Actionable insights tailored for policy makers, analysts, and stakeholders.
For stakeholders, these insights offer a pathway for meaningful interventions. Policymakers can utilize affordability ratios, like those we calculated between median incomes and housing prices, to design targeted housing policies or poverty alleviation programs. Employers can address systemic wage disparities identified in our research where White individuals consistently earned more than Black and Hispanic counterparts through transparent pay structures and wage equity audits. Urban planners can prioritize mixed income housing developments in areas where affordability issues intersect with high poverty rates, such as Mississippi and Louisiana, as revealed in our analyses. Furthermore, academics and researchers can use the data we’ve provided, sourced from reliable governmental institutions, to explore underrepresented communities and develop actionable recommendations. By leveraging these findings, stakeholders can drive equitable, impactful solutions while fostering public trust and transparency in decision-making. This approach ensures that progress is not only sustainable but also inclusive for all.
Our recommendations are centered on the importance of data driven decision making for stakeholders and data users alike, shaped by the insights gathered from our project. Firstly, for data collectors, we emphasize the need for granularity in data collection, such as gathering information at the county or ZIP code level, to better capture localized trends in housing affordability, poverty, and income disparities. We observed significant variations in housing prices, such as those in Puerto Rico, highlighting the importance of analyzing data at the ZIP code level. This level of analysis is crucial for understanding the complex relationship between wealth disparity and housing prices across different locations. We are also in favor of longitudinal data being utilized to observe changes over time, uncovering causal relationships between critical variables like wages, housing costs, and demographic shifts. For example, our analysis highlighted the wide variation in housing affordability across states, with Maryland exhibiting affordability issues despite high housing prices and California serving as a clear outlier with its exceptionally high prices relative to incomes. Additionally, incorporating diverse metrics including gender, race, and geographic disparities is essential, as shown in our findings on systemic gender wage gaps and disparities among White, Black, and Hispanic groups. Rigorous data validation methods, such as those used in our project to clean inconsistencies in poverty, income, and housing data, are critical to building a strong foundation for actionable insights.