${gi('physocc')}

${F.physOcc}%

▼ CBSA median ${F.cbsaOccMedian}%

${F.netTenantsToTarget} net tenants to reach ${F.targetOcc}%

Monthly revenue${gi('inplace')}

${k$(F.actualRev)}

${money(F.grossPot)} gross potential

${g('econocc','economic occupancy')} ${F.econOcc}%

Open recommendations

${ACTIONS.length}

${money(total)} / mo if all applied

actions overlap — see note below

${g('sortrank','Avg. Marketplace rank')}${gi('sortrank')}

4.2

▼ REITs average 1.8

${g('bidmult','bid')} unchanged at 2.5× for 12 mo

Recommended actions — ranked by monthly impact

${ACTIONS.map((x,i)=>`

${i+1}

${x.mod}

${x.name}

${x.ctx}

conf ${x.conf}%

${statusChip(x.status)}

${x.impact}

${x.impactk.split('·')[0]}

`).join('')}

These actions overlap — do not add them up naively. Rate Optimization and Conversion both change prices on the same units, and Conversion and Demand Optimizer both drive move-ins. Scheduling all five together yields less than ${money(total)}; the agent de-duplicates the overlap when you build a plan.

Every recommendation opens a report with two parts: the supporting data that justifies it and the forecast if you apply it. Nothing changes until you approve — the agent runs in recommend mode. All figures are illustrative sample data computed from one canonical facility dataset.

`;} function openReport(id){const a=ACTIONS.find(x=>x.id===id);if(!a)return; $('dMod').textContent=a.mod;$('dTitle').textContent=a.name;$('dCtx').innerHTML=a.ctx; const drv=a.drivers.map(d=>`

${d[0]}${d[1]}

`).join(''); const fc=a.forecast.map(f=>`

${f[0]}

${f[1]}

`).join(''); $('dBody').innerHTML=`

Supporting data — why the agent recommends this

${drv}

${a.why}

Forecast if you apply

${fc}

Illustrative sample data. Forecast measured against a matched-facility baseline over a 30-day ${g('attrwin','attribution window')}.

`; $('scrim').classList.add('open');$('drawer').classList.add('open');} function closeReport(){$('scrim').classList.remove('open');$('drawer').classList.remove('open');} /* ==================== RATE OPTIMIZATION ==================== */ let rSel='c10',rApp={}; function pageRate(){ return dhead('Rate Optimization','What should each unit type price at to win the tenant?', `A facility-wide pricing review. The agent scores every unit type, ranks them by dollar impact, and explains each recommendation down to the driver. Work the queue top to bottom, or apply the whole set at once — nothing moves until you approve it.`, ['i-rate','Rate & Volatility']) +`

Pricing queue — all ${UT.length} unit types, ranked by impact

Why the agent compares revenue, not rate. Raising price costs a little occupancy; cutting it buys occupancy back. Each row models both sides — ${g('occafter','the occupancy after')} the change — and reports the ${g('steady','steady-state')} revenue difference. That is why a price cut on 10×20 is the largest single opportunity in the facility.

Driver contributions sum exactly to the recommended change for each unit type — verified at load. Market position uses public rates aggregated across ${F.compFacilities} facilities in the Encinitas submarket; no competitor rate is identified. Illustrative sample data.

`;} function renderRateQ(){ const changed=UT.filter(t=>dPct(t)!==0),done=Object.keys(rApp).length; const openRev=changed.filter(t=>!rApp[t.k]).reduce((s,t)=>s+dRev(t),0); const appRev=changed.filter(t=>rApp[t.k]).reduce((s,t)=>s+dRev(t),0); $('rateProg').innerHTML=`

Review progress

${done} of ${changed.length} recommendations applied · ${UT.length-changed.length} types need no change

