Identify high-value prospects and streamline lead management prioritisation through Outra's automated lead enrichment API.
Our lead Enrichment data product addresses lead management and optimisation within organisations by providing lead scoring and grading, enabling more effective & efficient targeting of higher intent prospects.
API enriches leads with a wealth of insights, including demographic data such as age, income, location, lifestyle attributes and property attributes such as property type, size, condition & location factors.
The lead enrichment API unlocks a wealth of insights from demographics such as age, income, location, lifestyle attributes and property attributes such as property type, size, condition and geo-location factors. This enrichment ultimately enables sales teams to gain a comprehensive understanding of customer needs, and prioritisation of resources to improve sales efficiency.
Enhanced lead profiles.
We deliver the best predictive data
Bullishness and determination count for a great deal in sales, and nowhere more so than in property sales. They cannot, however, do much to counter the uncertainty the market that will carry over from 2023. Sellers will enter 2024 continuing to weigh lower valuations than they’d like against the prospect of increasingly lower purchase prices and mortgage rates that may begin to edge down again soon – unless they don’t!
In a market with this level of instability, beating sales goals calls for a far more dependable, which means data driven, strategy.
"In the dynamic landscape of real estate”, argues Giles Mackay, founder of real estate data analytics leaders, Outra, “harnessing the power of data is not just a strategy; it's a necessity. Early identification of motivated sellers is a prime example of how data can be the catalyst for exceeding sales targets."
How can data help real estate professionals meet and exceed sales targets?
Mr Mackay’s assertion is not unjustified. Data can empower anyone involved in residential sales, by providing actionable insights that help optimise effort, streamline processes and which will, ultimately, lead to beating of sales targets.
With effective predictive analytics, anyone in residential sales has comprehensive insights into market trends, buyer behaviour, and seller patterns at their disposal. By understanding these trends, they can anticipate market movements, adjust strategies accordingly, and target their efforts more effectively.
Leveraging data then allows for precise targeting of potential clients. By analysing demographics, preferences, and past behaviours, it becomes possible to identify and focus on the most promising leads, optimising use of both time and effort. By using predictive analytics to forecast when properties are likely to be listed for sale, sales teams can engage proactively with sellers, gaining competitive edge and increasing the likelihood of closing listings.
Data-driven insights also offer a solid foundation for making informed decisions around sales strategy. Whether on pricing or geographic targeting, for example, comprehensive data combined with robust analytics enables calls to be made with vastly improved confidence. In addition, at individual listing level, data can help build stronger client relationships by providing insights into client needs and preferences. This allows agency sales teams to deliver more personalised advice, guidance and service, fostering trust and increasing loyalty.
“The real Focus for everybody in the real estate space, “ says Fred Jones , COO of ‘quick sale’ cash buyer, Upstix, “is to get sellers before anybody else gets there. If we can do that then we've revolutionized the way anybody runs the real estate space.”
How identifying motivated sellers before they list improved performance.
Being able to identify motivated sellers before they list their properties delivers substantial advantages in achieving sales targets. Connecting with sellers identified as highly likely to list before their property hits the market lets you establish rapport and build relationships. This increases the likelihood of securing the listing, giving a head start in the sales process. It also, of course, reduces competition from other agents. By being the first point of contact, you have a better chance to negotiate terms, potentially agreeing more favourable conditions for both you and the seller.
Data analytics also offers the potential to offer upcoming sellers the option of an off-market deal. This means you may be able to offer a solution to specific needs or wishes, such as privacy concerns or the desire for a quick completion, and potentially expedite their sale.
With the insights provided by high grade analytics making it possible to understand a seller's motivation early on, sales teams also have an improved basis on which to tailor sales strategies. Whether this is by highlighting certain property features, offering more flexible terms, or addressing a specific concern a seller may have, the data insights make it possible to craft personalised approaches which maximise the chances of landing the instruction and achieving a successful sale.
