Real Estate AI Agent - Complete Configuration & Implementation Guide
By team on 17 min read
The Real Estate AI Agent transforms property discovery through intelligent search capabilities, market insights, and automated enquiry management. This comprehensive guide covers everything you need to configure, customize, and deploy the Real Estate AI Agent in your property platform.
Overview
The Real Estate AI Agent provides intelligent property assistance through natural language processing, automated property matching, and comprehensive market analysis. It integrates seamlessly with property platforms to enhance user experience and increase lead conversion rates.
Key Capabilities
- Natural Language Property Search - Users can describe their property needs conversationally
- Intelligent Property Matching - AI-powered property recommendations based on preferences
- Market Insights & Analytics - Real-time market data and suburb analysis
- Automated Enquiry Generation - Smart lead capture and follow-up management
- Multi-channel Deployment - Web, mobile, WhatsApp, and Messenger integration
System Architecture
graph TB
User[User] --> Interface[Multi-Channel Interface]
Interface --> |Web Portal| WebPortal[Property Website]
Interface --> |Mobile App| MobileApp[Real Estate App]
Interface --> |WhatsApp| WhatsApp[WhatsApp Business]
Interface --> |Voice| VoiceInterface[Voice Assistant]
WebPortal --> NLP[NLP Engine]
MobileApp --> NLP
WhatsApp --> NLP
VoiceInterface --> NLP
NLP --> PropertyMatcher[Property Matching Engine]
PropertyMatcher --> PropertyDB[(Property Database)]
PropertyMatcher --> MarketData[(Market Data)]
PropertyMatcher --> UserProfile[(User Profiles)]
PropertyDB --> |Property Details| PropertyAPI[Property APIs]
MarketData --> |Market Insights| MarketAPI[Market Data APIs]
PropertyAPI --> |CoreLogic| CoreLogic[CoreLogic API]
PropertyAPI --> |Domain| Domain[Domain API]
PropertyAPI --> |REA| REA[RealEstate.com.au]
MarketAPI --> |Pricing| PricingEngine[Pricing Engine]
MarketAPI --> |Analytics| AnalyticsEngine[Analytics Engine]
PropertyMatcher --> LeadEngine[Lead Management]
LeadEngine --> CRM[CRM Integration]
LeadEngine --> EmailSystem[Email Automation]
LeadEngine --> Notifications[Push Notifications]
CRM --> |Salesforce| Salesforce[Salesforce CRM]
CRM --> |HubSpot| HubSpot[HubSpot CRM]
Core Features Configuration
1. Natural Language Property Search
The Real Estate AI Agent uses advanced NLP to understand complex property requirements and preferences.
Search Configuration Parameters
const propertySearchConfig = {
// Language understanding settings
nlp: {
languages: ["en", "es", "fr"], // Supported languages
confidence_threshold: 0.75,
fallback_behavior: "clarify_requirements",
},
// Property entity extraction
entity_extraction: {
property_types: {
enabled: true,
types: [
"house",
"apartment",
"townhouse",
"villa",
"studio",
"penthouse",
],
synonyms: {
flat: "apartment",
condo: "apartment",
home: "house",
},
},
location: {
enabled: true,
include_suburbs: true,
include_postcodes: true,
include_landmarks: true,
radius_search: true,
max_radius_km: 50,
},
price_range: {
enabled: true,
currency: "AUD", // Default currency
flexible_range: 0.1, // 10% flexibility
include_rent: true,
include_sale: true,
},
property_features: {
bedrooms: { min: 0, max: 10 },
bathrooms: { min: 0, max: 10 },
parking: { min: 0, max: 10 },
land_size: { unit: "sqm", min: 0, max: 10000 },
building_size: { unit: "sqm", min: 0, max: 1000 },
},
amenities: [
"pool",
"gym",
"garden",
"balcony",
"garage",
"air_conditioning",
"heating",
"dishwasher",
"built_in_wardrobes",
"study",
"ensuite",
],
},
};
Implementation Steps
- Initialize Property Search Engine
import { PropertySearchEngine } from "@avestalabs/realestate-agent";
const searchEngine = new PropertySearchEngine(propertySearchConfig);
await searchEngine.initialize();
// Load property database
await searchEngine.loadPropertyData({
data_source: "your_property_database",
update_frequency: "hourly",
include_off_market: false,
});
- Configure Search Handlers
const searchHandlers = {
property_search: handlePropertySearch,
suburb_analysis: handleSuburbAnalysis,
price_estimate: handlePriceEstimate,
market_trends: handleMarketTrends,
property_comparison: handlePropertyComparison,
};
searchEngine.registerSearchHandlers(searchHandlers);
2. Intelligent Property Matching
Configure the AI matching algorithm to provide personalized property recommendations.
