Real estate data platforms can provide water-saving insights for city housing and infrastructure planning, drought management and sustainability. A study by Stanford University using data from Zillow is already informing better water use.
A new Stanford University study has identified residential water use and conservation trends by analysing housing information available from the prominent real estate website Zillow.
Study senior author Newsha Ajami said in a statement: “Creating water-resilient cities under a changing climate is closely tied to how we can become more efficient in the way we use water as our population grows.”
While city growth is a consistent trend, the types of residential dwellings being constructed and neighborhood configurations are less uniform. The people living within these communities have different water use behaviours based on age, ethnicity, education and income. However, when planning for infrastructure changes, decision-makers only take population, economic growth and budget into account.
The Stanford researchers used data from real estate website Zillow to gather single-family home information, including lot size, home value and number of rooms in Redwood City, California, and paired this with U.S. Census Bureau demographic information for the city. They then applied machine learning methods to identify five community groupings, or clusters.
They found the two lowest income groups scored average on water use despite having a higher number of people living in each household. The middle-income group had high outdoor water use but ranked low in winter water use, signaling efficient indoor water appliances. The two highest income groups were the most dissimilar. One cluster – younger residents on smaller lots with newer homes in dense, compact developments– had the lowest water use of the entire city. The other high-income cluster consisting of older houses built on larger lots with fewer people turned out to be the biggest water consumer.
The research lays the framework for integrating big data into urban planning, providing more accurate water use expectations for different community configurations, say the researchers.
Image credit: Tim J Keegan, flickr/Creative Commons