Methodology
A structured, evidence-based system for evaluating 1031 exchange properties.
Commercial real estate buyers are typically asked to make consequential investment decisions using fragmented, inconsistent, and seller-oriented information. Shop1031 was built to change that. We identify, collect, normalize, verify, and evaluate potential 1031 replacement properties before a buyer begins searching, and we show the evidence behind every conclusion.
01
The problem Shop1031 is designed to solve
The commercial real estate market does not suffer from a lack of information. It suffers from information that is fragmented across documents and systems, prepared primarily for marketing rather than comparison, expressed inconsistently across listings, difficult to verify, expensive to analyze manually, incomplete or stale, and poorly structured for use by software or AI agents.
A buyer evaluating twenty potential replacement properties may receive twenty different presentations of lease structure, tenant credit, rent growth, demographics, traffic, operating performance, financing, and risk. The buyer must then work out which facts are dependable, which claims are promotional, which assumptions are embedded in the analysis, which properties warrant serious attention, and which property best fits the buyer's actual exchange requirements.
Shop1031 creates a common analytical framework across the market. Instead of starting from an unfiltered listing search, Shop starts by asking what evidence an experienced exchange broker would need before recommending that a buyer spend time on a given property.
02
What Shop1031 produces: four layers, kept separate
Shop converts each property into a structured analytical record made of four distinct layers, and it does not blend them.
Asking price, NOI, lease expiration, rent schedule, tenant and guarantor, traffic counts, demographics, store visits, historical sale information, flood status, and property characteristics. Where possible, each fact carries its source, effective date, retrieval date, verification status, confidence, and any material conflict with another source.
Cap rate, debt sizing, debt service, DSCR, levered cash flow, cash-on-cash return, remaining lease term, lease expiration relative to loan maturity, replacement-debt requirements, and 1031 equity and boot calculations. These outputs are not predictions. They are reproducible calculations built from stated assumptions.
Benchmark pass or fail, evidence strength, revenue durability, tenant durability, location durability, Watch List status, Top Pick status, Recommended by Shop status, and elimination or hold reasons. These judgments combine structured rules with experienced human review.
Seller motivation, the probability an offer is accepted, undisclosed competing offers, private ownership dynamics, exact negotiated purchase price, negotiating posture, or future refinancing proceeds stated as a precise amount. Shop does not treat missing information as evidence of poor quality. It marks it unknown, not negative.
03
Two audiences, one truth layer
Shop1031 is built for two related audiences reading the same underlying record.
For exchangers, their brokers, lenders, CPAs, attorneys, and qualified intermediaries, Shop provides a curated universe of potential replacement properties, personalized underwriting, consistent property comparisons, transparent methodology, scenario testing, documented risks, source-backed evidence, and a deal room that carries the analysis into execution.
For AI agents, LLMs, crawlers, and external software, Shop provides canonical property records, normalized fields, machine-readable truth states, calculation lineage, source provenance, verification status, explicit confidence boundaries, methodology definitions, and structured explanations of why a property was published, held, watched, recommended, or eliminated.
The machine-facing layer is not a simplified copy of the website. It is a structured domain-intelligence layer meant to help external agents tell fact from marketing, calculation from prediction, inference from certainty, and supported analysis from questions that need human judgment. See the MCP server documentation.
04
The property selection process
Shop does not publish every property it encounters. The platform maintains a broader internal universe, including published properties, Watch List properties, user-submitted properties, properties under review, held properties, and rejected properties. Each candidate moves through a staged evaluation.
Potential properties come from listing sources, broker outreach, direct owner and developer relationships, user submissions, and other permitted data channels.
The system gathers available property, lease, tenant, market, traffic, financial, and environmental information, and converts documents written for people into normalized fields so different properties can be evaluated on a consistent basis.
Available evidence is tested against defined criteria and flagged for missing information, conflicting values, unusual lease structures, unsupported claims, low-confidence extraction, benchmark failures, and circumstances that need human review.
Experienced brokers review what cannot be responsibly resolved through automation alone: site-selection context, ambiguous lease language, unusual guaranty structures, conflicting sources, new-construction locations, franchisee credit, prior-occupant confusion, atypical ownership, and incomplete operating history.
