Singapore URA Tender Plot Ratio Analyzer using Vercel AI SDK
Singapore's Urban Redevelopment Authority (URA) frequently releases prime land sites like Marina Gardens Lane with strict plot ratio caps. Build a full-stack land tender bidding assistant using the Vercel AI SDK (TypeScript) that parses URA release notices and generates maximum allowable Gross Floor Area (GFA) proposals.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an interactive TypeScript application with Vercel AI SDK streaming tools that extracts plot ratios and zoning restrictions from URA press releases to optimize tender land bids.
How work is evaluated
Evaluates URA site tender parsing and gross floor area calculation accuracy.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
GFA Calculation Exact Match
Verifies that max_gfa_sqm exactly equals site_area * gross_plot_ratio.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
unit_yield_error
Absolute error in predicted residential unit yield • target: 0 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a streaming web interface using Vercel AI SDK `generateText` and `streamUI`.
Parse Singapore URA land sale parameters including site area and Gross Plot Ratio (GPR).
Calculate total maximum Gross Floor Area (GFA) and projected residential unit yield.
Format structured bid submission objects according to URA regulatory standards.
Reference links and supporting material
Dataset of 25 Singapore URA land sale announcements, including site areas, gross plot ratios, land use zones, and winning bid histories.
Challenge setup
Evaluates URA site tender parsing and gross floor area calculation accuracy.
Challenge input
JSON containing site_location, site_area_sqm, gross_plot_ratio, and estimated_avg_unit_size_sqm
AI SDK
Mandatory Vercel framework for streaming AI applications.
Evaluated output
JSON with max_gfa_sqm, estimated_unit_yield, and tender_bid_ceiling_sgd
- Verifies that max_gfa_sqm exactly equals site_area * gross_plot_ratio.
- Absolute error in predicted residential unit yield • target: 0 • range: 0-10
- Benchmark: Urban Planning Technical Benchmark
- Unit Yield Error target: 0
- 1 public reference case
- JavaScript execution harness
- javascript 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
- Vercel AI SDK · Required
- Sacred · Optional
- Synthflow · Optional
- Vercel AI SDK · Required
Evaluation contract
- GFA Calculation Exact Match · Weight 1
- unit_yield_error · Weight 1
Metrics
- unit_yield_error · Target 0
Benchmarks
- Urban Planning Technical Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing site_location, site_area_sqm, gross_plot_ratio, and estimated_avg_unit_size_sqm
Output: JSON with max_gfa_sqm, estimated_unit_yield, and tender_bid_ceiling_sgd
Execution harness: JavaScript
Evaluation tools
- AI SDK · TypeScript Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.