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.

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.

The Importance of Social Media in the Residential Property Market

Having a well polished web site is all well and good, but how much better is it for your client and their network, to receive regular reminders via social media apps that showcase your properties, drive them to your site and ultimately secure more sales?

Lets look at some stats. Every second, on average, around 6,000 tweets are tweeted on Twitter, corresponding to over 500 million tweets per day? On Facebook there were 1.94 billion monthly active users as of March 2017, 32 million user accounts just in the UK.

Social media apps such as Twitter, Facebook, Instagram and Linkedin are of increasing importance in the residential property market, having the advantage of allowing notifications without ad blocking. When a user follows your page your posts are seen by people in their network, an easy way to get your latest featured property to a wider audience. Additionally, if linked correctly, your post takes the user to your website without the client needing to use a search engine, or your company needing a dedicated app.

Traditional Email versus Social Media Channels

So what does this mean for your e-mailed mailshots, will they fall out of fashion as much as big shoulder pads and the glossy A4 fliers of the 1980s? Deloitte’s 2016 Consumer Survey shows the reach of e-mail and apps are rising in parallel. Every smartphone comes with an email app, hence its popularity. It is a simple way to communicate, however, blanket group e-mails are both impersonal and counterproductive. Posting to your own Social Media page drives clients towards you and gives much wider and more targeted coverage.

The best of both worlds

The future lies in combining these tools, for example personalised mailshots, which link to fully interactive, cross platform compliant reports. Here at Houseprice.AI, our property app, Horizon, includes cutting edge interactive reports that can be e-mailed to your clients and also posted to social media for other followers to read, combining the best of both worlds. Horizon also has a smart feature that enables you to chat directly with your potential clients from the interactive report, whether they click on the e-mail link or the social media post. In fact these conversations are not only with clients that were e-mailed the report, but with any interested buyer who clicks on that post.

When To Post or Not To Post

It is important to establish the perfect balance of social media posts – just enough to keep people engaged, but not so much as to irritate them. Automatic and poorly scheduled posts that tweet the same post on the hour every hour quickly become tedious and a turn off. You can use Facebook and Twitter Analytics tools to make your posts more effective. For example Twitter statistics show that the most popular time to tweet is 12-1pm, the most read are first thing, and the most liked in the evening. Use the tools to help you decide what works best for your business.

There is no doubt that social media channels will play an increasingly important role in the marketing of property sales and lettings. These need to be quickly embraced and adapted as the powerful marketing and business generating vehicles they can be, and as forward thinking 21st century estate agents become hybrid agencies, with a presence in both virtual highways and local high streets.

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