Houseprice.AI - our data, your knowledge

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Houseprice.AI

  • Market leading ML technology
  • Incredibly accurate valuations
  • Reactive value reports
  • Proprietary API

Houseprice.AI is a data company specialising in identifying real value. We have the most comprehensive and proprietary data sets in the industry and have developed industry leading Machine Learning algorithms.

Applications across a range of markets

Our clients include: surveyors, banks, developers, buyers, vendors, investors and agents.

A disruptor in the value of Real Estate
  • Saves time and cuts costs
  • Raises standards and simple to use
  • Superior market intelligence
Information packed reactive value report

Including: comparables, historicals, forecasts, sales values, listings, rental values, greenspace, neighbourhood, transport, economics, EPC and much more.

Project Management

From one property to an entire portfolio. Revalue at any time. Change property specs and obtain the value of the property after the improvements. Revalue an individual property or the entire portfolio at any time.

Out of the box valuation widget

A valuable lead generator that can be embedded in your website

Superior market intelligence

The HP.AI Risk/Reward index, is a proprietary index that further enhances residential real estate data knowledge and improve decision making and profitability.

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Please contact us if you have questions about Houseprice.AI , our AI data analytics app, want access to our API, or would like to schedule a demo.

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Houseprice.AI is a RICS Tech Affiliate

Unwrapping 2019

Having just exited 2018, and as we step blinking into 2019, the team at Houseprice.AI thought that we should take a look at the year ahead. So to start with, we want to talk about property predictions and their accuracy.


As the Chinese proverb goes, the appropriately named year of the Pig promises to be an interesting time and clearly not just for the property market. All, largely due, to the ever surprising package we call Brexit.
So even though all the presents of 2018 are now unwrapped, one is sitting there and as we give the box a squeeze, we still have no clue what this one marked 'Brexit' will actually look like because the tag still reads 'Only open on March 29th'.

To help us we have reviewed a number of possible outcomes and scenarios and we have combined a number of resources and estimates from the following sources: Bank of England (BOE), Office for Budget Responsibility (OBR), Institute for Fiscal Studies (IFS), Centre for European Reform (CER) , Centre for Economic Policy Research (CEPR) all of which offer current best estimates for the UK’s economy and either explicitly or implicitly, associated potential impacts on the UK's residential property market.

Now many readers will say, 'ahah here's yet another crystal ball, mystic Meg exercise'. However, prediction is actually our business, and we are pretty good at it when it comes to property. By using objective data, paired with Machine Learning (ML), all expressed within a probabilistic framework, we are able to estimate property values to average errors of 2-3%. Our approach is scientific, by this we mean it is expressed as how probably wrong we are, not how absolutely right we are.

However there are three points that need to be stressed.

Firstly, it must be stated that the following are scenarios not forecasts. The scenarios illustrate what could happen, not even necessarily what is most likely to happen under a set of key assumptions. It is therefore a spectrum of outcomes that we worked with.


Secondly, whilst Brexit is clearly the major domestic factor for the UK, the global macro-economic environment is clearly another major concern. We could obviously mention the current fall in global stock markets, the prospect of further trade tensions between the US and China, the start of Quantitative Tightening (QT) and the flattening and inversion of the yield curve. All of the above point to much increased economic downside risks in addition to Brexit. For example, the chart below is reason for concern, since flattening of the yield curve, historically, is more often than not, a pretty good predictor of housing property declines.

Thirdly, whilst real estate is one of the largest asset classes, it is also the most heterogeneous. By comparison, traded stocks, bonds and commodities whilst also very diverse are far more standardised, so a further caveat we must make is that individual properties values can vary very greatly within a single location. Using our ML based algorithms we have high confidence for the predictive accuracy of our appraisal predictions at both a micro and property specific level. The image below links to an interactive map and this example in Stevenage - postcode outer SG4 - shows that HP.AI's average prediction error was 1.10% for 93 Terrace properties that sold and that we predicted over the previous 12 trailing months.

It should be mentioned that this is just one sample postcode area randomly taken from over 2,100 postcode areas that we estimate precise individual property values for, every month, based on our estimates vs actual Land Registry transactions. In fact, if you click on the map image you can see the sample for 12 months up to November 2018 has over 530,000 property valuations. Incidentally why only 530,000? Well we have to calibrate our ML models on the others, so some 700,000 in the last 12 months along with another 15 million from previous years.

Click the image to go to the reactive map

So whilst we are probably wrong, but we are also probably more likely to be less wrong than someone who is authoritatively pointing a finger in the air and citing subjective and anecdotal evidence. The interactive map below shows how accurate our valuations are in every postcode in England & Wales.

