Data logic

Data-driven decision making is transforming the real estate sector.

We are witnessing an incredible pace of change. Data is optimising decision making and actions across the entire cycle, from concept to feasibility, to acquisition to design, from development to operation, to repurposing and disposal.

A data-driven approach can pay dividends. According to PwC, data driven organisations, can outperform competitors by 6% in profitability and 5% in productivity and a recent study by Forrester found that data driven businesses are 162% more likely to significantly surpass revenue goals than their counterparts.

In this article, and over the next few weeks, we’ll explore how data is being used in the real estate sector to optimise decision making, reduce risks, and increase the chances of better outcomes.

Informing real estate strategy
Data is having a profound impact on strategic decision making around valuation of space, commercial models, and development feasibility. Traditionally this relied heavily on manual appraisals and subjective assessments. However, predictive analytics is changing the way the sector analyses historical data, identifies patterns, and predicts future market trends, property demand, and investment opportunities.

For example, Israeli-based Skyline AI utilises a massive commercial property transaction database, blending traditional and alternative data to reveal key insights into economic growth. It discovered that the number of Airbnb listings correlates with rent price fluctuations and that car ownership rates and credit card data can serve as reliable metrics of investment feasibility in an area.

Data science is helping companies identify areas experiencing significant growth potential and invest in property before prices change. This allows them to arrive at more accurate valuations and assessments of suitability of locations, capitalise on emerging opportunities and maximise their returns.

Revealing consumer behaviour
Understanding the potential audience for a development is critical, but until recently this relied on static and often out-of-date catchment and demographic insights.

Anonymised consumer mobility data is now increasingly used to understand the volume and types of consumer segments that visit and use locations on different days and times of the day, including key insights such as frequency and dwell time. This data is available in many countries and is used to inform the suitability of locations for different uses, ongoing performance, and decisions for renewal and repurposing of assets.

In the US, Spatial.ai has developed the ‘GeoSocial’ dataset, which sources and analyses data from social media conversations across 72 segments, allowing organisations to better predict both consumer demand and lifestyle preferences in different locations.

These and similar approaches are providing more accurate, timely, and holistic consumer insights for driving property strategy and feasibility decisions.

AI supported architecture and design
Artificial Intelligence founded on vast quantities of high-quality data is significantly impacting architecture and design in general, reducing the time from concept to build. It is being used by architects to generate and evaluate design options faster than ever. For example, generative design software, such as text to image applications Midjourney and Dall E 2 are being used to generate design concepts quickly, so architects can evaluate options more effectively.

AI is also being used to in conjunction with building information models to allow designers to create digital models of their spaces, to identity issues and improvements. Machine learning and simulation is also being used to optimise energy efficiency of buildings at the design stage.

Operations and monitoring of performance
IoT (The Internet of Things) devices and machine learning are increasingly deployed in property management to better understand the performance of buildings and spaces. Sensors can be placed in heating systems, elevators, and workspaces to understand insights such as energy consumption, people movement and occupancy. For example, PointGrab uses a computer vision and machine learning platform to optimise the utilisation of workspaces, while BuildinIQ’s Predictive Energy Optimisation service uses sensors in heating, ventilation, and air conditioning (HVAC) systems to improve the energy efficiency of buildings.

Increasingly, real estate organisations are bringing together metrics on economic (such as revenue), environmental (such as energy usage) and social (such as impact on local workforce) to evidence the overall performance of their assets.

Data-driven decision making is proving invaluable in improving operational efficiency, reducing costs, evidencing green credentials, and enhancing human experience of spaces.

These examples only scratch the surface of the vast potential of data-driven decision making in real estate. Organisations that recognise and embrace the power of data are improving the quality and speed of their operations and gaining significant competitive advantage in the market.

However, making progress requires a strategic effort to become data-centric, requiring clear objectives for managing and analysing data, coupled with robust processes, technologies, and above all a skilled and performance focused culture.

James Miller