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Challenge

Vacation Market Yield Indexing Engine with LlamaIndex Advanced RAG

Exclusive resort markets like Lake Geneva, WI experience high seasonal demand fluctuations affecting rental yields. Build an advanced RAG indexing system using LlamaIndex to query vacation rental market trends and predict seasonal yield premiums with 90% indexing precision.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an advanced LlamaIndex RAG architecture utilizing vector index routers and metadata filters to analyze vacation home sales and short-term rental yields.

Delivery guide

How work is evaluated

Evaluation

Evaluates LlamaIndex query engine retrieval precision and numerical RevPAR yield accuracy.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Confidence Threshold Test

Ensures RAG context retrieval confidence score exceeds 0.85.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

revpar_mae

Mean Absolute Error of calculated projected monthly RevPAR • target: 100 • range: 0-5000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build LlamaIndex VectorStoreIndex and Document Summary Index pipelines over vacation market reports.

  • Implement AutoMergingRetriever and MetadataFilters for hyper-local geography queries.

  • Calculate seasonal RevPAR (Revenue Per Available Room) premiums and occupancy forecasts.

  • Synthesize real estate market spotlight summaries for vacation investors.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 45 vacation rental property records in Lake Geneva and Midwest resort markets, containing ADR history, occupancy rates, waterfront distance, and seasonal revenue figures.

Challenge setup

Evaluates LlamaIndex query engine retrieval precision and numerical RevPAR yield accuracy.

Challenge input

JSON query string specifying location, waterfront_status, and peak_season_months

LlamaIndex

Mandatory framework for advanced vector indexing and RAG query engines.

Evaluated output

JSON with predicted_summer_adr, peak_occupancy_rate, projected_monthly_revpar, and confidence_score

Evaluation checks
  • Ensures RAG context retrieval confidence score exceeds 0.85.
  • Mean Absolute Error of calculated projected monthly RevPAR • target: 100 • range: 0-5000
Configured evaluation evidence
  • Benchmark: Domain RAG Retrieval Benchmark
  • Revpar Mae target: 100
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • Exa · Optional
  • Lyzr · Optional

Evaluation contract

  • Confidence Threshold Test · Weight 1
  • revpar_mae · Weight 1

Metrics

  • revpar_mae · Target 100

Benchmarks

  • Domain RAG Retrieval Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON query string specifying location, waterfront_status, and peak_season_months

    Output: JSON with predicted_summer_adr, peak_occupancy_rate, projected_monthly_revpar, and confidence_score

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Data & RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Vacation Market Yield Indexing Engine with LlamaIndex Advanced RAG