Apers vs. Crelytic

Overview

Crelytic is a CRE analytics and data intelligence platform that provides property-level analysis, market insights, and portfolio analytics. It's focused on the data and analytics layer — helping teams understand market conditions, benchmark property performance, and identify investment patterns.

Apers is a deal underwriting and financial modeling system. It starts from deal documents, generates Excel models, sizes debt, models waterfalls, and produces IC-ready output. Apers is focused on the modeling and decision layer — turning data into investment analysis.

The comparison is between analytics (understanding the market) and modeling (making the deal work on paper). Both inform investment decisions, but from different angles.

Feature Comparison

Table 1 — Crelytic provides analytics and market intelligence. Apers generates financial models for specific deals. Different layers of the investment workflow.

Analytics vs. Modeling: Different Questions

Analytics and modeling answer fundamentally different questions, and the distinction matters when choosing tools.

Analytics answers "what's the market doing?" Cap rate trends across submarkets. Absorption rates by asset class. Supply pipeline — how many units are under construction and when they deliver. Rent growth trajectories. Vacancy patterns. These are market-level questions that inform strategy: where should we be looking, and what macro conditions affect our thesis?

Modeling answers "does this specific deal work?" What are the projected returns at this basis? Does the debt coverage ratio hold under stress? How do waterfall distributions flow at different exit cap rates? What's the GP promote at a 1.8x equity multiple vs. a 2.2x? These are deal-level questions that produce investment decisions: should we commit capital to this specific opportunity?

Both inform the investment process, but at different altitudes. Analytics operates at the portfolio and market level — broad patterns that shape where you deploy capital. Modeling operates at the deal level — specific structures that determine whether a particular opportunity meets your return thresholds.

The confusion between these two layers is common, and it leads teams to buy the wrong tool. A team that needs better market intelligence won't solve that problem with a modeling engine. A team drowning in deal documents and Excel spreadsheets won't solve that problem with a dashboarding platform. Identifying which layer is your actual bottleneck is the first step to choosing correctly.

When Crelytic Wins

• Market analysis and benchmarking. Understanding submarket trends, comparable property performance, and market risk indicators. Crelytic's analytics inform the market assumptions that go into your financial models.
• Portfolio monitoring. Tracking performance across a portfolio of owned assets, benchmarking against market data, and identifying properties that are underperforming or outperforming.
• Investment strategy. Which markets are attractive? What cap rate trends are emerging? Where is supply outpacing absorption? Analytics-level questions that inform where to deploy capital.

When Apers Wins

• Deal-level underwriting. Going from "we're interested in this property" to "here's the IC-ready model with returns, debt, waterfall, and sensitivity." Analytics inform assumptions; models produce decisions. See how this works in our underwriting workflow.
• Document-to-model pipeline. Taking the broker's OM, extracting data with Apers' document intelligence engine, reconciling against the T-12, and producing a complete financial model. This is underwriting work, not analytics work.
• Complex deal structures. LIHTC, development pro formas, multi-tranche debt, preferred equity — these require financial modeling, not market analytics.
• IC preparation. Producing the Excel workbook, sensitivity tables, and investment summary that your committee reviews before committing capital.

How to Evaluate

Ask yourself: is your bottleneck understanding the market, or modeling the deal? If you need better market data and portfolio analytics, Crelytic's focus serves that need. If you need to go from deal documents to IC-ready models faster, Apers handles that workflow.

Many institutional teams need both — analytics to inform strategy, and modeling to execute on specific deals. The tools are complementary rather than competitive. For more comparisons, see our full comparison overview.

The Combined Workflow

In practice, sophisticated investment teams layer their analytical tools. Each layer serves a different purpose, and the output of one feeds the input of the next.

Layer 1: Market identification. Platforms like Crelytic identify attractive markets and submarkets. Which MSAs show favorable supply-demand dynamics? Where are cap rates compressing or expanding? What submarkets have absorption that outpaces new deliveries? This narrows the geographic and asset-class focus from "everywhere" to a manageable target list.

Layer 2: Property-level comps. Data providers like CoStar supply the property-level detail — recent sale comps, lease comps, asking rents, and specific building characteristics. Once you've identified a target market, comp data helps you evaluate whether a specific opportunity is priced appropriately relative to recent transactions.

Layer 3: Deal-specific modeling. Apers takes the specific deal — with its actual rent roll, its actual debt terms, its actual waterfall structure — and produces the financial model that answers whether the deal meets your return thresholds. This is where market assumptions from Layer 1 and comp data from Layer 2 get translated into projected cash flows, returns, and sensitivities.

The analytical stack is layered because investment decisions are layered. You don't model a deal before you've identified the market. You don't identify a market without data. And you don't commit capital without a model. Each tool handles the layer it was built for. Trying to force any single tool across all three layers produces mediocre results at every level.