Capital that replaces an earlier piece of financing — e.g. a permanent loan that “takes out” (pays off) the construction loan once the building is built and leased. A “take-out buyer” buys the finished asset.
The party that ultimately takes the project off your hands at a given stage — the buyer of the stabilized building or its long-term loan. A “pension-fund take-out” means a pension fund is that off-taker.
A rule requiring an institution to invest only in assets meeting environmental, social and governance criteria. If a deal can’t prove it qualifies, that capital legally can’t buy it.
Low-Income Housing Tax Credit equity — federal tax credits sold to investors that bring cash into a deal in exchange for setting aside affordable units.
The eligible cost on which LIHTC credits are calculated. “Eligible basis” is the portion of development cost that qualifies for the credit, so how you spend money affects how much credit equity you raise.
Commercial Real Estate Collateralized Loan Obligation — a vehicle that bundles transitional / bridge property loans. A common lease-up-stage lender; typically higher-leverage and floating-rate.
Government-backed permanent mortgages (Fannie Mae, Freddie Mac, HUD). Long amortization, non-recourse, low-cost — but sized strictly on debt coverage, and HUD carries prevailing-wage rules.
Insurance covering a building while it is under construction (fire, theft, weather damage). A construction-stage cost that the capital stack has to fund and time.
“Wrap-up” insurance programs — one master policy covering everyone on a job site. OCIP is owner-controlled, CCIP is contractor-controlled. Affects cost and who controls claims.
Pension-fund general account
#The main investment pool of a pension fund — patient, long-hold capital that favors large, stable, income-producing assets, increasingly with an ESG mandate.
Insurance-company general account
#An insurer’s core investment pool backing its policies — similar appetite to a pension fund: large, safe, long-duration income, and a major source of long-term real-estate debt.
The point where a building has leased up to its normal occupancy and income has settled — the moment most long-term buyers and lenders underwrite to.
The period after opening when a building fills from empty to stabilized occupancy.
Refinancing — replacing one loan with another, usually a cheaper long-term loan once the building is stabilized.
The end of the hold — selling the asset (or its loan) to realize the return.
The dollar amount a given investor wants to deploy per deal. Some capital won’t look below (or above) a certain ticket.
The minimum (or maximum) deal value a capital source will consider — many institutional buyers won’t touch anything below, say, ~$50M.
A condition that disqualifies a deal for a given capital source no matter how good the rest is — e.g. no affordability for a LIHTC investor, or an uninsurable site for a pension fund.
A switch (single asset / portfolio / district) that tells the model how many assets to underwrite and whether to show a roll-up — so the same engine serves a single building and a whole district.
The cross-cutting economics of a multi-asset, neighborhood-scale project — shared infrastructure, phasing, cross-collateralization, a district-scale capital source — that only appear when more than one asset is in play.
One building underwritten on its own; the default case (N=1).
Several assets underwritten together and rolled up — a district is the portfolio case with shared economics.
Backwards-propagation of expectations
#Planning a project backward from the capital that will eventually own it: if you want a pension-fund take-out, you bake in the asset size, ESG data and cash-flow shape it needs at the very start, before the design locks.
Making sure the project’s metrics and decisions at each stage (pre-dev, construction, lease-up, stabilized, refi, exit) match what the capital that owns that stage expects.
Whether a property can actually be insured against climate risk (flood, wildfire, wind) at a price the deal can carry, for the whole hold. Increasingly a yes/no deal-gate, not just a cost line.
How willing insurance carriers are to write coverage in a given submarket. In some markets it is shrinking toward zero, which can strand an otherwise-good deal.
Insurance that pays a fixed amount when a measured trigger is hit (e.g. wind speed over a threshold) rather than reimbursing assessed losses — sometimes the only coverage available in high-risk markets.
The knock-on chain when one input moves — raise efficiency → more rentable area → more rent → higher value. The model traces it automatically.
An input the engine supports but no agent proposes a value for, so it just sits at its default until a human moves it.
Extra units or height a city grants in exchange for something (often affordable units) — a key way an entitlements move can unlock the affordable → tax-credit financing path.
The yield a future buyer demands; value ≈ annual income ÷ cap rate, so a lower cap means a higher sale price. Often the single biggest driver of the return.
Debt-Service-Coverage-Ratio floor — the minimum ratio of operating income to loan payments a lender requires (1.20 conventional, 1.176 HUD here). Below it, the loan sizes down.
The maximum loan size the model allows (~$95M here) — a hard constraint the agents cannot exceed.
A hard rule the engine enforces (the DSCR floor, the facility cap). A move that breaks one is penalized and can’t count as a “go.”
The orchestrator that sequences the agents — reads the binding constraint, asks agents for moves, and composes the cheapest path that pencils.
The planned shape with the ten-agent fleet: cross-cutting agents set constraints / objectives, lever-owning agents propose moves within them, and the Lead Agent composes.
An agent that doesn’t own sliders; it sets targets / constraints the other agents’ moves must satisfy (e.g. Capital Sourcing, ESG, Insurance).
An agent that proposes actual input values — it moves sliders (e.g. Program, Cost, Revenue). After the steel-man pass these are called “composers.”
An agent that proposes actual engine inputs — it moves sliders: Program, Cost, Revenue, Operations, Capital Stack, Risk & Returns. The “real agents” in the propose-a-number sense.
An agent that sets constraints or objectives the composers must work within, rather than moving sliders itself (Capital Sourcing, ESG). A policy, wearing a persona for the user.
An agent that is a binary checkpoint — a yes/no deal-killer (Insurance: insurable or not). A validator with judgment, not a move-proposer.
A program that translates code from one language to another — here, the small tool that reads the Python engine and writes out the equivalent JavaScript automatically.
A transpiler that reads code by its structure (its Abstract Syntax Tree) rather than as plain text, so the translation is reliable rather than find-and-replace.
A test that throws thousands of random inputs at the system to catch edge cases that hand-picked examples would miss.
Rounding a value that is exactly halfway to the nearest even number (2.5 → 2, 3.5 → 4) instead of always up. The standard accounting convention; what the engine now uses.
The old hand-written engine, kept untouched as a reference the tests check against — like keeping the original signed contract in a drawer. It doesn’t ship.
Generator-fidelity test
#The test proving the generated JavaScript matches the Python engine number-for-number, over the scenarios plus thousands of random inputs.
How close two computed numbers must be to count as equal (here one part in a million) — needed because different platforms’ math can differ in the last digit.
A test that freezes today’s known-good outputs and flags if a change moves any number, so nothing drifts silently.
An operation you can run repeatedly with the same result — e.g. a build script that regenerates a section cleanly instead of duplicating it.
Arguing the strongest possible case for a position before judging it — here, making the best case both for keeping the agent fleet and for tearing it down, then acting on what survived.
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