feat(ai): AI pre-market rent recommendation from regional comparables, supply & demand

- IAIService.recommendPreMarketRent: recommended price + range, verdict (UNDERPRICED/FAIR/AMBITIOUS), drivers, rationale, confidence - MockAIService: deterministic recommendation from locationIntelligence — regional comp median, vacancy (supply), demand strength + days-on-market, rent trend (forward for pre-market) - BackendAIService: LLM prompt with market context + mock fallback - usePreMarketRentRecommendation hook; PreMarketPriceAdvisor component shows the recommendation per released unit with verdict ("zu günstig" when underpriced) + adjustable expected price + "Empfehlung übernehmen" - Replaces the simple indexed suggestion with a market-driven AI recommendation that flags underpricing Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-21 00:22:14 +02:00
parent 3e1e945661
commit fb029cf0bc
7 changed files with 239 additions and 53 deletions
@@ -176,6 +176,27 @@ export interface FitOutAdvice {
  estimatedNetInvestment: string
 }

+// ── Pre-market rent recommendation ─────────────────────────────────────────────
+
+export interface PreMarketRentInput {
+  city: string
+  assetType: string
+  areaSqm: number
+  currentRentPerSqm: number
+  availableFrom?: string   // ISO — Pre-Market liegt in der Zukunft
+}
+
+export interface PreMarketRentRecommendation {
+  recommendedPerSqm: number
+  rangeMinPerSqm: number
+  rangeMaxPerSqm: number
+  verdict: 'UNDERPRICED' | 'FAIR' | 'AMBITIOUS'   // Bewertung des heutigen Preises
+  deltaVsCurrentPct: number                        // Empfehlung vs. heutiger Preis
+  drivers: string[]                                // Vergleichsmiete, Angebot, Nachfrage, Trend …
+  rationale: string
+  confidence: 'LOW' | 'MEDIUM' | 'HIGH'
+}
+
 // ── Legacy types (kept for backward compatibility) ────────────────────────────

 export interface CriteriaExtractionResult {
@@ -220,6 +241,9 @@ export interface IAIService {
  // Fit-out investment advice (demand side)
  generateFitOutAdvice(input: FitOutAdviceInput): Promise<AIResponse<FitOutAdvice>>

+  // Pre-market rent recommendation (supply side) — based on regional comparables, supply & demand
+  recommendPreMarketRent(input: PreMarketRentInput): Promise<AIResponse<PreMarketRentRecommendation>>
+
  // Legacy methods
  extractCriteria(input: string): Promise<AIResponse<CriteriaExtractionResult>>
  generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<AIResponse<string[]>>
@@ -57,6 +57,8 @@ import type {
  MarketSignalClassification,
  FitOutAdviceInput,
  FitOutAdvice,
+  PreMarketRentInput,
+  PreMarketRentRecommendation,
 } from '../IAIService'
 import { ServiceErrorCode } from '../../types'
 import { AppError } from '../../errors'
@@ -82,6 +84,7 @@ import { buildDecisionBriefPrompt } from '../prompts/decisionBriefPrompt'
 import { buildDataQualityPrompt } from '../prompts/dataQualityPrompt'
 import { buildMarketSignalPrompt } from '../prompts/marketSignalPrompt'
 import { MockAIService } from '../mock/MockAIService'
+import { getCityIntelligence, getMarketRent } from '../../../lib/locationIntelligence'

 // ── Config ────────────────────────────────────────────────────────────────────

@@ -606,6 +609,41 @@ Bitte analysiere die Situation und empfiehl die beste Option für den Mieter.`
    }, () => MockAIService.generateFitOutAdvice(input))
  },