Property Matching Algorithm Flow
flowchart TD
UserQuery[User Search Query] --> ParseQuery[Parse Requirements]
ParseQuery --> ExtractEntities[Extract Entities]
ExtractEntities --> Location[Location Preferences]
ExtractEntities --> Budget[Budget Range]
ExtractEntities --> Features[Property Features]
ExtractEntities --> Lifestyle[Lifestyle Preferences]
Location --> LocationScore[Calculate Location Score]
Budget --> BudgetScore[Calculate Budget Score]
Features --> FeatureScore[Calculate Feature Score]
Lifestyle --> LifestyleScore[Calculate Lifestyle Score]
LocationScore --> WeightedScore[Apply Weighted Scoring]
BudgetScore --> WeightedScore
FeatureScore --> WeightedScore
LifestyleScore --> WeightedScore
WeightedScore --> UserProfile[Consider User Profile]
UserProfile --> PersonalizationBoost[Apply Personalization Boost]
PersonalizationBoost --> MarketContext[Add Market Context]
MarketContext --> FinalScore[Calculate Final Score]
FinalScore --> Threshold{Score > Threshold?}
Threshold -->|Yes| IncludeProperty[Include in Results]
Threshold -->|No| ExcludeProperty[Exclude Property]
IncludeProperty --> RankResults[Rank by Score]
RankResults --> DiversityCheck[Apply Diversity Rules]
DiversityCheck --> FinalResults[Present Final Results]
Matching Algorithm Configuration
const matchingConfig = {
// Scoring algorithm
scoring: {
weights: {
location_match: 0.25,
price_match: 0.2,
size_match: 0.15,
features_match: 0.15,
amenities_match: 0.1,
condition_match: 0.1,
market_potential: 0.05,
},
// Matching thresholds
thresholds: {
excellent_match: 0.85,
good_match: 0.7,
fair_match: 0.55,
minimum_match: 0.4,
},
},
// Personalization settings
personalization: {
enabled: true,
learning_rate: 0.1,
user_feedback_weight: 0.3,
browsing_history_weight: 0.2,
similar_users_weight: 0.1,
},
// Recommendation diversity
diversity: {
ensure_variety: true,
max_similar_properties: 3,
include_stretch_options: true,
stretch_budget_factor: 1.2,
},
};
Custom Matching Rules
const customMatchingRules = {
// Location-based rules
location_rules: {
sydney: {
popular_suburbs: ["bondi", "manly", "paddington", "surry_hills"],
transport_proximity_weight: 0.3,
school_zone_importance: 0.2,
beach_proximity_bonus: 0.1,
},
melbourne: {
popular_suburbs: ["fitzroy", "richmond", "st_kilda", "south_yarra"],
cafe_culture_weight: 0.2,
tram_access_importance: 0.25,
laneway_proximity_bonus: 0.05,
},
},
// Buyer persona rules
buyer_personas: {
first_home_buyer: {
budget_priority: 0.4,
growth_potential_weight: 0.3,
stamp_duty_considerations: true,
first_home_buyer_grants: true,
},
investor: {
rental_yield_weight: 0.35,
capital_growth_weight: 0.25,
maintenance_costs_factor: 0.2,
tenant_demand_analysis: true,
},
family: {
school_zones_weight: 0.3,
safety_rating_weight: 0.25,
park_proximity_weight: 0.2,
family_amenities_bonus: 0.15,
},
downsizer: {
maintenance_ease_weight: 0.3,
accessibility_features_weight: 0.25,
proximity_to_services_weight: 0.2,
security_features_bonus: 0.15,
},
},
};
3. Market Insights & Analytics
Configure comprehensive market analysis and suburb insights.
