Inspiration
During the Welcome Ceremony, we were introduced to this challenge and immediately recognized the real-world problem that buying a car is one of the most significant financial decisions people make, yet the process of understanding financing options is often overwhelming and confusing. Coming from families that own Toyota vehicles, we've experienced firsthand how challenging it can be to compare financing versus leasing, understand APR rates, and determine what fits within a budget. We wanted to create a solution that would have helped our own families and countless others make more informed, confident decisions when purchasing their Toyota vehicles.
What it does
Toyota Financial Services is an intelligent web application that personalizes vehicle financing and leasing recommendations based on individual financial profiles. Users complete a comprehensive questionnaire covering:
- Annual income and credit score
- Available down payment and desired loan term
- Vehicle type preferences (sedans, SUVs, trucks, electrified, performance)
- Financing preference (lease vs. finance) The application then analyzes this data against our database of current 21 Toyota models and intelligently recommends the top 3 best-matched vehicles. Each recommendation includes:
- Detailed monthly payment breakdowns
- APR rates and total interest calculations
- Down payment requirements
- Total cost of ownership
- Lease terms and mileage limits (for leasing options) Users can save multiple plans to their account, compare options side-by-side, and access their saved plans across sessions with secure authentication.
How we built it
We built the application using React for a dynamic, responsive single-page application experience. Key technical components:
- Smart Recommendation Engine: Custom JavaScript algorithm that filters and scores vehicles based on multiple criteria including budget constraints, credit requirements, income thresholds, and vehicle preferences
- User Authentication System: Secure sign-in/sign-up flow with password validation (minimum 8 characters)
- Persistent Storage: LocalStorage implementation for user sessions and per-user saved plans
- Comprehensive Vehicle Database: 21 Toyota models across all categories (sedans, SUVs, trucks, hybrids, electric, performance) with accurate financing and leasing data
- Responsive Design: Mobile-first approach using Tailwind CSS for styling and Lucide React for icons
The recommendation algorithm uses a sophisticated scoring system that filters vehicles by affordability (monthly payment, down payment), validates credit score and income requirements, matches vehicle type preferences, cores results based on budget fit and user priorities, and provides fallback options when exact matches aren't available
Challenges we ran into
One of our biggest challenges was ensuring our financial calculations were realistic. We spent considerable time researching current APR rates, typical down payment requirements, and how credit scores affect financing eligibility to make our recommendations credible and useful. Managing user authentication, questionnaire data, selected plans, and saved plans across multiple components also required careful state management. We had to ensure data flowed correctly between the App component and all child components while maintaining localStorage synchronization.
Accomplishments that we're proud of
We built a functional Toyota financing app in just 24 hours, featuring user authentication, personalized recommendations, and data persistence. The app includes all 21 Toyota models with realistic financing and leasing details, powered by a smart matching system that tailors suggestions based on multiple factors. With a clean, user-friendly design and a polished, responsive interface, it makes complex financial decisions simple and accessible. Users can save their plans, return later, and enjoy a smooth, end-to-end experience from sign-in to personalized results.
What we learned
Building this application deepened our understanding of auto financing, APR calculations, credit score impacts, and the true cost of vehicle ownership. We learned how many factors influence what someone can actually afford. We learned how to guide users through complex decision-making processes by breaking information into digestible steps and presenting comparisons clearly. W gained hands-on experience with prop drilling, lifting state up, and managing complex application state across multiple components. We developed skills in creating scoring systems and multi-criteria filtering algorithms that balance competing priorities. We discovered how much more valuable an application becomes when it tailors recommendations to individual circumstances rather than showing generic options.
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