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Challenge

LlamaIndex Urban Land Tender Valuation Agent for Singapore URA Sites

Urban land tender participants struggle to rapidly parse complex site requirements and land plot ratio limits set by municipal planners. Build a LlamaIndex RAG agent to digest Singapore URA land sale notices and estimate optimal bid ranges. Achieve gross development value extraction accuracy of 92%.

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Challenge brief

What you are building

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

Build a LlamaIndex Advanced RAG agent with vector index routing to parse Singapore Urban Redevelopment Authority (URA) tender packages and generate bid metrics.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of extracted Gross Floor Area (GFA) and projected land rate valuation.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

gfa_calculation_match

Checks exact match of GFA calculation (site area * plot ratio)

Binary check

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

Dimension 2

extraction_accuracy

Accuracy of extracted tender planning attributes • target: 0.92 • range: 0-1

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

  • Implement a LlamaIndex vector store index (`VectorStoreIndex`, `SummaryIndex`) over URA tender release PDFs

  • Create custom query engine tools for querying land plot ratio (GPR), maximum gross floor area (GFA), and designated building uses

  • Parse land tender guidelines for special planning conditions (e.g., green mark standards, public transit connections)

  • Formulate baseline bid evaluation summaries with estimated land rate per square meter per plot ratio

Resources and assets

Reference links and supporting material

Dataset notes

Summarized land sale tender documentation released by Singapore Urban Redevelopment Authority (URA).

How this agent runs

Evaluates accuracy of extracted Gross Floor Area (GFA) and projected land rate valuation.

Challenge input

JSON containing site_area_sqm, gross_plot_ratio, land_use_zone

LlamaIndex

Data framework for structured indexing and retrieval over tender documents.

Evaluated output

JSON containing total_allowable_gfa_sqm, recommended_bid_psm, target_use

Checks for
  • Checks exact match of GFA calculation (site area * plot ratio)
  • Accuracy of extracted tender planning attributes • target: 0.92 • range: 0-1
Proof of success
  • Benchmark: Urban Land Tender RAG Benchmark
  • Extraction Accuracy target: 0.92
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • ZenML · Optional
  • Synthflow · Optional

Evaluation contract

  • gfa_calculation_match · Weight 1
  • extraction_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about LlamaIndex Urban Land Tender Valuation Agent for Singapore URA Sites