Building a relationship with a seller before they list their property may also lead to a higher closing rate, through being able to better understand seller expectations, manage negotiations, and guide the sales process more effectively.
With HMRC National Statistics recording a provisional, non-seasonally adjusted, estimate of the number of UK residential transactions in September 2023 down 19% on September 2022 (and down 2% on August 2023), advantages of the kind gained through identifying motivated sellers before they list could step any sales team closer to not only achieving, but impressively outperforming, their 2024 targets.
The best data and analytics tools to help you pass 2024 targets.
Proptech as a sector is booming, but while platforms aimed directly at buyers and sellers proliferate, the market for serious data and analytics designed specifically to help agents identify sellers ahead of listing is dominated by Outra’s ground-breaking product, Pre-Mover.
“In all my years involved in residential development and estate agency”, says Dominic Grace, former Head of London residential Development at Savills, “I've never seen anything as phenomenal, in terms of its power, as the Outra platform. It gives you lots of information at a really granular level… so it means you can run your whole business much more efficiently and effectively.”
Pre-Mover tracks more than 2,300 attributes each on 30.8m UK households, amounting to 75 billion data points at property level. According to Outra’s Chief Data and Technology Officer, Peter Jackson, “We make 900,000 predictions every month; they've not even listed their house yet and we're predicting when they're going to list. That's quite remarkable performance.”
Upstix’s Fred Jones agrees. “The UK housing market is hugely volatile and has changed massively over the last 12 months alone. The Outra data helps us be agile and adapt our marketing strategies to respond to that. It's allowed us to be on the front foot and capture new leads rather than being reactive.”
No longer ‘Nice to have’. ‘Must have’ in 2024.
It would be foolish to imagine 2024 won’t harbour significant challenges for the residential sector, compounded by those lingering market uncertainties. Against this unpredictability, however, data stands out as the key driver for success. Leveraging data-driven insights has become not only ‘advantageous’, but essential for exceeding sales targets.
By understanding market trends and buyer behaviours, and anticipating seller patterns, directors, area leaders and managers in firms of any size can gain a comprehensive understanding of the landscape and use this to recalibrate strategies, precisely target potential clients, and engage proactively with prospective sellers before their properties hit the market.
It is this last benefit, the ability to identify motivated sellers before their official listing, that is the real game-changer. Establishing early relationships opens doors for negotiations, reduces competition, and allows for tailored sales approaches that really impress and win over sellers.
As 2024 approaches, the use of data-driven insights can no longer be viewed as a ‘nice to have’ competitive advantage. It’s now the linchpin for success; the single most decisive factor in empowering you to not just meet, but to significantly exceed, 2024 targets.
Leadership brings challenges. For anyone guiding the fortunes of a business of any scale, these challenges go with the territory. Indeed, many would say these are the territory.
While every commercial sector has its own issues to wrestle with, for anyone leading a UK residential real estate business at this time, the hurdles to be overcome (and, on the flipside, opportunities to be embraced) arise largely from the uncertainty against which the sector finds itself operating.
In the words of former Foxton’s CEO, Nic Budden, "The UK residential estate agency market is one of the most competitive and dynamic in the world, but it is also one of the most challenging and complex. We have to deal with the fluctuations and uncertainties of the housing market, the evolving preferences and behaviours of our customers, and the increasing competition and regulation in the industry. We have to be agile and innovative to stay ahead of the curve and differentiate ourselves from the crowd.” The accuracy of tis view has only been compounded by further economic turbulence.
Staying ahead of the crowd, as Mr Budden observed, really is the key issue. In a heavily populated market, how can real estate businesses develop strategies capable of securing real market advantage, when borrowing rates are high, buyers are nervous and hesitant, sellers are uncertain, the economy is sluggish and policy from the Bank and Government must be viewed as ‘continuously subject to change’?
Data analytics, real estate and a glimpse into the future.
In the age of sophisticated data science and Artificial Intelligence tools, the answer seems certain to lie in innovative and timely use of analytics.
