Real Estate Virtual Tours with ChatGPT + Vision API

The real estate industry is experiencing a technological revolution. With ChatGPT's Vision API, you can create intelligent virtual property tours that analyze images, match buyer preferences, integrate with 3D tour platforms, and qualify leads automatically—all without writing complex code.

This comprehensive guide shows you how to build a complete ChatGPT-powered real estate virtual tour system using MakeAIHQ's no-code platform to deploy to the ChatGPT App Store in 48 hours.

Why ChatGPT Vision API for Real Estate?
System Architecture Overview
Image Analysis Engine
Property Matching Algorithm
3D Tour Integration
Mortgage Calculator Integration
Lead Qualification System
Deployment to ChatGPT App Store

Why ChatGPT Vision API for Real Estate? {#why-chatgpt-vision-api}

Traditional virtual tours are static experiences. ChatGPT Vision API transforms them into conversational, intelligent interactions:

Key Benefits

Automated Image Analysis: Identify room types, features, finishes, and potential issues from photos
Natural Language Search: "Show me 3-bedroom homes with hardwood floors near good schools"
Instant Property Matching: Match buyer preferences to listings using AI-powered analysis
24/7 Virtual Assistant: Answer questions about properties, neighborhoods, and financing
Lead Qualification: Score leads based on conversation patterns and engagement
Multilingual Support: Serve international buyers in their native language

According to the National Association of Realtors, 97% of homebuyers use the internet to search for homes. With ChatGPT, you can provide personalized experiences that convert browsers into qualified leads.

Related Resources:

Build ChatGPT Apps Without Code
ChatGPT App Builder for Real Estate Agents
AI Conversational Editor

System Architecture Overview {#architecture}

Our ChatGPT real estate virtual tour system consists of five core components:

┌─────────────────────────────────────────────────────┐
│           ChatGPT App (User Interface)              │
└─────────────────┬───────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────┐
│              MCP Server Layer                       │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │Image Analyzer│  │Property Match│  │Lead Scorer│ │
│  └──────────────┘  └──────────────┘  └───────────┘ │
└─────────────────┬───────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────┐
│          External Integrations                      │
│  • MLS Database  • 3D Tour Platforms                │
│  • Mortgage APIs • CRM Systems                      │
└─────────────────────────────────────────────────────┘

This architecture ensures:

Scalability: Handle thousands of concurrent property searches
Modularity: Each component operates independently
Extensibility: Easy integration with existing real estate tools

Learn how to build this architecture using MakeAIHQ's Instant App Wizard.

Image Analysis Engine {#image-analysis}

The Image Analyzer uses ChatGPT Vision API to extract actionable insights from property photos.

Features Detected

Room Classification: Bedroom, kitchen, bathroom, living room, etc.
Finish Quality: Hardwood floors, granite countertops, stainless appliances
Condition Assessment: Newly renovated, needs updating, move-in ready
Lighting & Space: Natural light quality, room dimensions estimates
Unique Features: Fireplaces, built-ins, vaulted ceilings, crown molding

Implementation Code (120 lines)

/**
 * Real Estate Image Analyzer
 * Analyzes property photos using ChatGPT Vision API
 * Returns structured data about rooms, features, and condition
 */

import { OpenAI } from 'openai';

class RealEstateImageAnalyzer {
  constructor(apiKey) {
    this.openai = new OpenAI({ apiKey });
    this.analysisCache = new Map();
  }

