Within the real estate sector, the way data is collected and used is nothing like it is, say, in the world of e-commerce. We explain how allmyhomes gains insights and uses them for excellent experiences.
Tech giants including the likes of Amazon, Facebook, Google, and Netflix can use big data to gain insights and generate recommendations tailored to their users on that basis. This requires a high volume of transactions and as many interactions on and with the platform as possible. The result is an insightful sample and plenty of raw data that can be analyzed and used to identify trends.
Compared to the world of e-commerce or, say, the consumer goods sector, the number of transactions within the real estate sector is naturally low. After all, most people looking to buy a home will be making a “once in a lifetime” decision. The price of a newly developed residential unit is incredibly high – especially when compared to the average Amazon basket, which may contain a USB cable and a couple of books. As you might well imagine, there isn’t much data to collect and it doesn’t come about all that frequently. And yet it is so relevant. Just think of how much more thought and emotion is involved in the decision to buy a residential property. Analyzing this data in a meaningful way and drawing insightful conclusions is one of the major challenges faced within this sector.
There are several ways of working with what is known as small data. One potential option is to use suppositions as a way of defining the focus. This relies on industry experience. Another approach is to combine a number of data attributes to generate clear findings or link one’s own internal data points with external data (if available). Lots of public sources can be drawn upon, for example, to gather comparative prices for specific properties, such as a penthouse in central Berlin.
When it comes to using small data effectively, allmyhomes relies on an in-depth understanding of the real estate sector, which is recorded in a data model (in the form of a digital specification). This provides an accurate outline of events and specific business processes. Allmyhomes constantly involves relevant stakeholders from various departments, customers, and partners to continually validate the relevance of the data. Not all raw data is always useful and so allmyhomes checks, for example, which data may be key at which point in the buying process and in which form. The system has to be constantly updated and adapted in line with the latest findings.
As far as allmyhomes is concerned, being data driven also means staying open to unexpected results that may have been missed out of the initial assessment. At the end of the day, as fewer transactions take place, it is all the more important to collect comprehensive raw data created at every stage of a transaction. This makes it possible to confirm whether or not a supposition, relating to the reason for a purchase for example, has been validated.
And here it is ultimately possible to reap the benefit of an end-to-end approach (internal link to article):
The quality of data collected using metadata can be refined on the basis of a full understanding of the real estate transaction as a whole. Taking the context of the whole transaction makes it possible to turn small
data into smart data. In turn, this can help us fully understand what exactly it is that buyers are looking for.