Click the image to go to the reactive map
We hope you have a good week!

Eldred Buck is CEO and Co-Founder of Houseprice.AI Ltd. He has over 25 years experience in capital markets and banking, specialising in quantitative models and derivatives trading across all major asset classes. Previously he founded Eiger Trading Advisors, a leading fintech company.
Need More Information?

Please contact us if you have questions about Houseprice.AI , our AI data analytics app, want access to our API, or would like to schedule a demo.

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Did you know that there is more to Houseprice.AI than values?

Do you want to improve your data analytics? Using AI and machine learning we can provide you with the tools to increase your profitability.
Our new  proprietary reports and benchmarks provide fast and objective comparative analyses between residential projects, from single family homes through to large scale developments.
The Insight module within our app,Horizon, distils billions of data points to help you identify emerging areas with steady home appreciation and high rental returns.
Our API delivers our proprietary data into your own applications and business processes and will give you a competitive edge over your competition.

We want to work alongside you and help you every step of the way, as little or as much as you require.

Need More Information?

Please contact us if you have questions about Houseprice.AI , our AI data analytics app, want access to our API, or would like to schedule a demo.

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Eldred Buck talks about Data sets and AI

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How important are data sets

One of the intriguing facts about Houseprice.AI is the fact that our data sets are very vast and that means that we are able to accurately estimate the appraisal value of a property by virtue of these data sets and that means that for the 22.5 million properties in the UK we can arrive at a valuation in 10 seconds provided that we put the correct data in, most of which we actually have.

Who can makes use this data

The market place that we’re serving is everybody from home buyers though to, essentially, all the intermediaries involved in that process and all the professionals involved with the industry and sector. Bearing in mind that we have a residential market of 6.2 trillion pounds, in the order of 3 times the GDP of the country. It’s a very substantial market that we are looking to apply our technology to, and there are a myriad number of applications that come from being able to value property on an objective set of criteria which are universally applied across the geographies that we are involved in. Which in this case is England and Wales.

Why did you start Houseprice.AI

The reason that we started Houseprice.AI was in order to meet the need for objective information in the residential market. We saw that actually with the application of Machine Learning and the use of supervised learning sets as part of Artificial Intelligence that we would be able to provide consumers with a far better set of objective criteria in terms of valuing their property.

Eldred Buck is CEO and Co-Founder of Houseprice.AI Ltd and a Non Executive Director at Sequant Capital. He has over 25 years experience in capital markets and banking, specialising in quantitative models and derivatives trading across all major asset classes. Previously he founded Eiger Trading Advisors, a leading fintech company.
Need More Information?

Please contact us if you have questions about Houseprice.AI , our AI data analytics app, want access to our API, or would like to schedule a demo.

* info@houseprice.ai


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Gio Miano talks about Insight

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Why Houseprice.AI created Insight

We created Houseprice Insight, which is part of our offering, and is essentially a way to allow our users to provide their customers with much more granularity, data analysis and insight on a specific area.

How does Insight help the user?

We created the ability for someone to show that they effectively know their area. For example, what’s going to happen if a new Waitrose down the street opens up, what’s going to happen if the school kids are doing better at school, what’s going to happen if the NHS push more money to improve their service in the area. How that is going to affect the value of the area has to affect the value of a property. And how you can actually extrapolate this to have a meaningful forecast.

The significance of using AI and Machine Learning

I think that the problem that we now have is because we value things based on the past, and what we want to do, is we want to try to extrapolate with the ability to do a meaningful prediction in the future. Which is not just drawing a straight line between points and know that is the future value.

What Insight can demonstrate

We want to understand why and what impact each single amenity has. The little park that they are planning to build and how that is going to affect the area, as a community project. Where is going to be the best place to open a park. Where is it going to affect the value of an area. How is it is going to affect the value of an area.

We are helping to create sustainable projects, that also have the minimum impact on peoples life, and also on their most important asset. Their house.

Giovanni Miano is Co-founder and CTO of Houseprice AI. Gio has been involved with HFT projects across the globe and is experienced in designing and implementing highly complex IT solutions. Previously he has served as technical consultant for several global firms such as BP, Trafigura, Morgan Stanley and Goldman Sachs.
Need More Information?

Please contact us if you have questions about Houseprice.AI , our AI data analytics app, want access to our API, or would like to schedule a demo.

