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.



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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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Analysis of London borough rental properties EPC - compare boroughs

One of the most visible indicators of the UK’s commitment to battling global climate change is the ubiquitous Energy Performance Certificate. This colourful barchart lookalike is not only on electrical appliances but has also featured on every estate agent’s brochure for the last ten years.

Most of us assume that they are a reasonable guide to the amount of energy that we will need to pump into our homes to keep us warm over Christmas and like the new dog, it is with us for life. But it looks like we could all be in for a shock as inconsistencies have cropped up in the system that suggest that pre 1920’s properties are causing anomalies which require a re-calibration of the EPC system.

On top of this from April 2018, any rental property will need to achieve a rating of E or better. With 20% of the housing stock under public sector control and approximately 20% of these properties being below the E rating, this should be seen as a call to action.

Houseprice.AI’s analysis indicates that London’s local authorities have over 110,000 properties that are not just below the EPC rating guidelines, but are also of the age before cavity walls, double glazing, insulated attics and efficient boilers. Continuing to ignore the need to upgrade these properties, is as misguided as buying new buckets, when we really need to spend money on the roof.

Houseprice.AI can also demonstrate that 25% of these properties are in areas where additional investment is also likely to lead to higher than normal ROI’s as the average price per sq ft is in excess of 10% higher than the value of the properties.

In a typical London Borough, we have identified houses in one street owned by the Local Authority which are identical, apart from their poor energy performance, to their privately owned neighbours. Not surprisingly the Local Authority Homes are achieving sales prices of 40% below their real market values. In this street the value range from the highest to the lowest is 200%

Retrofitting the poorer quality housing to achieve an A or a B rating will cost around £15,000 for a typical 3 bedroom home, but could yield a return of 25 to 30% based on rental uplift, as well as saving 50% of the energy bills.

What dog would not be happy in that new home?

Philip Challenor
Chairman
Houseprice.AI

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Data Cleansing for AI

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Houseprice AI is selected by PWC

Houseprice.AI has been chosen as the sole UK company to take part in the PWC Collider Accelerator program. Houseprice,AI was selected to be one of the 12 successful candidates from a highly competitive field of nearly 300 global applicants.

PWC aims to take the most innovative companies using Artificial Intelligence, Big Data analytics and Machine learning, and honing their business models for the international marketplace.

Click this link to read the full PWC announcement

Houseprice.AI COO Rob Marsden who championed the submission said:

"the PWC qualification process was extremely rigorous and forced us to ask searching technical and commercial questions of ourselves, many for the first time. It is a great accolade for the team to know that our answers chimed with such a major international advisor as PWC."

Founder and CEO Eldred Buck added

"We are delighted to be selected from an intensely competitive field to work with PWC in Europe”.

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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Do you need more insight?

Introducing Insight

our latest Horizon module

Distill billions of data points to help you identify emerging areas with steady home appreciation and high rental returns.
Compare different areas to identify opportunities. Filter by data indicators including sales value, number of sales, rental yield and more.

Click the location report in the pop up window and drill down into detailed reports on that postcode.

Analyse Trends

Quickly and easily view across England and Wales residential property market using the heat map.

Monitor Risk

There are an increasing number of schemes which are being sold by traditional housebuilders looking for risk reduction in a less than certain new build flats marketplace.

With access to the most up to date supply, demand and price data, you can significantly increase risk awareness in your portfolio.

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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