An Imperfect Model for an Imperfect Market

Want to try the simulation? Try it here. Instructions for downloading and running locally are provided below.

Homeownership: A Shifting Foundation
For generations, homeownership has been a cornerstone of the American narrative—a key marker of financial stability and a primary vehicle for building wealth. The 2008 financial crisis, however, fundamentally reshaped this landscape, and its aftershocks continue to define the market today.

The Post-2008 Housing Landscape

To understand the modern housing market, it’s crucial to look at the data. According to the U.S. Census Bureau, the national homeownership rate (see the data on FRED) was 67.8% in the first quarter of 2008. In the aftermath of the crisis, it entered a prolonged decline, hitting a generational low of 62.9% in 2016. While the rate has since recovered to roughly 66% as of mid-2025, it highlights a market that is fundamentally more complex than it was a generation ago.

This new environment is shaped by a series of rational, yet often competing, economic forces:

  • For an existing homeowner, rising property values feel like a clear financial win.
  • For a seller, accepting the highest bid is the logical outcome of an efficient market.
  • For an institutional investor or local landlord, purchasing properties to meet rental demand is a sound business decision based on established metrics.

None of these actions are inherently disruptive. Yet, when aggregated, they can produce collective outcomes that are challenging for the community as a whole, particularly for first-time buyers. A home’s soaring market price may increase the paper wealth of its owner, but its fundamental utility—as a place to live—remains unchanged. This growing gap between an asset’s market price and its practical value is a defining feature of the post-2008 economy.

Modeling the Market

How do these individual, logical decisions create complex feedback loops that drive up prices and impact affordability? To explore this question and visualize how these forces interact, I developed a simulation that models the behavior of different actors within a simplified housing market.

Try It Yourself: Interactive Housing Simulation
Interactive Housing Simulation

Simulation Parameters:
The weights and constants used in the simulation are available in
housing_weights.json

Download & Run the Simulation Locally

Requirements:

Setup Instructions:

  1. Download the code above or clone the repository: 
    git clone https://github.com/TJAdryan/astro_blog.git
cd astro_blog
  2. Install dependencies: 
    npm install
  3. Start the development server: 
    npm run dev
  4. Open your browser to the local address shown in the terminal (usually http://localhost:3000).

You only need the code in src/components/HousingSim/HousingSimulation.jsx to experiment with the simulation logic, but running the full project gives you the interactive UI.

The Widening Gaps: From Credentials to Housing

As much as I tried to make this an impartial look at a simplified version of today’s housing market, I have opinions and probably some biases. It’s a symptom of a larger economic reality defined by two powerful trends: the rising bar for entry and the shifting psychological rewards for clearing it.

1. Credential Inflation
More jobs now require a bachelor’s degree, even when the work hasn’t become more complex. Research from Harvard Business School, such as the “Dismissed by Degrees” report, shows that this often functions as a screening tool in a crowded labor market, rather than a true measure of skill, creating costly barriers for individuals without improving performance.

2. The Psychology of Inequality
For years, the prevailing wisdom came from a 2010 study by Daniel Kahneman and Angus Deaton (see the original PNAS study). They found that while life satisfaction continued to rise with income, day-to-day emotional well-being tended to plateau at around $75,000 per year.

However, a more recent 2023 study by Kahneman and Matthew Killingsworth refined this view, finding that for most people, happiness does continue to rise with income (see the study in PNAS). The critical context is inequality. The Relative Income Hypothesis suggests our happiness is deeply tied to our economic standing relative to others. In a society with vast wealth gaps, each additional dollar has a higher “happiness utility” because it buys insulation from precarity and a greater sense of status and security.

The Appreciation Trap

Home prices rise, creating “paper wealth” for owners, but this increase does not improve a home’s utility as shelter. Owners often oppose development that could make housing more affordable, seeking to protect their appreciation. Meanwhile, investors with a cash advantage win bidding wars, efficiently turning homes into vehicles for rental income rather than family shelter. The conversion of homes to short-term rentals further maximizes financial returns but reduces the supply of long-term housing, increasing scarcity and raising costs for the broader community.

The Dashboard of the Divide

The simulation’s dashboard reveals the measurable outcomes of these dynamics:

  • Median Homeowner Income (the asset owners) pulls away from
  • Median Seeker Income (those seeking the utility of shelter)
  • Median Rent Burden shows the direct cost of renting a home’s utility when you cannot afford to buy the asset itself

It is the real-time measurement of the market decoupling from the community it’s meant to serve.

Realigning Price and Purpose

This model, I hope, doesn’t have a conclusion. I built it to help as tool to broaden my understanding. If it fails to that for you, I regret that. That is why I included the code. I would love to see other people’s models. I think at the very least this is a good place for anyone trying to better understand how markets work.

But this is just one interpretation—an imperfect model for an imperfect market. If you find flaws in my logic, I would love to hear them. I know I have bias, as much as I like to see myself as logical and clear minded, I am well aware I am often wrong before I am right. I also welcome idealogical criticism, should you be intersted in providing it.

Best, Dominick