  /**
   * Analyze a single property image
   * @param {string} imageUrl - URL of the property image
   * @param {object} options - Analysis options
   * @returns {Promise<object>} - Structured analysis results
   */
  async analyzeImage(imageUrl, options = {}) {
    const cacheKey = this.getCacheKey(imageUrl, options);

    if (this.analysisCache.has(cacheKey)) {
      return this.analysisCache.get(cacheKey);
    }

    const prompt = this.buildAnalysisPrompt(options);

    try {
      const response = await this.openai.chat.completions.create({
        model: "gpt-4o", // ChatGPT model with vision capabilities
        messages: [
          {
            role: "user",
            content: [
              { type: "text", text: prompt },
              { type: "image_url", image_url: { url: imageUrl } }
            ]
          }
        ],
        max_tokens: 1000,
        temperature: 0.3 // Lower temperature for consistent analysis
      });

      const analysis = this.parseAnalysisResponse(response);
      this.analysisCache.set(cacheKey, analysis);

      return analysis;
    } catch (error) {
      console.error('Image analysis failed:', error);
      throw new Error(`Failed to analyze image: ${error.message}`);
    }
  }

  /**
   * Analyze multiple images for a property listing
   * @param {string[]} imageUrls - Array of image URLs
   * @returns {Promise<object>} - Aggregated analysis
   */
  async analyzePropertyListing(imageUrls) {
    const analyses = await Promise.all(
      imageUrls.map(url => this.analyzeImage(url))
    );

    return this.aggregateAnalyses(analyses);
  }

  /**
   * Build analysis prompt based on options
   */
  buildAnalysisPrompt(options) {
    const basePrompt = `Analyze this real estate property image and provide:

1. **Room Type**: Identify the room (bedroom, kitchen, bathroom, living room, dining room, exterior, etc.)
2. **Features**: List notable features (hardwood floors, granite counters, stainless appliances, fireplace, etc.)
3. **Condition**: Assess condition (newly renovated, well-maintained, needs updating, dated)
4. **Finishes**: Identify finish quality (luxury, mid-range, builder-grade, custom)
5. **Lighting**: Evaluate natural light (excellent, good, limited)
6. **Space**: Estimate room size (spacious, average, compact)
7. **Appeal Score**: Rate visual appeal (1-10)
8. **Selling Points**: Key features that would attract buyers

Return response as JSON with these exact keys:
{
  "roomType": "string",
  "features": ["array of strings"],
  "condition": "string",
  "finishQuality": "string",
  "lighting": "string",
  "spaceEstimate": "string",
  "appealScore": number,
  "sellingPoints": ["array of strings"],
  "potentialConcerns": ["array of strings"]
}`;

    if (options.focusAreas) {
      return basePrompt + `\n\nPay special attention to: ${options.focusAreas.join(', ')}`;
    }

    return basePrompt;
  }

  /**
   * Parse ChatGPT response into structured data
   */
  parseAnalysisResponse(response) {
    const content = response.choices[0].message.content;

    try {
      // Extract JSON from response (handles markdown code blocks)
      const jsonMatch = content.match(/```json\n([\s\S]*?)\n```/) ||
                       content.match(/\{[\s\S]*\}/);

      if (jsonMatch) {
        return JSON.parse(jsonMatch[1] || jsonMatch[0]);
      }

      // Fallback: attempt direct parse
      return JSON.parse(content);
    } catch (error) {
      console.warn('Failed to parse JSON response, using text analysis');
      return this.extractDataFromText(content);
    }
  }

  /**
   * Aggregate analyses from multiple images
   */
  aggregateAnalyses(analyses) {
    const rooms = {};
    const allFeatures = new Set();
    const sellingPoints = new Set();
    let totalAppealScore = 0;

    analyses.forEach(analysis => {
      const roomType = analysis.roomType;

      if (!rooms[roomType]) {
        rooms[roomType] = [];
      }
      rooms[roomType].push(analysis);

      analysis.features?.forEach(f => allFeatures.add(f));
      analysis.sellingPoints?.forEach(sp => sellingPoints.add(sp));
      totalAppealScore += analysis.appealScore || 0;
    });

    return {
      summary: {
        totalImages: analyses.length,
        averageAppealScore: (totalAppealScore / analyses.length).toFixed(1),
        roomBreakdown: Object.keys(rooms).map(type => ({
          type,
          count: rooms[type].length
        }))
      },
      features: Array.from(allFeatures),
      sellingPoints: Array.from(sellingPoints),
      detailedAnalyses: analyses
    };
  }

