by CMANarrative (LX AI)
I started CMANarrative after watching sellers stare at a grid of numbers and feel no closer to a price they could defend. The grid tells you what sold; the narrative tells you why it matters to the person holding the listing.
When a seller sees a table of recent sales, the brain looks for a pattern that fits a personal goal – maybe a quick sale, maybe a top‑dollar offer. Numbers alone leave that pattern to imagination, and imagination often fills gaps with fear or hope. A short, clear story connects each comparable to a concrete reason: the renovated kitchen that added $15 k, the lot size that lowered the price per square foot, the timing that shows a seasonal dip. By giving the seller a cause‑effect chain, the narrative reduces anxiety and builds trust in the price range you propose.
A grid shows sale price, date, size, and maybe a few columns of notes. It does not show the reasoning behind those numbers. Without that reasoning, a seller may see a high outlier and think the market is hotter than it is, or see a low outlier and worry the market is collapsing. The grid also hides the context that makes a comparable truly comparable – things like school district changes, new zoning, or a recent foreclosure cluster.
I designed the tool to take the raw comps you already have and turn them into a short, seller‑focused script. The process is deterministic: you feed in the data, the system applies a set of rules derived from real‑world agent conversations, and it outputs a paragraph you can copy into an email or a listing presentation.
Below is a concrete walk‑through using a fictional set of ten comps from a suburban neighborhood. The table shows each step, the input required, and the output you receive.
| Step | Action | Input | Output |
|---|---|---|---|
| 1 | Upload comps | CSV with 10 rows (price, date, sqft, beds, baths, lot, notes) | Validated dataset; 2 rows flagged for missing lot size |
| 2 | Set narrative angle | Choose “emphasize renovation premium” or “highlight seasonal slowdown” | Selected angle stored for rule set |
| 3 | Run pattern detection | System analyzes price per sqft over time, groups by renovation flag | Detects +8 % price premium for renovated kitchens; notes a 0.5 % monthly decline | 4 | Generate draft | Apply rules: lead with renovation premium, add trend note, adjust for lot size outliers | Paragraph: “Renovated kitchens in this area have commanded roughly $14 k more per 100 sq ft over the past six months. While the overall market has softened slightly, homes with larger lots (>0.25 acre) have held steady. Based on these patterns, a list price between $420 k and $445 k aligns with recent buyer behavior.” |
| 5 | Review and tweak | Read draft, edit tone, add local detail (e.g., “near the new park”) | Final seller‑ready talk track |
Type or paste 3-5 comparable sales and the subject into the form. CMANarrative does not import an MLS search, and it does not check a CSV of columns.
Choose list at market, list slightly high, or aggressive / fast sale. That choice only frames the hint. It does not set the seller's price.
The note restates the comps you pasted and offers a listing-price hint. It does not compute a live market statistic or pull sold prices you did not type.
Copy the draft and change it. This is not an appraisal and it does not promise the home will sell at the hint.
It is important to be clear about the limits of the tool so you do not over‑promise to a client.
CMANarrative does not replace a licensed appraisal. It does not consider all the factors an appraiser must weigh, such as cost approach details or specific condition adjustments required by USPAP.
The narrative reflects patterns in the data you provide. It does not promise that a home will sell for any specific number within the suggested range.
You still need to know the nuances of your market – upcoming infrastructure projects, school district changes, or builder incentives. The tool surfaces what the numbers say; you add the qualitative insight.
It does not check for fair‑housing language or advertising rules. You must review the final copy for any regulated claims.
Even with a good story, mistakes can happen. Being aware of them helps you keep the conversation productive.
I keep a written checklist next to every release that touches this topic. The list is boring on purpose: name the owner, name the evidence artifact, and name the rollback. If a step cannot produce a file, a ticket, or a log line, it is not a step — it is a wish.
| Signal | What to record | Stop-ship? |
|---|---|---|
| Unexpected write or send | Tool name, args, identity | Yes until gated |
| Missing confirmation on destructive action | Call path and bypass | Yes |
| Untrusted text treated as instruction | Source surface and resulting action | Yes for agent reach |
| Scanner clean, judgment open | Deferred items + owner | Only if core path safe |
cmanarrative helps with the measurable first pass. Architecture choices, legal sign-off, and customer-facing claims still need a human owner. Treat every checklist row as decision support, not a certificate.
When a prospect asks whether the product "covers everything," I answer with the boundary: automated checks catch known patterns; novel chains and policy judgment still need review. That honesty keeps support load down and keeps the blog aligned with what the product actually does.
If you need a second opinion, pair this write-up with the references below and with your own staging reproduction. Do not skip the reproduction step just because a model wrote a confident summary.
Turn comps into a pricing story sellers believe.
Want to try the product? CMANarrative
Generated 2026-09-17 · primary sources only · no fabricated traffic metrics