MARKET: Real Estate — Listing Intelligence & Lead Scoring

examples/44-market-realestate.halo policies |* parallel

Ingest new MLS listings, enrich with market data, score leads, auto-draft marketing copy, and route to the right agent based on expertise

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// ============================================================
// MARKET: Real Estate — Listing Intelligence & Lead Scoring
// PURPOSE: Ingest new MLS listings, enrich with market data,
//          score leads, auto-draft marketing copy, and route
//          to the right agent based on expertise
// ============================================================

// --- Policies ---
policy FairHousing   : Block(DiscriminatoryLanguage) & Block(SteeringLanguage)
policy PII           : Redact(SSN) & Redact(CreditCard)
policy CostGuard     : MaxSpend(1.50)

// --- Tools ---
tool MLSAPI        : REST("api.spark.com/v1/listings", apiKey: Env.MLS_KEY)
tool ZillowAPI     : REST("api.bridgedataoutput.com/api/v2/zestimates", apiKey: Env.ZILLOW_KEY)
tool CRM           : POST("api.followupboss.com/v1/events", apiKey: Env.FUB_KEY)
tool AgentSlack    : POST("hooks.slack.com/services/listings-team")

// --- Store ---
tool MarketTrends   : VectorStore(Env.PINECONE_KEY, namespace: "market-comps")
tool AgentExpertise  : VectorStore(Env.PINECONE_KEY, namespace: "agent-specialties")

// --- Struct ---
schema ListingIntel {
  listing_id     : String
  estimated_value : Number
  lead_score     : Number
  marketing_copy : String
  assigned_agent : String
}

// --- Agents ---
agent MarketAnalyst [openai/gpt-4o-mini, temp: 0.1]
  equips MarketTrends, MLSAPI, ZillowAPI
  requires CostGuard

agent Copywriter [anthropic/claude-sonnet-4, temp: 0.7]
  requires FairHousing, CostGuard

agent LeadScorer [openai/gpt-4o-mini, temp: 0.0]
  equips AgentExpertise
  requires CostGuard

// --- The Pipeline ---
@timeout(3m)
@retry(2, 10s)
pipeline ProcessListing(listing: MLSListing) -> ListingIntel:
  listing
    // Step 1: Parallel enrichment — market data + comps
    |* {
         MarketAnalyst.pull_comps(
           tool: MLSAPI,
           address: context.data.listing.address,
           radius: "0.5mi",
           sold_within: "90d"
         ),
         MarketAnalyst.get_zestimate(
           tool: ZillowAPI,
           address: context.data.listing.address
         )
       }

    // Step 2: Market analysis — pricing recommendation
    |> MarketAnalyst.analyze_pricing(
         listing: context.data.listing,
         comps: context.data.pull_comps,
         zestimate: context.data.get_zestimate,
         local_trends: MarketTrends
       )

    // Step 3: Generate Fair Housing-compliant marketing copy
    |> Copywriter.draft_listing(
         property: context.data.listing,
         pricing: context.data.analyze_pricing,
         tone: "luxury",
         max_words: 150
       )

    // Step 4: Score and route the lead
    |> LeadScorer.score_and_route(
         listing: context.data.listing,
         pricing: context.data.analyze_pricing,
         agent_pool: AgentExpertise
       )

    // Step 5: Push to CRM and notify assigned agent
    |> CRM.post(
         event: "new_listing_matched",
         listing_id: context.data.listing.mls_id,
         agent: context.data.score_and_route.assigned_agent,
         lead_score: context.data.score_and_route.score,
         copy: context.data.draft_listing
       )