Beyond the Labels: Housing Realities

Say goodbye to “good” or “bad” labels! Housing Realities uses data to unveil the real stories behind neighbourhoods. We challenge assumptions and provide insights that empower homebuyers.

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Beyond the Labels: Housing Realities

Say goodbye to “good” or “bad” labels! Our dashboard uses data to unveil the real stories behind neighborhoods. We challenge assumptions and provide insights that empower homebuyers.

Inspiration

When it comes to choosing a home, everyone’s needs are unique. Our goal isn’t to label neighborhoods as "good" or "bad." Instead, we aim to reveal if common beliefs and assumptions about homes and neighborhoods hold true or false—using data to uncover insights.

What it does

Our Tableau dashboard examines several popular assumptions about housing. Analyzing key data and verifying if these assumptions align with reality, helping potential homeowners to make informed choices.

How we built it

We identified a few common assumptions about housing and explored the data to validate them. Specifically, we analyzed:

  1. If a property is registered under a valid Rental Registry, does it have fewer code violations?
  2. Does paying more tax correlate with better infrastructure and lower crime rates?
  3. Does the age of a property affect its vacancy status?

Challenges we ran into

In developing our dashboard, we encountered several challenges and applied various solutions to make our data meaningful and accurate:

  • Identifying insightful attributes: We calculated correlations in Excel to identify highly relevant attributes, ensuring our analysis focused on the most impactful factors.
  • Incorporating city crime data from Syracuse: This dataset provided only crime coordinates, so we used the OpenStreetMap API to extract corresponding zip codes, allowing us to integrate crime data effectively into our analysis.
  • Categorizing code violations: To ensure consistency, we used the ICC digital codes of NY State, which allowed us to group and analyze code violations systematically.
  • Creating calculated fields: We formatted date fields for consistency and created calculated fields to analyze relationships, such as whether a crime occurred while a property had an active rental registry.
  • Standardizing tax rates: We calculated the average tax per acre to standardize tax rates across different properties, merging datasets based on SBL (parcel identification numbers) and zip codes for a seamless integration.

Each of these steps enhanced the accuracy and relevance of our insights, enabling a more reliable and user-friendly dashboard.

Accomplishments that we're proud of

We’re proud to make complex data accessible and actionable, providing users with an intuitive tool to understand housing trends and assumptions better. Our dashboard empowers users to make informed, data-driven housing decisions.

What we learned

Through this project, we validated key assumptions about housing and uncovered new insights:

  1. If a property is registered under a valid Rental Registry, does it have fewer code violations?
    Properties with active rental registry accounts typically have fewer code violations, indicating a possible association with safer and better-maintained neighborhoods.

  2. Does paying more tax correlate with better infrastructure and lower crime rates?
    Our analysis showed that higher tax-paying counties, except for areas near our university, generally have lower crime rates. In university-adjacent areas, larceny (often bike theft in student-heavy neighborhoods) is common, and a slight positive correlation was observed between higher vacancy rates and crime. This suggests that certain local conditions, like student populations, may affect crime rates in specific high-tax areas.

  3. Does the age of a property affect its vacancy status?
    We found that age and vacancy status are not strongly correlated. However, many vacant properties are part of government initiatives like RNI (Revitalization Neighborhood Initiative) and NRSA (Neighborhood Revitalization Strategy Areas), which aim to improve housing quality and potentially make these properties more suitable for future occupancy.

Working on this project taught us the nuances of housing data analysis and highlighted the importance of challenging assumptions with data rather than relying solely on preconceived notions. This approach has allowed us to provide meaningful, data-backed insights for potential homeowners and policymakers.

What's next for Beyond the Labels: Housing Realities

Moving forward, we aim to:

  • Expand our analysis to include more housing attributes, such as property quality and proximity to amenities.
  • Integrate additional data sources to provide even deeper insights.
  • Refine our dashboard with more user-focused metrics and predictive analytics to further empower homebuyers in making data-informed decisions.
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