Research · Existing Building Retrofit

Retrofitting: NYC Rowhouses

A toolkit for high-performance row house retrofits.
Author
Jenny (Xin Yu) Ye
Program
M.Arch, Cornell AAP
Year
2025–2026
Focus
Existing building retrofit
Deep retrofitEmbodied carbon Rowhouse typologyDecarbonization
Retrofitting NYC Rowhouses thesis poster
Thesis poster: Retrofitting NYC Rowhouses, a toolkit for high-performance row house retrofits.

Design can operationalize the decarbonization of New York City's rowhouses by translating existing building-performance data into typology-based retrofit frameworks.

Abstract

New York City's row houses are a large and historically significant share of the housing stock, and a major source of operational carbon. Because most of them will still be standing for decades, retrofitting them well is central to the city's climate goals. Yet retrofit is constrained by heritage, cost, and the practical complexity of working on occupied, aging buildings.

This thesis develops a toolkit that turns building-performance data into typology-based retrofit frameworks, giving owners and designers clear, sequenced strategies rather than one-off fixes.

Key directions

The toolkit is organized around the moves that matter most for row house performance, paired with the frameworks needed to apply them responsibly:

  • Envelope upgrades
  • Air-tightness & moisture control
  • Mechanical systems & ventilation
  • Window & shading retrofits
  • Phasing & construction logic
  • Cost-performance framework
  • Heritage & performance integration

Approach

The work classifies representative row house conditions, tests staged interventions against performance and cost, and accounts for embodied carbon so that improvements are net-positive over time. Heritage and performance are treated together, so that upgrades respect the character of the block while meeting a tightening regulatory landscape.

Why it matters

Framing retrofit as a toolkit, rather than a single design, changes who the work is for. Owners, developers, and design teams need decisions they can act on: what to keep, what to improve, and how to prove performance. The aim of this research is to make those decisions clearer and better supported by evidence.

Full thesis in progress. For the current draft, please get in touch.