True affordability: UX case study

True affordability: UX case study

Overview

In today's challenging housing market, homebuyers struggle with outdated affordability tools that focus on lender approvals rather than realistic monthly budgets and lifestyle impact. As lead designer, I developed and tested affordability prototypes that visualized personalized monthly costs based on actual take home pay and expenses, moving beyond traditional debt to income calculations. User testing overwhelmingly validated the approach and gained strong stakeholder buy in, though the project was ultimately paused due to shifting business priorities.

Objectives

  • Help users understand what they could comfortably afford

  • Provide transparency into the hidden costs of home ownership

  • Reduce financial anxiety and build confidence in home-buying decisions

  • Create personalized experiences that met users' unique lifestyles and goals

  • Help users understand what they could comfortably afford

  • Provide transparency into the hidden costs of home ownership

  • Reduce financial anxiety and build confidence in home-buying decisions

  • Create personalized experiences that met users' unique lifestyles and goals

  • Help users understand what they could comfortably afford

  • Provide transparency into the hidden costs of home ownership

  • Reduce financial anxiety and build confidence in home-buying decisions

  • Create personalized experiences that met users' unique lifestyles and goals

Design process

As a first step, I reviewed recent user interviews about home searching in today's market. The research revealed that users wanted more transparency into true home affordability and a simpler, less confusing way to determine what they could comfortably afford based on their lifestyle and goals.

Users were juggling multiple tools to calculate affordability because they didn't trust the home affordability calculators on real estate sites. They struggled to understand how these calculators factored in their actual take-home pay and expenses, and frequently set aside additional money for unexpected costs that weren't accounted for in existing tools.

Next, I conducted a comprehensive audit of our existing calculators and found they all fell short of current user expectations, confirming that innovation in this space was needed to address these pain points.

Current Rocket Mortgage Affordability calculator:

As a first step, I reviewed recent user interviews about home searching in today's market. The research revealed that users wanted more transparency into true home affordability and a simpler, less confusing way to determine what they could comfortably afford based on their lifestyle and goals.

Users were juggling multiple tools to calculate affordability because they didn't trust the home affordability calculators on real estate sites. They struggled to understand how these calculators factored in their actual take-home pay and expenses, and frequently set aside additional money for unexpected costs that weren't accounted for in existing tools.

Next, I conducted a comprehensive audit of our existing calculators and found they all fell short of current user expectations, confirming that innovation in this space was needed to address these pain points.

Current Rocket Mortgage Affordability calculator:

I audited our home affordability tools and identified key areas for improvement based on users' biggest pain points:

  • Gross vs. net income: Tools used gross income, which doesn't reflect users' actual take-home pay

  • Missing expense calculations: Most calculators ignored monthly expenses beyond debt payments

  • List price vs. total monthly costs: Users could determine if they qualified to buy but not whether they could actually afford the ongoing costs

  • Hidden costs and maintenance: Tools mentioned budgeting for these expenses but provided no guidance on calculating them

  • Lack of user control: Users wanted to decide their comfort level rather than being told a fixed amount they "could afford"

  • Fragmented experience: Users lacked confidence in any single tool and were using multiple calculators to cross-reference results

These existing tools were designed to give users a prequalification estimate based on information mortgage lenders typically request. While the business goal was driving users toward loan approval, there was no reason we had to use industry jargon upfront or present information in the traditional format.

Additionally, just because users could qualify for a certain loan amount didn't mean it was truly affordable for their lifestyle. The affordability calculation remained static regardless of the specific home they were considering.

After extensive iteration, we decided to test two prototypes that departed from traditional affordability calculators. We wanted to gather user feedback early before investing in a more robust experience.

Testing was conducted with 12 users who were intending to purchase a home within the next 12 months. Users included 6 first time, 6 repeat, 8 states, 4 regions and a mix of other demographics (age, gender, job, income).

