06 Sep For Real Estate Search, Disruption is around the Corner
In terms of real estate search, it seems as if little has changed in recent years. Today, it seems to be only a matter of time until new technologies, based on big data, will radically change the customer experience in the real estate search market.
The last major revolution of property search happened about twenty to twenty-five years ago. Back then, the only way to find properties was either by engaging a realtor or by studying print media. This meant tedious and time-consuming screening of property ads and finding supra-regional real estate was almost impossible. Everyone was completely dependent on the realtor – from property owners, landlords, tenants, to buyers. Nobody was able to get a complete, transparent overview of the market.
Later, in the late 1990ies, the first online real estate platforms such as immowelt.de (1994), followed by realestate.com.au (1995), seloger.fr (1996), immobilienscout24.de (1998), rightmove.co.uk (2000) and trulia.com (2004) were on the rise, and with them, property search underwent a radical transition.
User-friendliness as the key to success
Online market places such as immowelt.de dramatically revolutionized the real estate market. Suddenly, it was possible for the user to search for supra-regional properties and comparing prices became effortless; a single ad reached a significantly bigger audience and administrative expenses decreased for landlords, realtors, and property owners.
The time-consuming search for real estate got easier for property seekers as well. Potentially avoidable on-site visits were prevented, as preferences regarding price and facilities simply could be entered online. Comparing real estate had just become a whole lot easier. It was this new level of user-friendliness that caused the major shift away from print media and towards the online segment in the real estate portal business.
Little innovation due to low competition
Today, the market is well represented by up to three real estate platforms in each country with little differences among each other. The reason is simple: the way prospects can search for property has stayed the same for twenty years. While there are different versions for various devices and the design is more appealing than twenty years ago, the underlying search mechanisms remained the same. The user has to know where and what kind of property he or she is looking for. In consequence, real estate portals only benefit those who have already made up their minds by about 80%.
Yet, the decision-making process for real estate works completely different. Rather than considering whether the portal’s displayed ads are a fit, prospects want to decide on the basic qualifications of a location and its surroundings first.
Intelligent algorithms already help in the early stages
Choosing the right location is a highly individual decision and normally beings with questions of connectivity and travel times from and to potential places of residence. This is a simple task for a prospect who has already decided on one or more potential locations. However, since all online analysis tools require a more or less specific location to start the search, if one hasn’t decided on a location yet, current online solutions won’t be able to help. That being said, today’s technology is already capable of doing what the prospects need – choosing the best location by considering certain demands.
Here’s an example:
Person A’s workplace is in Oxford Street and his/her partner works at Trafalgar Square – both want to reach their workplaces in approximately the same time. Which areas should they consider for their new apartment?
The graphic above shows potential places of residence from which both can reach their workplaces in approximately the same time. Using this technology, the map also reveals certain patterns which no one, including experienced realtors, could have predicted.
Current solutions miss out on the technological potential
In the mentioned example, the prospect can now compare and evaluate the various displayed suggestions for a place of residence. In this context, surroundings of a potential location play a vital role. Depending on one’s individual requirements, different questions emerge:
How good is the care and supply situation regarding doctors, kindergartens, schools, municipal institutions, and others?
- What does cultural life offer: restaurants, bars, bakeries, cafes?
- What is the neighborhood like?
- Is the district safe?
- What kind of sports clubs exist?
- Is there sufficient parking?
- How high are the rental prices?
All these aspects must be part of the analysis now. There is plenty of data available for these questions and, partially, platforms are already visualizing single aspects such as kindergartens and schools in the surrounding area.
Yet, most portals require a specific location as a prerequisite to take these aspects into consideration. This means that although it’s possible to create an environment analysis for a single real estate offer, this information doesn’t provide any value for someone who wants to choose their location depending on the surroundings – and not the other way around. The prospect wants to make a weighed decision regarding the potential place of residence.
New technology is here – disruption is coming
Interestingly, state-of-the-art technology is already capable of processing the available data to compare different regions, and to find the potential place of residence without looking at a single listing – promising to significantly simplify real estate search.
Looking into the near future, the next generation of real estate portals will move the prospect’s needs into the center of attention – because as history has shown before, it’s what users like that wins the race.
Stephan is Managing Director of Targomo and former MD of Immobilienscout24.de. With Targomo, he helps businesses and public organizations to harvest the power of advanced spatial analysis. Targomo’s technology simplifies online search for spatial objects by applying sophisticated route planning and travel time algorithms and visualization.
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