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Connecting Lending Discrimination and Auto Ownership: A Los Angeles County case study - Financial accessibility is crucial for transportation equity, particularly in areas where systemic constraints exist. This study looks at the relationship between car ownership trends in Los Angeles County and Home Mortgage Disclosure Act (HMDA) denial rates, which are assumed as a proxy for barriers to auto loans. This work provides a thorough method for comprehending the relationship between lending practices and mobility outcomes by integrating individual-level discrete choice analysis with aggregate spatial regression models.
The main hypothesis is that those areas with higher mortgage denial rates are more likely to encounter similar difficulties with auto loans, leading to lower rates of car ownership. However, the strength of this relationship varies across the county’s diverse demographic and geographic landscape. The study focuses on four main objectives: (1) analyzing the relationship between HMDA denial rates and car ownership behavior, (2) identifying spatial patterns in this relationship, (3) evaluating how lending barriers improve the predictive power of car ownership models in model specification stages, and (4) addressing the equity implications of these barriers for different communities and identify disadvantages.
In the first step, spatial econometric techniques are employed with the HMDA dataset to assess how socio-demographic and geographic factors influence denial rates at the census tract level throughout LA County. The initial analysis uses an Ordinary Least Squares (OLS) model, followed by Moran’s I to detect spatial autocorrelation. Where spatial patterns are identified, Spatial Lag or Spatial Error Models are applied to account for interactions between neighboring tracts. Variables such as racial composition, income, poverty rates, and housing characteristics are examined alongside denial rates. The second step links aggregate-level findings to individual car ownership data from the 2012 California Household Travel Survey (CHTS). Using unordered response models, the analysis explores how denial rates—combined with individual socio-demographics and built environment factors—affect car ownership choices (0, 1, 2, or 3+ vehicles). Spatial regression techniques are also applied where dependencies exist.
Initial results reveal a trend of disparities in denial rates and car ownership. Higher denial rates are linked to lower car ownership in minority-majority neighborhoods, historic redlining neighborhoods as well as low-income areas, highlighting persistent inequities. These findings agree with previous research on how financial barriers hinder access to personal transportation and contribute to broader mobility challenges.
This research aims to underscore the need for policy interventions that target those who are affected by lending barriers, improving both financial inclusion and transportation access. While mortgage denial data is not a perfect proxy for auto lending practices, this innovative approach offers a valuable method for examining equity in mobility outcomes. By connecting community-level lending patterns to individual car ownership decisions, the study provides a robust framework for policymakers and planners to address systemic inequities and develop more inclusive urban mobility strategies.