Think of the advantage to be gained at every level of the business, from operational and budgetary consideration at local office level, to organisation-wide forecasting and planning, were residential agency businesses to exploit relevant consumer and market data on a continuous basis.
In what ways could AI-driven data analytics of this kind give agents ready to embrace their forensic power and invaluable insights a competitive edge?
Fed with extensive and expertly defined data points and signals, data analytics could provide a tactical commercial weapon of a kind never before seen in real estate. Able to analyse market trends, consumer behaviour and economic indicators in myriad ways, agency leaders would be able to identify emerging market patterns, prioritise areas in which listings were about to blossom or to slow or, at a granular level, predict accurately, months in advance, when particular properties were likely to list.
At an organisational level, consistent use of data analytics in managing the business could provide estate agents with the ‘crystal ball’ of their dreams, enabling informed decisions, insight-driven business planning and early moves to obtain instructions, and providing the keen competitive edge needed to cut through the uncertainties of the market and outpace competitors.
The advantages for agents of identifying listings ahead of time.
Accurate and reliable foresight into future listings could provide strategic advantage for real estate businesses in numerous ways. Early access to information about properties coming onto the market would enable agents to engage proactively with potential sellers, reaching out to offer their expertise and services before the property lists. An approach of this kind not only demonstrates a proactive operation, but positions the agent as a dependable professional partner.
Agencies able to identify soon-to-list properties early would also lay claim to the opportunity to secure the listing exclusively, gaining greater control over the marketing process and allowing for a more tailored and focused strategy to maximise a property's visibility. Similarly, with data-driven ability to analyse comparable properties and market trends, agencies could take a more authoritative position in helping sellers set competitive and realistic prices. As well-informed pricing enhances the chances of a successful transaction, this stands to minimise the time properties spend on the market.
There would be further advantages to be gained, too. In property marketing terms, amed with early visibility of upcoming listings across an area, agents would enhance their ability to optimise sale price for their clients, advising on when precisely to release a property onto the market to maximise its value. Similarly, by identifying properties before they enter the market and engaging early with vendors, agents would be able to streamline the transaction processes, preparing documentation, conducting pre-listing inspections, and addressing potential issues ahead of actual listing. This would again reduce the time the property spends on the market, creating a smoother sale process for all parties.
With the ability to predict upcoming property listings, agencies could become more proactive market leaders, able to control deals and run even the largest chain with greater precision and, as a result, enhanced success.
How early, data-driven insights show the way through uncertain economic times.
By now, most real estate agency management teams have come to terms with the lingering uncertainty in the landscape. It’s ‘the way it is’. But while it’s a far from ideal environment in which to try to solidify success, managements equipped with powerful, AI-driven data analytics tools could gain high value wins.
Underpinned by the right dataset, a robust real estate tool could facilitate accurate forecasting of economic shifts by analysing predictive indicators such as employment rates, inflation and interest rates. Armed with these insights, managements would be able to anticipate how changes in the broader economy might impact the housing market, allowing them to adjust strategies on pricing, marketing or investment recommendations, accordingly.
Reaching beyond raw economic data, an effective tool might tap into sentiment analysis of market participants, letting agency teams observe shifts in consumer confidence and adjust approaches accordingly. For example, with indications of positive sentiment returning, an agency team might consider it a good time to turn up marketing efforts, while negative sentiment might advise greater caution.
Inevitably, economic uncertainty gives rise to trends and shifts in consumer behaviour. Able to analyse a myriad of data points, however, real estate businesses could observe emerging trends such as shifts in housing preferences, demand for specific amenities, or changes in preferred locations, all before these became widely apparent. Businesses with access to these insights would then be able to position themselves ahead of the curve, aligning their offerings with evolving consumer preferences.
For every business in the sector, there is also the clear need to assess and mitigate the new and increased risks inherent to transactions in times of uncertainty. Through analysis of historical data and market trends, powerful real estate tools will help agency managements identify the potential for, say, market downturns or fluctuations in values. This knowledge would make it possible for leaders to develop contingency plans and advise clients accordingly, greatly enhancing resilience.
