The promise of data has been around for at least ten years now. As a society and in the real estate industry, we have learned that data is only useful when it is interpreted meaningfully and presented in easy-to-understand formats. ERA R&D works with clients to process data into insights and develop bespoke interactive digitally-enabled ways to present this data. From better ways to make investment decisions to more easily calculating economic and social impact or understanding the way the people in organisations interact in office designs, this is an intro into how you can use data science and digital interactivity in innovative ways.
Data for place
Meg begins by providing an overview of the kinds of data you can use to make real estate decisions. The primary focus is data for the place. ERA-co believe that they can most closely predict the future by understanding patterns from the past. ERA-co analyses the data spectrum across several variables, including financial, economic, spatial, social, and linguistic analysis.
Most people are familiar with financial and economic analysis. For example, this includes development, feasibility, and cost-benefit analysis. On the other hand, what is less familiar is the use of variables such as spatial and linguistic analysis.
According to Meg, there is a correlation between spatial and economic outcomes, especially in urban cities. Spatial analysis includes street network type analysis, block modelling, and 3D modelling of the city to look at different metrics of space. Social and demographic analysis includes the census and providing breakdowns of age, gender, ethnicity, and the underlying demographic characteristics of an area.
The more interesting and new fields include linguistic and psychographic analysis. Here, the focus is on using natural language processing techniques to really understand the sentiment of a community. Alternatively, using psychographic analysis and survey methods to really understand their values and their drivers. Of equal importance is looking at where data points from the various variables overlay. For example, where you put financial with spatial analysis or image analysis with sentiment analysis. Often, the data will present interesting patterns that allow for deep insight.
Where is the next great place? How to find the opportunities
Meg is frequently asked three primary questions when it comes to real estate: 1) Where should I invest? 2) What type of property should I develop? 3) How can I determine the impact of my investment?
Meg focuses on the first question, i.e., how to find good real estate opportunities. ERA-co has created a model that helps you find the next great real estate location. The model is underpinned by four main variables: predictive analytics, clustering models, regression analysis and geospatial filtering.
Predictive analytics uses time-series data to predict future states from current conditions. Specifically, it highlights potential patterns in the market or population that can signal a change in potential for an investment opportunity. Because real estate is a long-term investment, predictive analytics can be highly beneficial.
Clustering models build unsupervised machine learning models on data to categorize areas or locations. Clustering models aid in determining which locations are similar for investment opportunities or sites that are best suited for specific uses. Regression analysis seeks correlations between different variables to understand better the factors that influence pricing or social outcomes in projects and the degree to which they influence each other.
Finally, geospatial filtering is ERA-co’s bespoke urban data visualization portal which allows users to filter data based on variable levels, revealing areas and locations with desirable characteristics for investment or impact.
What works in a brief here? What are the sticky components?
In terms of the briefing, a common point of contention is understanding what will attract people to a place and make it an incredible experience for those who already live there. ERA-co aims to quantify the character and identity of a locale. This is accomplished through analysing social media data, the types of food people consume, the types of clothing they wear, and the kind of activities they attend.
ERA-co provides a breakdown of several categories that measure the characteristics of a location, including social/lifestyle profile, local sentiment, benchmark places, spatial opportunities, and land use studies.
The social/lifestyle profile uses social media and other data to provide a snapshot of the local community, their values, and lifestyle preferences. Local sentiment exploration uses text-based analysis to determine local sentiment or perspectives on issues, including Twitter, newspaper or other text-based media. Benchmark places measure the spatial and economic properties of benchmark locations that are desirable exemplars of the kind of place to emulate.
Furthermore, spatial opportunities determine the reach of a development by walking, public transport, or vehicular travel, including street network properties that surround a site. This helps to provide an understanding of the target market area and their accessibility to a place and the best locations for retail and commercial activity. Finally, land use studies identify existing and proposed land uses.
What impact will you make? What can you measure?
Meg explains that there has been renewed focus over the last year on ensuring that real estate projects make a holistic impact on the surrounding environment. The impact factor can be categorized into four distinct metrics: economic, social, and community, spatial opportunities, and environmental.
First, economic metrics evaluate the Gross Value Added (GVA) across the industries to be housed in a development, financial contribution during construction, the number of jobs provided, and the jobs for under-employed segments of the population. Second, the social and community metric focuses on access to education, fresh food, and culture. Moreover, it forecasts future trends and interests of the community, support for young talent, and increasing overall community wellbeing.
Third, spatial opportunities look at expanding the movement potential of pathways through the city linked to town centre vitality and the success of retail. Fourth, the environmental metric assesses the access to and overall quantum of green space added to a city. This includes the number of trees planted, air quality, and surface water runoff reuse.
Interactive Analytics Portal
Lastly, Meg provided a tutorial of the way ERA-co’s data insights can be used via their interactive analytics portals (Figure 1). Users can log in and walk around your developments and understand how those developments are performing from a data perspective. For example, geospatial filtering allows you to filter for certain variables, such as employment and impact metrics. As a developer, you can adjust your metrics and see the impact on other variables, for example, the number of jobs or GVA in a development.