Graduation Year

2025

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

School of Geosciences

Major Professor

Ambe J. Njoh, Ph.D.

Committee Member

Fred L. Mannering, Ph.D.

Committee Member

Steven Reader, Ph.D.

Committee Member

Martin Bosman, Ph.D.

Keywords

Residual Income Method, Geographically Weighted Regression, Spatial Heterogeneity, Housing Price Modeling, Housing Affordability, Policy Interventions

Abstract

Housing affordability assessments rely on the 30 percent income method and generalized linear regression (GLR)-based hedonic price modeling. Both provide broad insights but fail to capture spatial heterogeneity in affordability challenges. This study upgrades housing affordability assessment approaches by incorporating the residual income method and Geographically Weighted Regression (GWR). The primary objective of this study is to determine whether methodological improvements, residual income method and GWR, yield significantly different results and provide a more accurate and spatially adaptive understanding of affordability dynamics in Hillsborough County, Florida.

The 30 percent income method is widely used and classifies households as cost-burdened if they spend more than 30 percent of their gross income on housing. However, it fails to account for non-housing expenses such as transportation, childcare, and healthcare. The residual income method, by contrast, evaluates discretionary income after essential expenses, offering a more precise measure of financial strain. Similarly, traditional GLR-based hedonic price modeling assumes a global relationship between housing prices and explanatory variables, neglecting local variations in price determinants. GWR improves upon this by allowing relationships to vary across space, capturing localized affordability pressures and economic disparities.

Findings reveal that the residual income method identifies affordability crises beyond low-income neighborhoods, including middle-income areas where high living costs significantly reduce financial flexibility. Meanwhile, GWR results indicate substantial spatial heterogeneity in housing price determinants, with factors such as proximity to amenities, employment centers, and transportation infrastructure influencing affordability differently across the county. South Tampa and northern Hillsborough County emerge as high-stress areas where traditional affordability measures underestimate financial strain.

These results lead to the rejection of Null Hypothesis 2, confirming that both affordability assessments and housing price models differ significantly when incorporating spatial and financial complexity. The study emphasizes the need for location-specific housing policies that reflect the diverse economic realities of different communities. Policy recommendations include targeted financial assistance, zoning reforms, and infrastructure improvements to enhance affordability and accessibility. By integrating spatial econometrics and refined affordability metrics, this research provides a more nuanced and actionable framework for addressing housing affordability challenges in Hillsborough County.

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