What is Houseprice.AI?

This video explains how Houseprice.AI is at the forefront of Proptech innovation by developing a machine valuation model that draws on Big Data and AI to predict astonishingly accurate valuations.

What's the fair price? Houseprice.AI.

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If you would like to know more information about Houseprice.AI or our Estate agents' App Horizon, please feel free to contact us through the online chatbox or by email at support@houseprice.ai.

How do 2017-2018 students pay the rent?

The Government has increased the maintenance loan for students starting in September 2017 which means that freshers will be 2.8 % better off than those who are already doing their degree. This increase is matched by an increase in tuition fees by 2.8% in line with inflation which pretty much nullifies any benefit.

In London rental prices have decreased slightly, according to the Guardian, the typical new rent in London has fallen by 3% in a year, but the cost of food has increased by 2.3 % in the last 3 months and transport has risen. The Office for National Statistics said the Retail Price Index (RPI) measure of inflation, which is used to calculate train ticket prices, rose by 3.6% in July, up from 3.5% in June.

The Government expects parents who earn over 25 K to subsidise their student child. This varies enormously throughout the country, and sticks a hefty burden on parents looking at the rental market depending where their bright spark has decided to study.

For example take 4 sets of average parents in the Midlands all of whom earn over 50K
EC studies at Manchester. GW studies in Leeds. EB studies in London. LC studies in Sheffield. All 4 receive a Tuition loan of £9000.

Manchester Sheffield Leeds london
Tuition Loan £9000 £9000 £9000 £9000
Maintenance £5256 £3994 £5256 £5479
10 mths rent £6190 £0 £2320 £7312
Surplus £934 £3994 £2936 £1833

This means that the maintenance loan for GW and LC, who commutes from home, not only covers their rent but also gives them money for transport and food, maybe even the odd book, heaven forbid. We do remember they are supposed to be studying and even ebooks cost money?

LC has the best financial situation, although living at home may not give a student the life lesson of independence, budgeting and the social freedom living in student accommodation allows. EB has the worst situation. Her Maintenance loan does not cover her rent and her transport costs are higher in London. Like EC she needs financial assistance from her parents just to pay the rent, only twice as much. Food, transport, clothes, all this has to come from somewhere, and yet if you remember, our sample set of parents all earn roughly the same.

Parents with enough savings, or their own mortgage paid off may consider investing in accommodation. Outside of London the market is still slowly rising, but within housing has dipped. The Office of National Statistics (ONS) records London experiencing a £3,000 drop in the average price of a home to £482,000 in June from the previous month, but remains the most expensive UK region in which to own a property. So the investment may not be as sensible as it seems if they are left with a mortgage for a property that is worth less than they bought it for in 3 years.

This is not just in London, one of our clients recently shared an anecdote about student accommodation in Portsmouth. He was looking at the value of a flat that had previously been rented out to students. The owner reported that the value was now slightly depressed as a new student block had been recently erected adjacent to his property. Students who had previously lived in the surrounding area had moved into the new accommodation which resulted in the value of the previously tenanted housing dropping.

According to a recent survey on savethestudent.org 80 % of students worry about money. Apart from scrounging money from friends, family and credit cards, many students are forced to work to supplement their income, often adversely impacting on the amount of time that they have to study. Parents without significant savings are taking out second mortgages or even relying on their own parents to help.

This also makes the student applying for a University place take the location into consideration. A University with a short commute time from home gives them the option of living at home and not having to pay any rent. Those outside London also have a small surplus of cash after paying rent. These students actually have the funds for a social life. Remember that?

Students at universities outside London pay significantly less rent so opting for a University based on location rather than the course is becoming increasingly more biased. Surely that is wrong, the emphasis should be on the aptness of the course not its location.

We haven’t mentioned that there is a proposal in the pipeline to increase tuition fees to £9,250. This is not only for new students, but also existing ones. How can this be when they have signed a contract? Apparently on page 3 of this contract the Government has the right to increase the fees at will, giving a further financial strain on this loan that will be with the student for the next 30 years. Interest rates on loans are rising to 6.1%, which will push up average student debt on graduation to more than £50,000, including maintenance loans. Students in England leave university with higher debts than almost anywhere else in the developed world, the Institute for Fiscal Studies said in July.

Charging £9,250 a year for an undergraduate degree makes England a real outlier by international standards. Even in the UK England does not compare well, Scotland has no fees for Scottish students, and fees in Wales and Northern Ireland are much lower.

There is no reflection in the cost of tuition as to the amount of hours the student is getting for their money. Those studying science require far more hours than those studying, say, English literature or history and yet this has no bearing on the cost of tuition.

Overall location pays a key role in the financial balance for both parents and students. But just remember, it is only for 3 years. Unless they decide to do a PHD of course!

For more information on Horizon contact: info@houseprice.ai 

https://www.gov.uk/education/funding-and-finance-for-students https://www.theguardian.com/business/2017/mar/13/uk-average-annual-rents-fall-stamp-duty-hike-letting

Horizon and Social Media

Here at Houseprice.AI we want to help our Estate Agent clients to win those vital new instructions, so we have designed Horizon’s interactive and customised reports specifically to help you to achieve this. Click the image below to watch our short video.

Recently there are a few new features within Facebook that you may not be aware of and we are always happy to help you to enhance your posts and make the most of these features as we have years of experience!

There are also features that you can use built into other every day software such as Microsoft Office. Did you know that you can turn a simple Powerpoint of your images into an MP4? You can arrange photos and plans using a template and upload them to a video site such as Youtube or Vimeo and post that to your Horizon report, and your social media.

If you would like to know more hints and tips that will save you time and money please feel free to contact us through the online chatbox or by email at support@houseprice.ai.

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.


Graph 1

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.
Graph 3

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.
Graph 5


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.
Graph 7

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.


Graph 9

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


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.