Home value check
Home value check
Home value check

Home Value Check

tl;DR

We developed an online tool to estimate your home value, based on a learning algorithm. The feature was built iteratively, with an integrated customer feedback loop as the steering wheel for improvements.

details
Role
UX Designer
Timeframe
H2 2021
Platforms
Web + App

Home Value Check

tl;DR

We developed an online tool to estimate your home value, based on a learning algorithm. The feature was built iteratively, with an integrated customer feedback loop as the steering wheel for improvements.

details
Role
UX Designer
Timeframe
H2 2021
Platforms
Web + App

Home Value Check

tl;DR

We developed an online tool to estimate your home value, based on a learning algorithm. The feature was built iteratively, with an integrated customer feedback loop as the steering wheel for improvements.

details
Role
UX Designer
Timeframe
H2 2021
Platforms
Web + App
Context
Team
We started off with a small development team, consisting of 1 UX Designer (that’s me!), 1 Front-End Engineer, 2 Back-End Engineers, 1 Data Scientist and a Product Owner.
Company
Funda is the biggest real estate marketplace in the Netherlands and is mainly known for being the place where home searchers can find their (next) dream home.
Goal
As a company, Funda wanted to expand beyond just the searching & finding of objects, playing a bigger role in the journey of buying, selling & owning a home. After some less successful pilots in the moving journey, we started exploring the journey of the home-owner and the potential (future) home-seller.
Initial research
During initial interviews it became clear that home-owners are often thinking about what their home is currently worth and are using a multitude of methods to ‘guesstimate’ their home value. There were a few online tools available already, but none had mass adoption in the Netherlands and often were inaccurate.

What also became apparent is that there were often clear flaws in the way they estimated their home value. Home owners for example looked at the asking price of homes for sale in their area, but couldn’t take the actual sale price into account as this isn’t public available data (for consumers).
Problem
Home-owners struggle to estimate their home value as they don’t have access to relevant market data.
Design Challenge
How can we make it possible for all home-owners in the Netherlands to estimate their home value with an acceptable level of accuracy?
Context
Team
We started off with a small development team, consisting of 1 UX Designer (that’s me!), 1 Front-End Engineer, 2 Back-End Engineers, 1 Data Scientist and a Product Owner.
Company
Funda is the biggest real estate marketplace in the Netherlands and is mainly known for being the place where home searchers can find their (next) dream home.
Goal
As a company, Funda wanted to expand beyond just the searching & finding of objects, playing a bigger role in the journey of buying, selling & owning a home. After some less successful pilots in the moving journey, we started exploring the journey of the home-owner and the potential (future) home-seller.
Initial research
During initial interviews it became clear that home-owners are often thinking about what their home is currently worth and are using a multitude of methods to ‘guesstimate’ their home value. There were a few online tools available already, but none had mass adoption in the Netherlands and often were inaccurate.

What also became apparent is that there were often clear flaws in the way they estimated their home value. Home owners for example looked at the asking price of homes for sale in their area, but couldn’t take the actual sale price into account as this isn’t public available data (for consumers).
Problem
Home-owners struggle to estimate their home value as they don’t have access to relevant market data.
Design Challenge
How can we make it possible for all home-owners in the Netherlands to estimate their home value with an acceptable level of accuracy?
Context
Team
We started off with a small development team, consisting of 1 UX Designer (that’s me!), 1 Front-End Engineer, 2 Back-End Engineers, 1 Data Scientist and a Product Owner.
Company
Funda is the biggest real estate marketplace in the Netherlands and is mainly known for being the place where home searchers can find their (next) dream home.
Goal
As a company, Funda wanted to expand beyond just the searching & finding of objects, playing a bigger role in the journey of buying, selling & owning a home. After some less successful pilots in the moving journey, we started exploring the journey of the home-owner and the potential (future) home-seller.
Initial research
During initial interviews it became clear that home-owners are often thinking about what their home is currently worth and are using a multitude of methods to ‘guesstimate’ their home value. There were a few online tools available already, but none had mass adoption in the Netherlands and often were inaccurate.

