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housing-market

Estimate housing attribute values and construct repeat-sales house price indices.

33|6|Updated Mar 17, 2026
Search Tags:#housing-market-analysis#hedonic-pricing#repeat-sales-index#spatial-econometrics#affordability-metrics#urban-economics
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill housing-market
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: housing-market
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/18-urban-science/housing-market
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill housing-market

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you analyze housing prices and market dynamics by estimating attribute “implicit prices,” constructing repeat-sales price indices, measuring affordability, and detecting spatial clustering.

Core Features & Use Cases

  • Hedonic pricing with spatial effects: Estimate how structural and location variables jointly determine log house prices, and assess spatial spillovers.
  • Repeat-sales (Case-Shiller style) indices: Build a house price index from sequential property sales without needing full attribute data at each time.
  • Affordability and investment metrics: Compute affordability measures such as price-to-income and Housing Affordability Index, and evaluate supply constraint relationships and capitalization/yield-style concepts.

Quick Start

Use the housing-market Skill to estimate hedonic regressions, build a repeat-sales price index, and produce affordability metrics from a housing transactions dataset you provide.

Frequently Asked Questions about housing-market

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a repeat-sales house price index using Python?▼

To build a repeat-sales house price index, you construct a Case-Shiller-style index from sequential property sales using Python scientific libraries like pandas and statsmodels without needing full attribute data at each time period.

What is hedonic pricing and how do spatial spillovers affect house prices?▼

Hedonic pricing estimates how structural and location variables jointly determine log house prices, while spatial spillovers assess spatial lag and clustering effects using geospatial data and spatial econometrics techniques.

Can I calculate housing affordability metrics like price-to-income ratios in Python?▼

You can calculate housing affordability metrics like price-to-income ratios and the Housing Affordability Index by processing housing transactions data with numpy and pandas to quantify market accessibility.

Do I need geospatial data to detect spatial price clustering in housing markets?▼

Yes, detecting spatial price clustering requires geospatial and time-indexed housing data, utilizing optional geopandas alongside scipy and statsmodels to perform spatial econometrics and spillover analysis.

How do I estimate implicit prices for housing attributes with regression?▼

You estimate implicit prices for housing attributes by running hedonic regressions on structural and location variables to determine their joint impact on log house prices using statsmodels and scikit-learn.

What's the best way to quantify urban housing market dynamics for research?▼

The best way to quantify urban housing market dynamics is applying reproducible numerical workflows covering hedonic regression, repeat-sales index estimation, and affordability dashboards using Python scientific libraries.

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