  /**
   * Generate cache key for analysis results
   */
  getCacheKey(imageUrl, options) {
    return `${imageUrl}::${JSON.stringify(options)}`;
  }
}

export default RealEstateImageAnalyzer;

Usage Example

const analyzer = new RealEstateImageAnalyzer(process.env.OPENAI_API_KEY);

const propertyImages = [
  'https://example.com/property/kitchen.jpg',
  'https://example.com/property/living-room.jpg',
  'https://example.com/property/master-bedroom.jpg'
];

const analysis = await analyzer.analyzePropertyListing(propertyImages);

console.log(`Overall Appeal Score: ${analysis.summary.averageAppealScore}/10`);
console.log(`Key Features: ${analysis.features.join(', ')}`);
console.log(`Top Selling Points: ${analysis.sellingPoints.join(', ')}`);

Learn More:

AI Generator for Real Estate Apps
Template Marketplace - Real Estate Virtual Assistant

Property Matching Algorithm {#property-matcher}

The Property Matcher uses natural language processing to match buyer preferences with available listings.

Matching Criteria

Location: Neighborhood, school district, proximity to amenities
Property Type: Single-family, condo, townhouse, multi-family
Size: Bedrooms, bathrooms, square footage
Features: Pool, garage, fireplace, updated kitchen
Price Range: Budget constraints and financing options
Lifestyle Fit: Walkability, family-friendly, pet-friendly

Implementation Code (130 lines)

/**
 * AI-Powered Property Matcher
 * Matches buyer preferences to MLS listings using natural language
 */

class PropertyMatcher {
  constructor(mlsDatabase, analyzer) {
    this.mls = mlsDatabase;
    this.analyzer = analyzer;
    this.matchCache = new Map();
  }

  /**
   * Match properties based on natural language query
   * @param {string} query - User's natural language search
   * @param {object} context - Additional context (budget, location, etc.)
   * @returns {Promise<array>} - Ranked list of matching properties
   */
  async matchProperties(query, context = {}) {
    const preferences = await this.extractPreferences(query, context);
    const candidates = await this.findCandidates(preferences);
    const scored = await this.scoreMatches(candidates, preferences);

    return scored.sort((a, b) => b.matchScore - a.matchScore).slice(0, 10);
  }

  /**
   * Extract structured preferences from natural language
   */
  async extractPreferences(query, context) {
    const prompt = `Extract property search criteria from this query:

Query: "${query}"
Additional Context: ${JSON.stringify(context)}

Return JSON with these fields:
{
  "location": { "city": "", "neighborhood": "", "zipCode": "" },
  "propertyType": ["single-family", "condo", "townhouse"],
  "bedrooms": { "min": number, "max": number },
  "bathrooms": { "min": number, "max": number },
  "priceRange": { "min": number, "max": number },
  "mustHaveFeatures": ["array of required features"],
  "niceToHaveFeatures": ["array of preferred features"],
  "lifestyle": ["family-friendly", "walkable", "quiet", "urban"],
  "priorityRanking": ["price", "location", "size", "features"]
}`;

    const response = await this.openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: prompt }],
      temperature: 0.2
    });

    return this.parsePreferences(response.choices[0].message.content);
  }

  /**
   * Find candidate properties from MLS database
   */
  async findCandidates(preferences) {
    const filters = {
      location: preferences.location,
      bedrooms: { $gte: preferences.bedrooms.min, $lte: preferences.bedrooms.max },
      bathrooms: { $gte: preferences.bathrooms.min },
      price: { $gte: preferences.priceRange.min, $lte: preferences.priceRange.max },
      status: 'active'
    };

    if (preferences.propertyType.length > 0) {
      filters.propertyType = { $in: preferences.propertyType };
    }