I audited our home affordability tools and identified key areas for improvement based on users' biggest pain points:

  • Gross vs. net income: Tools used gross income, which doesn't reflect users' actual take-home pay

  • Missing expense calculations: Most calculators ignored monthly expenses beyond debt payments

  • List price vs. total monthly costs: Users could determine if they qualified to buy but not whether they could actually afford the ongoing costs

  • Hidden costs and maintenance: Tools mentioned budgeting for these expenses but provided no guidance on calculating them

  • Lack of user control: Users wanted to decide their comfort level rather than being told a fixed amount they "could afford"

  • Fragmented experience: Users lacked confidence in any single tool and were using multiple calculators to cross-reference results

These existing tools were designed to give users a prequalification estimate based on information mortgage lenders typically request. While the business goal was driving users toward loan approval, there was no reason we had to use industry jargon upfront or present information in the traditional format.

Additionally, just because users could qualify for a certain loan amount didn't mean it was truly affordable for their lifestyle. The affordability calculation remained static regardless of the specific home they were considering.

After extensive iteration, we decided to test two prototypes that departed from traditional affordability calculators. We wanted to gather user feedback early before investing in a more robust experience.

Testing was conducted with 12 users who were intending to purchase a home within the next 12 months. Users included 6 first time, 6 repeat, 8 states, 4 regions and a mix of other demographics (age, gender, job, income).

I audited our home affordability tools and identified key areas for improvement based on users' biggest pain points:

  • Gross vs. net income: Tools used gross income, which doesn't reflect users' actual take-home pay

  • Missing expense calculations: Most calculators ignored monthly expenses beyond debt payments

  • List price vs. total monthly costs: Users could determine if they qualified to buy but not whether they could actually afford the ongoing costs

  • Hidden costs and maintenance: Tools mentioned budgeting for these expenses but provided no guidance on calculating them

  • Lack of user control: Users wanted to decide their comfort level rather than being told a fixed amount they "could afford"

  • Fragmented experience: Users lacked confidence in any single tool and were using multiple calculators to cross-reference results

These existing tools were designed to give users a prequalification estimate based on information mortgage lenders typically request. While the business goal was driving users toward loan approval, there was no reason we had to use industry jargon upfront or present information in the traditional format.

Additionally, just because users could qualify for a certain loan amount didn't mean it was truly affordable for their lifestyle. The affordability calculation remained static regardless of the specific home they were considering.

After extensive iteration, we decided to test two prototypes that departed from traditional affordability calculators. We wanted to gather user feedback early before investing in a more robust experience.

Testing was conducted with 12 users who were intending to purchase a home within the next 12 months. Users included 6 first time, 6 repeat, 8 states, 4 regions and a mix of other demographics (age, gender, job, income).

Prototype 1

Highlights of what we included in this prototype:

  • After users went through a light onboarding we showed them a monthly home cost amount instead of list price

  • Showed recommended home costs a user could afford based on their current lifestyle

  • Used net monthly income instead of gross annual income

  • Included debts and expenses (used fixed for prototype but idea was to incorporate variable later)

  • Recommended monthly breakdown (1/3 net income towards home costs) instead of a calculation based on DTI (debt-to-income)

  • Estimated monthly cost breakdown for each listing including estimated utilities and maintenance costs

Highlights of what we included in this prototype:

  • After users went through a light onboarding we showed them a monthly home cost amount instead of list price

  • Showed recommended home costs a user could afford based on their current lifestyle

  • Used net monthly income instead of gross annual income

  • Included debts and expenses (used fixed for prototype but idea was to incorporate variable later)

  • Recommended monthly breakdown (1/3 net income towards home costs) instead of a calculation based on DTI (debt-to-income)

  • Estimated monthly cost breakdown for each listing including estimated utilities and maintenance costs

Highlights of what we included in this prototype:

  • After users went through a light onboarding we showed them a monthly home cost amount instead of list price

  • Showed recommended home costs a user could afford based on their current lifestyle

  • Used net monthly income instead of gross annual income

  • Included debts and expenses (used fixed for prototype but idea was to incorporate variable later)

  • Recommended monthly breakdown (1/3 net income towards home costs) instead of a calculation based on DTI (debt-to-income)

  • Estimated monthly cost breakdown for each listing including estimated utilities and maintenance costs

See prototype

See prototype

See prototype

Research Findings

Income Preferences: Users preferred net income over gross because gross income felt "generic" and didn't reflect additional income streams or deductions for taxes and retirement savings.

Monthly Payment Focus: Unanimously, buyers focused on what they could confidently pay per month rather than overall list price. Monthly payments allowed for direct comparison to their current rent or mortgage.

