Commutes, Greenspace, Parks and how to grow your own decision tree

We are all well aware if you ask any decent estate agent they will pride themselves on knowing every local detail of their stomping ground, from amenities to zoos. They will also know about transportation, schools, hospitals, council tax bands, shopping and parks, where they are and be able to rate how good they are. However if you were to ask them exactly how much each of these characteristics are actually worth in pounds per square foot, then they would probably quite rightly reply that it was all in the eye of the beholder, since buyers all have very different motivations.

So while we all know that public resources such as transportation, hospitals, parks, and schools are very important factors in housing prices, we do not have an attribution to value of what these factors actually represent. However we do know that, in aggregate, if enough buyers rate, say, proximity to a school, as sufficiently important a factor, or proximity to transportation, this will obviously lead to such properties being more or less desirable and hence more or less expensive.

Re-engineering The Decision Tree

Of course if all buyers had exactly the same motivations, then describing how these variables influenced price would be a trivial exercise. Furthermore in this respect, property portals do not help at all, since the decision tree to selecting a property is so restrictive, effectively forcing all buyers into the same space, with the same decision tree. For example, the Zoopla/Rightmove search order goes a) define location b) set the price band c) choose between flat, terrace, semi or detached house d) define number of bedrooms. To see how really unhelpful this is, imagine if these property portals were selling paintings, the listings would appears as follows: ‘Rectangle landscape, size 120 sqft, blue and green with lilies and water’, or ‘sought after portrait, size 1 sqft, mostly dark with funny smile’. Well that's a Claude Monet and a Leonardo da Vinci marketed, job done!

What if buying decisions could be based on a person’s actual unique motivations? This is where Big Data and Artificial Intelligence (AI) come in. To see why, think when people say it’s all about location, location, location, what do they really mean? Actually this translates to all those things that make up a place, which will be everything you can measure, from shopping footfall, to reported crime, schools, health, sports clubs, aspect, environment, air quality, noise pollution, transportation and so on. Indeed everything that makes a place. So far we have identified around fifty different objective measures and while it very unlikely that a buyer will list all 50 of these, they may well have a very different say, top four or five?

For an example, consider a buyer who was interested in flats with a maximum commute time to central London of 45 mins, green space, transportation and wanted to be close to a park, where would be the places they could look to buy in London?

To date, several studies on property value have largely concentrated on transportation effects and only a few studies have focused on the effect that green space has on property values. In these researchers have mainly focused on specific parks, for example Hyde or Green Park, within different communities rather than parks in general, to study the average impact of green space on housing prices. Using both parks and actual visible green space we have been able to quantify the effect of public resources on property value, especially green space and/or parks, using AI.

Machine Learning

To do this we took transaction price data and the structural attributes of 84,747 properties in and around London that have sold over the course of the last 18 months. These were then cross referenced with additional supporting data, which included structural attributes, location variables, and environmental variables. In this study, Inner London is defined as the 150 inner London postcodes. Outer London is classed as within 16 and 35 km of central London. While these studies are quite basic, they demonstrate the way the Machine Learning evolves to arrive at a precise value.

Chart1

Graph 1

Chart2
Graph 2

Graphs 1 and 2 show that the average percentage of green space has far more impact on price per square meter (PSQM) the closer you are to the center of London, this again is as one would expect, with green space being more highly prized the more built up a city becomes.
Chart3
Graph 3

Chart4
Graph 4

As the graphs 3 and 4 show, London property price per square meter is inversely related to commute distance, which is not a surprise, but it is also more widely dispersed the further you radiate out, representing cheaper PSQM for similar commute times.
Chart5-1
Graph 5

Chart6-1

Graph 6

Graphs 5 and 6 show the effect of being near a train or tube station. What is interesting is that if a property is too close, this reduces the price per square meter of a property, where noise disturbance becomes a factor to consider. In Outer London, up to 2 km away from a station or tube line and you can see very large differences in PSQM, which again shows the opportunities that exist in these areas.
Chart7
Graph 7

Chart8
Graph 8

Charts 7 and 8, show the considerable effect of living close to a park, in fact in Inner London, properties fall by and average of £4,264 PSQM every 1000 meters you move way from a park! This effect is lessened in Outer London, though it is still clearly a factor as graph 8 shows.

In fact if we look a just one property type, we get an even more impressive demonstration of how certain factors drive values. Here we are just looking at flats, but we have all property types listed under the Land Registry and based on commute distances up to 90 km to central London, we have also expressed house prices as the natural log of the price per square meter so now we can see the very clear correlation at work in graph 9 with an R squared of 0.72.

Chart9

Graph 9

We can see the effect of proximity to a park on flats as well in graph 10.

Chart10

Graph 10

Living in a Bubble

Graph 11

Graph 12


The last two graphs, 11 and 12 show that as we pull together all the unique drivers our buyer has chosen, we start to narrow down to the specific areas and property types that meet the criteria. In fact we have now the means to select individual properties, that meet the precise requirements the buyer seeks. We arbitrarily chose central London and 5 particular factors out of a much longer list, but with Houseprice.AI you could do this for any city in the UK and for 40+ other drivers.

By using AI and Machine Learning, we can arrive at assessing any property purely on the basis of objective values thanks to the breadth, detail and quality of our data. In fact this attention to detail actually leads to a more natural and holistic approach to finding property. So, property searches can be made far more intuitive, personalised and quantifiable, thereby allowing buyers to identify and locate properties far more efficiently and objectively and to be able to verify value for money on both an absolute and comparative basis.

Eldred Buck

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