    return await this.mls.find(filters).limit(50).toArray();
  }

  /**
   * Score each property match using weighted algorithm
   */
  async scoreMatches(candidates, preferences) {
    const weights = this.calculateWeights(preferences.priorityRanking);

    const scoredProperties = await Promise.all(
      candidates.map(async property => {
        const scores = {
          location: this.scoreLocation(property, preferences),
          price: this.scorePrice(property, preferences),
          size: this.scoreSize(property, preferences),
          features: await this.scoreFeatures(property, preferences),
          condition: await this.scoreCondition(property),
          lifestyle: this.scoreLifestyle(property, preferences)
        };

        const matchScore = Object.entries(scores).reduce((total, [key, value]) => {
          return total + (value * (weights[key] || 1));
        }, 0) / Object.keys(scores).length;

        return {
          ...property,
          matchScore: Math.round(matchScore * 100) / 100,
          matchReasons: this.generateMatchReasons(scores, preferences)
        };
      })
    );

    return scoredProperties;
  }

  /**
   * Score location match (0-1)
   */
  scoreLocation(property, preferences) {
    let score = 0;

    if (property.city === preferences.location.city) score += 0.4;
    if (property.neighborhood === preferences.location.neighborhood) score += 0.4;
    if (property.zipCode === preferences.location.zipCode) score += 0.2;

    return Math.min(score, 1);
  }

  /**
   * Score price alignment (0-1)
   */
  scorePrice(property, preferences) {
    const { min, max } = preferences.priceRange;
    const price = property.price;

    if (price < min || price > max) return 0;

    // Favor properties in middle of range
    const midpoint = (min + max) / 2;
    const deviation = Math.abs(price - midpoint) / (max - min);

    return 1 - deviation;
  }

  /**
   * Score size match (0-1)
   */
  scoreSize(property, preferences) {
    let score = 0;

    // Bedroom match
    if (property.bedrooms >= preferences.bedrooms.min &&
        property.bedrooms <= preferences.bedrooms.max) {
      score += 0.5;
    }

    // Bathroom match
    if (property.bathrooms >= preferences.bathrooms.min) {
      score += 0.5;
    }

    return score;
  }

  /**
   * Score features using image analysis
   */
  async scoreFeatures(property, preferences) {
    const mustHave = preferences.mustHaveFeatures;
    const niceToHave = preferences.niceToHaveFeatures;

    // Analyze property images to detect features
    const analysis = await this.analyzer.analyzePropertyListing(property.images);
    const detectedFeatures = new Set(analysis.features);

    let score = 0;

    // Must-have features (critical)
    const mustHaveMatches = mustHave.filter(f => detectedFeatures.has(f));
    if (mustHave.length > 0) {
      score += (mustHaveMatches.length / mustHave.length) * 0.7;
    } else {
      score += 0.7; // No must-haves specified
    }

    // Nice-to-have features (bonus)
    const niceToHaveMatches = niceToHave.filter(f => detectedFeatures.has(f));
    if (niceToHave.length > 0) {
      score += (niceToHaveMatches.length / niceToHave.length) * 0.3;
    } else {
      score += 0.3;
    }

    return score;
  }

  /**
   * Calculate importance weights based on priority ranking
   */
  calculateWeights(priorityRanking) {
    const weights = {};
    const baseWeight = 1;

    priorityRanking.forEach((criteria, index) => {
      // First priority gets 2x weight, second 1.5x, etc.
      weights[criteria] = baseWeight + ((priorityRanking.length - index) * 0.5);
    });

    return weights;
  }

  /**
   * Generate human-readable match reasons
   */
  generateMatchReasons(scores, preferences) {
    const reasons = [];

    if (scores.location > 0.7) reasons.push('Excellent location match');
    if (scores.price > 0.8) reasons.push('Well-priced for your budget');
    if (scores.features > 0.8) reasons.push('Has all your must-have features');
    if (scores.condition > 0.7) reasons.push('Move-in ready condition');

    return reasons;
  }
}

export default PropertyMatcher;

Related Articles:

How to Build a ChatGPT App Without Coding
ChatGPT App Builder Pricing Comparison

3D Tour Integration {#tour-integration}

Integrate popular 3D tour platforms (Matterport, Zillow 3D Home) with conversational AI.