Market Data Configuration
const marketAnalyticsConfig = {
// Data sources
data_sources: {
primary: "corelogic", // or 'domain', 'realestate_com_au'
secondary: ["rpdata", "onthehouse", "homely"],
government_data: ["abs", "planning_portals"],
update_frequency: "daily",
},
// Analytics modules
analytics_modules: {
price_trends: {
enabled: true,
time_periods: ["1m", "3m", "6m", "1y", "2y", "5y"],
property_types: "all",
include_forecasts: true,
},
suburb_profiles: {
enabled: true,
demographics: true,
infrastructure: true,
schools_analysis: true,
transport_connectivity: true,
lifestyle_factors: true,
},
market_indicators: {
enabled: true,
days_on_market: true,
auction_clearance_rates: true,
price_growth_rates: true,
rental_yields: true,
vacancy_rates: true,
},
comparative_analysis: {
enabled: true,
similar_suburbs: true,
price_per_sqm: true,
feature_comparisons: true,
market_positioning: true,
},
},
};
Custom Market Insights
const marketInsightRules = {
// Market condition indicators
market_conditions: {
hot_market: {
criteria: {
price_growth_3m: ">5%",
days_on_market: "<14",
auction_clearance: ">80%",
},
buyer_advice: "Act quickly, consider pre-approval",
seller_advice: "Excellent time to sell, price competitively",
},
buyers_market: {
criteria: {
price_growth_3m: "<-2%",
days_on_market: ">45",
auction_clearance: "<60%",
},
buyer_advice: "Great negotiation opportunities",
seller_advice: "Consider holding or price realistically",
},
},
// Investment insights
investment_analysis: {
high_growth_potential: {
factors: [
"infrastructure_development",
"population_growth",
"employment_opportunities",
"transport_improvements",
],
risk_factors: [
"oversupply_risk",
"economic_dependency",
"natural_disaster_risk",
],
},
},
};
4. Automated Enquiry Management
Configure intelligent lead capture and follow-up automation.
Lead Management Workflow
sequenceDiagram
participant User
participant Agent
participant LeadEngine
participant CRM
participant EmailSystem
participant RealEstateAgent
User->>Agent: Express interest in property
Agent->>LeadEngine: Capture lead information
LeadEngine->>LeadEngine: Qualify lead (scoring)
alt High Quality Lead
LeadEngine->>CRM: Create high-priority lead
LeadEngine->>RealEstateAgent: Immediate notification
LeadEngine->>EmailSystem: Send instant follow-up
EmailSystem->>User: Property details & agent contact
RealEstateAgent->>User: Personal call within 2 hours
else Medium Quality Lead
LeadEngine->>CRM: Create standard lead
LeadEngine->>EmailSystem: Schedule follow-up sequence
EmailSystem->>User: Property insights (4 hours)
EmailSystem->>User: Financing options (2 days)
EmailSystem->>User: Market update (1 week)
else Low Quality Lead
LeadEngine->>CRM: Create nurture lead
LeadEngine->>EmailSystem: Add to nurture campaign
EmailSystem->>User: Educational content
end
CRM->>LeadEngine: Update lead status
LeadEngine->>Agent: Provide lead updates to user
Enquiry Configuration
const enquiryConfig = {
// Lead capture settings
lead_capture: {
required_fields: ["name", "email", "phone"],
optional_fields: ["budget", "timeframe", "financing_status"],
qualification_questions: [
"Are you pre-approved for finance?",
"What is your ideal timeframe?",
"Is this for investment or owner-occupation?",
],
auto_qualification: true,
},
// Follow-up automation
follow_up: {
immediate_response: {
enabled: true,
template: "enquiry_acknowledgment",
include_property_details: true,
include_agent_contact: true,
},
drip_campaign: {
enabled: true,
sequences: {
hot_lead: [
{ delay: "1h", template: "property_insights" },
{ delay: "1d", template: "similar_properties" },
{ delay: "3d", template: "market_update" },
],
warm_lead: [
{ delay: "4h", template: "property_insights" },
{ delay: "2d", template: "financing_options" },
{ delay: "1w", template: "market_update" },
],
},
},
},
// Agent assignment
agent_assignment: {
auto_assign: true,
assignment_rules: {
location_based: true,
specialization_match: true,
workload_balancing: true,
performance_based: true,
},
escalation_rules: {
high_value_threshold: 2000000, // AUD
vip_client_flag: true,
urgent_timeframe: "< 2 weeks",
},
},
};
Advanced Features Configuration
5. Property Valuation Engine
Configure AI-powered property valuation and price estimation.
