Data Manifold of the NYC Housing Market Varying Through Time [OC] Statistics

What This Visualization Shows

This data visualization displays "Data Manifold of the NYC Housing Market Varying Through Time [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on This is from a personal machine learning research project of mine. I call this a Madelung Object and used python to create it. It's effectivley a map of liquidity in the New York City housing market. For more information you can check out my unpublished write up here: https://docs.google.com/document/d/1EqnGlw3XImvt_XWcBC0XKeulhVmalJCY7-RyBsxWcak/edit?usp=sharing

GitHub: HavensGuide/DissipativeValue

Data sourced from the NYC Department of Finance (adjusted for inflation in my code): https://www.nyc.gov/site/finance/property/property-rolling-sales-data.page#.

This project is very cross-disciplinary. Scales described below:

**Y-Axis: Log Price Per Sq Ft (Value)**

**Range:** -4 to 12 This covers everything from essentially free land to the most expensive real estate on Earth.

| Log PPSF | Real Price ($/sq ft) | Context | | :--- | :--- | :--- | | **-4** | **$0.02** | *Statistical artifact / Data error (essentially zero)* | | **0** | **$1.00** | Nominal transfer / Family gift | | **4** | **$55** | Raw industrial land / Distressed foreclosure | | **5** | **$148** | Low-cost housing / Outer borough fixer-upper | | **6** | **$403** | **Standard Market:** Median Queens/Brooklyn condo | | **7** | **$1,096** | **Premium Market:** Standard Manhattan apartment | | **8** | **$2,980** | **Luxury:** High-end TriBeCa / Upper West Side | | **9** | **$8,103** | **Ultra-Luxury:** Billionaire's Row (217 W 57th St) | | **10** | **$22,026** | Global record-breaking penthouse territory | | **12** | **$162,754** | Outlier / Data error |

**X-Axis: Log Building Area (Physical Scale)**

**Range:** 4 to 16 This covers everything from a closet to a stadium.

| Log Area | Real Area (sq ft) | Context | | :--- | :--- | :--- | | **4** | **55** | *Likely a storage closet or parking spot* | | **6** | **403** | Micro-studio / Tiny retail kiosk | | **7** | **1,096** | **Standard 2-Bedroom Apartment** | | **8** | **2,980** | Large single-family home / Restaurant | | **10** | **22,026** | Mid-size 5-story walk-up apartment building | | **12** | **162,754** | Large commercial office building | | **14** | **1,202,604** | **Skyscraper:** Empire State Building is ~2.7M (Log 14.8) | | **16** | **8,886,110** | *Entire Hudson Yards complex / Massive logistics center* |

Here are some html files created with plotly that have full 3d maneuverability and show different aspects of the Madelung Object:

Base 3D Visualization: https://github.com/HavensGuide/DissipativeValue/blob/main/market_evolution_4d(10YRS%2C%20REAL%20USD).html

Trace of the Spatial Fisher Information Metric: https://github.com/HavensGuide/DissipativeValue/blob/main/metric_instability_field.html

Information Kinetic Energy Density: https://github.com/HavensGuide/DissipativeValue/blob/main/kinetic_energy_density.html

Where Each Borough Dominates: https://github.com/HavensGuide/DissipativeValue/blob/main/nyc_borough_territories.html

Video of the Streamlines of Fisher Information: https://github.com/HavensGuide/DissipativeValue/blob/main/market_flow_2d.mp4, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.

Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.