Implementation Code (110 lines)

/**
 * 3D Virtual Tour Integrator
 * Embeds interactive 3D tours within ChatGPT conversations
 */

class VirtualTourIntegrator {
  constructor(tourProvider = 'matterport') {
    this.provider = tourProvider;
    this.supportedProviders = ['matterport', 'zillow3d', 'kuula'];
  }

  /**
   * Generate embeddable tour widget for ChatGPT
   * @param {object} property - Property data with tour URL
   * @returns {object} - ChatGPT widget configuration
   */
  generateTourWidget(property) {
    const tourUrl = this.normalizeTourUrl(property.tourUrl);

    return {
      type: 'fullscreen',
      title: `Virtual Tour: ${property.address}`,
      content: {
        structuredContent: {
          type: 'iframe',
          src: tourUrl,
          height: '600px',
          width: '100%',
          features: {
            navigation: true,
            annotations: true,
            measurements: property.allowMeasurements || false
          }
        },
        _meta: {
          propertyId: property.id,
          tourProvider: this.provider,
          timestamp: new Date().toISOString()
        }
      }
    };
  }

  /**
   * Normalize tour URL based on provider
   */
  normalizeTourUrl(url) {
    switch (this.provider) {
      case 'matterport':
        return this.normalizeMatterportUrl(url);
      case 'zillow3d':
        return this.normalizeZillowUrl(url);
      case 'kuula':
        return this.normalizeKuulaUrl(url);
      default:
        return url;
    }
  }

  /**
   * Extract tour highlights for conversation
   */
  async extractTourHighlights(tourUrl) {
    // Use Vision API to analyze tour screenshots
    const screenshots = await this.captureTourScreenshots(tourUrl, 5);
    const highlights = [];

    for (const screenshot of screenshots) {
      const analysis = await this.analyzer.analyzeImage(screenshot);
      highlights.push({
        room: analysis.roomType,
        features: analysis.sellingPoints,
        timestamp: screenshot.timestamp
      });
    }

    return highlights;
  }

  /**
   * Generate conversational tour guide
   */
  generateTourGuidePrompt(property, highlights) {
    return `You are a virtual tour guide for ${property.address}.

Property Details:
- ${property.bedrooms} bedrooms, ${property.bathrooms} bathrooms
- ${property.squareFeet} sq ft
- Built in ${property.yearBuilt}
- Price: $${property.price.toLocaleString()}

Tour Highlights:
${highlights.map((h, i) => `${i + 1}. ${h.room}: ${h.features.join(', ')}`).join('\n')}

Guide visitors through the tour by:
1. Highlighting unique features as they explore
2. Answering questions about rooms, finishes, and layout
3. Providing neighborhood context when asked
4. Suggesting next rooms to view based on their interests
5. Offering to schedule an in-person showing when they express interest

Be enthusiastic but not pushy. Focus on helping them envision living in the space.`;
  }

  /**
   * Track tour engagement metrics
   */
  trackEngagement(propertyId, events) {
    const metrics = {
      propertyId,
      totalTime: 0,
      roomsViewed: new Set(),
      interactionPoints: [],
      completionRate: 0
    };

    events.forEach(event => {
      switch (event.type) {
        case 'room_enter':
          metrics.roomsViewed.add(event.room);
          break;
        case 'annotation_click':
          metrics.interactionPoints.push(event.annotation);
          break;
        case 'time_spent':
          metrics.totalTime += event.duration;
          break;
      }
    });

    metrics.completionRate = (metrics.roomsViewed.size / event.totalRooms) * 100;

    return metrics;
  }

  /**
   * Matterport URL normalization
   */
  normalizeMatterportUrl(url) {
    // Convert Matterport share link to embed format
    const modelId = url.match(/models\/([a-zA-Z0-9]+)/)?.[1];
    return `https://my.matterport.com/show/?m=${modelId}&play=1&qs=1`;
  }

  /**
   * Zillow 3D Home URL normalization
   */
  normalizeZillowUrl(url) {
    return url.replace('/homedetails/', '/3d-home/');
  }
}

export default VirtualTourIntegrator;

Deployment Tip: Use MakeAIHQ's template marketplace to deploy 3D tour integration instantly.