In a market in which economic conditions can vary significantly at regional and micro-market levels, granular analysis also has the potential to help teams leading chains operating across larger areas to understand the nuances within different locales. Localised insight of this kind could prove invaluable in tailoring strategies to specific regions, optimising marketing efforts, or capitalising on opportunities that may be unique to certain areas.
Outra’s Pre Mover analytics management leads the way for real estate businesses.
The application of powerful data analytics to residential real estate is a paradigm shift that can redefine success in the industry for everyone from single-office agencies through to the largest, nationwide chains.
Innovation in the space is led by Outra’s disruptive ‘Pre Mover’ product. Observing over 30 million UK households with more than 2,300 data points and 130 signals on each, Pre Mover generates highly accurate predictions about properties which are soon to be listed. It integrates analysis of real world events, such as residential sales and rental data, with a broad array of household insights, demographic data and other influencing factors to accurately predict households planning to list as far as six months in advance.
Decision time for agency leadership.
Data analytics promises bold and ambitious real estate management teams a level of disruptive and transformative power similar to those obtained in comparable sectors by organisations willing to embrace early and remodel operations and strategies around its impactful insights.
While the market maintains its current unpredictability, the consideration for all management teams should be the risk of being left for dead by competitors taking this opportunity to re-engineer their strategies and approaches around potent, AI-driven data science.
In early 2020, overnight working from home (WFH) became widespread across the globe. As lockdowns have ended in the UK, we have embraced ‘the new normal’ with hybrid working models more common than ever. However, for some, the weekly commute has returned. This change in working behaviour across the UK has, over the past 2 years, dramatically impacted house prices.
With such disparity between working behaviours, Outra’s data science team has sort to identify who are these hybrid or remote workers, and how this is influencing the UK’s housing market. By analysing commuting data, we have looked to identify where the resulting ‘race for space’ is occurring. To start to understand Outra used data from a variety of sources, modelled to provide insight and identify trends, including;
- passenger data for the 40 UK stations with themost marked difference in passenger numbers - the 20 highest and the 20 lowest.
- we identified suburban stations with the highest and lowest change in entry and exit volumes as a proxy for WFH.
- the numbers of people who commute to work using the Office of Road and Rail information for the 12 months ending in March 2021.
Pre and post lockdown trends
In locations where there were minimal increases in house prices, compared to the wider UK trend, we identified 3 key factors. Where these were present, homes near these back-to-work stations, prices only rose by 8.7%.
- Train stations largely located in and around London, where properties are higher in value. Occupants are more likely to work full time, travelling to and from the office.
- Higher number of flats and apartments. Fewer bedrooms compared to more central properties, meaning occupants are less likely to have access to adequate WFH space.
- Property occupants near train and bus stations are more likely to be in managerial roles. Increased seniority requires a more regular presence in the office — resulting in more frequent commuting.
Where we are seeing the ‘race for space’, is where people in the inner cities sought to transition to WFH locations, which in turn has fuelled the UK’s property price growth. Property values near WFH stations have increased by 13.5%, outpacing average country-wide growth since 2020.
- Local properties are more often owner-occupied and bigger in size, giving less incentive to commute.
- Property occupants living far away from their offices and nearest stations are more likely to work from home.
Working from home trends today
Now the UK has fully opened up, a majority of employers have embraced new ways of working. As workers demand a better work life balance, Covid WFH practices also demonstrated that businesses could still be just as productive. As we saw with key worker lockdown policies, working from home is more feasible for some types of workers than others. High earners are the ones most likely to be benefiting from a hybrid work environment, with 38% of workers earning £40,000 or more currently working both in the office and at home.
Meanwhile, lower earners are less likely to WFH at all. Lower earners who reported hybrid working between 27 April and 8 May 2022 included:
- 8% of those earning up to £15,000
- 24% of those earning between £15,000 and £20,000
- 21% of those earning between £20,000 and £30,000
- 32% of those earning between £30,000 and £40,000
There also seems to be an age element to those either able to or wanting to WFH, as those aged 30 to 49 most likely to do so.