Property Valuation Process
graph TD
PropertyInput[Property Details] --> DataCollection[Collect Comparable Data]
DataCollection --> RecentSales[Recent Sales Data]
DataCollection --> MarketTrends[Market Trend Analysis]
DataCollection --> PropertyFeatures[Property Feature Analysis]
RecentSales --> ComparableAnalysis[Comparable Market Analysis]
MarketTrends --> MarketAdjustment[Market Adjustment Factors]
PropertyFeatures --> FeatureValuation[Feature-based Valuation]
ComparableAnalysis --> HedonicModel[Hedonic Pricing Model]
MarketAdjustment --> HedonicModel
FeatureValuation --> HedonicModel
HedonicModel --> CostApproach[Cost Approach Model]
HedonicModel --> IncomeApproach[Income Approach Model]
CostApproach --> EnsembleModel[Ensemble Model]
IncomeApproach --> EnsembleModel
EnsembleModel --> ConfidenceInterval[Calculate Confidence Interval]
ConfidenceInterval --> ValidationCheck[Validation Against Market]
ValidationCheck --> |Pass| FinalValuation[Final Valuation Report]
ValidationCheck --> |Fail| ModelAdjustment[Adjust Model Parameters]
ModelAdjustment --> EnsembleModel
FinalValuation --> ValuationReport[Generate Valuation Report]
ValuationReport --> User[Present to User]
const valuationConfig = {
// Valuation models
models: {
primary_model: "hedonic_pricing",
secondary_models: ["comparative_market_analysis", "cost_approach"],
ensemble_weighting: {
hedonic_pricing: 0.6,
comparative_market_analysis: 0.3,
cost_approach: 0.1,
},
},
// Valuation factors
factors: {
property_characteristics: {
weight: 0.4,
factors: ["size", "bedrooms", "bathrooms", "parking", "condition"],
},
location_factors: {
weight: 0.3,
factors: [
"suburb_desirability",
"transport_access",
"schools",
"amenities",
],
},
market_conditions: {
weight: 0.2,
factors: ["recent_sales", "market_trends", "supply_demand"],
},
unique_features: {
weight: 0.1,
factors: [
"views",
"heritage",
"architectural_significance",
"land_potential",
],
},
},
// Accuracy settings
accuracy: {
confidence_intervals: [80, 90, 95], // percentage
margin_of_error: 0.1, // 10%
minimum_comparables: 5,
maximum_age_comparables: 180, // days
},
};
6. Virtual Property Tours
Configure virtual tour integration and property visualization.
const virtualTourConfig = {
// Tour types
tour_types: {
"360_photos": {
enabled: true,
provider: "matterport", // or 'ricoh', 'insta360'
quality: "high",
auto_generate_floorplan: true,
},
video_tours: {
enabled: true,
formats: ["mp4", "webm"],
quality: "1080p",
include_narration: true,
},
ar_visualization: {
enabled: true,
furniture_placement: true,
renovation_preview: true,
measurement_tools: true,
},
},
// Integration settings
integration: {
embed_in_listings: true,
share_via_link: true,
mobile_optimized: true,
vr_headset_support: true,
},
};
Integration Channels
7. WhatsApp Business Integration
Configure WhatsApp for property enquiries and updates.
const whatsappPropertyConfig = {
// Business API settings
business_api: {
phone_number: "+61400000000",
business_account_id: "your_business_account_id",
access_token: "your_access_token",
webhook_url: "https://your-domain.com/webhooks/whatsapp",
},
// Property-specific templates
templates: {
property_alert: {
name: "property_alert",
language: "en",
components: [
{
type: "header",
format: "image",
},
{
type: "body",
text: "New property match found! {{1}} in {{2}} for ${{3}}",
},
{
type: "footer",
text: "Reply STOP to unsubscribe",
},
],
},
inspection_booking: {
name: "inspection_booking",
language: "en",
components: [
{
type: "header",
format: "text",
text: "Inspection Confirmed 🏠",
},
{
type: "body",
text: "Your inspection for {{1}} is confirmed for {{2}} at {{3}}",
},
],
},
market_update: {
name: "market_update",
language: "en",
components: [
{
type: "header",
format: "document",
},
{
type: "body",
text: "Here's your monthly market update for {{1}}",
},
],
},
},
// Interactive features
interactive_features: {
property_search: true,
inspection_booking: true,
price_alerts: true,
document_sharing: true,
location_sharing: true,
},
};
8. Website Widget Integration
Embed the property search agent into your real estate website.
