Can AI predict societal changes?

The advent of artificial intelligence (AI), data science, and machine learning (ML) has provided a toolkit of novel analytical techniques that can generate predictions from analysing patterns in data. These tools can be used to predict economic and societal changes that are relevant to the real estate industry.

Identifying and anticipating societal patterns and changes can help real estate developers and investors assess where and what to target, ensuring that projects meet current and future demands. AI can identify emerging hotspots and predict shifts in housing needs, enabling more informed decisions that align with market dynamics. For governments and local authorities, predicting societal changes is a proactive way of anticipating future demands and resource allocation. For businesses, AI can be a valuable tool to provide insights into consumer behaviour and market trends, helping companies to innovate and stay competitive.

How AI can forecast population growth and economic shifts

AI and ML are being increasingly used in demographic studies to predict population growth. They use intelligent algorithms, such as neural network, to analyse vast and complex population data, enabling accurate predictions of population trends and dynamics. Additionally, AI can scrutinise vast amounts of population patterns and understand key themes and relationships between various factors like age, gender, and education. AI's use in population studies extends to predicting migration, fertility, marriage, ageing patterns, analysing social media's impact on population changes, health analysis, mortality, and data integration and predictive modelling. The resulting insights are invaluable in supporting efficient public sector resource planning.

Similarly, AI can also be used to predict economic shifts. It enables real time market analysis, identifying emerging trends and detecting sectors ready for growth. This can be used to attract investment and unlock revenue streams.

How machine learning (ML) can help forecast gentrification

Recent research provides strong empirical support for using ML to provide insight into changes in residential neighbourhoods, and ultimately predict gentrification. Successful prediction requires defining the right features of the data collection the ML model will use. Key indicators include education, income, amenities, and house prices. Two notable studies have effectively used longitudinal census data to predict gentrification. The first, which analysed census data from 1970 to 2010, identified education, rising home values, and the presence of amenities as significant factors in predicting gentrification. The second, conducted by researchers at King's College London, used variables like household income, house prices, occupational share, and job qualifications to predict uplift across areas in London. Both studies trained their ML models with historical data and achieved high predictive accuracy when their forecasts were compared with observed prices in subsequent years.

Both studies also highlighted the importance of understanding the impact of these models on policy. For a model to have utility, the insights it creates must be used by humans to inform equity and policy. Furthermore, although these forecasts were made retrospectively, the prerequisite data was available earlier, indicating the potential for proactive use of these models in the future.

How AI can predict public health trends

Predicting public health trends is another area where utilising ML and AI technology have shown much promise. Such insights can aid the healthcare sector by informing public health policies and interventions, contributing to healthier communities. AI's application in public health has grown significantly, aiding in disease prediction, outbreak detection, and healthcare delivery. It allows for large-scale data analysis, predictive modelling, and real-time monitoring of health trends, enhancing medical diagnosis accuracy and facilitating personalised treatment. Its integration presents challenges, including the need for diverse datasets and addressing ethical considerations like data security. Robust regulatory frameworks and interdisciplinary collaboration are necessary to maximise AI benefits and minimise potential risks. Despite these challenges, AI holds immense potential to revolutionise public health and improve population wellbeing.

AI is also transforming public services, using spatial data and satellite imagery to enhance service delivery, disaster responsiveness, and the development of smarter, more sustainable cities. Models are used to analyse large data sets and produce real-time metrics, leading to more efficient and precise provision of essential services and infrastructure.

AI’s potential for social impact

The ability of ML and AI systems to predict various societal changes has profound implications for the real estate sector, including urban planners, and policy making. By forecasting shifts such as population growth, economic changes, and health trends, these technologies can provide valuable insights that can inform real estate investment and development.

The power to predict societal changes presents an opportunity for the real estate sector to play a proactive role in shaping sustainable, resilient, and inclusive urban environments. The insights derived from these technologies can guide data-driven decision-making, ensuring that development projects are not only economically viable, but also socially equitable and environmentally sustainable. However, it is vital to remember that while AI and ML provide powerful predictive capabilities, they are tools to aid human decision-making, not replace it.