Your CRM Has a Scoring Model. Here Is What It Is Actually Scoring.
Every major real estate CRM now claims AI-powered lead scoring. The pitch is the same: our algorithm identifies your hottest leads so you stop wasting time on people who will never buy or sell.
The reality is more complicated.
Think of it like a teacher grading papers — except instead of looking at answers, the AI looks at behavior. Did the lead open the email? Visit the same listing 8 times? Check mortgage rates at midnight? Each CRM watches different behaviors, weighs them differently, and produces scores that mean entirely different things.
Fello AI tracks over 400 data points per homeowner, including ownership length, home equity, and engagement signals. BoldTrail (the platform behind kvCORE) claims 400+ behavioral data points per lead. But tracking data points is not the same as scoring accurately. The model behind those data points matters more than the count.
Here is the problem most agents miss: a "hot" lead in kvCORE is not the same as a "hot" lead in Ylopo. Different inputs, different weights, different thresholds. Comparing scores across platforms is like comparing Fahrenheit to Celsius and wondering why the numbers don't match.
Agents waste 60%+ of follow-up time on leads that will never convert. The right scoring model doesn't eliminate that waste entirely, but it narrows it. The question is which model narrows it best for your business.
How Each CRM Scores Differently
Four platforms dominate AI scoring in real estate CRMs. Each takes a fundamentally different approach.
kvCORE/BoldTrail — Behavioral Automation
kvCORE's parent company BoldTrail built its Marketing Autopilot around behavioral tracking. The system monitors 400+ data points per lead: website visits, listing saves, email opens, property search patterns, and engagement timing. The AI uses these signals to trigger automated follow-up sequences.
BoldTrail claims their automation drives 5-10X more engagement than standard drip campaigns. The strength is breadth — few platforms track as many behavioral signals. The weakness is complexity. Most agents never configure it properly, which means the scoring model runs on incomplete data.
Starting price: approximately $299/month.
Lofty — Agentic AI (New February 2026)
Lofty launched what they call the first Agentic AI Operating System for real estate in February 2026. Instead of just scoring leads, Lofty deploys six autonomous AI agents that plan and execute workflows: lead qualification, follow-up scheduling, task routing, and more.
This is the most ambitious approach on the market. The AI doesn't just flag who to call — it handles parts of the follow-up itself. The strength is autonomous action. The weakness is obvious: it launched weeks ago. No independent conversion data exists yet. Early adopters are testing it in production right now.
Starting price: approximately $449/month.
Ylopo — AI Texting + Scoring
Ylopo's differentiator is AI-powered text messaging. Their system engages leads via text conversation, qualifies them through natural dialogue, and scores them based on response patterns and stated intent.
The data is compelling. Ylopo reports that AI texting converts 50% more leads than human agents. A mid-sized California brokerage documented a 40% increase in conversion rate after implementing Ylopo's AI texting. The strength is proven conversion data with real numbers behind it. The weakness is scope — Ylopo is texting-focused, not a full CRM. You likely need it paired with another platform.
Starting price: approximately $295/month.
Follow Up Boss — Integration-First
Follow Up Boss takes a different philosophy. Instead of building a native AI scoring model, they built the best integration layer in the industry. FUB connects to Ylopo, CINC, Sierra Interactive, and dozens of other lead sources, pulling in their scoring data and centralizing it.
The strength is flexibility. You pick the scoring model you trust and route it through FUB's workflow engine. The weakness is that FUB itself doesn't score leads — it depends entirely on whatever system feeds it. No integration, no scoring.
Starting price: $69/month.
Scoring Model Comparison
| Feature | kvCORE | Lofty | Ylopo | Follow Up Boss |
|---|---|---|---|---|
| Native AI scoring | Yes | Yes (agentic) | Yes (texting) | No (via integrations) |
| Data points tracked | 400+ | Not disclosed | Behavioral + text | Varies by integration |
| Autonomous follow-up | Partial | Full (6 agents) | Text only | No |
| Proven conversion data | 5-10X engagement | Too new | 50% more than human | N/A |
| Starting price | ~$299/mo | ~$449/mo | ~$295/mo | $69/mo |
Pricing is approximate and may vary by team size and features selected.
Before and After: Maria's Team in Phoenix
Maria runs a six-agent team in Phoenix. Before implementing AI scoring, her team processed 800 leads per month manually. Response times averaged 4 hours. Conversion rate: 2.1%.
She implemented Ylopo AI texting for initial lead engagement and kvCORE's scoring model for prioritization. Response time dropped from 4 hours to 4 minutes. The AI handled the first touch. Her agents focused on the leads the scoring model flagged as ready for a real conversation.
The math: 800 leads at 2.1% conversion = 17 deals per month. After implementation, conversion rose to 3.4% = 27 deals per month. Ten extra deals at $8,500 average GCI = $85,000 per year in additional revenue.
This tracks with broader data. A Seattle-area brokerage documented similar results: response times under 4 minutes, 11.2% conversion rate, 80 deals per year (a 300% increase), and what they calculated as a 25,000% ROI on their AI investment.
The numbers are real. But they require two things the platforms don't advertise: clean data going in, and agents who actually follow up on the leads the AI flags.
Common Mistakes
Mistake 1: Trusting the score without understanding the inputs. Garbage in, garbage out. If your CRM data is stale — wrong phone numbers, duplicate contacts, leads from three years ago mixed with fresh ones — the scoring model learns from noise. Clean your database before expecting useful scores.
Mistake 2: Setting it and forgetting it. AI scoring needs data hygiene on an ongoing basis. New lead sources, changed tagging conventions, agents who forget to log calls — all of these degrade the model over time. Schedule a quarterly audit.
Mistake 3: Comparing scores across CRMs. Each platform uses a different model with different weights. A score of 85 in kvCORE and a score of 85 in Ylopo mean completely different things. Don't compare them. Evaluate each system's scores against actual conversion data within that system.
Mistake 4: Ignoring the human layer. AI scores flag who to call. They don't tell you what to say. The agent who calls a high-scoring lead with a generic script wastes the advantage. Pair AI scoring with role-prompted AI scripts for follow-up conversations.