"Monthly payment is the number I care about. I only think about what hits my bank account every month… I have a hard time getting away from looking at a monthly number."

Financial Anxiety: Anxiety over being "house poor" was a common theme. Seeing "leftover cash" after all financial responsibilities provided significant comfort in their decision-making.

"What's left at the end is what I care about."

Customization Needs: Users reacted very positively to cost and budget breakdowns and wanted even more ability to edit inputs to reflect their lifestyles. They were particularly delighted by the inclusion of "expenses" where they could input groceries and subscriptions they considered essential.

"I want to play around with the numbers. If my phone bill or a subscription changes, how does that affect my cash flow?"

Current Behaviors: Many users maintained detailed spreadsheets tracking every spending category and used multiple tools for cross-checking accuracy. They relied on habits and estimates for decision-making but remained prepared for surprise expenses by rounding up and adding healthy buffers.

"There's a difference between what the bank says I can afford and what feels responsible."

"Savings rate is non-negotiable. We cut everything else before cutting savings."

Users emphasized the importance of rainy day funds for major repairs, moving, or life changes - some maintained separate "buckets" while others kept a single savings reserve but some weren't sure if that should be on the listing itself. Several users wanted more transparency into how the numbers were calculated and what data was used.

Prototype 2

Highlights of what we included in this prototype:

  • Allowed users to input current home costs including utilities (even if renting)

  • Home listings shown as monthly amount instead of list price

  • Showed listings as amounts above or below what users were currently paying

  • Broke down monthly home costs for each listing and compared expenses to current home expenses

Highlights of what we included in this prototype:

  • Allowed users to input current home costs including utilities (even if renting)

  • Home listings shown as monthly amount instead of list price

  • Showed listings as amounts above or below what users were currently paying

  • Broke down monthly home costs for each listing and compared expenses to current home expenses

Highlights of what we included in this prototype:

  • Allowed users to input current home costs including utilities (even if renting)

  • Home listings shown as monthly amount instead of list price

  • Showed listings as amounts above or below what users were currently paying

  • Broke down monthly home costs for each listing and compared expenses to current home expenses

See protoype

See protoype

See protoype

Research Findings

Price Comparison Features: Buyers unanimously loved seeing price flags that showed whether properties were above or below their current monthly home costs. Our solution, showcasing "monthly home costs" delivered the pricing value buyers were looking for. The tool was seen as highly valuable and likely to drive app usage over competitors.

Utility Information: Users appreciated seeing utilities broken down by individual properties but questioned the accuracy of the estimates.

Next steps

Immediate Enhancements: Continue exploring monthly budget and net-income based features, including realistic "cost to own" expense planning with customization options for comfort buffers and "leftover cash" displays on each listing.

Transparency and Control: Provide greater transparency into data sources and calculation methodologies. Allow users to compare financial scenarios visually and explore how this experience might scale across all 12 market segments.

Integration Opportunities: Investigate integrating budgeting tools directly (e.g., Rocket Money) or connecting bank accounts with an API (Plaid) to eliminate manual input and improve affordability accuracy. Research and test whether adding a lifestyle scenario tool would benefit buyers by showing projections for major life changes (divorce, having a baby, job changes, etc.).

Future Considerations: While these prototypes focused on core affordability factors, we intentionally excluded other variables that affect home affordability, such as down payment amounts, existing home equity, and credit scores. Also, depending on the market users are buying in, the 1/3 rule might not be realistic so more research needs to be done there. The plan was to incorporate these elements in subsequent testing phases if the initial prototypes performed well.

Conclusion

The affordability prototypes served as an initial step to gauge user reactions early in the design process. User testing overwhelmingly validated that our approach aligned with what users want and need in today's market, generating significant stakeholder buy-in to continue development.

Users' desire to visualize home affordability in a new way became evident through testing. Rather than focusing on list prices and complex debt-to-income ratios that are difficult to understand, users wanted personalized results based on their actual monthly take-home pay and expenses.

Current tools available are outdated and unhelpful, forcing users to leverage multiple tools across different platforms which is a fragmented and time-consuming process. Users clearly want an experience to meet them where they are and offer valuable insights into how their lives would look if they purchased a particular home.

Unfortunately, this work was paused when business priorities shifted away from nurturing initiatives.