Lead Management

AI Lead Scoring Worksheet Template for Real Estate Agents

AI Systems Instructor • Real Estate Technologist

Quick Answer: This fill-in-the-blank lead scoring worksheet helps you build a weighted system that ranks leads by likelihood to transact. Define your criteria, assign weights, set thresholds, and let AI score each lead so you know exactly who to call first every morning.

Most agents treat every lead the same. That's why they burn out chasing people who registered on Zillow at 2 AM and never came back. This worksheet gives you a weighted scoring system so AI can rank your leads by likelihood to transact—and you can spend your time on the ones who matter.

The Template

You are a [ROLE] specializing in [MARKET_AREA]. I need you to score the following lead based on the weighted criteria below. Scoring Criteria (each scored 1-10, multiplied by weight): 1. [CRITERIA_1] — Weight: [WEIGHT_1] 2. [CRITERIA_2] — Weight: [WEIGHT_2] 3. [CRITERIA_3] — Weight: [WEIGHT_3] 4. [CRITERIA_4] — Weight: [WEIGHT_4] 5. [CRITERIA_5] — Weight: [WEIGHT_5] 6. [CRITERIA_6] — Weight: [WEIGHT_6] Lead Information: - Name: [LEAD_NAME] - Source: [LEAD_SOURCE] - First contact date: [FIRST_CONTACT] - Last activity: [LAST_ACTIVITY] - Behaviors observed: [BEHAVIORS] - Budget range: [BUDGET] - Timeline stated: [TIMELINE] Scoring threshold: [THRESHOLD] points = hot lead, route to agent immediately. Below [THRESHOLD_MID] = nurture sequence. Below [THRESHOLD_LOW] = long-term drip only. CRM system: [CRM_SYSTEM] Output format: Score each criterion, show the math, give the total weighted score, and recommend the appropriate action (immediate call, nurture, or drip). Include a one-sentence justification for the recommendation.

Placeholders to Fill In

[ROLE]

The AI persona for scoring context

e.g., real estate lead analyst and conversion strategist

[MARKET_AREA]

Your geographic market

e.g., Nashville and Middle Tennessee

[CRITERIA_1 through CRITERIA_6]

Your scoring factors (e.g., timeline urgency, budget clarity, engagement frequency, mortgage status, property views, response rate)

e.g., Timeline Urgency (buying within 0-3 months vs 12+ months)

[WEIGHT_1 through WEIGHT_6]

Multiplier for each criterion based on importance (1x-3x)

e.g., 3x (highest priority)

[LEAD_SOURCE]

Where the lead originated

e.g., Ylopo Facebook ad, Zillow inquiry, open house sign-in

[BEHAVIORS]

Observable actions the lead has taken

e.g., Viewed 14 listings in East Nashville, saved 3 favorites, opened last 4 emails

[THRESHOLD]

Score that triggers hot-lead routing

e.g., 70 out of 100

[THRESHOLD_MID]

Score that triggers nurture sequence

e.g., 40 out of 100

[THRESHOLD_LOW]

Score below which lead enters long-term drip

e.g., 25 out of 100

[CRM_SYSTEM]

Your CRM for implementation notes

e.g., Follow Up Boss

5 Essentials + HOME Framework

How to Use This Template

Follow these steps to get the best results. Each step maps to proven frameworks taught in AI Acceleration.

Define Your Scoring Criteria

5 Essentials - Essential 2: Context Engineering

Pick 5-6 factors that actually predict whether someone will transact. Timeline urgency and mortgage pre-approval are almost always the two highest-weight factors. Engagement frequency (how often they search, open emails, respond to texts) tells you if they're actively in-market. Avoid vanity metrics like 'number of page views' unless paired with recency.

Assign Weights That Reflect Your Market

HOME Framework - M (Materials)

Not all criteria matter equally. In Nashville's competitive market, a pre-approved buyer with a 60-day timeline is worth 3x a casual browser. Assign weights of 1x, 2x, or 3x to each criterion. Your weights should reflect YOUR conversion data—if open house attendees close at 2x the rate of online leads in your market, weight that higher.

