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PropChain Seed Round Modeling Assumptions
A comprehensive framework for evaluating PropChain's Seed economics, product roadmap, and capital deployment strategy. This document details the assumptions underlying our $1.0M–$1.6M raise (target $1.4M) on a YC-style post-money SAFE with $15M cap, designed to fund ~24 months of runway as we build our AI-first, mobile-centric real estate platform that unifies discovery, valuation, and closing into one guided workflow.
Round Structure & Capital Planning
Seed Round Size & Band
  • Instrument: YC-style post-money SAFE
  • Valuation cap: $15M post-money
  • Raise band: $1.0M–$1.6M
  • Target: $1.4M
  • Minimum acceptable close: $1.0M
  • Maximum (hard cap): $1.6M
Implicit: No other concurrent priced equity round or SAFEs with conflicting terms.
Runway Math & Planning Horizon
  • Planning horizon: ~24 months from close
  • At $1.4M:
  • Base-case runway ≈ 22–24 months
  • Low-burn runway ≈ 31 months
  • High-burn runway ≈ 17–18 months
Communication: "Target ~24 months with downside case ~18 months if PropChain accelerates spend."
Runway is approximated as: Runway ≈ Raise ÷ (steady-state monthly burn), smoothing CapEx and ignoring timing of inflows/outflows.
Base Monthly Burn Assumptions
(pre-scanning overlay)
  • Low-burn: ≈ $45k/month
  • Base-case steady-state: ≈ $58k/month
  • High-burn: ≈ $80k/month
These scenarios are driven primarily by:
  • Cash activation timing for DS/MLE and FSMAD
  • Hiring timing for 2nd engineer and Sales/CS
  • GTM intensity (ad spend, travel, etc.)
  • Degree of contingency usage
Treatment of Contingency / Reserve
Baseline "no-scanning" plan at $1.4M:
  • $1.22M planned operating spend over 24 months
  • $180k contingency / operating reserve (~13% of round)
Contingency is assumed to:
  • Sit outside the base P&L projection until "activated"
  • Be available for:
  • Upside cash activation for equity-first roles
  • GTM acceleration if CAC/LTV is favorable
  • Covering downside (data overages, legal, slower revenue)
For scanning overlays, scanning spend is assumed to come on top of the $1.22M base, reducing the effective reserve unless other categories are cut.
Use-of-Funds Structure at $1.4M
(base, no incremental scanning)
Over 24 months:
  • Headcount & Contractors: $900k
  • Data, Cloud & Tooling: $90k
  • Hardware & Equipment: $30k
  • GTM, Marketing & Partnerships: $90k
  • Legal, Compliance, Insurance & Admin: $70k
  • Ops, Office, Travel & Misc: $40k
  • Contingency / Reserve: $180k
This base table is assumed to exclude incremental multi-rig scanning OpEx (that's in Section 9 overlays), except for the first rig's CapEx being embedded in the $30k hardware line.
Historical Financials & Starting Position
Historical P&L Assumptions
  • 2023 and 2024 are modeled on a cash basis
  • No revenue to date (2023–2024)
  • Total expenses over 2023–2024: ≈ $47,055
Allocation assumptions:
  • R&D + Product/Design ≈ $43,340 (~92% of total)
  • Remaining ≈8% split across G&A and marketing (software, travel, website)
Implicit: No other material liabilities or off-balance sheet commitments beyond what's mentioned.
Starting Balance Sheet at Seed Close
  • Cash balance pre-raise: ≈ $500
  • Credit card/payables: ≈ $2,000 (curb-scanner hardware)
  • Additional short-term hardware/networking obligations: ≈ $2,000