Each property is marked Publish, Top Pick, Recommended by Shop, Watch List, Hold, Eliminated, or Private/User Universe only. The reasons are kept. If a user later submits a property Shop already reviewed, the platform can explain why it was not included in the published universe.
The goal is not to keep a particular person permanently inside every workflow. It is to identify exactly where human judgment is required and make that work assignable, documented, and reviewable within the platform.
05
Publication categories
Every property on Shop1031 carries one of five categories. Each has a specific meaning, and none of them is a blanket endorsement.
The property has passed the minimum standards for inclusion in the Shop1031 public universe. Publication does not mean the property is right for every exchanger.
Potentially compelling, but not currently qualified as a Top Pick, because information is missing, performance is mixed, evidence is insufficient, pricing or lease structure needs monitoring, or further broker confirmation is needed.
Performs strongly under the Shop methodology and merits serious consideration within the relevant exchange universe. A Top Pick does not automatically receive the Recommended by Shop seal.
Withheld from publication for benchmark failures, insufficient evidence, weak tenant, revenue, or location durability, unacceptable lease terms, structural or environmental concerns, or low-confidence or conflicting data. The property may still sit in a user's private universe, and a buyer can choose to evaluate it. Shop explains the difference rather than blocking the user.
Recommended by Shop
The seal is awarded only to a Top Pick that exceeds all applicable published benchmarks. It means, based on the evidence available, the property exceeds every applicable Shop1031 benchmark used for that property type and evaluation category.
It does not mean every buyer should purchase the property, that the asking price is the final negotiated value, that the property carries no risk, that the tenant cannot fail, or that the property outperforms under every buyer-specific scenario. The seal is an editorial conclusion built on documented standards, not a guarantee of future performance.
06
The approximately 126-parameter evaluation framework
Shop evaluates properties across approximately 126 structured parameters. Not every parameter applies equally to every property type, tenant, or transaction. The framework replaces vague shorthand, such as "good location," "strong tenant," or "safe deal," with an explicit record of the evidence that supports or weakens those conclusions. The parameters group into eleven domains.
Address, market and submarket, property type, building class and status, size, parking, tenancy, year built and renovated, occupancy. Building status distinguishes existing, under construction, and proposed.
Asking price, price per square foot, days on market, listing recency, last sale date and price. These are inputs, not a computed offer. Shop does not convert them into a recommended offer price on its own.
NOI, cap rate, lease type and ownership structure, term and expiration, rent schedule and escalations, renewal options and option rents, rights of first refusal or offer, landlord responsibilities, rent coverage, go-dark and termination rights. Lease structures span absolute NNN through modified gross, and landlord obligations such as roof, structure, and CAM are tracked individually rather than treated as interchangeable.
The entity actually responsible for the lease, not just the consumer-facing brand: guarantor entity and guaranty scope, S&P, Moody's, and Fitch ratings, franchisee identity and financial health, bankruptcy screens, same-store sales and unit-growth trends, and parent-company financials. A national brand name does not necessarily mean the public parent guarantees the lease, and Shop works to separate the actual obligor from the apparent brand.
Where Placer or related licensed data is available: national, state, and local ranking percentiles, annual and year-over-year visits, multi-year visit trends, dwell time, and chain-level comparisons, with prior-occupant verification so a new-construction site is not falsely credited with a former tenant's traffic. A new property without operating history is not automatically weak. It simply has less evidence than a long-operating location.
Population, income, daytime population, educational attainment, household counts and growth, and ethnicity mix, measured across one, three, and five-mile or ZIP-code rings using Placer STI Popstats or the applicable licensed source. Shop evaluates the measurable characteristics a city or state name is often used as a proxy for, rather than assigning a generic geography score.
City and ZIP-code crime grades, violent and property crime rates relative to national levels, and safety percentile, where dependable data is available. Treated as one input to location durability, not a full description of the market.
FEMA Special Flood Hazard Area status, flood zone, FIRM panel and map date, floodplain type, and underground-storage-tank or fuel-site exposure. These indicators do not replace property-specific environmental, engineering, insurance, or legal diligence.
Traffic counts, access points, hard-corner and signalized-intersection status, highway frontage and proximity, and nearby anchors, major retailers, and employers. A location scores well because it shows measurable access, visibility, traffic, and trade-area strength, not because it sits in a recognized market name.