Applied so far

${money(appRev)} / mo

Still on the table

${money(openRev)} / mo

`; $('rateQ').innerHTML=` #Unit typeUnits${g('physocc','Occ.')} ${g('street','Current')}${gi('street')}${g('mktmed','Mkt median')}${gi('mktmed')} RecommendedΔ${g('occafter','Occ. after')}${gi('occafter')} ${g('steady','Δ rev / mo')}${gi('steady')}AnnualConf.Status ${[...UT].sort((a,b)=>dRev(b)-dRev(a)).map((t,i)=>{const d=dPct(t),ap=rApp[t.k],sel=rSel===t.k,dv=dRev(t); const dc=d>0?'var(--good)':d<0?'var(--sedona)':'var(--muted)'; const stat=ap?'Applied':d===0?'No change':'Recommended'; return ` ${i+1} ${t.n} ${t.u}${t.occ}% $${t.cur}$${t.med} $${t.rec} ${d>0?'+':''}${d===0?'—':d+'%'} ${d===0?'—':t.occ+'% → '+occAfterPct(t)+'%'} ${dv?money(dv):'—'} ${dv?k$(dv*12):'—'} ${t.conf}%${stat} ${d!==0&&!ap?``:''}`;}).join('')} Facility total ${F.units}${F.physOcc}% → ${F.occAfterAll}% ${money(F.rateOpp)} ${k$(F.rateOpp*12)}`;} function renderRateDetail(){ const t=UT.find(x=>x.k===rSel),d=dPct(t),ap=rApp[t.k]; const pos=v=>((v-t.rng[0])/(t.rng[1]-t.rng[0])*100).toFixed(1); $('rateDetail').innerHTML=`

${t.n} · ${F.name}

${t.u} units · ${t.occ}% occupied · ${vacU(t)} vacant · ${g('stale','staleness')} ${t.stale} days · ${g('revsqft','$'+revSqFt(t).toFixed(2)+' / sq ft')}

${ap?'Applied':d===0?'No change recommended':'Recommended'}

${g('street','Current street')}

$${t.cur}

Recommended

$${t.rec} ${d!==0?`${d>0?'+':''}${d}%`:''}

Rate split${gi('webrate')}

${g('rackrate','Standard')} $${t.rec}

${g('webrate','Web')} $${t.web}

${t.note}

Why this price — driver contribution hover each driver for its definition

Drivers sum to ${d>0?'+':''}${d}% · confidence ${t.conf}%

${d!==0&&!ap?``:''} ${ap?'':''} ${t.k==='rv'?``:''} ${t.k==='10x10'?``:''}

Where $${t.rec} sits in the market

Aggregated

$${t.rng[0]}

$${t.rng[1]}

${g('mktmed','Market median')} $${t.med} · you are at the ${t.pctile}th ${g('pctile','percentile')} today · ${g('compset','comp set')} of ${F.compFacilities} facilities.
● current ● recommended

${g('q2r','Conversion')} at each price

Value if applied

${g('steady','Δ rev / mo')}

${dRev(t)?money(dRev(t)):'—'}

Annualized

${dRev(t)?k$(dRev(t)*12):'—'}

Revenue math

${dRev(t)?`${t.uA}×$${t.rec} − ${occU(t)}×$${t.cur}`:'no change'}

`; const mx=Math.max(...t.drv.map(x=>Math.abs(x[2]))); $('rDrv').innerHTML=t.drv.map(([key,l,v])=>{const w=Math.abs(v)/mx*100,p=v>=0; return `