Set Action Thresholds

OODA Loop - Orient

Define three tiers: hot (agent calls within 5 minutes), warm (enters AI nurture sequence), and cold (long-term drip). Most agents set the hot threshold at 65-75% of the maximum possible score. Test your thresholds against your last 20 closed deals—if those clients would've scored below your hot threshold, your weights are wrong.

Test Against Real Leads

OODA Loop - Decide and Act

Pull 10 recent leads from your CRM. Five that closed, five that went cold. Run them through the scoring template. If the closed leads don't score above your hot threshold, adjust your criteria and weights. This calibration step separates a useful scoring system from a theoretical exercise.

Automate in Your CRM

5 Essentials - Essential 4: Process Integration

Once your scoring model is calibrated, build it into your CRM. Follow Up Boss uses smart lists and tags. kvCORE has built-in lead scoring. If your CRM doesn't support scoring natively, use the AI template to score leads manually each morning—it takes 5 minutes and tells you exactly who to call first.

Before & After

Filled Example

Template with Your Details

You are a real estate lead analyst and conversion strategist specializing in Nashville and Middle Tennessee. I need you to score the following lead based on the weighted criteria below.

Scoring Criteria (each scored 1-10, multiplied by weight):
1. Timeline Urgency (buying within 0-3 months = 10, 3-6 months = 7, 6-12 months = 4, 12+ = 2) — Weight: 3x
2. Mortgage Pre-Approval Status (pre-approved = 10, in process = 7, not started = 3) — Weight: 3x
3. Engagement Frequency (daily searches = 10, weekly = 7, monthly = 4, none = 1) — Weight: 2x
4. Budget Clarity (specific range stated = 10, vague = 5, unknown = 2) — Weight: 2x
5. Response Rate (responds within 1 hour = 10, same day = 7, days = 4, never = 1) — Weight: 1x
6. Lead Source Quality (referral/repeat = 10, open house = 8, organic = 6, paid ad = 5, Zillow = 4) — Weight: 1x

Lead Information:
- Name: Sarah Chen
- Source: Ylopo Facebook ad (East Nashville condos)
- First contact date: January 15, 2026
- Last activity: February 8, 2026 (viewed 6 listings, saved 2)
- Behaviors observed: Viewed 22 listings in East Nashville over 3 weeks, saved 4 favorites all in $350-425K range, opened 6 of 7 emails, responded to 1 rDrip text
- Budget range: $350,000-$425,000 (stated in rDrip conversation)
- Timeline stated: 'Moving in March or April'

Scoring threshold: 70 points = hot lead, route to agent immediately.
Below 45 = nurture sequence.
Below 25 = long-term drip only.

CRM system: Follow Up Boss

AI-Generated Result

LEAD SCORE: Sarah Chen 1. Timeline Urgency: 9/10 × 3x = 27 points 'March or April' is 1-2 months out. Scored 9 instead of 10 because no specific move date mentioned yet. 2. Mortgage Pre-Approval: 3/10 × 3x = 9 points No pre-approval mentioned in any conversation. This is the biggest gap to close. 3. Engagement Frequency: 9/10 × 2x = 18 points 22 listings viewed in 3 weeks with 4 saved = near-daily engagement. Highly active searcher. 4. Budget Clarity: 9/10 × 2x = 18 points Stated $350-425K range in conversation. Specific and consistent with saved listings. 5. Response Rate: 5/10 × 1x = 5 points Responded to 1 of what appears to be multiple outreach attempts. Opens emails but doesn't reply often. 6. Lead Source Quality: 5/10 × 1x = 5 points Paid Facebook ad lead. Standard source quality. TOTAL WEIGHTED SCORE: 82/120 (68%) RECOMMENDATION: HOT LEAD — Route to agent immediately. Sarah's timeline urgency and engagement frequency are among the strongest indicators of a near-term buyer. The critical next step is a phone call focused on mortgage pre-approval status. If she's not pre-approved, connect her with your lending partner today—her March timeline means she needs to start the process this week. Tag in Follow Up Boss: Hot, East Nashville, Condo, $350-425K. Set a task for same-day callback.

