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.

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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.

* info@houseprice.ai

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Mad about Metrics

Houseprice.AI Value

We have introduced 5 different metrics to suit all of our property clients. As a company we are mad about metrics and passionate about using AI data analytics.
A core part of this is to interpret and predict the fairest and most accurate value.

We realise that each sector of the property market has different needs. As an example, an agent wants sales regression for an accurate appraisal, a consumer wants our fair value to protect their investment, whilst a vendor the guided listing price to get the the maximum return on the property.

We give you the tools to help you to make the right decisions for your property interests.

Current estimated value This value is what we call the fair price, property can go higher or lower but this is our AI deduced benchmark value based on over 40 drivers.

Range Includes variables relating to changes in factors such as, aspect, environmental factors, fittings and improvements.

Sales Regression Method used by traditional AVMs. Deriving a value by fitting a line between the recent sales of comparable properties factored for PSQM/PSQF .

Weighted Average A weighted average of sale prices over the past 6 months

Guided Listing price Recommended listing price based on analytics of time on the market and supply demand measures for the area.

Confidence level Statistical measure that is based on how many observations there are for the specific area/property type and deviation from the mean.

Adjusted Value
Gain more precision by adjusting property details and watch the value adjust instantly. The more details you are able to provide, the more precise our adjusted value will be.

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.
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

‘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.


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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Bravish New World

AI. Helping you see beyond the Big Red Book

Aldous Huxley probably didn't have financial regulation in mind when he wrote his novel about a dystopian world order, but the slew of newly formed global rules, that today govern international finance, does have a touch of that Brave New World feeling.

Yes, a lot has been happening in the world of Finance lately.
The Bank for International Settlements (BIS) Basel III regulatory framework was finalised last December, with a staged implementation phase over the next 5 years; 10 if you include the Tier 1 capital ratio buffers.

Five years might sound a lot, but when you know that the best banks will want to adopt these new standards well ahead of their competition and the BIS's own deadlines, you know the race is well and truly on. Additionally this January, both the International Financial Reporting Standard 9 (IFRS9) and MiFID 2 went live too.

A cynic might argue that ultimately all of these new regulations have been brought in to ensure that the next financial crisis is not like the last one. There is more than a grain of truth in that. However, let's not forget that the 2007/2008 Global Financial Crisis's Pudding Lane, was the US sub-prime mortgage market.

Ten years on and now all of this may seem like distant thunder to participants in real estate, but actually a great part of what all these new financial fire break regulations do, is put an intense focus on Pudding Lane and particularly on property based finance and securitization.

Amongst the myriad effects designed to improve financial stability through Basel III, these new standards demand regular and better stress testing of the left hand side of the lenders’ balance sheet and specifically Loan To Value (LTV) bands and their associated Risk Weightings.

So for Assets, think real estate, commercial and residential property valuations and any lending based on these, mortgages, MBS and RMBS. These are in addition to the more obvious aspects of credit risk analysis of borrowers and credit default probabilities, along with forecasting and stress testing of future risks and then provisioning for them. All together, quite tricky stuff.

World Keeps Spinning

Meanwhile the real world has not quite stopped while these new regulatory frameworks were being figured out, let alone implemented. During this time it was not surprising that traditional mortgage lending remained and continues to remain subdued, whilst these participants have their financial probity medicine administered. Equally unsurprising that while this happened, a host of 'alternative finance' new entrants have entered into the property lending space. Now what is interesting, is that obviously these new lenders come at a cost and that actually any non-bank or non-regulated lender will likely have a much higher cost of capital, all of which will be passed on to the borrower. This is instructive, as it shows that the actual cost of a mortgage works out as the sum of the credit worthiness of the lender and the borrower plus the risk free rate. However what both traditional lenders and new participants all need, is a clear objective estimate of the collateral (underlying property), the fair value, which actually leads to the mortgage offer and thus the LTV.

Quickly you can see there is a problem, the Pudding lane problem. The GFC fire started with the tinder of poor real estate valuations, the oxygen of leverage through securitization and fanned with the accelerant of fraudulent lending criteria. We cant do anything about the last two, but we can get better valuations thanks to applying AI, Machine Learning and Big data.

Fortune favours the bravish

Over the next few weeks, the team here at Houseprice.AI will go over how several of these factors can be better measured and modelled using ML and Big Data with accurate, objective property valuations. Not exactly heroic of course, but bravish enough to manage this new regulatory maze. Our Property experts will go through how the checklist of the RICS Red Book can be better supported with objective datasets from a variety of drivers, leading to better property valuations. Our Finance and Asset Management experts will show how more accurate portfolio analytics, pricing and granularity directly affects the cost of funds and risk weighted returns for the lender. That then in turn leads directly to an end-users cost for funding property acquisitions, through a variety of distribution channels.

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.

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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From gut feel to the real deal

Like the winter weather, referenda results and Arsene Wenger’s future, forecasting property values is a risky business.

We can all be experts on past transactions but predicting the fair price for a property today, yet alone tomorrow, needs more than just the ‘I know my patch' gut feel that influences the majority of property transactions.

In the past rising markets have covered over a trail of over-optimistic estimations, minimising Professional Indemnity Insurance claims. But with uncertainties likely to continue to affect the market for several years, and with billions of pounds put at risk, much smarter and objective advice is demanded by a customer base that has increasing thirst for more information, thanks to mobile Apps and widgets on their smartphones.

Artificial Intelligence (AI) and Machine Learning (ML) have been around for over a decade in the banking sector and yet have made very little impact on the world of property. That is until now.

When these are paired with Big Data and powerful Cloud based processing capacity, the next generation of valuation toolkits can be delivered to users, which are far smarter and responsive than those built with MRA and standard matrix analysis, methods which currently form the basis of most AVMs (Automatic Valuation Model).

The ML is constantly reacting and learning from changes in and around the marketplace and can be trained to look at other marketplaces to broaden the range of issues to be considered within a single valuation. Thus the components affecting the valuation criteria are being constantly adjusted as millions of bits of new data are assimilated.

As soon as a property sale is recorded on the Land Registry, a new public transport route announced, a new school opened, or an increase in local air pollution levels registered, the AI system will model the impacts, produce a modified valuation figure and then model the outcomes in a number of different ways, advising on the most likely scenarios.

These scenarios can also be further processed within industry specific hybrid models that combine specific levels of sophistication, maximising the ability to use the information creatively, yet ensuring accuracy and reliability. This allows, for instance real estate developers to work within much clearer risk parameters.

Does a developer continue to build residential blocks to sell, revert to a wholly or partial rental scenario, or sell on and move onto the next project? All scenarios can be modelled, stress tested and risk assessed.

There is now no need to wait until the market conditions ‘have picked up post Brexit’ or the ‘overseas investor becomes more comfortable’ or even that ‘new government initiatives will re-ignited a stagnant marketplace’.

Decisions can be made now on a vastly more intelligent prognosis than ‘gut feel’ and a list of apocryphal or redacted sales comparison list.

Who knows, with the ability to assimilate vast amounts of hitherto undreamt of information, AI and ML make real estate valuation a science and not just an art. A science that does not depend on the hunger of the buyer, the desperation of the seller. or the charm and salesmanship of the broker.

Arsene’s next career?

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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No business like snow business for 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 info@houseprice.ai.

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