Building a vendor evaluation scorecard for proptech AI tools

# Building a vendor evaluation scorecard for proptech AI tools

A leasing manager at a 40-property multifamily portfolio recently signed with an AI vendor promising "40% faster tenant screening." Six months later, the tool couldn't export data to their property management system, the vendor wouldn't disclose which credit bureau data fed its risk model, and switching costs turned out to exceed the original contract value. This is not a rare story. It's why proptech (property technology) buyers need a structured scorecard, not a sales deck, before signing.

Why proptech AI evaluation is different

Real estate AI tools sit on top of decades-old data infrastructure: MLS (Multiple Listing Service) feeds, PMS (property management systems) like Yardi or RealPage, and fragmented local records (tax assessors, permitting offices). A vendor's model can be technically excellent and still fail in production because it can't talk to your CRMCRMCustomer Relationship Management: software and strategy to manage and analyse customer interactions throughout their lifecycle.Voir la définition complète → or your accounting system.

Evaluation therefore needs four lenses, not one:

1. Data provenanceData provenanceData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète →: where does training and inference data come from

2. Model transparency: can you explain a decision to a tenant, regulator, or investor

3. Integration: does it plug into existing PMS/CRMCRMCustomer Relationship Management: software and strategy to manage and analyse customer interactions throughout their lifecycle.Voir la définition complète → stacks without custom engineering

4. Vendor lock-in risk: what happens if you want to leave in two years

The scorecard structure

Score each vendor 1 (poor) to 5 (excellent) per criterion, then weight by what matters most for your use case (leasing risk tools need higher weight on transparency; maintenance scheduling tools need higher weight on integration).

| Criterion | Weight | Vendor A | Vendor B | Vendor C |

|---|---|---|---|---|

| Data provenanceData provenanceData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète → | 25% | 4 | 2 | 5 |

| Model transparency | 25% | 3 | 2 | 4 |

| PMS/CRMCRMCustomer Relationship Management: software and strategy to manage and analyse customer interactions throughout their lifecycle.Voir la définition complète → integration | 30% | 5 | 4 | 2 |

| Lock-in risk (reverse-scored) | 20% | 3 | 5 | 2 |

Worked calculation (Vendor A): (4×0.25) + (3×0.25) + (5×0.30) + (3×0.20) = 1.0 + 0.75 + 1.5 + 0.6 = 3.85/5

Run the same formula for B and C. The point isn't the decimal precision, it's forcing an explicit tradeoff conversation instead of an emotional one driven by a slick demo.

Criterion 1: Data provenanceData provenanceData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète →

Ask vendors directly: what data trained this model, and what data does it use at inference time?

For tenant screening tools, this matters because of the Fair Credit Reporting Act (FCRA) in the US, which governs how consumer report data (credit history, eviction records) can be used in tenant decisions. If a vendor can't tell you whether their model uses eviction court records that may contain errors or outdated filings, you inherit legal exposure, not just model risk.

In Europe, GDPR (General Data Protection Regulation) requires that automated decisions with legal effect (like rejecting a rental application) be explainable to the data subject on request. A vendor with a black-box scoring model and no audit trail is a compliance liability, not just a technical shortcoming.

Red flag questions:

• "Can you show us the data sources feeding this specific score?"
• "Do you retrain on our portfolio's data, and who owns that derivative model?"
• "Is any training data scraped from listing sites without consent?"

Criterion 2: Model transparency

Transparency doesn't mean you need the source code. It means you need a reason code.

For AI-driven valuation tools (AVMs, automated valuation models, used by companies like Zillow for its Zestimate or CoreLogic for lender-facing valuations), ask whether the vendor can produce a factor breakdown: comparable sales weight, condition adjustments, location premium. If a vendor says "it's proprietary, trust the number," that's a transparency failure, not a trade secret protection.

A useful public reference for how valuation model risk should be documented is the Federal Reserve's SR 11-7 guidance on model risk management, originally written for banks but widely adopted as a best-practice framework by real estate lenders and increasingly by proptech vendors as a due diligence standard.

Simple transparency test: ask the vendor to explain one wrong output. If they can trace it to a specific input error, the system has real interpretability. If they say "the model just does that sometimes," walk away.

Criterion 3: Integration with existing systems

Most portfolio-level AI failures are integration failures, not model failures.

Check specifically for:

• API availability (Application Programming InterfaceApplication Programming InterfaceApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →, the technical connector that lets systems exchange data) with your PMS (Yardi, RealPage, AppFolio, Entrata are the common incumbents in the US)
• Data format compatibility: does the vendor require manual CSV exports, or is there a live sync
• Latency: for maintenance-prediction or dynamic-pricing tools, a 24-hour data lag can make the output useless

Simple integration check (ask the vendor to answer directly):
1. Native API to [your PMS]? Yes/No
2. Real-time sync or batch upload only?
3. Who owns the integration code if we build custom connectors?
4. Average implementation time from contract signature to live data flow?

If the answer to question 4 is "3 to 6 months" for a tool marketed as "plug and play," that's a material finding, not a footnote.

Criterion 4: Vendor lock-in risk

Lock-in isn't just about contract length. Three concrete risk markers:

1. Proprietary data formats: if exiting means you can't extract your own historical data cleanly, that's lock-in

2. Model dependency: if a leasing decision engine has been trained on 18 months of your portfolio's outcomes, switching vendors means losing that tuning, effectively resetting to a generic model

3. Single point of integration: if one vendor's tool has become the hub that three other tools depend on, replacing it becomes a system-wide project, not a swap

Ask for an exit clause that specifies data portability in a standard format (CSV, JSON, or via APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →) within a defined window (30 to 90 days is reasonable) after contract termination. If a vendor resists putting this in writing, treat it as a scorecard flag, not a negotiating point to concede.

Putting it together: a realistic vendor comparison

Consider three archetypal categories active in the market today:

• Established PMS-adjacent AI (features built into Yardi or RealPage's existing suites): strong integration by default, weaker on independent transparency since it's bundled