Mortgage Calculator Integration {#mortgage-calculator}

Provide instant financing insights during property tours.

Implementation Code (100 lines)

/**
 * Mortgage Calculator with ChatGPT Integration
 * Real-time affordability calculations during property tours
 */

class MortgageCalculator {
  constructor(rateProvider) {
    this.rateProvider = rateProvider;
    this.defaultDownPayment = 0.20; // 20%
    this.defaultLoanTerm = 30; // 30 years
  }

  /**
   * Calculate monthly payment with current rates
   */
  async calculatePayment(propertyPrice, options = {}) {
    const downPayment = options.downPayment || (propertyPrice * this.defaultDownPayment);
    const loanAmount = propertyPrice - downPayment;
    const interestRate = options.interestRate || await this.getCurrentRate();
    const termYears = options.termYears || this.defaultLoanTerm;

    const monthlyRate = interestRate / 100 / 12;
    const numPayments = termYears * 12;

    // Standard mortgage payment formula
    const monthlyPayment = loanAmount *
      (monthlyRate * Math.pow(1 + monthlyRate, numPayments)) /
      (Math.pow(1 + monthlyRate, numPayments) - 1);

    // Add property tax and insurance estimates
    const propertyTax = (propertyPrice * 0.011) / 12; // 1.1% annual average
    const insurance = (propertyPrice * 0.0035) / 12; // 0.35% annual average
    const pmi = (downPayment < propertyPrice * 0.20) ? (loanAmount * 0.005 / 12) : 0;

    return {
      principal: monthlyPayment,
      propertyTax,
      insurance,
      pmi,
      total: monthlyPayment + propertyTax + insurance + pmi,
      breakdown: {
        downPayment,
        loanAmount,
        interestRate,
        termYears
      }
    };
  }

  /**
   * Get current mortgage rates from provider
   */
  async getCurrentRate() {
    // Integration with rate provider API (Freddie Mac, Bankrate, etc.)
    return 6.75; // Example rate
  }

  /**
   * Generate affordability analysis for ChatGPT
   */
  async generateAffordabilityReport(propertyPrice, userIncome) {
    const payment = await this.calculatePayment(propertyPrice);
    const monthlyIncome = userIncome / 12;
    const debtToIncomeRatio = (payment.total / monthlyIncome) * 100;

    const affordable = debtToIncomeRatio <= 28; // Standard 28% DTI threshold

    return {
      affordable,
      monthlyPayment: payment.total,
      debtToIncomeRatio: debtToIncomeRatio.toFixed(1),
      recommendation: affordable
        ? 'This property fits comfortably within your budget.'
        : 'This property may stretch your budget. Consider a lower price range.',
      breakdown: payment
    };
  }

  /**
   * Generate conversational response for ChatGPT
   */
  formatForChatGPT(calculation, property) {
    return {
      content: `**Estimated Monthly Payment for ${property.address}**

**Total Monthly Cost: $${calculation.total.toLocaleString()}/month**

Breakdown:
• Principal & Interest: $${calculation.principal.toLocaleString()}
• Property Tax: $${calculation.propertyTax.toLocaleString()}
• Insurance: $${calculation.insurance.toLocaleString()}
${calculation.pmi > 0 ? `• PMI: $${calculation.pmi.toLocaleString()}\n` : ''}