These changes in working patterns have had a material impact on the housing market. Any further changes or an impending recession will either reverse or accelerate those shifts. Only by understanding the unique data points that create each individual householder can businesses adjust to new trends.
View our Interactive map
Outra’s data has been featured in the The Spectator on why many people simply cannot afford to become homeowners.The Spectator used our data and found the proportion of millennials buying a home has dropped 11% in five years – a fall in spend of just under £25bn-a-year.The data is yet further evidence of a gaping generational divide in the UK property market and raises concerns that financially hard-pressed millennials are struggling to get on the housing ladder, which is likely to have a significant impact on their financial future given that a house is normally the most valuable thing a person owns.To read the full article - click here
The retail, utilities and credit sectors are facing one of the greatest challenges of the past 40 years: As the economic situation worsens, a recession becomes more and more likely. Households feel the pressure and businesses need to adapt to survive. So, what can companies do to weather the storm and thrive during this new challenge?
The perfect storm
In a post-Covid world there were glimmers of growth, but as the world has opened back up, we’ve faced a new wave of challenges. As a result, the UK is seeing a dramatic reduction in spending. On June 24th, the Office of National Statistics (ONS) announced that the volume of goods sold in-store and online in May 2022 fell by 0.5%.
With inflation at its highest level since the 1980s, we’re experiencing all-time lows in consumer confidence. As food and energy prices continue to rise, this trend is likely to continue throughout the summer.
Some of the industries most affected by the crisis are retail, credit, and utility. Each industry faces unique challenges and in turn, will need to deliver a unique strategy in response.
- Retail businesses will need to be smarter at customer targeting and retention to ensure a healthy flow of new shoppers while preventing the loss of existing loyal customers.
- Credit providers must address the increased need for credit along with an increased risk of defaults or fraud.
- Utility companies must improve their processes for supporting vulnerable customers to adhere to regulations, along with a moral duty to support these households.
Data insights and the 2022 consumer
Consumers are adopting more defensive spending behaviours to ensure they have enough money to pay for the essentials. These tactics include self-imposed checkout limits at supermarkets, reducing expenditure on luxuries, or borrowing to fund everything from clothes to food.
The change in consumer behaviour creates a reduction in disposable income for businesses in the affected sectors. With a reduction in income comes a hit to companies’ bottom lines — threatening the survival of many of them. Small businesses are particularly at risk as they lack the cash flow necessary to recover from low-income periods.
Each UK household is unique. For consumer-focused businesses, understanding each of these, whether they are an existing customer or not, will be key to success. By using data that provides detailed insight at an individual post box level, businesses can gain a true understanding of the UK consumer.
Leveraging customer data science and data modelling provides the ability to turn data into wisdom — which in turn, forms the bedrock for any business or marketing strategy through an understanding of the unique nature of each customer, and the unique issues they are facing.
In the current environment, identifying which UK households are most likely to be affected by the cost of living crisis helps businesses adapt to the needs of their customers. By knowing the financial status of customers at a household level, businesses get a live snapshot of current behaviours. And through modelling that live snapshot, we can then predict customers' future behaviours.
All consumer-facing businesses can use this insight to understand:
- Who are my customers, and how are they dealing with increased living costs?
- What is important to my customers?
- How do I ensure my marketing messaging is effective?
- Who should I target, and what’s the best channel to use to do it?
But this data can also help overcome unique challenges within each sector.
For Retail, this insight can identify which physical store locations might face the most amount of pressure from reduced takings. Those located in the most hard-hit locations are likely to see a reduction in foot traffic and profits. Using data science, retailers can implement strategies ahead of time to address the issue, such as stocking cheaper products, creating targeted promotions or advertisements, or even reducing the number of physical stores.