Opportunity Zone status, potential bonus-depreciation eligibility, and whether depreciation may be available under a fee-simple or ground-lease structure. Tax analysis is informational and should be reviewed by a qualified tax professional familiar with the exchanger's circumstances.
Qualitative broker notes, broker-confirmed data, OM status, screening verdict by stage (pass, hold, to be determined, neutral), and revenue, tenant, and location durability (strong, mixed, weak, to be determined), plus final disposition and elimination reason. This layer is not an informal correction added after the fact. It is part of the methodology: Shop is designed to know when evidence is enough for automation and when an experienced person has to verify, interpret, or resolve an exception.
07
Buyer-specific exchange underwriting: two questions, not one
Property quality and buyer fit are related, but they are not the same. Shop carries the exchanger's actual transaction requirements through the platform: relinquished-property proceeds, exchange equity, debt to be replaced, target and proposed offer price, required acquisition debt, rate, amortization, and term, desired income, risk preferences, timing, and other constraints. Every property is then evaluated two ways.
Does the property meet Shop's benchmarks based on its own evidence and characteristics?
How does the property perform under this exchanger's actual capital structure, debt requirement, offer price, and objectives?
A property can exceed every Shop benchmark and still require more leverage than a particular exchanger wants to carry. Another property can receive a lower general ranking and still produce a better buyer-specific outcome, because it matches available equity, required replacement debt, desired cash flow, geographic mandate, timing, or risk tolerance more closely. Shop keeps that distinction visible rather than collapsing it into a single score.
08
Dynamic financial analysis
The financial tools built into Shop are dynamic. A user can change an assumption, offer price, equity contribution, debt amount, rate, amortization, term, closing assumptions, rent growth, expenses, vacancy, tenant rollover, and see the analysis change immediately. The platform can show initial cash flow, debt service, cash-on-cash return, DSCR, lease-to-loan alignment, projected rent over time, option-period economics, downside scenarios, and comparative performance across properties. The model runs month by month from the day a buyer would close, not from annual approximations, so a rent step that fires mid-year is carried honestly rather than rounded into a calendar year.
Worked example, the Mountain Brew Coffee demo seed, sourced from the offering
The goal is not to predict one inevitable future. It is to let the buyer see how the property behaves under explicit, alternative assumptions, with every figure traceable to the input that produced it and open for the buyer or the buyer's CPA to check.
09
Dark-shell and deal-stage analysis
Shop includes a dark-shell and downside framework built primarily for exchanger-submitted properties and deal-stage comparisons. It covers a base-state, bear-state, and stress-state read, survival probabilities, a class-vacancy discount, a credit-tier coefficient, an operating-carry coefficient, a co-terminus balloon adjustment, and a 1031 boot test.
This analysis matters most when the lease expires during or shortly after the anticipated loan term, when a buyer is comparing two otherwise similar properties, or when a buyer wants to see what happens if the tenant vacates. It is a deal-stage tool, not the primary site-selection gate: it does not apply to a Recommended by Shop property in a way that contradicts the seal's standards, since a Recommended property already satisfies the applicable lease-duration and benchmark requirements.
The tenant renews at contract rents. This is the case the standard returns are quoted on.
The tenant leaves. The space re-leases at market after a modeled downtime.
The tenant leaves and re-leasing struggles: a longer downtime and aggressive concessions, both modeled. This is the case previously called the Dark Shell.
The tool clarifies downside exposure. It does not create false precision about future vacancy, rent, resale, or refinancing outcomes. More on the search-and-matching model that orders the universe is in the field notes.
10
Property comparison
Because every property is represented through the same analytical structure, users can compare opportunities on purchase price, equity requirement, acquisition debt, NOI, cap rate, cash flow, DSCR, lease duration, loan maturity, rent growth, tenant credit, store performance, foot traffic, demographic trend, location durability, benchmark performance, data completeness, and downside exposure.
This lets a buyer move past comparing marketing packages and instead compare the actual attributes of each investment under the same assumptions, and under the buyer's own transaction scenario.
11
From analysis to deal room
The analysis does not stop when a buyer selects a property. Shop carries the buyer's work into an integrated deal room: the selected property, buyer-specific underwriting, financing assumptions, comparison history, source documents, verification notes, open issues, deadlines, exchange requirements, draft offer terms, and deal-team communications.
