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.
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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.
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Impressionism for AI

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New features

This week we have introduced some great new features to our value report.

Comparables

Our comparable listings now has links to the vendor agent for both current sales listings and current lettings listings. Also new this month are pictures of each property to help you evaluate the external condition and period of the property. Add, remove, filter, and sort homes you feel are most comparable.
You can view comparables by recorded sales, buy listings or rental listings.

PDF Report

At Houseprice.AI we listen. Our PDF report has been completely revamped, by request of a number of our clients, to reflect the layout of our interactive value report. Designed to be viewed landscape across all devices, you can download, email or print out your valuation, comparables and demographics.

Transparency Clarity Consistency
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.
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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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Happy Easter from Houseprice.AI



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A message to all Stakeholders

In light of the recent headlines concerning Cambridge Analytica and Facebook, we would like to make the following clear to all of our clients and any other stakeholders in Houseprice AI.


We, like many new fintech/proptech firms, use Big Data, Social Media and AI as a major part of our business activity and of the services we offer. So even if all of our data sets are always anonymised and encrypted and we obviously already follow all the General Data Protection Regulation GDPR which takes effect on 25 May 2018, we additionally have one basic rule. We never do anything in business that in turn, we would not want done to us, as private individuals ourselves. It’s a simple rule and powerful, but treating others as you yourself would wish to be treated, is effective and common sense. Some call this business ethics, we would call it a fundamental right of all us.


If you have any concerns about how your data is being used, please feel free to contact us on support@houseprice.ai at any time.

Many thanks

Eldred and the Team at Houseprice.AI

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.



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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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