+  // ── recommendPreMarketRent ──────────────────────────────────────────────────
+  recommendPreMarketRent(input: PreMarketRentInput): Promise<AIResponse<PreMarketRentRecommendation>> {
+    return withFallback('recommendPreMarketRent', async () => {
+      const intel = getCityIntelligence(input.city)
+      const comp = getMarketRent(input.city, input.assetType)
+      const system = `Du bist Schweizer Gewerbeimmobilien-Marktanalyst. Empfiehl einen Pre-Market-Mietpreis (CHF/m²/Jahr) auf Basis regionaler Vergleichsmieten, Angebot (Leerstand) und Nachfrage. Antworte als JSON:
+{
+  "recommendedPerSqm": number,
+  "rangeMinPerSqm": number,
+  "rangeMaxPerSqm": number,
+  "verdict": "UNDERPRICED" | "FAIR" | "AMBITIOUS",
+  "deltaVsCurrentPct": number,
+  "drivers": ["kurze Treiber auf Deutsch"],
+  "rationale": "2-3 Sätze Begründung auf Deutsch",
+  "confidence": "LOW" | "MEDIUM" | "HIGH"
+}`
+      const user = `Stadt: ${input.city}
+Nutzung: ${input.assetType}
+Fläche: ${input.areaSqm} m²
+Heutiger Preis: CHF ${input.currentRentPerSqm}/m²
+Vergleichsmiete (Median): ${comp ?? 'unbekannt'}
+Leerstand: ${intel?.vacancyRatePct ?? '?'}%
+Nachfrage: ${intel?.demandStrength ?? '?'}
+Miettrend 12M: ${intel?.rentTrend12m ?? '?'}%
+Ø Vermietungsdauer: ${intel?.avgDaysOnMarket ?? '?'} Tage`
+      const raw = await chat(system, user)
+      const json = extractJSON<PreMarketRentRecommendation>(raw)
+      if (!json || typeof json.recommendedPerSqm !== 'number') {
+        const fb = await MockAIService.recommendPreMarketRent(input)
+        return { ...fb, provenance: makeProvenance('mock', true, false, { fallbackReason: json ? 'schema_validation' : 'json_parse' }) }
+      }
+      return { data: json, provenance: makeProvenance('ai', false, true) }
+    }, () => MockAIService.recommendPreMarketRent(input))
+  },
+
  // ── Legacy: extractCriteria ─────────────────────────────────────────────────
  extractCriteria(input: string): Promise<AIResponse<CriteriaExtractionResult>> {
    return withFallback('extractCriteria', async () => {
@@ -12,12 +12,15 @@ import type {
  MarketSignalClassification,
  FitOutAdviceInput,
  FitOutAdvice,
+  PreMarketRentInput,
+  PreMarketRentRecommendation,
 } from '../IAIService'
 import { mockProvenance } from '../IAIService'
 import { aiTraceStore } from '../tracing'
 import { mockParseNeed } from './needParser'
 import { buildComparisonSummary } from './compareBuilder'
 import { buildMockDecisionBrief } from './decisionBrief'
+import { getCityIntelligence, getMarketRent } from '../../../lib/locationIntelligence'

 const SIMULATED_DELAY = { fast: 300, medium: 600, slow: 1800 }
 const delay = (ms: number) => new Promise(r => setTimeout(r, ms))
@@ -338,6 +341,71 @@ export const MockAIService: IAIService = {
      return { data, provenance: mockProvenance() }
    }),