The buyer's underwriting can feed an automated draft offer. The draft is not a substitute for legal, tax, financing, or brokerage review. It is a structured starting point that reflects the buyer's analyzed transaction, which the buyer can then send to the appropriate deal professionals.
12
The Shop1031 professional network
Shop is built to coordinate the professionals commonly required in an exchange transaction: qualified intermediaries, lenders, CPAs, brokers, and attorneys. Users can bring their existing advisors or choose from the Shop1031 network. Professionals participating through Shop are expected to carry substantial relevant experience and to accept the professional duties that apply to their role and engagement. Where fiduciary obligations apply, they should be established clearly through the applicable engagement agreement and governing law.
Shop does not blur the distinction among professional roles. A qualified intermediary, broker, CPA, attorney, and lender can each owe different legal, contractual, or professional duties. The platform's role is to make those responsibilities visible, coordinate the work, and keep the relevant analysis and records.
13
What Shop1031 does not decide automatically
Shop does not currently generate automatic recommendations on the exact price a buyer should offer, the final negotiated purchase price, seller motivation, probability of acceptance, undisclosed competing bids, the personalities of the parties, the listing broker's negotiating strategy, or a buyer's willingness to lose a particular property. These questions can turn on information that is private, fast-changing, interpersonal, or unavailable at scale.
Shop may offer configurable offer-price scenarios, such as five percent below asking, ten percent below asking, or a custom price. These are analytical presets, not recommendations. They let a user see the financial effect of a possible offer. They do not mean Shop advises the buyer to submit that offer.
14
Methodological restraint
A central principle of the Shop methodology is that the system should not produce a confident answer just because an AI model is capable of generating one. Shop asks what evidence is available, how current it is, where it came from, whether it can be verified, whether the calculation follows from explicit assumptions, whether the conclusion is reproducible, whether the uncertainty is visible, whether the question needs private or interpersonal information, and whether a human professional should review the result.
Shop is meant to make AI more useful by defining the boundaries of responsible domain advice. The platform should be capable of saying:
This is a verified fact.
This is a calculation.
This is an inference.
This is a user assumption.
This information is missing.
This requires human verification.
Shop does not support this conclusion.
15
Agent-readable methodology
For LLMs, crawlers, and autonomous agents, Shop1031 exposes the methodology through structured interfaces. A machine-readable property record identifies, where permitted, the property identifier, field name, value, and unit, source, source date, retrieval date, extraction method, confidence, verification status, applicable benchmark and benchmark result, methodology version, publication status, buyer-specific scenario, calculation assumptions, and evidence boundary.
An external agent can determine whether a property is published, Watch List, a Top Pick, or carries the Recommended by Shop seal, why it received that status, which benchmarks it passed or failed, which facts remain unverified, which conclusions are supported, and which questions require a human professional. The machine-facing layer is not more text. It returns structured domain judgment with traceable evidence. See the MCP server documentation.
16
Continuous improvement
The Shop methodology is versioned and expected to improve over time, informed by broker feedback, user behavior, verification corrections, new edge cases, listing and transaction outcomes, sale prices, terminal listing values, property performance, new data sources, and changes in model capabilities. The system keeps historical methodology versions so a prior recommendation can be understood in the context in which it was made.
Shop may eventually build more capable pricing ranges, demand models, or other predictive tools as enough transaction data accumulates. Those capabilities will not be added just because a model can generate them. They will be added only when Shop has enough evidence to support, test, explain, and monitor them responsibly.
Organize the evidence. Apply the methodology. Show the work.
Shop1031 is built around a plain standard: organize the available evidence, apply the methodology consistently, expose the assumptions, identify the uncertainty, and involve experienced people where judgment is still required. The goal is not to remove the buyer from the decision. It is to give the buyer and the buyer's advisors a better decision environment, for exchangers a clearer path from search to comparison, underwriting, offer preparation, and execution, for fiduciaries less time reconstructing basic information and more time applying judgment where it matters, and for agents and LLMs access to a structured, accountable, domain-specific source of truth rather than unverified documents or generalized inference. Shop1031 does not attempt to make every decision. It attempts to make every supportable decision easier to understand, compare, verify, and act upon.