AI Real Estate & Property Management with Chinese LLMs

# Property valuation with multi-factor analysis import requests property_data = { "property_type": "Residential apartment", "location": "Hangzhou, Gongshu District, near West Lake", "size_sqm": 89, "bedrooms": 3, "floor": "12 of 28", "year_built": 2019, "orientation": "South-facing", "condition": "Newly renovated, furnished", "comparable_sales": [ {"address": "Same complex, 85sqm, 8F", "sold_price_yuan": 3850000, "sold_date": "2026-06"}, {"address": "Adjacent complex, 92sqm, 15F", "sold_price_yuan": 4100000, "sold_date": "2026-07"} ], "market_factors": { "metro_line_extension": "Line 3 extension opens Q4 2026, 800m from property", "new_school": "Top-tier primary school branch opening 2027 within 1km", "policy": "Hangzhou easing purchase restrictions for non-local buyers", "interest_rate": "Mortgage rate cut to 3.85% (LPR - 10bp)" } } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a real estate valuation AI. Analyze property data, comparable sales, and market factors. Generate valuation ranges with confidence levels and explain key drivers. Format as structured JSON."}, {"role": "user", "content": f"Value this property: {json.dumps(property_data, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Tenant application risk assessment import requests tenant_application = { "applicant": {"age": 29, "occupation": "Software engineer", "employer": "Tencent (3 years)"}, "monthly_income_yuan": 28000, "proposed_rent_yuan": 6500, "credit_history": "No delinquencies, credit score 720", "previous_landlord_reference": "Paid on time for 2 years, gave 60 days notice, left apartment in good condition", "red_flags": ["Changed jobs 3 times in 4 years", "Previous address only 8 months"], "application_completeness": "All documents provided, verified income", "co_applicant": "Spouse, income 18000/month, stable government job" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are a tenant screening AI. Assess application risk, calculate rent-to-income ratios, evaluate stability, and recommend approval with conditions. Comply with fair housing principles. Format as structured JSON."}, {"role": "user", "content": f"Screen this tenant: {json.dumps(tenant_application, ensure_ascii=False)}"} ], "max_tokens": 1500 } ) print(response.json()["choices"][0]["message"]["content"])

# Lease clause analysis and tenant rights assessment import requests lease_analysis = { "lease_type": "Residential (Shanghai)", "monthly_rent_yuan": 8500, "lease_term_months": 12, "key_clauses": { "deposit": "3 months rent (25,500 yuan)", "rent_increase": "Landlord may increase rent by up to 15% upon renewal with 30 days notice", "early_termination": "Tenant forfeits entire deposit if terminating before 12 months", "subletting": "Prohibited without written consent (consent may be withheld for any reason)", "maintenance": "Tenant responsible for all repairs under 500 yuan", "utilities": "Tenant pays all utilities including property management fees" }, "tenant_concerns": ["deposit seems high", "early termination penalty feels excessive"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a lease analysis AI. Explain clauses in plain Chinese, flag terms that may violate Shanghai tenancy regulations, suggest negotiation points, and estimate tenant total cost. Be fair to both parties."}, {"role": "user", "content": f"Analyze this lease: {json.dumps(lease_analysis, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# HVAC system predictive maintenance analysis import requests maintenance_data = { "equipment": "Central HVAC chiller unit (Carrier 19XL)", "installation_year": 2018, "operating_hours": 28400, "last_service": "2026-05-15 (routine filter replacement)", "sensor_readings": { "vibration_mm_s": 6.8, "discharge_temp_c": 42, "suction_pressure_psi": 65, "oil_pressure_psi": 78, "power_consumption_kw": 245 }, "recent_issues": ["Slight refrigerant leak detected 3 months ago, topped up", "Unusual noise reported by facilities staff"], "maintenance_history": ["2025-11: Compressor overhaul", "2025-03: Condenser cleaning"], "criticality": "High - serves 15 floors, 200+ tenants" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a building systems maintenance AI. Analyze equipment data, predict failure risks, recommend maintenance actions, and estimate costs. Prioritize by tenant impact."}, {"role": "user", "content": f"Assess HVAC maintenance needs: {json.dumps(maintenance_data, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Building energy optimization strategy import requests building_data = { "building": "Grade A office tower, 35 floors, 85,000 sqm", "location": "Beijing CBD", "occupancy_pattern": "70% weekdays 9-18h, 20% weekends, 10% after-hours", "current_consumption": { "annual_kwh": 4200000, "hvac_pct": 52, "lighting_pct": 18, "elevators_pct": 8, "plug_loads_pct": 22 }, "utility_rates": {"peak_yuan_kwh": 1.35, "off_peak_yuan_kwh": 0.45, "peak_hours": "9:00-12:00, 13:00-17:00"}, "equipment_age": "HVAC 8 years, LED lighting 3 years, elevators 12 years", "sustainability_goals": "Reduce carbon 30% by 2028, achieve carbon neutrality 2035" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are a building energy optimization AI. Analyze consumption patterns, recommend efficiency measures, calculate ROI, and develop sustainability roadmaps. Consider Chinese utility pricing and green building standards."}, {"role": "user", "content": f"Optimize building energy: {json.dumps(building_data, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])

# Real estate development feasibility study import requests development_project = { "site": "Chengdu Tianfu New Area, 12,000 sqm plot", "proposed_use": "Mixed-use: retail podium + residential towers", "zoning": "R2 residential with 20% commercial allowance", "market_context": { "population_growth_5yr": "+18%", "avg_income_yuan": 85000, "competing_projects": "3 residential towers under construction within 2km (1,200 units total)", "retail_vacancy": "22% in 1km radius", "metro_access": "Line 18 station 600m away, opening 2027" }, "financial_parameters": { "land_cost_yuan": 180000000, "construction_cost_yuan_sqm": 4500, "target_selling_price_yuan_sqm": 18000, "expected_fsi": 3.5 }, "risk_factors": ["Oversupply in residential segment", "High retail vacancy", "Metro not yet operational"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a real estate development feasibility AI. Assess projects, calculate financial metrics, identify risks, and recommend positioning strategies. Use Chinese real estate development standards."}, {"role": "user", "content": f"Assess development feasibility: {json.dumps(development_project, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])