What also became apparent is that there were often clear flaws in the way they estimated their home value. Home owners for example looked at the asking price of homes for sale in their area, but couldn’t take the actual sale price into account as this isn’t public available data (for consumers).
Problem
Home-owners struggle to estimate their home value as they don’t have access to relevant market data.
Design Challenge
How can we make it possible for all home-owners in the Netherlands to estimate their home value with an acceptable level of accuracy?
Beta phase
Start small
We quickly developed both the product & the data model to a basic level to get it to our users, with the intention to start learning from user feedback as fast as possible. This also meant that the product started with a simplified back-end, in essence, just a CSV-file.
The first iteration of the Home Value Check in beta phase
Feedback loop
To get feedback from users, we implemented an integrated feedback tool, within the product itself. This feedback, in addition to planned user interviews, helped us to learn more about user expectations in the product & gave us a quantitative feeling of satisfaction.
The feedback tool popped into the UI and was easy to dismiss
Targets
Before we were confident enough to open up the product to all of the NL and launch it, we set some targets that were the key drivers for our iterations.
Satisfaction
70%
Weekly average rating from home-owners
Coverage
80%
Of all homes in the Netherlands
Beta phase
Start small
We quickly developed both the product & the data model to a basic level to get it to our users, with the intention to start learning from user feedback as fast as possible. This also meant that the product started with a simplified back-end, in essence, just a CSV-file.
The first iteration of the Home Value Check in beta phase
Feedback loop
To get feedback from users, we implemented an integrated feedback tool, within the product itself. This feedback, in addition to planned user interviews, helped us to learn more about user expectations in the product & gave us a quantitative feeling of satisfaction.
The feedback tool popped into the UI and was easy to dismiss
Targets
Before we were confident enough to open up the product to all of the NL and launch it, we set some targets that were the key drivers for our iterations.
Satisfaction
70%
Weekly average rating from home-owners
Coverage
80%
Of all homes in the Netherlands
Beta phase
Start small
We quickly developed both the product & the data model to a basic level to get it to our users, with the intention to start learning from user feedback as fast as possible. This also meant that the product started with a simplified back-end, in essence, just a CSV-file.
The first iteration of the Home Value Check in beta phase
Feedback loop
To get feedback from users, we implemented an integrated feedback tool, within the product itself. This feedback, in addition to planned user interviews, helped us to learn more about user expectations in the product & gave us a quantitative feeling of satisfaction.
The feedback tool popped into the UI and was easy to dismiss
Targets
Before we were confident enough to open up the product to all of the NL and launch it, we set some targets that were the key drivers for our iterations.
Satisfaction
70%
Weekly average rating from home-owners
Coverage
80%
Of all homes in the Netherlands
Research
user interviews
To improve the product for our users, we had to understand our users. We set up multiple interview sessions, in which we talked with 5 potential users in-depth for 45 min in each session, to really get to understand the problems and potential within the product.

These interview insights were supplemented with quantitative insights about user interactions with the feature and the open feedback provided through the feedback tool. This led to these following key insights:
Users wanted to be able to edit used home details
Users reported that their home details were sometimes incorrect (gathered from an external source). They also wanted to be able to tweak their home details to see the impact of potential home extensions on their home value.
Home maintenance state not taken into account
Home-owners often compared their own home with other homes in the area, but they would base their relative home value on the maintenance state. After validating that the parameter improved estimations, we added it to the product.
Low trust due to ‘black-box’ experience
The initial simplified UX of the tool lowered the perceived validity of the home value estimate, as they felt it was ‘too easy’ and they were unsure on what data the estimate was based.
Research
user interviews
To improve the product for our users, we had to understand our users. We set up multiple interview sessions, in which we talked with 5 potential users in-depth for 45 min in each session, to really get to understand the problems and potential within the product.

These interview insights were supplemented with quantitative insights about user interactions with the feature and the open feedback provided through the feedback tool. This led to these following key insights:
Users wanted to be able to edit used home details
Users reported that their home details were sometimes incorrect (gathered from an external source). They also wanted to be able to tweak their home details to see the impact of potential home extensions on their home value.
Home maintenance state not taken into account
Home-owners often compared their own home with other homes in the area, but they would base their relative home value on the maintenance state. After validating that the parameter improved estimations, we added it to the product.
Low trust due to ‘black-box’ experience
The initial simplified UX of the tool lowered the perceived validity of the home value estimate, as they felt it was ‘too easy’ and they were unsure on what data the estimate was based.
Research
user interviews
To improve the product for our users, we had to understand our users. We set up multiple interview sessions, in which we talked with 5 potential users in-depth for 45 min in each session, to really get to understand the problems and potential within the product.

These interview insights were supplemented with quantitative insights about user interactions with the feature and the open feedback provided through the feedback tool. This led to these following key insights:
Users wanted to be able to edit used home details
Users reported that their home details were sometimes incorrect (gathered from an external source). They also wanted to be able to tweak their home details to see the impact of potential home extensions on their home value.
Home maintenance state not taken into account
Home-owners often compared their own home with other homes in the area, but they would base their relative home value on the maintenance state. After validating that the parameter improved estimations, we added it to the product.
Low trust due to ‘black-box’ experience
The initial simplified UX of the tool lowered the perceived validity of the home value estimate, as they felt it was ‘too easy’ and they were unsure on what data the estimate was based.

