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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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What you are building

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

How work is evaluated

Evaluation

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

Datasets

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

How submissions are scored

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

  • 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

Dataset notes

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

Challenge setup

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

Evaluation checks
  • Checks exact match of GFA calculation (site area * plot ratio)
  • Accuracy of extracted tender planning attributes • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: Urban Land Tender RAG Benchmark
  • Extraction Accuracy target: 0.92
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

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

Metrics

  • extraction_accuracy · Target 0.92

Benchmarks

  • Urban Land Tender RAG Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing site_area_sqm, gross_plot_ratio, land_use_zone

    Output: JSON containing total_allowable_gfa_sqm, recommended_bid_psm, target_use

    Execution harness: Python

Evaluation tools

  • LlamaIndex · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

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