Assumption: No other debt or payables large enough to affect the plan.
Pro forma cash at $1.4M:
  • Gross raise: $1,400,000
  • Less ~$4,000 to clear short-term obligations
  • Pro forma cash ~ $1.396M, treated as effectively the full seed capital for the 24-month window
FP&A, Revenue & P&L Assumptions
1
24-Month FP&A Structure
Split into:
  • Year 1: Months 1–12 post-close
  • Year 2: Months 13–24
FP&A assumptions:
  • Contingency/reserve is excluded from the base P&L and treated as off-to-the-side buffer
  • No explicit modeling of interest income/expense or taxes
2
Revenue Assumptions
  • Year 1: $0 (focus on infrastructure, product, and pilots)
  • Year 2: $200k revenue:
  • Treated as the midpoint of a $150–250k ARR range
  • Sources: subscriptions + early data/API and B2B contracts
Implicit:
  • Revenue is recognized on a simple cash/ARR-like basis, without complex GAAP deferrals
  • No additional revenue sources (e.g., one-off services, consulting) are assumed unless part of these categories
Cost Structure Assumptions (Base-Case P&L)
Over 24 Months:
  • Total spending (excluding contingency): ≈ $1.22M
  • Average monthly burn: ≈ $50.8k (Year 1 ~40.4k; Year 2 ~61.2k)
Assumes:
  • R&D includes the hardware completion for the first rig within that $1.22M
  • Sales & Marketing scaling in Year 2 (to $172k) aligns with GTM gating and ad-budget tranches
  • G&A includes founder/ops compensation, legal, insurance, admin, office tools, and travel
Contingency Usage Assumptions
The full $180k reserve:
  • Is NOT spent by default in base-case
  • Under base-case, 50–75% may be activated by end of Year 2, pushing Year 2 burn to roughly $65–70k/month
Assumes reserve draw is discretionary and depends on:
  • GTM performance (CAC/LTV)
  • Data/legal/insurance actuals relative to budget
  • Revenue ramp vs. plan
Headcount, Comp & Hiring Assumptions
Headcount Budget Assumptions
Total cash envelope over 24 months: $900k
Roles included in that $900k:
  • Founder/CEO
  • DS/MLE (equity-first, warrant-heavy; optional cash)
  • FSMAD (full-stack / multi-domain; equity-first, optional cash)
  • UI/UX
  • Business Ops / PM
  • 2nd Engineer
  • Sales & Customer Success (later)
  • Digital Marketing Lead (cash optional, warrants-based baseline)
Assumes:
  • Headcount plan can be sequenced and gated, not all roles are hired in Month 1
  • Some roles are part-time or contractor-based, not all full-time salaried
Equity-First Structure Assumptions
DS/MLE and FSMAD:
  • Engaged as warrant-heavy contractors
  • Cash activation is optional and can be turned on/off via amended term sheets
Digital Marketing Lead:
  • Initially non-cash (warrant-only)
  • Warrant cap: up to 2,000 Common NV units
  • Cash component only added later from reserve or post-Seed capital
ETL Data Engineer:
  • A dedicated contractor role with no base cash
  • Compensation: up to 2,000 warrants
  • Timing aligned to Terra Net and CurbScan data ingestion milestones
Assumes:
  • Talent can be recruited and retained with this equity-heavy structure
  • Warrant grants remain a minority share relative to founder ownership
Hiring & Cash-Activation Gating
01
Sales/CS Hire Gate
  • ≥ 10 paying accounts or ≥ 25 paying seats
  • Rolling pipeline ≥ $200k ACV
  • Early cohorts with NRR ≥ 100%
02
Digital Marketing Lead Cash Activation Gate
  • ≥ 2 live Alpha/Beta pilots with consistent weekly activity
  • ≥ 1,000 monthly active free users
  • ≥ 3 validated trigger events tied to conversion
03
GTM Ad Budget (within $50k)
  • Tranche 1 ($15k): at Beta launch with 3–5 live pilots & good NPS
  • Tranche 2 ($20k): ≥ 50 SQLs and ≥ 10 paying customers with acceptable CAC/LTV
  • Tranche 3 ($15k): once annualized ARR ≥ $150k

Implicit: Hiring and spend can be slowed or paused if KPIs are not met. There is a mechanism (and discipline) to enforce KPI gates.
Data, MLS, Infrastructure & Tooling Assumptions
MLS / Data-Aggregator Pricing
Baseline cost: S2 "Moderate Grow" for ListHub/MLS:
  • ≈ $1,670/month → $20,050/year
  • Rounded to $40k over 24 months in the base plan
That $40k is baked into:
  • The $90k Data, Cloud & Tooling line as "MLS/data"
Higher MLS/Data Buffer for Scaling Scenarios
For scanning-heavy scenarios:
  • Planning buffer: $3,000/month for MLS + aggregator combinations
Treatment:
  • The $3k/month figure is used only as a stress-test / scenario-planning value in Section 9
  • Any spend above the base $40k envelope:
  • Draws from contingency, or
  • Requires small reallocations from other non-core buckets
Assumes:
  • The S2 Moderate scenario is realistic for minimum viable MLS access
  • The $3k/month buffer safely overestimates potential data spend as CurbScan scales
Cloud and Infrastructure Costs
Total over 24 months: $40k for:
  • AKS/NAS hybrid infra (Azure + on-prem NAS)
  • Enough to support Terra Net & Terra Engine at Seed scale
Assumes:
  • No major surprise infra bills (e.g., ultra-high egress, extreme data usage) beyond this envelope
  • CurbScan storage/processing above baseline is partly captured in the per-rig OpEx ($40/month per rig for extra storage/processing)
Tooling Costs
Dev and analytics tools budget: $10k over 24 months
Assumes:
  • Existing stack (e.g., Figma, M365, Slack, dev tools) can be maintained/upgraded within this line
CurbScan Hardware & Operations Assumptions
$14K
Total CapEx per Rig
Hardware BOM + install & calibration
$5.9K
Monthly OpEx per Rig
Driver, vehicle, fuel, connectivity & misc ops
1,050
Scanning Miles/Month
Per rig coverage capacity
$5.60
Cost per Scanned Mile
OpEx only, conservative estimate
Hardware CapEx per Rig
Baseline BOM: $13,000 per rig:
  • Jetson AGX Orin
  • Sensors (LiDAR/RGB/depth/thermal where applicable)
  • SSD, modem, enclosure, cabling, rig mount
  • Share of NAS-side infra
  • Install & calibration: $1,000 per rig
  • Total planning CapEx per rig: $14,000
  • Useful life: 3 years (36 months)
  • "Planning depreciation" (for FP&A, not GAAP): ≈ $390/month per rig