const propertyWidgetConfig = {
// Widget appearance
appearance: {
theme: "auto", // 'light', 'dark', 'auto'
primary_color: "#2563eb",
accent_color: "#f59e0b",
position: "bottom-right",
size: "large",
border_radius: "16px",
shadow: "large",
},
// Widget behavior
behavior: {
auto_open: false,
greeting_message:
"Hi! I'm here to help you find your perfect property. What are you looking for?",
typing_indicators: true,
property_cards: true,
map_integration: true,
save_searches: true,
},
// Search features
search_features: {
voice_search: true,
image_search: true, // Search by property photos
map_search: true,
saved_searches: true,
price_alerts: true,
},
// Integration settings
integration: {
container_id: "property-agent-widget",
api_endpoint: "https://api.avestalabs.ai/realestate-agent",
session_persistence: true,
analytics_tracking: true,
lead_capture: true,
},
};
// Widget initialization
window.PropertyAgent.init(propertyWidgetConfig);
9. CRM Integration
Integrate with popular real estate CRM systems.
const crmIntegrationConfig = {
// Supported CRM systems
crm_systems: {
salesforce: {
enabled: true,
api_version: "v54.0",
sandbox: false,
custom_fields: {
property_preferences: "Property_Preferences__c",
search_criteria: "Search_Criteria__c",
lead_score: "AI_Lead_Score__c",
},
},
pipedrive: {
enabled: true,
api_version: "v1",
custom_fields: {
budget_range: "budget_range",
property_type: "property_type",
preferred_suburbs: "preferred_suburbs",
},
},
hubspot: {
enabled: true,
api_version: "v3",
contact_properties: [
"property_budget",
"property_type_preference",
"location_preference",
"timeline_to_purchase",
],
},
},
// Data synchronization
sync_settings: {
real_time_sync: true,
batch_sync_interval: 3600, // 1 hour
conflict_resolution: "crm_wins", // or 'agent_wins', 'manual'
sync_direction: "bidirectional", // or 'to_crm', 'from_crm'
},
// Lead scoring
lead_scoring: {
enabled: true,
factors: {
engagement_level: 0.3,
budget_qualification: 0.25,
timeline_urgency: 0.2,
financing_readiness: 0.15,
property_specificity: 0.1,
},
score_ranges: {
hot: [80, 100],
warm: [60, 79],
cold: [0, 59],
},
},
};
Customization Options
10. Branding and UI Customization
Customize the agent to match your real estate brand.
const realEstateBrandingConfig = {
// Brand identity
brand: {
name: "Your Real Estate Agency",
logo_url: "https://your-domain.com/logo.png",
primary_color: "#1e40af", // Professional blue
secondary_color: "#f59e0b", // Accent gold
font_family: "Inter, sans-serif",
},
// Agent personality
personality: {
name: "PropertyBot",
avatar_url: "https://your-domain.com/agent-avatar.png",
tone: "professional", // 'professional', 'friendly', 'casual'
expertise_level: "expert",
specializations: ["residential", "investment", "luxury"],
},
// Custom responses
custom_responses: {
greeting:
"Welcome to [Brand Name]! I'm your AI property specialist. How can I help you find your perfect home today?",
property_found:
"Great news! I found some properties that match your criteria. Let me show you the best options.",
no_results:
"I couldn't find exact matches, but I have some similar properties that might interest you.",
enquiry_success:
"Perfect! I've sent your enquiry to our specialist agent. They'll contact you within 2 hours.",
goodbye:
"Thank you for using [Brand Name]. Feel free to return anytime for property assistance!",
},
// Market-specific customization
market_customization: {
currency: "AUD",
measurement_units: "metric",
date_format: "DD/MM/YYYY",
local_terminology: {
apartment: "unit",
realtor: "agent",
listing: "property",
},
},
};
11. Advanced Search Filters
Configure sophisticated property search and filtering options.