${l}

${p?'+':''}${v}%

`;}).join(''); mk('rConv','rConv',{type:'bar',data:{labels:t.conv.map(c=>'$'+c[0]),datasets:[ {data:t.conv.map(c=>c[1]),backgroundColor:t.conv.map(c=>c[0]===t.rec?CLR.lake:c[0]===t.cur?CLR.nickel:'#cfe0e6'),borderRadius:6}]}, options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false}, tooltip:{callbacks:{label:c=>c.parsed.y+'% quote→reservation'+(t.conv[c.dataIndex][0]===t.rec?' · recommended':t.conv[c.dataIndex][0]===t.cur?' · current':'')}}}, scales:{y:{ticks:{callback:v=>v+'%'},grid:{color:'#eef0f6'}},x:{grid:{display:false}}}}}); } function selUnit(k){rSel=k;renderRateQ();renderRateDetail();} function applyUnit(k){rApp[k]=1;const t=UT.find(x=>x.k===k);toast(t.n+' → $'+t.rec+' pushed to FMS (demo)'); const next=[...UT].sort((a,b)=>dRev(b)-dRev(a)).find(x=>!rApp[x.k]&&dPct(x)!==0);if(next)rSel=next.k; renderRateQ();renderRateDetail();} function applyAllRates(){UT.forEach(t=>{if(dPct(t)!==0)rApp[t.k]=1;});renderRateQ();renderRateDetail(); toast(UT.filter(t=>dPct(t)!==0).length+' rate changes pushed to FMS (demo)');} function initRate(){renderRateQ();renderRateDetail();} /* ==================== UNIT INTELLIGENCE ==================== */ const DEMPOOL={'5x5':9,'5x10':22,'10x10':31,'c10':18,'10x15':12,'10x20':5,'rv':25}; function pageUnit(){ const byRev=[...UT].sort((a,b)=>revSqFt(b)-revSqFt(a)); return dhead('Unit Intelligence','Is my unit mix right — and what is each attribute worth?', `Price at the unit-type and ${g('attrprem','attribute')} level rather than the facility average, and rank by ${g('revsqft','revenue per square foot')} rather than occupancy. Attribute premiums here are learned from live Marketplace conversion, not from FMS history.`, ['i-occ','Occupancy']) +`

${g('unitmix','Mix')} conversion · ${F.name}

10×20 at 45% occupancy with 29 units empty — weakest revenue per square foot

Flagged, not recommended

Today

${MIXCONV.nConv} × 10×20

If converted

${MIXCONV.newUnits} × 10×10

Δ revenue / mo

${money(MIXCONV.delta)}

Capex

${money(MIXCONV.capex)}

${g('payback','Payback')}

${MIXCONV.payback} mo

Positive, but the agent does not recommend it. ${MIXCONV.nConv} × 10×20 occupy ${MIXCONV.nConv*200} sq ft returning ${money(MIXCONV.before)} / mo. Converted, the same footprint returns ${money(MIXCONV.after)} / mo — a real gain of ${money(MIXCONV.delta)}. But at ${money(MIXCONV.capex)} of capex the payback is ${MIXCONV.payback} months, past your 36-month threshold. Fund the pricing moves first; revisit if 10×20 demand stays weak.

Measured ${g('attrprem','attribute premiums')}${gi('attrprem')}

Willingness-to-pay learned from 4,180 live Marketplace conversions in this submarket — not inferred from your own rate history.

Why this beats the incumbents. Veritec sells value/convenience pricing as a patented module and Prorize micro-segments, but both work off FMS history alone. Premiums learned from live conversion move when the market moves.

You are giving away the climate premium. Shoppers pay 8% more for climate, but your 10×10 climate and standard 10×10 are both priced at $132.

Unit mix — ranked by ${g('revsqft','revenue per square foot')}${gi('revsqft')}

${byRev.map(t=>{const r=revSqFt(t),best=revSqFt(byRev[0]); const read=t.occ>=88&&t.cur`;}).join('')}

Unit type	Sq ft	Units	${g('physocc','Occ.')}	Vacant	${g('street','Rate')}	Rev / sq ft	Relative	${g('dpool','Demand pool')}${gi('dpool')}	Read
${t.n}	${t.sf}	${t.u}	${t.occ}%	${vacU(t)}	$${t.cur}	$${r.toFixed(2)}		~${DEMPOOL[t.k]}	${read[0]}
Facility	${F.sqft.toLocaleString()}	${F.units}	${F.physOcc}%	${F.vacant}		$${F.revPerSqFt.toFixed(2)}

Small units earn the most per foot. Your 5×5s return $${revSqFt(UT.find(t=>t.k==='5x5')).toFixed(2)} / sq ft at 93% occupancy while the 10×20s return $${revSqFt(UT.find(t=>t.k==='10x20')).toFixed(2)} at 45%. Ranked by occupancy the 10×20 looks like a fill problem; ranked by revenue per foot it is a mix problem.