Solution

Iterative development

Based on the research insights, we decided to focus on (1) making the tool more flexible to fit user needs, (2) explaining how the tool works in an accessible way and (3) making it easier to keep track of your home value.

features

An online home value estimation tool, focused on ease of use & understandability

Fully editable

You're able to correct the prefilled home details or play around with them, to explore potential impact of a home extension.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Monthly updates

Opt-in to receive a monthly value update in your inbox, to stay on top of your home value development via email and/or push.

Solution

Iterative development

Based on the research insights, we decided to focus on (1) making the tool more flexible to fit user needs, (2) explaining how the tool works in an accessible way and (3) making it easier to keep track of your home value.

features

An online home value estimation tool, focused on ease of use & understandability

Fully editable

You're able to correct the prefilled home details or play around with them, to explore potential impact of a home extension.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Monthly updates

Opt-in to receive a monthly value update in your inbox, to stay on top of your home value development via email and/or push.

Solution

Iterative development

Based on the research insights, we decided to focus on (1) making the tool more flexible to fit user needs, (2) explaining how the tool works in an accessible way and (3) making it easier to keep track of your home value.

features

An online home value estimation tool, focused on ease of use & understandability

Fully editable

You're able to correct the prefilled home details or play around with them, to explore potential impact of a home extension.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Price history

To get a grasp on how your home value has changed, you can zoom out and look at the price development in the last 12 months.

Monthly updates

Opt-in to receive a monthly value update in your inbox, to stay on top of your home value development via email and/or push.

Results

After multiple iterations we developed a valuable tool for home owners to estimate their home value. We managed to reach an average customer satisfaction rate of 85% and have coverage for more than 70% of all homes in the Netherlands.

national coverage
80%

customer

satisfaction

85%

Estimates

per MoNTh

250K

Results

After multiple iterations we developed a valuable tool for home owners to estimate their home value. We managed to reach an average customer satisfaction rate of 85% and have coverage for more than 70% of all homes in the Netherlands.

national coverage
80%

customer satisfaction

85%

Estimates per MoNTh

250K

Scaling up

Full rollout

After reaching the goals, we started scaling up, by opening up the feature to all of the Netherlands and launching a marketing campaign, to boost awareness.

Landing page of the home value check

Mockups of the feature at rollout

Mobile apps integration

After learning, validating and iterating on web, it was time to integrate it in the mobile apps as well. All the learnings from web could directly be applied, as well as some improvements.

Push notifications

Home-owners could now receive a notification as soon as their new home value was calculated.

Input wizard

We learned that asking for all your home details at once felt overwhelming, so we created a step-by-step input wizard.

Input wizard

We learned that asking for all your home details at once felt overwhelming, so we created a step-by-step input wizard.

My home

A dedicated tab was added in the app for your home dashboard for which the home value check was the first feature.

Scaling up

Full rollout

After reaching the goals, we started scaling up, by opening up the feature to all of the Netherlands and launching a marketing campaign, to boost awareness.

Your home dashboard with the home value estimate

Mockups of the feature at rollout

Mobile apps integration

After learning, validating and iterating on web, it was time to integrate it in the mobile apps as well. All the learnings from web could directly be applied, as well as some improvements.

Push notifications

Home-owners could now receive a notification as soon as their new home value was calculated.

Input wizard

We learned that asking for all your home details at once felt overwhelming, so we created a step-by-step input wizard.

Input wizard

We learned that asking for all your home details at once felt overwhelming, so we created a step-by-step input wizard.

My home

A dedicated tab was added in the app for your home dashboard for which the home value check was the first feature.

Learnings

start small, listen, iterate

Start with the smallest product possible that provides user value. Then start learning from there and build iteratively. In the early stage of the product we optimized the feedback tool for quantity, so we could look at it in every refinement and see what the most pressing issues were to tackle.

data-driven product development

The data model defined our possibilities for estimating the home value, as we wanted to estimate home values within an acceptable level of confidence. If a certain home characteristic was essential in estimating a home value with confidence, we tried to figure out a way to get this data-point for from either reliable external sources or user input.

Learnings

start small, listen, iterate

Start with the smallest product possible that provides user value. Then start learning from there and build iteratively. In the early stage of the product we optimized the feedback tool for quantity, so we could look at it in every refinement and see what the most pressing issues were to tackle.

data-driven product development

The data model defined our possibilities for estimating the home value, as we wanted to estimate home values within an acceptable level of confidence. If a certain home characteristic was essential in estimating a home value with confidence, we tried to figure out a way to get this data-point for from either reliable external sources or user input.

Want to know more?

I'm happy to tell you more about it. Just reach out to me via email or Linkedin and I'll get in touch!

Rick Pijnenburg

Want to know more?

I'm happy to tell you more about it. Just reach out to me via email or Linkedin and I'll get in touch!

Rick Pijnenburg