Location
Analysis

Interactive Data Tool for
Property Developers

Background

As a UX designer at IS24 I've led the product discovery work for the data product.

It has started following a successful business pitch, which identified a gap in the property developers data products markets and opportunity to monetize IS24's search data.

Scenario and Problem

When a property developer considers buying a land plot for his project, he needs to analyse a lot of data regarding the area in order to maximise the revenue.

The goal of the tool is to solve following customer problems and answer following questions:

Is it worth to buy this land plot? - Price analysis and prediction, competitors analysis (existing projects in the area)
What should I build on this land plot? - Demand structure analysis
Who buys real estate in the area? - Target market analysis

Solution

Currently, most of the property developers in Germany are using PDF real estate and demographics reports from research institutions, or reports form other real estate companies that specialise in market analysis. This reports are more like well designed PDF magazines.

Accessibility of the data analysis, ability to play with input data, dig in into the charts make interactive data tool extremely attractive to customers and would constitute one of the main product USPs, besides proprietary search data.

Currently, there are some existing or being developed interactive real estate data tools on the market, but they are targeted at real estate investors and not property developers.

Walk-through of the MVP prototype, as presented to customers during interviews

Product discovery process

During the discovery phase, I followed the double diamond discovery process and made several iteration on a mid-fidelity prototype.

Analysis

When I started to work on the project, I analysed customer surveys results, real estate market PDF reports of competitors, derived possible product USPs and product vision from the Business Pitch.

Synthesis

At the next stage, I defined number of opportunities for the interactive data tool, framing possible customer problems and iteratively prototyped the solutions.

Validation

The main validation tool used was customer interviews. In collaboration with User Researcher, Product owner and pricing analyst, I've created an interview script. We've held around 20 interviews with property developers customers.

During video calls we've presented the interactive prototype and interviewed the customers regarding the tool features.

I also validated the solutions internally during presentations to Key Account Managers, PMs and other stakeholders.

In parallel, feasibility was checked with software developers and data analyst.

Iteration

Based on the results of the interviews and stakeholders presentations, I created several iterations of the prototype, reframing the customer problems and refining the solutions.

You can check the resulting MVP prototype here.

It's possible to select the time range of the Data set for the analysis in the fixed side pane in every section

Data is the queen!

One of the main challenges we've encountered during the customer interviews was making the analysis look trustworthy.

It was achieved by:

Exact explanation on which data each section is based on. For example, for the existing projects in the area, only listing data of objects belonging to a project is used. For other sections, the supply data is of all sell listings of apartments with completion date in the last 4 years.
Letting the user feel in charge in regard to which data the analysis is based on:

User can fine tune the radius of the area around the land plot location to use for the analysis (smaller in the dense areas, bigger in suburbs)
User can select time range for the search and listings data to be used.

On the onboarding screen, the user can preview the data set and then fine tune the analysis area radius

Data is the main USP

During the customer interviews the highest rating got the charts that was directly based on the IS24 proprietary search and listings data: Demand and Supply section and Purchase Price section. These where the first ones to be included in MVP.

Demand analysis according to apartment size in conjunction with number of rooms

Readiness to buy: analysis of supply and demand prices by number of rooms. Supply and demand structure

Existing projects in the area: supply analysis.

Population Analysis: is the area growing or shrinking? What is structure and income of a household?

Next hot spot prediction

Answers question of "Where is it better to build in my region right now?". It was feasible to predict the next hot spot, as an external company was ready to build the data model for this purpose.

During the validation we found out that

The developers usually know what are the fast developing areas in their region.
The main problem is to find any land plot to buy - there are very few available, and then decide whether to buy it.

Intelligent planning feature

Answers question of "What is the best project configuration to build on the land plot, in order to maximise the revenue?".