const advancedFiltersConfig = {
// Property characteristics
property_filters: {
basic: {
property_type: ["house", "apartment", "townhouse", "villa", "studio"],
bedrooms: { min: 0, max: 10, any: true },
bathrooms: { min: 0, max: 10, any: true },
parking: { min: 0, max: 10, any: true },
},
advanced: {
land_size: { min: 0, max: 10000, unit: "sqm" },
building_size: { min: 0, max: 1000, unit: "sqm" },
year_built: { min: 1900, max: 2025 },
condition: ["new", "excellent", "good", "fair", "renovation_required"],
},
features: {
indoor: [
"air_conditioning",
"heating",
"fireplace",
"built_in_wardrobes",
"study",
"ensuite",
"walk_in_closet",
"butler_pantry",
],
outdoor: [
"pool",
"spa",
"garden",
"balcony",
"deck",
"courtyard",
"outdoor_entertaining",
"shed",
"carport",
],
luxury: [
"wine_cellar",
"home_theater",
"gym",
"sauna",
"elevator",
"smart_home",
"solar_panels",
"water_tank",
],
},
},
// Location filters
location_filters: {
proximity: {
schools: { max_distance: 2000, rating_min: 7 },
transport: { max_distance: 1000, types: ["train", "bus", "tram"] },
shopping: { max_distance: 5000, types: ["mall", "strip", "market"] },
healthcare: {
max_distance: 10000,
types: ["hospital", "clinic", "pharmacy"],
},
},
lifestyle: {
beach_distance: { max: 50000 }, // 50km
city_distance: { max: 100000 }, // 100km
noise_level: ["quiet", "moderate", "busy"],
walkability_score: { min: 50, max: 100 },
},
},
// Investment filters
investment_filters: {
financial: {
rental_yield: { min: 0, max: 15 },
capital_growth: { min: -10, max: 30 },
cash_flow: ["positive", "neutral", "negative"],
depreciation_benefits: true,
},
market: {
vacancy_rate: { max: 5 },
days_on_market: { max: 60 },
auction_clearance: { min: 60 },
price_growth_12m: { min: -5, max: 50 },
},
},
};
Analytics and Reporting
12. Performance Analytics
Configure comprehensive analytics for the Real Estate AI Agent.
const realEstateAnalyticsConfig = {
// User interaction tracking
interaction_tracking: {
enabled: true,
events: [
"search_initiated",
"property_viewed",
"enquiry_submitted",
"inspection_booked",
"agent_contacted",
"property_saved",
"search_refined",
],
},
// Conversion metrics
conversion_tracking: {
funnel_stages: [
"visitor",
"searcher",
"property_viewer",
"enquirer",
"qualified_lead",
"inspection_booked",
"offer_made",
"sale_completed",
],
conversion_goals: {
enquiry_rate: { target: 15 }, // 15% of searchers
qualification_rate: { target: 60 }, // 60% of enquiries
inspection_rate: { target: 40 }, // 40% of qualified leads
offer_rate: { target: 25 }, // 25% of inspections
},
},
// Agent performance metrics
agent_metrics: {
response_accuracy: true,
user_satisfaction: true,
search_success_rate: true,
lead_quality_score: true,
conversation_completion_rate: true,
},
// Business intelligence
business_intelligence: {
popular_searches: true,
market_demand_analysis: true,
price_point_analysis: true,
geographic_heat_maps: true,
seasonal_trends: true,
},
};
Deployment and Maintenance
13. Production Deployment
Set up your production environment for the Real Estate AI Agent.
Environment Configuration
# Environment variables
REALESTATE_AGENT_API_KEY=your_production_api_key
REALESTATE_AGENT_ENVIRONMENT=production
REALESTATE_AGENT_LOG_LEVEL=info
# Database configuration
DATABASE_URL=postgresql://user:password@host:port/realestate_db
REDIS_URL=redis://host:port
ELASTICSEARCH_URL=https://host:port
# External service API keys
CORELOGIC_API_KEY=your_corelogic_key
DOMAIN_API_KEY=your_domain_key
GOOGLE_MAPS_API_KEY=your_google_maps_key
WHATSAPP_ACCESS_TOKEN=your_whatsapp_token
# CRM integration
SALESFORCE_CLIENT_ID=your_salesforce_client_id
SALESFORCE_CLIENT_SECRET=your_salesforce_client_secret
HUBSPOT_API_KEY=your_hubspot_key
# Security settings
JWT_SECRET=your_jwt_secret
ENCRYPTION_KEY=your_encryption_key
Docker Configuration
FROM node:18-alpine
WORKDIR /app
# Install dependencies
COPY package*.json ./
RUN npm ci --only=production
# Copy application code
COPY . .
# Create non-root user
RUN addgroup -g 1001 -S nodejs
RUN adduser -S realestate -u 1001
# Change ownership
RUN chown -R realestate:nodejs /app
USER realestate
EXPOSE 3000
CMD ["npm", "start"]
14. Monitoring and Maintenance
Implement comprehensive monitoring for production deployment.
