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Do we really want more of the same? Now is our chance to change it.

Do we really want more of the same? Now is our chance to change it.

Do we really want more of the same? Now is our chance to change it.

By Jess Cleland, Director of Strategic Analytics & Innovation, Cushman & Wakefield | BROLL

Much of our built environment has historically been designed for men, by men. Yes, progression has been made in more recent years and decades, but certain standards and assumptions are entrenched and their effects linger on.

Everything from the design of bathrooms to the standard settings on thermostats was calculated based on the requirements of a typical middle-aged man. But ask most women, or a wheelchair user, or a single dad, or the elderly, whether the facilities at their office or local shopping centre work for them, and the answer could be discouraging.

So what happens when we use past examples to train the artificial intelligence and machine learning models which will design the future? We’ll get more of the same.

What about when data on credit lending is used to train models to assess future creditworthiness? If underrepresented or previously disadvantaged segments of society have historically received lower funding, then the data model may “learn” this as a rule and perpetuate the bias, leading to even more unequal access to credit for women and others.

If a building management system is trained on historic temperature settings, which were designed using a male body reference point, then it will only apply more of the same. Or if an AI architecture program automatically designs bathrooms based on what has been done in the past, it will not know that what currently exists underserves the different needs of women.

Data models used for smart city planning also might not fully account for the specific needs and preferences of women or those with disabilities. For example, transportation data models might prioritise road construction over pedestrian-friendly infrastructure, potentially impacting safety and mobility in urban areas for all.

The real estate industry is moving quickly to leverage data and analytics across various domains, including property valuation, investment decisions, market analysis, and risk assessment. Machine learning algorithms can help forecast property prices, recommend investment opportunities, and enhance customer experiences. With technology driving the industry's transformation and decision, the importance of diverse insights in the data modelling process – and the data itself – becomes even more critical.

Despite the advantages of data-driven decision-making, there exist inherent risks of biases in datasets, models, and outputs. Biases may stem from historical data reflecting past unfair societal norms and gender stereotypes, resulting in gender-based disparities being perpetuated into the future. To ensure fair and unbiased results, it is imperative to maintain balanced and representative datasets.

It’s clear that the effectiveness and success of data models and artificial intelligence in real estate will be contingent on having diverse perspectives brought to the table. Women, with their unique skills and perspectives, can play a vital role in overcoming biases in data and models, while ensuring inclusive outcomes across the real estate industry.

Women bring empathy and diverse perspectives to data analysis, helping to ensure that datasets are well-balanced and free from not only gender bias but also other potentially unfair prejudices or predispositions against other segments of society, whether that be race, ability or sexuality.

By scrutinising models and questioning underlying assumptions, women can provide unique viewpoints to help identify potential problems in the data and the outcomes, leading to more accurate and equitable results.

Women's ability to interpret trends from a broader societal perspective ensures that data-driven insights are designed for the entire population. Women's inherent consideration for diverse user populations enables them to recognise flaws in AI/ML models that may exclude or misrepresent certain segments. By asking questions that cater to diverse user needs, women contribute to inclusive decision-making processes.

Incorporating women into the realm of data and analytics in the real estate industry, as we do at Cushman & Wakefield | BROLL, is a transformative step towards a fairer, more inclusive future. By bringing diverse skills and perspectives to the forefront, women play a vital role in mitigating biases, recognising potential problems, and enhancing the overall decision-making process. Their invaluable contributions ensure that data-driven insights cater to the needs of all stakeholders, paving the way for a thriving and equitable real estate industry. Embracing diversity in data and analytics empowers the industry to rise above challenges and embrace a future of inclusive and dynamic growth.

Jess Cleland | Director: Strategic Analytics & Innovation |