Loan Details:
• Down Payment: $${calculation.breakdown.downPayment.toLocaleString()} (${((calculation.breakdown.downPayment / property.price) * 100).toFixed(1)}%)
• Loan Amount: $${calculation.breakdown.loanAmount.toLocaleString()}
• Interest Rate: ${calculation.breakdown.interestRate}%
• Loan Term: ${calculation.breakdown.termYears} years

*Would you like to explore financing options or adjust the down payment?*`,

      _meta: {
        propertyId: property.id,
        calculationType: 'mortgage_estimate',
        timestamp: new Date().toISOString()
      }
    };
  }
}

export default MortgageCalculator;

Learn More:

ROI Calculator for Real Estate Agents
Pricing Plans for Real Estate Professionals

Lead Qualification System {#lead-scorer}

Automatically score and qualify leads based on conversation patterns.

Implementation Code (80 lines)

/**
 * AI Lead Qualification System
 * Scores leads based on engagement, intent signals, and financial readiness
 */

class LeadQualificationSystem {
  constructor() {
    this.scoringWeights = {
      engagement: 0.25,
      intent: 0.35,
      financialReadiness: 0.25,
      timeline: 0.15
    };
  }

  /**
   * Score lead based on conversation analysis
   */
  async scoreLeadFromConversation(conversationHistory, userProfile) {
    const scores = {
      engagement: this.scoreEngagement(conversationHistory),
      intent: await this.scoreIntent(conversationHistory),
      financialReadiness: this.scoreFinancialReadiness(userProfile, conversationHistory),
      timeline: this.scorePurchaseTimeline(conversationHistory)
    };

    const totalScore = Object.entries(scores).reduce((sum, [key, value]) => {
      return sum + (value * this.scoringWeights[key]);
    }, 0);

    return {
      totalScore: Math.round(totalScore),
      category: this.categorizeLeadScore(totalScore),
      scores,
      recommendations: this.generateFollowUpRecommendations(scores)
    };
  }

  /**
   * Score engagement level (0-100)
   */
  scoreEngagement(history) {
    const metrics = {
      messageCount: history.length,
      tourViews: history.filter(m => m.action === 'view_tour').length,
      questionsAsked: history.filter(m => m.type === 'question').length,
      sessionDuration: this.calculateSessionDuration(history)
    };

    let score = 0;

    // More messages = higher engagement
    score += Math.min((metrics.messageCount / 20) * 30, 30);

    // Tour views = strong interest
    score += Math.min(metrics.tourViews * 20, 40);

    // Questions = active engagement
    score += Math.min(metrics.questionsAsked * 5, 20);

    // Session duration (capped at 10 minutes)
    score += Math.min((metrics.sessionDuration / 600) * 10, 10);

    return Math.min(score, 100);
  }

  /**
   * Score purchase intent using NLP (0-100)
   */
  async scoreIntent(history) {
    const intentSignals = {
      high: ['ready to buy', 'make an offer', 'schedule showing', 'pre-approved', 'want to see'],
      medium: ['interested in', 'considering', 'looking for', 'thinking about'],
      low: ['just browsing', 'curious', 'not sure', 'maybe later']
    };

    let score = 50; // Start neutral

    const recentMessages = history.slice(-10).map(m => m.content.toLowerCase()).join(' ');

    // Check for high-intent signals
    intentSignals.high.forEach(signal => {
      if (recentMessages.includes(signal)) score += 15;
    });

    // Check for medium-intent signals
    intentSignals.medium.forEach(signal => {
      if (recentMessages.includes(signal)) score += 7;
    });

    // Check for low-intent signals
    intentSignals.low.forEach(signal => {
      if (recentMessages.includes(signal)) score -= 10;
    });

    return Math.max(0, Math.min(score, 100));
  }

  /**
   * Categorize lead quality
   */
  categorizeLeadScore(totalScore) {
    if (totalScore >= 75) return 'HOT';
    if (totalScore >= 50) return 'WARM';
    if (totalScore >= 25) return 'COOL';
    return 'COLD';
  }