Embedded assumption: The first rig's $14k is included within the existing $30k hardware budget. Remaining ~$16k covers enclosure work and spares.
Future Hardware Cost Trajectory
Expectation: future rigs will be cheaper per unit:
  • Possible move from Jetson Orin to Jetson Nano or comparable hardware
Despite that, planning keeps $13–14k/rig as conservative.
Per-Rig OpEx Assumptions (US Metro, 2025)
Per-Rig Coverage Assumptions
Scan Rate
10 curb-miles/hour during active scanning
Effective Scanning Time
8-hour shift with 5 hours effective scanning. Remaining 3 hours: transit, breaks, calibration, setup
Working Days
21 days/month
Derived Coverage
1,050 scanning miles/month/rig
Total miles (scanning + transit): ~1,500 miles
Unit Economics per Rig
  • Monthly OpEx: $5,900
  • Scanning miles/month: 1,050
  • Cost per scanned mile (OpEx only): 5,900 ÷ 1,050 ≈ $5.60/scanned mile
  • Total miles/month: 1,500
  • Cost per total mile: 5,900 ÷ 1,500 ≈ $3.93/mile
Assumed to be conservative, with an expectation that optimization and hardware improvements would reduce these costs over time.
Scanning Program Phases & Scenarios
1
Phase A: Seed + Alpha/Beta (Months 1–9)
  • Rigs: 1
  • Geography: NJ (Summit/Union County) + limited NY metro
  • Coverage: 1 rig × 1,050 mi/mo × 9 = 9,450 scanned miles
  • Focus: debug + dense NJ data moat
2
Phase B: Seed + GTM Build-Out (Months 10–18)
  • Rigs: 2 (add second rig around month 10)
  • Rig 1: NJ/NY
  • Rig 2: FL or TX (high-ORI-2 cluster, with a preference for FL unless GTM signals push to TX)
  • Coverage: 2 × 1,050 × 9 = 18,900 miles in Phase B
  • Combined A + B: 28,350 scanned miles
3
Phase C: Late Seed / Pre-Series A (Months 19–24)
  • Rigs: 3 (add third rig around month 19)
  • Rig 3: additional ORI-2 top-15 state (e.g., CA, WA, GA) chosen based on traction
  • Coverage: 3 × 1,050 × 6 = 18,900 miles
  • Total A + B + C: 47,250 scanned miles
4
Phase D: Post-Seed / Series A
  • Rigs: 5–10+ across the ORI-2 top-15 cluster
  • Explicitly not required for Seed economics; funded post-Series A based on performance
Cost by Phase and Total Scanning Program (A+B+C)
Rig-Months Breakdown
  • Phase A: 1 × 9 = 9 rig-months
  • Phase B: 2 × 9 = 18 rig-months
  • Phase C: 3 × 6 = 18 rig-months
  • Total: 45 rig-months
Total Cost (A+B+C, 3 rigs):
  • OpEx: 45 × $5,900 = $265,500
  • CapEx: 3 rigs × $14,000 = $42,000
  • Total: $307,500 over 24 months
Phase Scenario Cost Assumptions
  • Phase A only: $67.1k (53.1k OpEx + 14k CapEx)
  • Phases A + B: $187.3k (145.3k OpEx + 42k CapEx)
  • Phases A + B + C: $307.5k (265.5k OpEx + 42k CapEx)
Smoothed over 24 months:
  • A only: ≈ $2.8k/month incremental
  • A+B: ≈ $7.8k/month incremental
  • A+B+C: ≈ $12.8k/month incremental
Overlay Integration Assumptions at $1.4M
Keep base ops = $1.22M. Add scanning on top:

Assumes: Base plan categories are unchanged unless explicitly trimmed. Scanning is most cleanly funded by reallocating reserve or raising toward $1.6M.
Feasibility by Raise Size Assumptions (3-Rig Scenario)
At $1.0M
3-rig plan is not feasible without gutting the base plan
At $1.2M
Full A+B+C is effectively no; even A+B is tight
At $1.4M
A+B+C is an aggressive/stress-test scenario
At $1.6M
3 rigs in 3+ states is tight but feasible with moderate reserve and/or modest cuts to GTM or other lines
Implicit: 1-rig Phase A is the "safe" base and 2-rig is the natural upside if more capital or early revenue appears.
GTM, Pricing & Revenue Scenarios
GTM & Marketing Budget Use
Total GTM budget line: $90k over 24 months:
  • $50k paid digital ads
  • $20k PR/content/events
  • $20k partner enablement & pilot incentives

Assumes: Spending is tranche-based and tied to KPIs (not purely time-based). Ads can be scoped in/out based on early CAC performance.
Revenue Scenarios at 24-Month Mark
$100K
Low ARR
50–75 paying subscribers (mostly Standard-tier). Limited VTP/transaction and data/API revenue.
$250K
Base ARR
120–180 paying subscribers across Standard/Pro. 1–3 modest API/data/B2B contracts.
$400K+
High ARR
Faster path toward 15% freemium→paid conversion. Earlier/more robust API/data/B2B uptake.
Assumes:
  • Pricing tiers (Free, Standard, Pro, and likely API/data SKUs) are designed to support these ranges
  • Operational capability to sell and support that many paying customers with the modeled team
SAFE Terms, Equity & Product Scope
SAFE Terms
  • YC-style post-money SAFE
  • Valuation cap: $15M post
  • Discount: 20% or next round price, whichever is lower
  • MFN: Included
  • Pro-rata rights: For investors with checks ≥ $100k

Assumes: Standard YC SAFE mechanics; no unusual side letters that materially alter economics.
SAFE Ownership Percentages
At cap of $15M post:
6.7%
At $1.0M
Of fully diluted equity
9.3%
At $1.4M
Of fully diluted equity
10.7%
At $1.6M
Of fully diluted equity
The rest (≈90–93%) remains with pre-money holders (founder + contractors/advisors + any pool).
Implicit:
  • That the cap is binding (i.e., priced round is at or above cap)
  • There are no other senior or conflicting instruments ahead of this SAFE
Pre-Money Equity Distribution (Illustrative)
  • Founder: 80–90% of pre-money fully diluted equity
  • Contractor/advisor warrants (DS/MLE, FSMAD, Digital Marketing Lead, others): 5–10% combined
  • Unallocated pool: 0–10% (likely formalized at the priced round)
Assumes:
  • Founder remains majority owner post-warrants
  • New pool can be carved out at priced round without breaking these ranges too badly
Product Scope and Roadmap Assumptions
In-Scope for Seed
  • Predictive MVP / Terra Engine (PropPredict, Property Oracle, core recommendation/valuation)
  • CurbScan / Terra Net ingestion to support pilots
  • MLS-backed pilots in ORI-2 markets
  • Freemium app and early B2C/B2B traction for renovators, homeowners, pros
Out-of-Scope (Post-Seed / Series A)
  • Crowdfunding/investment marketplace
  • Full transaction pipeline / escrow / embedded finance
  • Broad multi-sided marketplace features
  • Fleet expansion to 5–10+ rigs beyond what's justified by Seed ARR and data economics

Assumes: Investors understand these as future upside, not funded out of Seed.
ORI-2 & Market Selection Assumptions
Strategic focus on top-15 ORI-2 states:
CA, NY, FL, TX, WA, NJ, MD, VA, CO, GA, NC, OR, MA, AZ, NV
Seed rig deployments are assumed to:
  • Start in NJ (Summit/Union County)
  • Extend to NY metro for lighthouse partners
  • Add FL or TX as second- and eventually third-state targets
  • Possibly a third state (CA/WA/GA) in Phase C, selected based on early signal
Assumes:
  • ORI-2 ranking is a good proxy for "Adoption & Engagement Potential"
  • CurbScan routes and climate make these geographies practical to scan
PropChain is a PropTech 3.0, AI-first, mobile-centric real estate platform that unifies discovery, valuation, and eventually closing into one guided workflow for buyers, sellers, homeowners, small investors, and real estate pros. Instead of static listings, PropChain adds a real-time predictive layer so users can discover, analyze, and act in one place.
Core value: "Discover, analyze, and close on property in one place—turning opaque, slow real estate workflows into a real-time, AI-guided experience for every stakeholder."