* info@houseprice.ai


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Avamore Capital partners with Houseprice.AI

In the run up to London Tech Week, 11th - 17th June, we were delighted to be asked by Avamore Capital to partner with them in their Proptech series that highlights the latest developments in property Technology.

They featured us in this article:
Introducing Houseprice.AI - the must have tool for every Developer

Avamore Capital also asked us to produce a short follow up film to demo some features of our app. Here is our CEO, Eldred Buck, talking about Insight.

Please click the image to watch the video on Avamore Capital's Youtube Channel

Vivienne Brooks is the CCO of Houseprice.AI She has a long history as a Technical Software Support Guru, is a graphic artist and also has a strong background in Marketing.



If you would like to know more information about Houseprice.AI , Horizon, or access to our API please feel free to contact us at info@houseprice.ai.


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Future Proptech 2018 DiscoTech Challenge Update

Last Wednesday we took part in the Innovation Challenge organised by DiscoTech.
Our challenger was the Ministry Of Housing, Communities & Local Government.

The challenge was a complex one, as it addressed the housing crisis, a problem that has been around for many years, and housing availability is a very complicated problem to address.

As the Future Proptech Innovation Challenge states: "While a large part of the issue is the need to improve the speed, raise the quality and achieve tenure diversity of new home delivery, solving the housing crisis requires more than just new homes." So how can digital tools help with this process?

The MHCLG set three challenge. planning of homes, delivery of homes and sales of homes. Putting our heads together we realised that AI can help with all three challenges, and Mat Colmer, who heads up Disco Tech, was keen for us to address all three, and see what we could do.

Eldred, Philip and I represented Houseprice.AI on the day, and we chose to address the points in bold on the above slide, as we only had a scant seven minute slot for Eldred and Philip to do the final presentation.

Disco Tech Innovators

There were 6 other innovators in the Disco Tech challenges: Disruptive Tech, BrikBit, LIQUID, Pixie Labs, Skyscape and DIRTT. The gallery at the business Design centre was buzzing with activity. Some of the innovators even deciding to merge there ideas to form groups that could better address their chosen challenge.

Can you spot who is meant to be whom?

As we enthusiastically brainstormed, David Vignolli recorded the day in some fabulous visual scribes. Jo Tasker and Julie Brim kept everything flowing,and did a great job looking after us all, keeping us focused, fed and social media a flutter.

Houseprice.AI hard at work!

The time flew past, with Eldred and Philip also fitting in some networking and plenty of the conference attendees wanting to know about both Horizon and our proprietary API. I even got to catch up with a couple of my favourite Proptech contacts, Antony Slumbers and Will Darbyshire.

Presentation time

4.30pm and we headed to the main stage for our presentations. Eldred and Philip decided to do ours as a 2 hander, and it was very effective. Seven precious minutes was not nearly enough time on such a big subject, but they managed to get the key points over really well. I felt rather proud as our team Houseprice.AI eloquent orators showed a taste of what we can do with Machine Learning and AI.

Houseprice.AI on the big screen
Go team Houseprice,AI!

Vivienne Brooks is the CCO of Houseprice.AI She has a long history as a Technical Software Support Guru, is a graphic artist and also has a strong background in Marketing.



If you would like to know more information about Houseprice.AI , Horizon, or access to our API please feel free to contact us at info@houseprice.ai.


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Houseprice.AI and the Future Proptech 2018 conference

The Future Proptech conference will showcase the very latest ideas and technologies that the industry has to offer to a diverse set of users.
Future Proptech 2018
2nd May at the
Business Design Centre
London

‘Housing Crisis’ are two words that appears on the conference website and that are all too often coupled, as indeed are; ‘greedy and housebuilders’ along with ‘planning and delays’ and not forgetting ‘special and interests’. Everyone is calling for less rhetoric and more action, however this is not easy since the Housing Crisis is clearly not a simple problem to solve and there are always myriad and competing opinions on how to address the issues that underpin this deep seated problem.

Consequently Houseprice.AI are delighted to be selected as one of the participants in the ‘Innovation’ stream at Future Proptech and we will specifically be looking to address some of the challenges that have been set out by MHCLG by applying Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to help better inform all participants and stakeholders involved with, planning, delivery and sales of housing.

We at Houseprice.AI are driven by the belief that transparency, clarity and consistency can be achieved through better data and leading edge technology and this combination allows for a more holistic approach to the housing problem. We want to show that adoption of a more data based approach, which objectively uses as many quantifiable drivers as possible, will naturally lead to more optimised and transparent solutions for all participants.