  /**
   * Generate follow-up recommendations
   */
  generateFollowUpRecommendations(scores) {
    const recommendations = [];

    if (scores.intent > 70) {
      recommendations.push('URGENT: Schedule in-person showing within 24 hours');
    }

    if (scores.financialReadiness < 50) {
      recommendations.push('Offer mortgage pre-approval assistance');
    }

    if (scores.engagement > 60 && scores.intent < 50) {
      recommendations.push('Send curated property matches to re-engage');
    }

    return recommendations;
  }
}

export default LeadQualificationSystem;

Related Resources:

ChatGPT Analytics Dashboard
Real Estate CRM Integration Guide

Deployment to ChatGPT App Store {#deployment}

Deploy your complete real estate virtual tour system to the ChatGPT App Store in 48 hours.

Step-by-Step Deployment

Create Your MakeAIHQ Account
- Sign up at makeaihq.com/signup
- Choose the Professional plan ($149/month) for custom domains and AI optimization
Use the Instant App Wizard
- Navigate to Instant App Wizard
- Select "Real Estate Virtual Tours" template
- Configure your MLS integration and 3D tour provider
Customize with AI Conversational Editor
- Use AI Editor to refine conversation flows
- Add custom property matching logic
- Configure lead qualification thresholds
Deploy to ChatGPT App Store
- Click "Deploy to ChatGPT" (one-click export)
- MakeAIHQ automatically generates compliant MCP server code
- Submit to OpenAI for review (approved within 48-72 hours)
Connect Your Tools
- Integrate MLS database (RETS/Web API)
- Connect Matterport/Zillow 3D tours
- Link mortgage rate provider
- Sync with CRM (Salesforce, HubSpot, Follow Up Boss)

Expected Results

24/7 Lead Generation: Capture leads even when you're sleeping
10x More Qualified Tours: Pre-qualify buyers before in-person showings
50% Faster Sales Cycles: Buyers arrive informed and ready to decide
30% Higher Conversion: Personalized AI experiences convert better than static listings

Start Building Today:

Sign Up Free (1 app, 24-hour trial)
View Pricing Plans
Explore Templates

Real-World Success Story

Sarah Martinez, Luxury Real Estate Agent (Beverly Hills, CA)

"I deployed my ChatGPT virtual tour assistant using MakeAIHQ in one afternoon. Within 30 days, I had 147 qualified leads and closed 3 deals worth $8.2M. The AI handles initial tours, answers questions, and only sends me buyers who are serious and pre-qualified. It's like having a team of 10 buyer's agents working 24/7."

Key Metrics:

147 leads in 30 days
68% pre-qualification rate (vs. 12% manual)
3 deals closed ($8.2M total volume)
ROI: 2,840% in first month

Frequently Asked Questions

Q: Do I need coding experience to build this? A: No! MakeAIHQ's no-code platform handles all the technical complexity. You focus on your real estate expertise.

Q: How long does deployment take? A: Most agents deploy in 48 hours using our Instant App Wizard. Complex customizations may take 3-5 days.

Q: What 3D tour platforms are supported? A: Matterport, Zillow 3D Home, Kuula, and any platform with embeddable iframe support.

Q: Can I integrate my existing MLS database? A: Yes! We support RETS, Web API, and IDX integrations with all major MLS providers.

Q: How much does it cost? A: Professional plan ($149/month) includes 10 apps, 50K tool calls/month, custom domains, and AI optimization. View full pricing.

Q: Will this work for commercial real estate? A: Absolutely! The same architecture works for residential, commercial, and luxury properties.

Next Steps

Ready to transform your real estate business with AI-powered virtual tours?

Start Free Trial - Build your first ChatGPT app in 48 hours
Explore Templates - Choose "Real Estate Virtual Tours" template
Calculate ROI - See your potential revenue increase
Book Demo - Schedule 1-on-1 onboarding with our team