POI's, I's, Baby
The world in 2020 may be flipped on its head, but some fundamental real estate truths remain.
“Even in a bad market, location, location, location is a way to still buy and sell property”. Sage advice from none other than 80's rap icon turned property mogul, Vanilla Ice. As an aside, the time you are spending reading this article could probably be better spent reading this one about Mr. Ice’s Real Estate adventures, and his pet Kangaroo, instead.
Yes, location is still king in real estate. What has changed, however, is how we are now able to more comprehensively assess locations, and what elements of location are becoming increasingly desirable.
According to McKinsey, 60% of our ability to predict property values is based on point-of-interest (POI) factors, such as proximity to nice hotels, restaurants, parks and cafes, making these factors more influential than the nature of the building itself. On the face of it, this shouldn’t be surprising; of course we take the surrounding neighbourhood into consideration when deciding where to live. What is more surprising is how frequently this information is overlooked by developers, investors and advisors when making decisions and recommendations. The availability of accurate and up-to-date point-of interest data now makes quantifiable what was previously based on gut feel (“it’s definitely an up and coming area”), and advances in AI-based technologies are increasing the ability to scalably build comprehensive POI libraries with increasing levels of accuracy.
Lockdown, homeschooling, WFH and various other changing trends are causing a rethink around what is actually important in a location. For example, in the US, residential searches are currently much more focused on smaller towns and proximity to open spaces, according to companies like RedFin.
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So what are points-of-interest? Basically they are the features and services to be found in our neighbourhoods, from stores and restaurants to temples and hospitals. The changing mix of these can inform the casual observer as to whether a neighbourhood is “on the up” (think cafes etc) or in decline (empty retail units, dollar stores), but an analytical approach can dramatically improve our ability to see the signs earlier and more measurably. This is truer now more than ever, as our cities are experiencing a time of unprecedented uncertainty, and no-one is truly capable of predicting what the great shake-up of 2020 (and 2021? and 2022?) will leave in its wake. POIs make up a core part of our location data work at Reomnify.
If there was a problem yo I'll solve it
A few ways you can derive value from POIs:
POI data can be analysed to identify lead indicators of commercial activity, invisible to the naked eye, that will help predict the future nature of neighbourhoods for eg retail location strategy. Just as the meteoric roll-out of stores like Starbucks have helped inform gentrification in the US, so too will we be able to identify struggling areas as the tides of new retail stores begin to retreat at different rates in different areas.
POI data is also especially powerful in less developed markets like India and parts of SouthEast Asia, where traditional types of real estate data is thin on the ground and the location of aspirational middle class stores and restaurants can play a huge role in the appeal of an area, for both living and working. “When we are picking the perfect location...you have to layer in many more important factors like infrastructure”, says Paayal Khanna, Director at JLL India, in her recent Unissu Connect video. POIs are now among the main ingredients in the growing area of automated residential valuations.
Another development that is rapidly elevating the importance of POI data is the emergence of mobile tracking solutions. I will expand more on this in the next article, but in essence it refers to the ability to track the movement of smartphone users who have opted into location tracking. If you know where a meaningful sample of citizens are located at any time, and you also know where all the POIs in a city are (down to the exact footprint of each site) then you suddenly learn a huge amount about how humans are interacting with the environment around them, in close to real time. This is incredibly powerful insight when it comes to monitoring retail activity, informing urban policy and identifying urban shifts like gentrification.
All right stop collaborate and listen...