Credit agencies and lenders can use the data to pinpoint households that are more likely to default. This goes beyond the process of deciding on whether to extend credit or not at the point of sale. It can identify households that already have credit, but now have a propensity to default. To better support those that face financial hardship as well as to manage credit risk.
The same is also the case for Utility providers. The current energy crisis has put an increased regulatory burden on providers. Our analysis has identified 5,055,359 vulnerable households out of the circa 30 million in the UK by modelling features beyond simple financial flags. Each one of those is considered a financially vulnerable household that they are obliged to actively provide additional support.
Data science at a household level can help consumer-facing businesses adapt and overcome the challenges that lay ahead. Through understanding where consumers are at this moment in time, and where they might be in six months, we can identify behaviours and keep your business ahead of the curve.
As winter begins to set in, the reality of the cost of living crisis is beginning to take hold. While inflation has been affecting the economy as a whole, the increase in UK energy costs has been especially steep. Even with government intervention, energy price hikes will place huge pressure on UK households. In particular, the most vulnerable, those with larger homes, and homes with a lower EPC rating.
The crisis isn’t affecting customers equally. Our data has identified regional variations in household finances and building quality which will mean some homes will face higher costs than others. Consumer facing businesses and regulators need to understand these regional differences at a household level, to not only maintain the bottom line, but to ensure the most vulnerable customers are being supported.
UK faces average energy bills of over £4,000
UK energy costs have increased due to the rising price of wholesale gas across 2021 and 2022. In April, household energy bills increased by 54% and consumers faced a further 80% rise in October.
In response, the UK Government established the Energy Price Guarantee. The programme caps the average unit price for dual fuel customers paying via direct debit from 1st October; 34.0p/kWh for electricity and 10.3p/kWh for gas. This is on top of the £400 Energy Bill Support Scheme.
What is important to note is that this means bills are not capped – they are still based on usage. So, those with larger homes, homes with a lower EPC rating, or those with additional heating needs (such as remote workers) will face bills far in excess of the governments proposed nationwide average.
The government has stated that the average household energy bill will be £2,500 per year. But our data has uncovered that due to these factors, the reality is that average UK energy costs will be more than £4,000 - a 150% increase from 2021.
“Across the UK, the minimum year-on-year increase according to our calculations will be 169%, which will result in more consumers having to tighten their belts and reduce their spending elsewhere to afford to pay their bills.”
Peter Jackson, Chief Data and Product Officer at Outra
What is the regional reality?
The UK has vast geographical differences, so a single average figure for bills can be extremely misleading. Financial disparity across the UK is nothing unexpected, but the scale at which the cost of living energy crisis will affect different regions could have long-term effects on households. By diving into Outra’s data, we have identified at a household and regional level where the rise will have the greatest impact.
Regional breakdown of UK energy bills from 1 October 2021
The chart demonstrates that while bills will rise to roughly £4,000 on average, some regions will face costs of nearer £5,000. On the other end of the spectrum, while London and the North East have the lowest annual bills, they will endure the largest relative price increases of around 200% each.
The reality of the cost of living crisis will be further exacerbated by the north/south income disparity. Colder areas like Scotland and the North, and more deprived regions in major cities will bear a larger cost burden.
Household data, means regional solutions
In the face of the cost of living crisis, understanding each individual household will be essential for any consumer facing business. It’s clear that national averages do not accurately represent the reality of the UK consumer. By being able to anticipate the economic forces impacting UK customers at a household level presents immense opportunities.
Whether you are looking to support vulnerable customers, retain existing customers when belts are being tighten, or looking to expand into a new customer base, household level data can provide the insights you need to be successful.
Every household is made of up its own unique data points. Each one builds upon the other to create detailed personas of who your existing customers are, and helps to identify new lookalike audiences.
Disposable income is being squeeze, and the reality of the cost of living is yet to set in. Only data driven marketing solutions can help your brand forecast risk, predict churn and deliver personalised messaging. Afterall, we’ve already identified around 5 million vulnerable UK households – do you know where they are?