Visualizing multidimensional data with Circle trees
By: Alexander Skorniakov
Tags: Real Estate, Data Visualization, Trees

Visualizing multidimensional data with Circle trees

Using real-estate data as a test case for how and why to use Circle trees

Data visualization may be an effective decision-making with many applications. In particular, it can help you to present multidimensional data in a way that is understandable. In this piece, I want to talk about one specific problem in the real estate world that practitioners encounter, and a solution which seems to be very interesting and promising: Circle trees!

Real estate developers, especially those involved in residential real estate on a relatively large scale, need to monitor sales of the dwelling stock they create very closely in order to adjust prices immediately — upwards for those properties that appear to be “washed out” (selling better) faster than others and downwards/freeze for those that are less attractive to potential buyers.

The lots that make up a residential building can be distinguished by a variety of features. Some of them are absolutely straightforward, like total area or living area in square meters, the number of rooms, and the floor on which it’s located. Others are more tricky to be assigned on a lot-by-lot basis on a massive scale and be converted into digital form, like view characteristics. There are some natural assumptions that you can make as to group all of the lots in a given residential building and to project which lots will “wash out” faster than others, but eventually you need to take into account your customers’ opinion on that (who vote with their bucks). This is where you come up with a problem of a compact visual representation of the lots in a given building based on their intrinsic features and also pricing and exposure period data and deals closed. Arc- of circle-shaped tree-like structures come as a natural choice here. Every level of such a tree represents a certain feature of the lots. Adding a new level, i.e. taking into consideration one more feature, or dimension of your data, expands the tree uniformly in all directions, so it stays rather compact (but more information-dense).

The deals in real estate are comparable to what airlines do in terms of tickets. As you know, some seats are better than others, and adjusting prices promptly so that an opportunity to buy a good seat is charged in fair respect to its demand, but still all of the seats are sold. This game is played all the time. The question revolves around money and companies — airlines or real-estate agencies want to “see” the market. So how do we represent real-estate lots in an overview while fitting the dynamics of demand?

Residential real estate developers seek various ways to visually represent value and demand dynamics of the lots in exposition to make its decisions on price adjustment as fact-based and shareable within its management team as possible. During my tenure as a head of project valuation group with a local developer in Moscow, one of the tasks of my group in respect to ongoing projects of the company was to regularly revalue its lots in exposition. This revaluation was generally very thoroughly discussed in a format of investment committee.

Limitations of current real-estate solutions

There was a solution that the company stuck to for a while. Each separate building within a given project was represented by an MS Excel table-like with its cells arranged in a way to mimic the building it represents (in respect to the number of floors and entrances), and each cell being highly loaded with numeric and color-based information on lots.

This solution may seem a bit obvious or simplistic, but it is absolutely practicable and your idea can be worked out to the smallest detail. To depict as much information as possible we used Excel plots that could be embedded in cells (sparklines) and conditional gradient fill patterns. I report below an example solution to give you a sense of how it all looks like.