Revenue per square foot

Vacant supply vs. ${g('dpool','in-market demand')}

Two opposite mismatches. 10×20 has ${vacU(UT.find(t=>t.k==='10x20'))} vacant against ~${DEMPOOL['10x20']} shoppers — genuine oversupply, which is what the conversion option above addresses. RV has ${vacU(UT.find(t=>t.k==='rv'))} vacant against ~${DEMPOOL.rv} shoppers, which is not a mix problem at all: it is a ${g('ranksup','rank problem')}.

Attribute premiums and demand-pool counts are illustrative. Revenue per square foot uses nominal unit footprints; the facility figure divides actual revenue by total rentable area, so it sits below the per-type rates because of vacancy.

`;} function initUnit(){ const A=[['Climate controlled',8],['Drive-up access',6],['24-hour access',4],['Ground floor',3],['Elevator only',-2]]; $('attrBars').innerHTML=A.map(([l,v])=>{const w=Math.abs(v)/8*100,p=v>=0; return `

${l}

${p?'+':''}${v}%

`;}).join(''); const byRev=[...UT].sort((a,b)=>revSqFt(b)-revSqFt(a)); mk('uRev','uRevChart',{type:'bar',data:{labels:byRev.map(t=>t.n),datasets:[ {data:byRev.map(t=>+revSqFt(t).toFixed(2)),backgroundColor:byRev.map(t=>{const r=revSqFt(t);return r>=1.4?CLR.good:r>=1.25?CLR.lake:CLR.cooper;}),borderRadius:6}]}, options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false},tooltip:{callbacks:{label:c=>'$'+c.parsed.y.toFixed(2)+' / sq ft'}}}, scales:{x:{grid:{display:false}},y:{ticks:{callback:v=>'$'+v.toFixed(2)},grid:{color:'#eef0f6'}}}}}); mk('uDem','uDemChart',{type:'bar',data:{labels:UT.map(t=>t.n),datasets:[ {label:'Vacant units',data:UT.map(t=>vacU(t)),backgroundColor:CLR.nickel,borderRadius:5}, {label:'In-market shoppers',data:UT.map(t=>DEMPOOL[t.k]),backgroundColor:CLR.lake,borderRadius:5}]}, options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{position:'top',labels:{boxWidth:12,usePointStyle:true}}}, scales:{x:{grid:{display:false}},y:{grid:{color:'#eef0f6'}}}}}); } /* ==================== CONVERSION ==================== */ function pageConvert(){ const st=[['Searches',FUN.searches,100,null,null,CLR.midnight,0], ['Leads',FUN.leads,64,+(FUN.leads/FUN.searches*100).toFixed(1),FUN.mktLead,'#2c3a76',0], ['Quotes',FUN.quotes,48,+(FUN.quotes/FUN.leads*100).toFixed(1),FUN.mktQuote,CLR.lake,0], ['Reservations',FUN.resv,30,+(FUN.resv/FUN.quotes*100).toFixed(1),FUN.mktResv,CLR.cooper,1], ['Move-ins',FUN.moveins,22,+(FUN.moveins/FUN.resv*100).toFixed(1),FUN.mktMove,CLR.seaweed,0]]; return dhead('Conversion Intelligence','How do I convert more leads into tenants?', `Rate and conversion are one decision. This workspace shows where the funnel leaks, what each lever buys in conversion points, and what the rate-for-volume trade is worth — so you can see both sides before you move.`, ['i-demand','Demand & Funnel']) +`

The funnel — facility-wide, last 30 days

${st.map(([n,c,w,step,mkt,col,leak])=>`

${n} · ${c.toLocaleString()}

${step===null?'top of funnel':`step rate ${step}% vs. market ${mkt}%`}${leak?'THE LEAK':''}

`).join('')}

One leak, precisely located. Every stage sits at market except ${g('q2r','quote → reservation')}, where you hold ${(FUN.resv/FUN.quotes*100).toFixed(1)}% against a market ${FUN.mktResv}%. Lead volume is not the problem — shoppers are asking for a price and walking away. That makes this a rate decision, not a marketing decision.

