In his Sunday Proptech Review, James Dearsley threw down the gauntlet for a UK company to take up an interesting challenge. Every year the American on line real estate data company, Zillow, use their analytics to create a light hearted look at the best cities in which to Trick or Treat.
So being a team that never shirk from a challenge, we at Houseprice.AI, have just crunched the numbers to find the best cities in the UK for your little monsters to score the most treats. Similar to the US based index, these cities have a population of at least 500,000 souls and are based on median home sales values, and favour those locations where the demographics show there are more children aged 10 and under and where there is high residential property density, meaning less walking from door-to-door. Lastly, because this is the country that brought Harry Potter to the world and is a global leader in potions and spells, we have added a little extra to the index, in the form of Gross Disposable Household Income. After all, you obviously would want to be knocking on doors where sweets are most likely to be lurking!
Check out the complete Trick-or-Treat Index, and the top areas in each city, below. Click on the map to see how your local area measures up on Trick-or-Treat Index!
|Name||Population||Child Rating||Median Price||TT Index|
The Houseprice AI Trick-or-Treat index is made up three equally weighted variables, the median sales price, the Regional gross disposable household income (GDHI) by each Local Authority and the proportion of all children up to and including 10 years of age as a percentage of the whole population by each Local Authority. Each of these variables is compared to the highest value nationally to create the component variable. The index is based on a sample of 149,783 property transactions from all cities and urban groupings in the UK with a population of over 500,000.
Happy Halloween from the team at Houseprice.AI!
If you would like to know more information about Houseprice.AI , Horizon, or access to our API please feel free to contact us at email@example.com.
The news on Friday that Transport for London (TfL) had denied Uber an Operator Licence for the capital has stirred up some intense feelings. TfL announced Uber “not fit and proper” to hold a private hire licence and that the company had shown a “lack of corporate responsibility” in relation to public safety. As soon as the judgment was announced the company stated its intention to challenge the ruling in the courts and a petition was launched which is now well on the way to reaching 800,000 signatures. TfL are now saying Uber needs to rethink its approach.
Uber claim that 3.5 million Londoners rely on them to get around London, so we thought that we would look to see exactly where these Londoners are using the App. To do this we used Uber’s own API to get waiting times from 1000 randomly selected locations across Greater London that were within a 50 km radius of Charing Cross and cross referenced it against some known property metrics.
Uber Response Rates
So what did we find? Well overall across the random sample of 1000 covering the most of Greater London, the average response time was 432 seconds, or 7 min 12 seconds, however the attached visualization shows that much like property values, response times lengthened dramatically for a few postcodes the further away from Central London you travelled. This is not so surprising, you would expect more Ubers to be located in the busy central areas.
However what is interesting is that actual response rates were very similar over a much larger area of London than you would expect and pretty fast too, with 2 to 3 minutes not unusual. The second chart shows the average price per square meter for properties in the same postcode areas. So if Uber were just used in central areas, you might expect the same distribution of response times as say, property prices/housing density, but as you can see, it is clearly not. In fact, response times in Outer London are very similar to Inner London and spread quite evenly, suggesting Ubers really are used by Londoners at pretty consistent levels all across the capital.
Uber Response Times Central London vs Outer London Please click the chart above to view our reactive study maps
As the chart above shows, even if you live in outer London (Green) you can have response times that are as fast as the most well served and expensive central areas (Red). So it would be very fair to say that if you are living in the most exclusive part of London, or indeed the cheapest bargain basement, in both cases your access to Uber in London's streets and alleys is about the same. That makes it a egalitarian mode of transport. This, all too often, is not the same for other modes of transportation across the capital that serve Londoners and suggests that TfL will need to rethink as much as Uber will have to smarten up its operations.
If you would like to see where the location you live ranks in London for Uber, or how other areas compare then please feel free to try out this link for our interactive charts on transportation and London property .
An extract of this blog post was written for http://www.jamesdearsley.co.uk/
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.
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.
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.
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.
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.
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.
We can see the effect of proximity to a park on flats as well in graph 10.
Living in a Bubble
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.
Kazuo Ishiguro’s Booker-winning story of unspoken love for anyone who’s ever held their true feelings back, seems to sum up the experience of many Remainers, where these voters were forced into a stubborn silence. Feeling bullied by the Government’s increasingly priapic rush to a Hard Brexit and vilified as Saboteurs and Remoaners; the 48% had their revenge through the ballot box by overturning Theresa May’s majority and returning a well hung parliament. This, it would seem, is the most frequently voiced synopsis of last week’s election; the Remains had their day, but is this really the explanation?
Whilst the Labour Party did pick up a huge number of seats, which had voted to stay in the EU, from the Conservatives, this would not explain the reason for the Phoenix like rise of the Conservatives in Scotland; a part of the UK that had voted to remain in the EU by a very significant margin. Labour too has been crowing about its results in England, but they are also particularly taciturn about their performance in Scotland. It wasn’t just about Brexit.
What then might also have been a significant factor in the election?Could Big Data help us we asked ourselves? So, we decided we would put our resident boffins to work, to see if HOUSEPRICE.AI could come up with something that might explain these unexpected electoral results. Across England & Wales there were 28 swings to Labour from the Conservatives, the average swing for those parliamentary constituencies was 12.14%, which is historically very large.
|Bristol North West||50.6||16.3|
|Crewe and Nantwich||47.1||9.4|
|Plymouth, Sutton and Devonport||53.4||16.7|
|Vale of Clwyd||50.2||11.9|
|Warwick and Leamington||46.7||11.8|
So what can we conclude from this quick analysis?
Battersea also stands out in having many more property transactions than the other constituencies, transacting over 74% more than the average of 2236.
Firstly, overall no surprises that there is no correlation between house prices and political outlook in this sample. The value of a home does not determine its owners political outlook. This will be a boon to pollsters and Labour political canvassers, who can legitimately door knock in Kensington and North Bury with equal hope.
Secondly, Estate Agents in Battersea must be worried about the rise of Internet Agents, 1.5% fees on an average Battersea property of £936,187, equate to fees of over £14,000 and with such a high number of transactions, that looks a sitting duckhouse.