Project 3: Property Value Choropleth with Price Prediction

Build a geospatial property analytics pipeline that combines choropleth mapping, spatial feature engineering, and explainable baseline prediction.

Quick Reference

Attribute	Value
Difficulty	Level 2: Intermediate (The Developer)
Time Estimate	16-24 hours
Main Programming Language	Python (Alternatives: R, SQL, Julia)
Coolness Level	Level 2: Practical but Forgettable
Business Potential	3. The “Service & Support” Model
Prerequisites	Spatial joins, basic modeling, cartographic basics
Key Topics	Choropleths, feature engineering, residual analysis

1. Learning Objectives

Join geocoded property transactions to administrative boundaries.
Design choropleths that communicate without distorting interpretation.
Engineer meaningful location-based features.
Train and evaluate a baseline predictor with transparent limitations.
Publish map and model report with uncertainty context.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Spatial Joins and Boundary Effects

Fundamentals

Point-to-polygon joins connect transactions to census tracts.
Boundary points and unmatched records require explicit policy.

Deep Dive The join stage can silently bias your entire model if unmatched or misassigned points are ignored. Always quantify matched vs unmatched records and inspect unmatched spatial distribution. Boundary behavior must be deterministic; otherwise repeated runs may alter per-tract aggregates.

2.2 Choropleth Design and Interpretation

Fundamentals

Classification method changes perceived spatial patterns.
Color ramps must align with data semantics and accessibility.

Deep Dive Choropleths can exaggerate cliffs and hide uncertainty. Publish class method and include transaction-count overlays so low-sample tracts are not over-interpreted. Compare at least two classing methods and document why one was chosen.

2.3 Spatial Feature Engineering and Leakage Control

Fundamentals

Distance and accessibility features can improve signal.
Leakage occurs when future or target-correlated features enter training.

Deep Dive Feature pipelines must include provenance metadata. For each feature, record source, timestamp, transformation, and leakage checks. Use temporal splits for evaluation when possible so scores reflect real deployment conditions.

3. Project Specification

3.1 What You Will Build

A pipeline that ingests property sales and geospatial context layers, then outputs:

tract-level choropleth,
feature table,
baseline model report with residual summary.

Included:

quality-controlled joins,
explainable map layers,
reproducible model evaluation.

Excluded:

production-grade AVM,
legal appraisal workflows,
fully automated valuation decisions.

3.2 Functional Requirements

Load and validate sales data and tract boundaries.
Geocode and spatially join records to tracts.
Create tract aggregates and choropleth output.
Engineer location-based model features.
Train baseline model and export evaluation report.

3.3 Non-Functional Requirements

Interpretability: Feature lineage and model limits documented.
Reproducibility: Fixed input snapshot and deterministic splits.
Auditability: Unmatched join and outlier handling logs retained.

3.4 Data Formats / Schemas

Model feature table columns (example):
- sale_id
- sale_price
- tract_id
- distance_to_cbd
- nearest_park_time
- nearest_school_time
- local_ndvi
- transaction_date

3.5 Edge Cases

Duplicate transactions
Extreme price outliers
Geocoding ambiguity
Tracts with very low sample counts
Null values in key contextual features

3.6 Real World Outcome

3.6.1 How to Run

$ python run_project3_property_pipeline.py --city "Austin, TX" --year 2025

3.6.2 Golden Path Demo

$ python run_project3_property_pipeline.py --fixture fixtures/austin_property_snapshot.parquet
[INFO] sales=38412 matched=37112 unmatched=1300
[INFO] features=12 columns; null-safe checks passed
[INFO] baseline MAE=28250.00
[DONE] outputs exported

3.6.3 Exact Terminal Transcript (Live)

$ python run_project3_property_pipeline.py --city "Austin, TX" --year 2025
[INFO] Loading transactions and tracts
[INFO] Joining properties to tracts
[INFO] Building choropleth classes
[INFO] Engineering spatial features
[INFO] Training baseline model
[INFO] Exporting outputs/property_choropleth.html
[INFO] Exporting outputs/property_model_report.md
[DONE] Completed in 4m 11s

4. Solution Architecture

4.1 High-Level Design

Sales + Geocode -> Spatial Join -> Tract Aggregation -> Choropleth Renderer
                          |
                          +-> Feature Engineering -> Baseline Model -> Evaluation Report

4.2 Key Components

Component	Responsibility	Key Decision
Join Engine	Property-to-tract assignment	Boundary and unmatched policy
Cartography Module	Classing and color design	Classification method selection
Feature Builder	Location-based predictors	Leakage-safe transformations
Model Runner	Baseline prediction	Evaluation split policy
Reporter	Export map + model card	Uncertainty and caveat format

4.3 Algorithm Overview

Validate and join data.
Build tract aggregates.
Engineer features.
Train/evaluate baseline model.
Export map and report.

5. Implementation Guide

5.1 Development Environment Setup

$ mamba create -n geo-p03 python=3.11 geopandas scikit-learn mapclassify folium -y
$ mamba activate geo-p03

5.2 Project Structure

project3/
├── src/
│   ├── join.py
│   ├── choropleth.py
│   ├── features.py
│   ├── model.py
│   └── main.py
├── fixtures/
├── outputs/
└── tests/

5.3 The Core Question You Are Answering

“How do we extract location signal from property data while keeping outputs interpretable and honest?”

5.4 Concepts You Must Understand First

Deterministic spatial joins.
Choropleth classing tradeoffs.
Feature provenance and leakage prevention.

5.5 Questions to Guide Your Design

Which records are excluded and why?
How are low-volume tracts flagged in map interpretation?
Which features are stable enough for deployment?

5.6 Thinking Exercise

Pick one tract with high residual error and list three plausible missing features that could explain it.

5.7 Interview Questions

How can choropleth design mislead interpretation?
What is feature leakage in spatial modeling?
Why must unmatched joins be reported?

5.8 Hints in Layers

Hint 1: Lock join policy before modeling.
Hint 2: Export transaction counts per tract.
Hint 3: Keep model simple first, then add features.
Hint 4: Publish residual map to diagnose blind spots.

6. Testing Strategy

Category	Purpose
Unit	Join rules, class assignment, feature transformations
Integration	End-to-end pipeline with fixture snapshot
Edge Case	Outliers, low-volume tracts, null contextual features

Critical tests:

Boundary-case property assignment is deterministic.
Feature table has no leaked future fields.
Model report includes uncertainty notes and residual summaries.

7. Common Pitfalls & Debugging

Pitfall	Symptom	Fix
Silent unmatched joins	Biased tract metrics	Log and inspect unmatched records spatially
Class break distortion	Misleading map patterns	Compare classing methods and document choice
Leakage inflation	Unrealistically high validation score	Enforce temporal split and lineage audit

8. Extensions & Challenges

Add temporal trend layers across multiple years.
Add explainable neighborhood profile cards.
Add fairness diagnostics by demographic overlays.

9. Real-World Connections

Real-estate market intelligence.
Municipal tax-base analysis.
Site-selection and investment screening.

10. Resources

GeoPandas docs: https://geopandas.org/en/stable/docs/user_guide.html
GeoParquet spec: https://geoparquet.org/
OGC SFA: https://www.ogc.org/standards/sfa/

11. Self-Assessment Checklist

I can justify my spatial join policy.
I can explain my choropleth classing choice.
I can identify and prevent feature leakage.
I can communicate model limitations clearly.

12. Submission / Completion Criteria

Minimum Viable Completion

Working choropleth and baseline report from fixture data.

Full Completion

Includes robust join QA and uncertainty overlays.

Excellence

Includes residual diagnostics and sensitivity analysis for classing choices.