By incorporating better data and AI - everything from asset price and mix through to local, socio economic and environmental impacts - planning, construction and property demand and sales can be far better modeled and analysed; both more objectively and much faster. Ultimately this improvement to existing processes, project evaluations and decision making, then leads directly to lower costs and greater efficiencies. Here are just a few examples.

AI as a Facilitator of Sustainable Regeneration

Multiple variables, socio-economic, demographic, access to green spaces, transportation, educational, recreational can be measured modeled and visualized, thereby allowing better and more objective decisions to be made with more confidence than ever before. More importantly these can then be modeled with forward looking factors leading to a whole new level of reliability based on predominantly ‘objective’ data.

This ensures that the number, size, mix of units, building types, tenure, phasing, etc all match the actual and future demands of local neighbourhoods adding value, vibrancy and vitality to existing communities. We believe that even aesthetics, such as building heights and design issues can also be modelled and optimised to create the ideal solution for each scenario.

Rethinking the Planning Process

Currently only a few local authorities succeed in hitting their government set targets for determining Planning Applications. We are confident that using AI and Big Data will allow users an opportunity to completely re-think the Planning Process. Just one example is to introduce site specific planning policy and townscape principles within a 3 dimensional planning framework that is coordinated on a GIS mapping system; the pre-Planning phase can be reduced. By allowing more objective analysis of Town Planning criteria and better engagement of local stakeholders, the whole preconstruction phase can start to be measured in months rather than years.

Creating new housing models fit for the future

Housing solutions are now so wide ranging that to simply continue to roll out the same old formula will inevitably be a wasted opportunity. There are now a range of options in the rental sector alone which, according to the Joseph Rowntree Foundation, 1.6m in London are currently locked into now for life. These options include:

Private Rental Sector-PRS.

This product is a hybrid aimed at these who wish to spend their moderate incomes on stylish living rather than a mortgage. Theirs is a discretionary, almost hedonistic philosophy as far divorced from the’ my home is my castle’ as its is possible to be. They will change their home almost as often as they will change their car. There is also a new class of investor as crowdfunding and even multiple small investor clubs create funds which are far less specific about risk and returns, but want to invest in creating more diverse and vibrant cities.

Post Grad Housing

Most University cities struggle to retain the graduates that were nurtured there, often due to the high cost of appropriate housing located near employment opportunities. An emerging model is flatted accommodation suitable for singles or couples in the pre-family face of their lives. These units typically feature flats clustered around a shared kitchen which preserves the ‘sharing, community feel that is a step or two up from student housing but retains the comfort and excitement of communal living. Each flat has its own ‘kitchenette’ for making drinks and breakfasts within their own private living room. There may also be a guest room in each cluster to accommodate the occasional visitor as most flats will only have one bedroom. Build costs can be as low as £75,000 for a one bed unit and the model is perfect for prefabricated solutions,

Mixed Tenure Housing

As couples plan for a family, a range of tenures tailored to the security needs of the occupiers is being introduced by a number of enlightened developers. This is a variance on the shared ownership scheme, allowing tenants to secure a five, ten or even longer term lease, fixing their overheads and meaning that they can invest in better quality fittings and decoration.

There may also be an element of self-build, reducing homeowners costs even further.

Private shared ownership housing.

This used to be the preserve of the social housing providers, but a model aimed at the aspirational middle income young family allows for an element of equity building that reduces the pain of jumping onto the property ladder. The first rung is normally set at 10% and similar tranches or more can be purchased at the owners obtain funding. So after ten years a family could own their own property outright without the painful process of finding big deposits and satisfying earnings hurdles to qualify for mortgages. Crowdfunding investors are ideally suited to this investment opportunity as building voids are virtually eliminated and investments can be withdrawn on an annual basis with a ten year maximum horizon.

Conclusion

It is clear that better more objective and therefore more creative solutions can arise by using better data and technology. Ultimately the Housing Crisis, which at first glance appears almost Gordian in complexity and scale, can be unpicked and solved by using clean data, consistent methods and transparent objective processes. The housing crisis is a problem that everyone acknowledges, but it will never be fixed by opinions, only by rational decisions based on real data and through objective planning, delivery and action.

We look forward to seeing you at the Business Design Centre, Islington on May 2nd 2018.

Philip Challinor is the chairman of Houseprice.AI and was part of the architects team at Denning Male Polisano who helped convert Highbury, the former home of Arsenal FC into 700 homes for local people.



If you would like to know more information about Houseprice.AI , Horizon, or access to our API please feel free to contact us at info@houseprice.ai.


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