Template Variations

Alternative versions for different use cases.

Buyer Lead Scoring

Optimized for scoring buyer leads with purchase-specific criteria

Score this buyer lead using the following criteria: 1. Timeline to purchase — Weight: 3x 2. Pre-approval status — Weight: 3x 3. Property search activity (frequency + recency) — Weight: 2x 4. Budget specificity — Weight: 2x 5. Communication responsiveness — Weight: 1x 6. Life event trigger (relocation, growing family, downsizing) — Weight: 1x Lead details: [PASTE LEAD INFO] Market: [MARKET_AREA] Hot threshold: [THRESHOLD] points

Seller Lead Scoring

Optimized for scoring potential seller leads

Score this seller lead using the following criteria: 1. Motivation level (must sell vs. testing waters) — Weight: 3x 2. Timeline to list — Weight: 3x 3. Home equity position (estimated) — Weight: 2x 4. Previous agent relationship (none = opportunity) — Weight: 2x 5. Engagement with home valuation content — Weight: 1x 6. Life event trigger (divorce, job change, estate, downsizing) — Weight: 1x Lead details: [PASTE LEAD INFO] Market: [MARKET_AREA] Hot threshold: [THRESHOLD] points

Investor Lead Scoring

For scoring real estate investor leads

Score this investor lead using the following criteria: 1. Capital availability (verified funds or proof of financing) — Weight: 3x 2. Purchase timeline — Weight: 2x 3. Investment experience (number of previous purchases) — Weight: 2x 4. Target criteria specificity (knows cap rate, neighborhoods, property type) — Weight: 2x 5. Decision speed (makes offers quickly vs. over-analyzes) — Weight: 2x 6. Communication responsiveness — Weight: 1x Lead details: [PASTE LEAD INFO] Market: [MARKET_AREA] Hot threshold: [THRESHOLD] points

Frequently Asked Questions

Why use AI for lead scoring instead of my CRM's built-in scoring?

Most CRM lead scoring is binary—it tracks actions (opened email = +5, viewed listing = +3) but doesn't understand context. AI can weigh the MEANING behind actions. Viewing 20 listings in a week means something different than viewing 20 listings over six months. AI also processes unstructured data like conversation transcripts, which CRM scoring ignores entirely. Use your CRM scoring as the baseline and AI scoring as the intelligence layer on top.

How many scoring criteria should I use?

Five to six. Fewer than five and you're missing important signals. More than eight and you're over-engineering a system that needs to be fast and actionable. The 80/20 rule applies: timeline urgency and financial readiness (pre-approval or proof of funds) predict 80% of conversions. Everything else is refinement. Start with those two at 3x weight and build from there.

How often should I recalibrate my scoring model?

Quarterly. Pull your closed transactions from the past 90 days and score them retroactively. If your model correctly identified 80%+ of them as hot leads, your weights are solid. If not, adjust. Markets shift—a scoring model built in a seller's market won't work the same in a balanced market because timeline urgency and price sensitivity change. The OODA Loop applies here: Observe your results, Orient against market conditions, Decide what to adjust, Act on the changes.

Can I use this template with any CRM?

Yes. The template generates a score and recommendation—you implement it however your CRM allows. Follow Up Boss users can create smart lists based on tags you apply after scoring. kvCORE has built-in scoring you can calibrate to match this model. Even if you use a simple CRM without scoring features, you can run this template each morning on your new leads and manually prioritize your call list. Five minutes of AI scoring beats an hour of guessing who to call first.

Learn the Frameworks

Lead Scoring AI CRM Lead Nurturing

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