1. Why is our occupancy so low? What should we do to right the ship?

• 500 homes | 425 occupied (85%) | 75 vacant (15%) | goal delta: 10% (50 homes)
• 10 pending move-in (20% of goal): What's the hold up? Anything we can do to get these tenants in faster?
  • Are we collect rent/fees for new tenants who are dragging out move-in?
  • Probably not much we can do at this stage. We've completed the turn and lease process, now we just let tenant schedules work their natural course. Of all vacant homes, this is probably the lever with least amount of leverage, so we should focus our efforts elsewhere.
• 25 listed (50% of goal): How do these listings compare to market comps? Are we priced right? Good photos/descriptions? Do we make it easy to schedule a showing?
  • Do we allow self-guided showings without having to have our own leasing agent on site? Do we allow same-day showings? Digital locks/security cams?
  • How quickly are we working a showing → screening → approval pipeline?
  • How quickly are we collecting all the necessary materials from prospect tenant to make a decision?
  • When necessary, how quickly are we routing a prospect tenant to the local housing authority for potential HAP benefits?
  • How quickly are we making our tenant approval decisions?
• 40 in turn (80% of goal): How do we make our turn process maximally efficient? We are tracking days on market for listed homes, we need to track days off market for each turn cycle and get that number as low as possible across all markets.
  • Do we have enough scopers in each market?
  • Is the turn scoping process itself efficient? What's the time between tenant move out and scoper on site? What's the time between scope report completed and first turn service starting work?
  • Do we have enough local service providers on our roster for each major service category across every market? Our turn time shouldn't get extended just because our top 1 or 2 choices were booked out.
  • Once we have a turn scoping report back, how quickly are we sending bid requests out to service providers? It should be instant, and we should have a standardized method for a provider to submit a bid to us, and preset approval thresholds so first qualifying bid at or under threshold gets auto approved and we get that work scheduled ASAP.
  • How are we managing waterfall service scheduling for turns? E.g. new interior paint needs to hit before new carpet, etc.
  • Are we incentivizing service providers on speed? Depending on market dynamics, maybe we pay a bonus if you can get our service done within X days.
  • How quickly are we verifying and approving turn work, and unblocking any potential downstream waterfall service providers?

2. Describe the tenant profile we want. What are your quantitative screening criteria?

• Based on sample data: 2 risk profile buckets
  • High risk: credit score <570 AND rent-to-income ratio ≥35%
    • 46 evictions / 380 tenants = 12% eviction rate
  • Low risk: credit score >570 OR rent-to-income ratio <35%
    • 37 evictions / 620 tenants = 6% eviction rate
  • RTI ≥ 35% is the primary signal in this data — it alone separates 5% from 11% eviction rate. Credit adds marginal separation within the high-RTI group.
• Vouchers are good. U of Chicago study: Voucher tenants collect at 96.7% vs. 86.2% for non-voucher tenants. Voucher tenants also had 60% lower eviction rates (11.9% vs. 30.1%), 61% lower move-out balances ($611 vs. $1,579), and 27% longer tenure (17.8 vs. 14.0 months).
• Generic ideal tenant:
  1. They pay rent on time, in full (maximal collections, minimal cost to collect)
     • Prior rental history
     • Savings buffer
     • Income stability over time
     • % of credit accounts in good standing
     • Utility/telecom payment history
  2. They stay a long time (minimal turns / listings)
     • Has kids (statistically stay in the same home longer)
     • Has pets (statistically pet owners stay 10 months longer than non-pet owners) HABRI report
     • On voucher (statistically stay in the same home longer)
     • Family size matches home size (they don't move out to upsize or downsize)
  3. They don't damage the property (minimal turn time + expense)
     • On voucher (HQS inspections)
     • Least amount of academic data for indicators here, consensus is that landlord actually sets the quality standard contract at final walkthrough key handoff
  4. They move out on good terms (no eviction expense)
     • No prior evictions
     • No rental-related collections

3. Besides occupancy and collections rate, what are 3 additional metrics you'd want to track? Tell us why they matter.

1. Homes (Cost/Supply Funnel): Days Off Market, broken down into:
   • Move out to scope
   • Scope to first service
   • First service to final service
   • Final service to listing
2. Residents (Revenue/Demand Funnel): Inquiry to Move-in, broken down into:
   • New prospect inquiry to first showing
   • First showing to approval decision
   • Approval to signed lease
   • Signed lease to move-in
3. Business (Overall): Net Operating Income, broken down into:
   • Gross potential rent (what a home should earn at market rate)
   • Minus vacancy loss (potential rent lost during turn, listing, pending move-in)
   • Minus collections loss (uncollected rent)
   • Minus operating costs (turn costs, eviction costs, collections costs)

Build rationale

Why I didn't choose B — Vacancy-risk radar

The homes table lacks:

• Location beyond a street name (no city, no market)
• Rent on the homes table
• Property attributes (beds, baths, sqft, age, condition)
• Turn duration for the 40 turn homes
• Historical vacancy data per home (how long was it vacant last time?)

This only leaves the option to build a simple cost table built on loose assumptions, and I'm not sure how relevant/helpful it is:

• Assume $1,262/mo rent per home (average of rent data from Residents table)
• $1,262/30 for a $42 assumed rent loss per day
• $4,000 turn cost from exercise guidelines
• For homes in rental-listed, cost = (daysOnMarket × $42) + $4,000
• For homes in turn, cost = $4,000

All this would really prove is that I was smart enough to grab the average rent value from residents table and apply it in the homes context.

Why I didn't choose C — Collections/eviction-timing model

Option C is kinda the same as option A. The difference is lifecycle timing: A decides who to rent to, C decides who to continue or stop renting to.

The residents table doesn't have a collections history — it has a single collectionsRate and collectionsCostMonthly per tenant. There's no time series, no month-over-month trajectory, no "they paid for 6 months then stopped." So "given a resident's collections history" reduces to a single break-even calculation: at what collections rate does the expected loss exceed the $5,000 eviction cost? That's one formula.

Why I chose A — Screening scorecard

I chose A because:

• It has the most data to work with (credit score, income, rent, rent-to-income ratio)
• It connects directly to discussion point 2 (tenant screening criteria)
• There's more to build and discuss (tuning, bucketing, tradeoffs, exceptions, etc)