Levers — select to model the combination

Combining levers applies a diminishing-returns factor — two levers never buy the simple sum of their individual lift.

Projected impact

Conversion vs. rate — the elasticity curve

A 6% ${g('webrate','web-rate')} cut buys roughly 8.7 points of conversion. Lower rate, more tenants, higher total revenue — the trade works while ${g('cac','CAC')} stays under ${g('ltv','LTV')}, and it stops working the moment it does not.

Cross-agent note. “Ari follow-up” hands the lead to the Customer Service & Sales worker; Ari returns conversion-by-segment, which recalibrates this curve over time.

Elasticity measured from live Marketplace and website conversion at each price point. Revenue figures compare ${g('movein','move-ins')} × effective rate against the current baseline of ${FUN.moveins} × $${STREET_BLEND}. Illustrative sample data.

`;} function renderLev(){ $('levWrap').innerHTML=LEVERS.map(([k,n,p])=> ``).join(''); const r=convCalc(); $('levProj').innerHTML=r.n===0 ?`

No levers selected — baseline is ${r.base}% conversion, ${FUN.moveins} move-ins per month at $${STREET_BLEND}.

` :`

${g('q2r','Conversion')}

${r.base}% → ${r.conv.toFixed(1)}%

${g('reservation','Reservations')} / mo

${FUN.resv} → ${r.resv}

${g('movein','Move-ins')} / mo

${FUN.moveins} → ${r.mi}

Rate / move-in

$${STREET_BLEND} → $${r.rate}

Net revenue / mo

${r.rev>=0?'+':''}${money(r.rev)}

${r.n} lever${r.n>1?'s':''} selected${r.n>1?` · diminishing factor ${r.dim.toFixed(2)}×`:''} · conversion capped at 64% (observed ceiling in this submarket)

`;} function togLev(k){levOn[k]=!levOn[k];renderLev();} function initConvert(){renderLev(); mk('cEl','cElChart',{type:'line',data:{labels:['$113','$120','$127','$134','$141'],datasets:[ {label:'Quote → reservation',data:[60,51.4,42.7,34,26],borderColor:CLR.lake,backgroundColor:'rgba(0,156,182,.12)',fill:true,tension:.35,borderWidth:2.8,pointRadius:4,pointBackgroundColor:CLR.lake}, {label:'Market',data:Array(5).fill(FUN.mktResv),borderColor:CLR.muted,borderDash:[5,4],borderWidth:1.8,pointRadius:0}]}, options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false},tooltip:{callbacks:{label:c=>c.dataset.label+': '+c.parsed.y+'%'}}}, scales:{x:{grid:{display:false},title:{display:true,text:'Blended web rate'}},y:{ticks:{callback:v=>v+'%'},grid:{color:'#eef0f6'}}}}});} /* ==================== ECRI ==================== */ function pageEcri(){ return dhead('Lifetime Rate / ECRI','How do I manage increases over the tenant’s lifetime?', `The largest single lever on tenant lifetime value — and the lowest-risk one, because it runs entirely on your own tenant data. Select ${g('cohort','cohorts')}, set the increase, and the agent models ${g('acceptance','acceptance')} and ${g('churnrisk','churn')} against your ${g('guardrail','guardrails')} before anything is scheduled.`, ['i-ecri','ECRI Capture']) +`

Own data only — lowest compliance exposure

Clear Space. Clear Mind.

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Affordable Rates

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