Health Checks
const healthChecks = {
// System health
system: {
memory_usage: { threshold: 85 }, // percentage
cpu_usage: { threshold: 80 },
disk_space: { threshold: 90 },
response_time: { threshold: 2000 }, // milliseconds
},
// External services
external_services: {
property_database: {
endpoint: "/health/property-db",
timeout: 5000,
retry_attempts: 3,
},
market_data_api: {
endpoint: "/health/market-data",
timeout: 10000,
retry_attempts: 2,
},
crm_integration: {
endpoint: "/health/crm",
timeout: 5000,
retry_attempts: 3,
},
},
// Business metrics
business_health: {
search_success_rate: { threshold: 90 }, // percentage
enquiry_conversion_rate: { threshold: 10 },
user_satisfaction_score: { threshold: 4.0 }, // out of 5
agent_response_accuracy: { threshold: 85 },
},
};
Automated Maintenance Tasks
const maintenanceTasks = {
// Daily tasks
daily: [
"update_property_listings",
"refresh_market_data",
"clean_expired_sessions",
"backup_user_preferences",
],
// Weekly tasks
weekly: [
"retrain_recommendation_model",
"update_suburb_profiles",
"analyze_search_patterns",
"generate_performance_reports",
],
// Monthly tasks
monthly: [
"full_market_analysis_update",
"user_behavior_analysis",
"model_performance_evaluation",
"security_audit",
],
};
Troubleshooting Guide
Common Issues and Solutions
1. Property Search Accuracy Issues
Problem: Agent returns irrelevant properties Solution:
// Improve search relevance
const searchOptimization = {
// Adjust matching weights
matching_weights: {
location_match: 0.35, // Increase location importance
price_match: 0.25,
features_match: 0.2,
},
// Add negative feedback learning
feedback_learning: {
enabled: true,
negative_feedback_weight: 0.5,
positive_feedback_weight: 0.3,
},
};
2. Market Data Synchronization Issues
Problem: Outdated or missing market data Solution:
// Implement data validation and fallback
const dataValidation = {
validation_rules: {
max_age_hours: 24,
required_fields: ["price", "suburb", "property_type"],
data_quality_threshold: 0.95,
},
fallback_strategy: {
use_cached_data: true,
alternative_sources: ["backup_api", "manual_data"],
alert_administrators: true,
},
};
3. High Response Times
Problem: Slow agent responses Solution:
// Implement caching and optimization
const performanceOptimization = {
caching: {
property_searches: { ttl: 300 }, // 5 minutes
market_data: { ttl: 3600 }, // 1 hour
suburb_profiles: { ttl: 86400 }, // 24 hours
},
database_optimization: {
connection_pooling: true,
query_optimization: true,
indexing_strategy: "comprehensive",
},
};
Best Practices
1. User Experience Optimization
- Progressive Search Refinement: Start broad, then narrow down based on user feedback
- Visual Property Presentation: Always include high-quality images and virtual tours
- Contextual Recommendations: Consider user's search history and preferences
- Transparent Pricing: Always display accurate, up-to-date pricing information
2. Data Quality Management
- Regular Data Validation: Implement automated checks for data accuracy and completeness
- Multiple Data Sources: Use redundant data sources to ensure reliability
- Real-time Updates: Keep property listings and market data current
- Quality Scoring: Implement quality scores for properties and data sources
3. Lead Management Excellence
- Rapid Response: Ensure immediate acknowledgment of all enquiries
- Lead Qualification: Implement intelligent lead scoring and qualification
- Personalized Follow-up: Tailor follow-up communications based on user preferences
- Agent Assignment: Match leads with the most suitable agents
Support and Resources
Getting Help
- Documentation: https://docs.avestalabs.ai/realestate-agent
- API Reference: https://api.avestalabs.ai/docs/realestate
- Support Email: realestate-support@avestalabs.ai
- Community Forum: https://community.avestalabs.ai/realestate
Training Resources
- Agent Configuration Workshop: Monthly online workshops
- Best Practices Guide: Comprehensive implementation guide
- Video Tutorials: Step-by-step configuration videos
- Case Studies: Real-world implementation examples
Conclusion
The Real Estate AI Agent provides a comprehensive solution for transforming property discovery and lead generation through intelligent automation and personalization. By following this configuration guide, you can deploy a fully functional property assistant that enhances user experience and drives business growth.
For enterprise features, custom integrations, or specialized requirements, contact our real estate solutions team at realestate@avestalabs.ai to discuss your specific needs.
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