+  // ── recommendPreMarketRent ──────────────────────────────────────────────────
+  recommendPreMarketRent: (input: PreMarketRentInput) =>
+    traceMock('recommendPreMarketRent', async () => {
+      await delay(SIMULATED_DELAY.fast)
+      const intel = getCityIntelligence(input.city)
+      const comp = getMarketRent(input.city, input.assetType)
+      const current = input.currentRentPerSqm
+      const ASSET_LABELS: Record<string, string> = { OFFICE: 'Bürofläche', LOGISTICS: 'Logistikfläche', LIGHT_INDUSTRIAL: 'Gewerbefläche', RETAIL: 'Retailfläche', PRODUCTION: 'Produktionsfläche' }
+      const assetLabel = ASSET_LABELS[input.assetType] ?? 'Fläche'
+
+      // Ohne regionale Vergleichsdaten: nur grobe Schätzung, niedrige Konfidenz
+      if (!intel || comp == null) {
+        const rec = Math.round(current * 1.02)
+        const data: PreMarketRentRecommendation = {
+          recommendedPerSqm: rec, rangeMinPerSqm: Math.round(rec * 0.93), rangeMaxPerSqm: Math.round(rec * 1.07),
+          verdict: 'FAIR', deltaVsCurrentPct: 0,
+          drivers: ['Keine regionalen Vergleichsdaten verfügbar'],
+          rationale: 'Keine ausreichenden Marktdaten für diese Region — Empfehlung beruht auf dem heutigen Preis.',
+          confidence: 'LOW',
+        }
+        return { data, provenance: mockProvenance() }
+      }
+
+      // Angebot/Nachfrage-Anpassung auf die regionale Vergleichsmiete
+      let adj = 0
+      if (intel.vacancyRatePct < 2.5) adj += 0.06
+      else if (intel.vacancyRatePct < 4) adj += 0.02
+      else if (intel.vacancyRatePct > 5.5) adj -= 0.06
+      else if (intel.vacancyRatePct > 4.5) adj -= 0.03
+      adj += { VERY_HIGH: 0.06, HIGH: 0.03, MEDIUM: 0, LOW: -0.05 }[intel.demandStrength]
+      if (intel.avgDaysOnMarket < 35) adj += 0.02
+      else if (intel.avgDaysOnMarket > 75) adj -= 0.03
+      const trendFwd = intel.rentTrend12m / 100   // Pre-Market liegt in der Zukunft → Trend vorwärts
+
+      const recommended = Math.round(comp * (1 + adj + trendFwd))
+      const rangeMin = Math.round(recommended * 0.93)
+      const rangeMax = Math.round(recommended * 1.07)
+      const deltaVsCurrentPct = Math.round(((recommended - current) / current) * 100)
+      const verdict: PreMarketRentRecommendation['verdict'] =
+        deltaVsCurrentPct >= 6 ? 'UNDERPRICED' : deltaVsCurrentPct <= -6 ? 'AMBITIOUS' : 'FAIR'
+
+      const supplyLabel = intel.vacancyRatePct < 3 ? 'sehr knappes Angebot' : intel.vacancyRatePct > 5 ? 'entspanntes Angebot' : 'ausgeglichenes Angebot'
+      const demandLabel = { VERY_HIGH: 'sehr hohe Nachfrage', HIGH: 'hohe Nachfrage', MEDIUM: 'mittlere Nachfrage', LOW: 'schwache Nachfrage' }[intel.demandStrength]
+      const drivers = [
+        `Vergleichsmiete Region: CHF ${comp}/m²`,
+        `Leerstand ${intel.vacancyRatePct}% (${supplyLabel})`,
+        demandLabel,
+        `Miettrend ${intel.rentTrend12m >= 0 ? '+' : ''}${intel.rentTrend12m}% (12 M)`,
+        `Ø Vermietungsdauer ${intel.avgDaysOnMarket} Tage`,
+      ]
+      const verdictText =
+        verdict === 'UNDERPRICED' ? `Ihr heutiger Preis (CHF ${current}/m²) liegt ${Math.abs(deltaVsCurrentPct)}% unter der Empfehlung — klarer Spielraum nach oben.`
+        : verdict === 'AMBITIOUS' ? `Ihr heutiger Preis liegt ${Math.abs(deltaVsCurrentPct)}% über der Markteinschätzung — ambitioniert.`
+        : 'Ihr heutiger Preis ist marktgerecht.'
+      const rationale = `Auf Basis vergleichbarer ${assetLabel} in ${input.city} (Median CHF ${comp}/m²), ${supplyLabel} und ${demandLabel}. Empfehlung für Pre-Market: CHF ${recommended}/m² (CHF ${rangeMin}–${rangeMax}). ${verdictText}`
+      const confidence: PreMarketRentRecommendation['confidence'] =
+        intel.demandStrength === 'LOW' || intel.avgDaysOnMarket > 75 ? 'MEDIUM' : 'HIGH'
+
+      const data: PreMarketRentRecommendation = {
+        recommendedPerSqm: recommended, rangeMinPerSqm: rangeMin, rangeMaxPerSqm: rangeMax,
+        verdict, deltaVsCurrentPct, drivers, rationale, confidence,
+      }
+      return { data, provenance: mockProvenance() }
+    }),
+
  // Legacy methods
  extractCriteria: (_input: string) =>
    traceMock('extractCriteria', async () => ({
@@ -24,5 +24,7 @@ export type {
  MarketSignalClassification,
  FitOutAdvice,
  FitOutAdviceInput,
+  PreMarketRentInput,
+  PreMarketRentRecommendation,
 } from './ai/IAIService'
 export { parseListingText } from './ai/mock/listingParser'